WO2021241980A1 - Composition for cancer prognosis prediction and kit comprising same - Google Patents

Composition for cancer prognosis prediction and kit comprising same Download PDF

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WO2021241980A1
WO2021241980A1 PCT/KR2021/006492 KR2021006492W WO2021241980A1 WO 2021241980 A1 WO2021241980 A1 WO 2021241980A1 KR 2021006492 W KR2021006492 W KR 2021006492W WO 2021241980 A1 WO2021241980 A1 WO 2021241980A1
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cancer
acta2
znf667
fendrr
group
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PCT/KR2021/006492
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French (fr)
Korean (ko)
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정재호
신민규
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연세대학교 산학협력단
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    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
    • C12Q1/6883—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
    • C12Q1/6886—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/118—Prognosis of disease development
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/136—Screening for pharmacological compounds
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/158—Expression markers

Definitions

  • the present invention relates to a composition for predicting cancer prognosis, a kit comprising the same, and a method for predicting cancer prognosis.
  • Cancer is a cell mass composed of undifferentiated cells that proliferate indefinitely while ignoring the necessary condition in the tissue, unlike normal cells, which can proliferate and suppress regularly and in a controlled manner according to individual needs.
  • Such unrestricted proliferation of cancer cells infiltrates into surrounding tissues and, in more severe cases, metastasizes to other organs of the body, causing severe pain and eventually death.
  • Cancer is broadly classified into blood cancer and solid cancer, and occurs in almost all parts of the body, such as stomach cancer, pancreatic cancer, breast cancer, oral cancer, liver cancer, uterine cancer, esophageal cancer, and skin cancer.
  • therapeutic agents are being used for the treatment of specific cancers, surgery, radiation therapy, and anticancer drug treatment using chemotherapeutic agents that inhibit cell proliferation are the main methods up to now.
  • chemotherapeutic agents since it is not a targeted therapy, the biggest problem with existing chemotherapeutic agents is the side effects and drug resistance due to cytotoxicity, which is a major factor that ultimately causes the treatment to fail despite the initial successful response to the anticancer agent. Therefore, in order to overcome the limitations of these chemotherapeutic agents, there is a continuous need to develop targeted therapeutics with a clear anticancer mechanism.
  • Gastric cancer is the third leading cause of cancer-related death and ranks fourth among the most common cancers.
  • personalized pharmaceuticals clinically and biologically close molecular analysis is required.
  • understanding of biological complexity has been improved through omics studies of gastric cancer at the whole genome level, and in particular, prognosis using biomarkers and responsiveness to adjuvant chemotherapy through analysis of mRNA gene expression data.
  • Great advances have been made in forecasting.
  • Molecular profiling at different levels in gastric cancer is expected to lead to the development of personalized treatment strategies.
  • lncRNA long non-coding RNA
  • the lncRNA acts as an important substance in the path of cancer, and is related to the development and progression of cancer.
  • the lncRNA is a transcript of 200 or more nucleotides without protein-coding potential. They have a higher number of constituent nucleotides than the protein-coding gene, are restricted to specific cell/tissue types to a greater extent than mRNA, and are promising as cancer-type specific therapeutic targets.
  • RNA-targeting Nucleic acid-based (RNA-targeting) therapeutics have shown clinical success in several diseases and preclinical success in some cancers by targeting lncRNA. However, there have been no reports of lncRNA targets in gastric cancer, and sufficient studies have not been conducted on their clinical relevance and biological functions.
  • One object of the present invention is to provide a biomarker composition for predicting the diagnosis, prognosis or therapeutic responsiveness of cancer.
  • Another object of the present invention is to provide a composition for diagnosing cancer, prognosis or predicting therapeutic responsiveness, and a kit comprising the same.
  • Another object of the present invention is to provide a method for providing information for predicting diagnosis, prognosis, or treatment responsiveness of cancer.
  • cancer is gastric cancer, ovarian cancer, colorectal cancer, breast cancer, liver cancer, pancreatic cancer, cervical cancer, thyroid cancer, parathyroid cancer, non-small cell lung cancer, prostate cancer, gallbladder cancer, biliary tract cancer, non-Hodgkin's lymphoma, Hodgkin's lymphoma, blood Cancer, bladder cancer, kidney cancer, melanoma, colon cancer, bone cancer, skin cancer, head cancer, uterine cancer, rectal cancer, brain tumor, perianal cancer, fallopian tube carcinoma, endometrial carcinoma, vaginal cancer, vulvar carcinoma, esophageal cancer, small intestine cancer, endocrine adenocarcinoma, part It may be renal cancer, soft tissue sarcoma, urethral cancer, penile cancer, ureter cancer, renal cell carcinoma, renal pelvic carcinoma, CNS central nervoussystem tumor, primary CNS lymphoma, spinal cord tumor, brainstem glioma or pituitary
  • ZNF667-AS1 ZNF667 Antisense RNA 1 (Head To Head)
  • RP11-572C15.6 FENDRR (FOXF1 Adjacent Non-Coding Developmental Regulatory RNA)
  • ACTA2-AS1 ACTA2 Antisense RNA 1
  • ZNF667-AS1 ZNF667 Antisense RNA 1 (Head To Head)
  • RP11-572C15.6 FENDRR
  • FENDRR F1 Adjacent Non-Coding Developmental Regulatory RNA
  • ACTA2-AS1 ACTA2 Antisense RNA 1
  • ACTA2 Antisense RNA 1 each are long-ratio
  • the ZNF667-AS1 may be represented by SEQ ID NO: 1
  • the RP11-572C15.6 may be represented by SEQ ID NO: 2
  • the FENDRR is SEQ ID NO: 3
  • the ACTA2-AS1 may be represented by SEQ ID NO: 4, but is not limited thereto.
  • biomarker composition for diagnosis of cancer comprising at least one selected from the group consisting of RP11-572C15.6, FENDRR and ACTA2-AS1.
  • the "target individual” refers to an individual who is uncertain whether or not the onset of the cancer has a high probability of onset.
  • the "biological sample” refers to any material, biological fluid, tissue or cell obtained from or derived from an individual, for example, whole blood, leukocytes, peripheral blood mononuclear peripheral blood mononuclear cells, buffy coat, plasma, serum, sputum, tears, mucus, nasal washes, nasal aspirate (nasal aspirate), breath, urine, semen, saliva, peritoneal washings, ascites, cystic fluid, meningeal fluid , amniotic fluid, glandular fluid, pancreatic fluid, lymph fluid, pleural fluid, nipple aspirate, bronchial aspirate, synovial fluid), joint aspirate, organ secretions, cells, cell extract, or cerebrospinal fluid, but is not limited thereto.
  • control group may be a normal control group that does not develop cancer.
  • the "diagnosis” refers to determining the susceptibility of a subject to a specific disease or disorder, determining whether the subject currently has a specific disease or disorder, or having a specific disease or disorder Determining a subject's prognosis (e.g., identifying a pre-metastatic or metastatic cancer state, staging the cancer, or determining the responsiveness of a cancer to treatment), or therametrics (e.g., for treatment efficacy); monitoring the state of an object to provide information).
  • the diagnosis is to determine whether or not the above-described cancer is onset or the possibility (risk) of the occurrence.
  • composition for diagnosis of cancer comprising an agent capable of measuring the expression level of one or more genes selected from the group consisting of RP11-572C15.6, FENDRR and ACTA2-AS1.
  • the agent capable of measuring the expression level of RP11-572C15.6, FENDRR or ACTA2-AS1 may include one or more selected from the group consisting of primers, probes and antisense nucleotides that specifically bind to the gene.
  • the "primer” is a fragment recognizing a target gene sequence, including a pair of forward and reverse primers, but preferably, a primer pair that provides analysis results having specificity and sensitivity. High specificity can be conferred when the primer's nucleic acid sequence is a sequence that is inconsistent with a non-target sequence present in the sample, thus amplifying only the target gene sequence containing the complementary primer binding site and not causing non-specific amplification. .
  • the "probe” refers to a substance capable of specifically binding to a target substance to be detected in a sample, and refers to a substance capable of specifically confirming the presence of a target substance in a sample through the binding.
  • the type of probe is not limited as a material commonly used in the art, but preferably PNA (peptide nucleic acid), LNA (locked nucleic acid), peptide, polypeptide, protein, RNA or DNA, and most preferably It is PNA.
  • the probe is a biomaterial derived from or similar thereto, or manufactured in vitro, and includes, for example, enzymes, proteins, antibodies, microorganisms, animal and plant cells and organs, neurons, DNA, and It may be RNA, and DNA includes cDNA, genomic DNA, and oligonucleotides, RNA includes genomic RNA, mRNA, and oligonucleotides, and examples of proteins include antibodies, antigens, enzymes, peptides, and the like.
  • kits for diagnosis of cancer comprising the composition for diagnosis of cancer according to the present invention.
  • the kit of the present invention may include a primer, a probe, or an antisense nucleotide selectively recognizing a marker for diagnosis of cancer, as well as one or more other component compositions, solutions, or devices suitable for an analysis method.
  • the cancer diagnosis kit of the present invention may be a microarray chip kit, a gene amplification kit, or a nanostring kit, but is not limited thereto.
  • the present invention comprising measuring the expression level of one or more genes selected from the group consisting of RP11-572C15.6, FENDRR and ACTA2-AS1 in a biological sample isolated from a subject of interest, It relates to a method of providing information for diagnosis of cancer.
  • the biological sample refers to any material, biological fluid, tissue or cell obtained from or derived from an individual, for example, whole blood, leukocytes, peripheral blood mononuclear cells ( peripheral blood mononuclear cells, buffy coat, plasma, serum, sputum, tears, mucus, nasal washes, nasal aspirate aspirate, breath, urine, semen, saliva, peritoneal washings, ascites, cystic fluid, meningeal fluid, amniotic fluid (amniotic fluid), glandular fluid, pancreatic fluid, lymph fluid, pleural fluid, nipple aspirate, bronchial aspirate, synovial fluid , joint aspirate, organ secretions, cells, cell extracts, or cerebrospinal fluid, but are not limited thereto.
  • peripheral blood mononuclear cells peripheral blood mononuclear cells, buffy coat, plasma, serum, sputum, tears, mucus
  • nasal washes nasal aspirate aspirate, breath, urine, semen
  • the present invention may include measuring the expression level of one or more genes selected from the group consisting of RP11-572C15.6, FENDRR and ACTA2-AS1 in the biological sample isolated as described above.
  • the agent for measuring the expression level of the RP11-572C15.6, FENDRR or ACTA2-AS1 gene may include one or more selected from the group consisting of primers, probes and antisense nucleotides that specifically bind to the gene. have.
  • RT-PCR reverse transcription polymerase reaction
  • RPA RNase protection assay
  • cancer when the expression level of one or more genes selected from the group consisting of RP11-572C15.6, FENDRR and ACTA2-AS1 measured with respect to a biological sample of a target individual increases compared to the control group, cancer occurs or can be predicted to have a high probability of developing the disease.
  • a biomarker composition for predicting the prognosis of cancer comprising at least one selected from the group consisting of RP11-572C15.6, FENDRR and ACTA2-AS1.
  • control group is a normal control group without cancer, the median value of the patient population with cancer (or the average value of the patient), or the median value of the patient population with high treatment responsiveness among patients with cancer (or average value of the patient).
  • the term “prediction of prognosis” refers to a process of predicting the treatment result of a pathological condition by collecting data on the progress of the pathological state and the treatment process.
  • the prognosis prediction may be interpreted as determining the probability of death after treatment of cancer, or the probability of death due to recurrence or metastasis after treatment, but is not limited thereto.
  • composition for predicting the prognosis of cancer comprising an agent capable of measuring the expression level of one or more genes selected from the group consisting of RP11-572C15.6, FENDRR and ACTA2-AS1 .
  • the agent capable of measuring the expression level of RP11-572C15.6, FENDRR or ACTA2-AS1 may include one or more selected from the group consisting of primers, probes and antisense nucleotides that specifically bind to the gene.
  • the analysis method for measuring the amount of the gene includes reverse transcription polymerase reaction (RT-PCR), Competitive RT-PCR, Real-time RT-PCR, RNase protection assay (RPA), Northern blotting, DNA chip, etc.
  • RT-PCR reverse transcription polymerase reaction
  • RPA RNase protection assay
  • Northern blotting DNA chip, etc.
  • the present invention is not limited thereto.
  • kits for predicting the prognosis of cancer comprising the composition for predicting the prognosis of cancer according to the present invention.
  • the kit of the present invention may include a primer, a probe, or an antisense nucleotide that selectively recognizes a marker for predicting the prognosis of cancer, as well as one or more other component compositions, solutions, or devices suitable for the analysis method.
  • the kit for predicting cancer prognosis of the present invention may be a microarray chip kit, a gene amplification kit, or a nanostring kit, but is not limited thereto.
  • the present invention comprising measuring the expression level of one or more genes selected from the group consisting of RP11-572C15.6, FENDRR and ACTA2-AS1 in a biological sample isolated from a subject of interest, It relates to a method of providing information for predicting the prognosis of cancer.
  • the biological sample is whole blood, leukocytes, peripheral blood mononuclear cells, buffy coat, plasma, serum, sputum , tears, mucus, nasal washes, nasal aspirate, breath, urine, semen, saliva, peritoneal washings, ascites, cystic fluid, meningeal fluid, amniotic fluid, glandular fluid, pancreatic fluid, lymph fluid, pleural fluid ), nipple aspirate, bronchial aspirate, synovial fluid, joint aspirate, organ secretions, cell, cell extract or cerebrospinal fluid, but is not limited thereto.
  • the present invention may include measuring the expression level of one or more genes selected from the group consisting of RP11-572C15.6, FENDRR and ACTA2-AS1 in the biological sample isolated as described above.
  • the agent for measuring the expression level of the RP11-572C15.6, FENDRR or ACTA2-AS1 gene may include one or more selected from the group consisting of primers, probes and antisense nucleotides that specifically bind to the gene. have.
  • RT-PCR reverse transcription polymerase reaction
  • RPA RNase protection assay
  • the prognosis when the expression level of one or more genes selected from the group consisting of RP11-572C15.6, FENDRR and ACTA2-AS1 measured with respect to the biological sample of the subject of the present invention is increased compared to the control group, the prognosis is poor can be predicted that
  • a biomarker composition for predicting therapeutic responsiveness to an anticancer agent for cancer comprising at least one selected from the group consisting of RP11-572C15.6, FENDRR, ACTA2-AS1 and ZNF667-AS1, a biomarker composition for predicting therapeutic responsiveness to an anticancer agent for cancer would like to provide
  • the prognosis when the expression level of RP11-572C15.6, FENDRR, ACTA2-AS1 or ZNF667-AS1 is increased compared to the control in the biological sample isolated from the subject of interest, the prognosis can be predicted to be poor. have.
  • control group means a normal control group without cancer, the median value of the patient population with cancer (or the average value of the patient), or the median value of the patient population with high treatment responsiveness among patients with cancer (or average value of the patient).
  • the anticancer agent may be an immune anticancer agent or an immunotherapeutic agent.
  • the immunotherapy or immunotherapeutic agent includes monoclonal antibodies, chimeric antigen receptor (CAR) T-cells, NK-cells, dendritic cells (DC), adoptive cell transfer (ACT), immune checkpoint modulators, cytokines, cancer vaccine, adjuvant, oncolytic virus, or a combination thereof, wherein said monoclonal antibody is PD-1, PD-L1, CTLA-4, IDO, TIM-3, LAG-3, 4- a signaling molecule selected from the group consisting of 1BB, OX40, MERTK, CD27, GITR, B7.1, TGF- ⁇ , BTLA, VISTA, arginase, MICA, MICB, B7-H4, CD28, CD137, and HVEM; It may be to modulate, and a preferred example may be an anti-PD-1 antibody or an anti-PD-L1 antibody, and specific examples, i
  • immunotherapy refers to a method of treating a disease by stimulating the immune system, and in the present invention, it means treating gastrointestinal cancer.
  • Passive immunotherapy is a treatment method that attacks cancer cells by injecting immune response components, such as immune cells, antibodies, and cytokines, made in large amounts outside the body, into cancer patients. It is a therapeutic method that attacks cancer cells by activating or producing them.
  • therapeutic responsiveness prediction refers to predicting whether a patient will respond favorably or non-preferably to an immune anticancer agent, or predicting the risk of resistance to an anticancer agent, prognosis of a patient after immunotherapy That is, it means predicting recurrence, metastasis, survival, or disease-free survival.
  • RP11-572C15.6, FENDRR, ACTA2-AS1 and ZNF667-AS1 for an anticancer agent for cancer comprising an agent capable of measuring the expression level of one or more genes selected from the group consisting of It relates to a composition for predicting therapeutic responsiveness.
  • the agent capable of measuring the expression level of RP11-572C15.6, FENDRR, ACTA2-AS1 or ZNF667-AS1 is one selected from the group consisting of primers, probes and antisense nucleotides specifically binding to the gene. may include more than one.
  • RT -PCR reverse transcription polymerase reaction
  • RPA RNase protection assay
  • the treatment responsiveness is predicted to be low.
  • kits for predicting therapeutic responsiveness to an anticancer agent for cancer comprising the composition for predicting the prognosis of cancer according to the present invention.
  • the kit of the present invention may include a primer, a probe, or an antisense nucleotide that selectively recognizes a marker for predicting the prognosis of cancer, as well as one or more other component compositions, solutions, or devices suitable for the analysis method.
  • the kit for predicting cancer prognosis of the present invention may be a microarray chip kit, a gene amplification kit, or a nanostring kit, but is not limited thereto.
  • measuring the expression level of one or more genes selected from the group consisting of RP11-572C15.6, FENDRR, ACTA2-AS1 and ZNF667-AS1 in a biological sample isolated from a subject of interest It relates to a method of providing information for predicting therapeutic responsiveness to an anticancer agent of cancer, including.
  • the biological sample is whole blood, leukocytes, peripheral blood mononuclear cells, buffy coat, plasma, serum, sputum , tears, mucus, nasal washes, nasal aspirate, breath, urine, semen, saliva, peritoneal washings, ascites, cystic fluid, meningeal fluid, amniotic fluid, glandular fluid, pancreatic fluid, lymph fluid, pleural fluid ), nipple aspirate, bronchial aspirate, synovial fluid, joint aspirate, organ secretions, cell, cell extract or cerebrospinal fluid, but is not limited thereto.
  • the present invention may include measuring the expression level of one or more genes selected from the group consisting of RP11-572C15.6, FENDRR, ACTA2-AS1 and ZNF667-AS1 in the biological sample isolated as described above.
  • the agent for measuring the expression level of the RP11-572C15.6, FENDRR, ACTA2-AS1 or ZNF667-AS1 gene is at least one selected from the group consisting of primers, probes and antisense nucleotides that specifically bind to the gene may include
  • the RP11-572C15.6, FENDRR, ACTA2-AS1 or ZNF667-AS1 gene is a process for confirming the presence and expression level of the gene.
  • As an analysis method for measuring the amount of the gene reverse transcription polymerase reaction (RT-PCR) , Competitive RT-PCR, Real-time RT-PCR, RNase protection assay (RPA), Northern blotting, DNA chip, etc.
  • RT-PCR reverse transcription polymerase reaction
  • RPA RNase protection assay
  • Northern blotting DNA chip, etc.
  • the present invention is not limited thereto.
  • the expression level of one or more genes selected from the group consisting of RP11-572C15.6, FENDRR, ACTA2-AS1 and ZNF667-AS1 measured with respect to a biological sample of a target individual is increased compared to the control group , it can be predicted that the treatment responsiveness will be low.
  • cancer comprising at least one selected from the group consisting of RP11-572C15.6, FENDRR, ACTA2-AS1 and ZNF667-AS1, epithelial-mesenchymal transition (EMT) ) to provide a diagnostic biomarker composition.
  • group consisting of RP11-572C15.6, FENDRR, ACTA2-AS1 and ZNF667-AS1, epithelial-mesenchymal transition (EMT) to provide a diagnostic biomarker composition.
  • epithelial mesenchymal metastasis subtype when the expression level of RP11-572C15.6, FENDRR, ACTA2-AS1 or ZNF667-AS1 is increased compared to the control in the biological sample isolated from the subject of interest, epithelial mesenchymal metastasis subtype can be predicted to be highly probable.
  • control group means a normal control group without cancer, the median value of the patient population with cancer (or the average value of the patient), or the median value of the patient population with high treatment responsiveness among patients with cancer (or average value of the patient).
  • epithelial mesenchymal transition refers to a process in which epithelial cells are transformed into mesenchymal cells. That is, it is a mutational process that loses the appearance of epithelial cells and acquires the characteristics of mesenchymal cells, which is known as an important process for individual formation and development, and is related to cancer cell growth, drug resistance, invasion and metastasis.
  • epithelial-middle of cancer comprising an agent capable of measuring the expression level of one or more genes selected from the group consisting of RP11-572C15.6, FENDRR, ACTA2-AS1 and ZNF667-AS1 It relates to a composition for diagnosing lobe metastasis (EMT).
  • EMT lobe metastasis
  • the agent capable of measuring the expression level of RP11-572C15.6, FENDRR, ACTA2-AS1 or ZNF667-AS1 is one selected from the group consisting of primers, probes and antisense nucleotides specifically binding to the gene. may include more than one.
  • RT -PCR reverse transcription polymerase reaction
  • RPA RNase protection assay
  • epithelial mesenchymal metastasis subtype when the expression level of RP11-572C15.6, FENDRR, ACTA2-AS1 or ZNF667-AS1 is increased compared to the control in the biological sample isolated from the subject of interest, epithelial mesenchymal metastasis subtype can be predicted to be highly probable.
  • the present invention relates to a kit for diagnosing epithelial-mesenchymal metastasis (EMT) of cancer, comprising the composition for diagnosing epithelial-mesenchymal metastasis (EMT) of cancer according to the present invention.
  • EMT epithelial-mesenchymal metastasis
  • the kit of the present invention may include a primer, a probe, or an antisense nucleotide that selectively recognizes a marker for predicting the prognosis of cancer, as well as one or more other component compositions, solutions, or devices suitable for the analysis method.
  • the kit for predicting cancer prognosis of the present invention may be a microarray chip kit, a gene amplification kit, or a nanostring kit, but is not limited thereto.
  • measuring the expression level of one or more genes selected from the group consisting of RP11-572C15.6, FENDRR, ACTA2-AS1 and ZNF667-AS1 in a biological sample isolated from a subject of interest It relates to an information providing method for diagnosing epithelial-mesenchymal metastasis (EMT) of cancer, including.
  • EMT epithelial-mesenchymal metastasis
  • the biological sample is whole blood, leukocytes, peripheral blood mononuclear cells, buffy coat, plasma, serum, sputum , tears, mucus, nasal washes, nasal aspirate, breath, urine, semen, saliva, peritoneal washings, ascites, cystic fluid, meningeal fluid, amniotic fluid, glandular fluid, pancreatic fluid, lymph fluid, pleural fluid ), nipple aspirate, bronchial aspirate, synovial fluid, joint aspirate, organ secretions, cell, cell extract or cerebrospinal fluid, but is not limited thereto.
  • the present invention may include measuring the expression level of one or more genes selected from the group consisting of RP11-572C15.6, FENDRR, ACTA2-AS1 and ZNF667-AS1 in the biological sample isolated as described above.
  • the agent for measuring the expression level of the RP11-572C15.6, FENDRR, ACTA2-AS1 or ZNF667-AS1 gene is at least one selected from the group consisting of primers, probes and antisense nucleotides that specifically bind to the gene may include
  • the RP11-572C15.6, FENDRR, ACTA2-AS1 or ZNF667-AS1 gene is a process for confirming the presence and expression level of the gene.
  • As an analysis method for measuring the amount of the gene reverse transcription polymerase reaction (RT-PCR) , Competitive RT-PCR, Real-time RT-PCR, RNase protection assay (RPA), Northern blotting, DNA chip, etc.
  • RT-PCR reverse transcription polymerase reaction
  • RPA RNase protection assay
  • Northern blotting DNA chip, etc.
  • the present invention is not limited thereto.
  • the expression level of one or more genes selected from the group consisting of RP11-572C15.6, FENDRR, ACTA2-AS1 and ZNF667-AS1 measured with respect to a biological sample of a target individual is increased compared to the control group , it can be predicted that cancer cells are highly likely to contain epithelial-mesenchymal transition subtypes.
  • the method comprising: treating a candidate drug to a biological sample isolated from a cancer subject or an animal model of cancer; And measuring the expression level of one or more genes selected from the group consisting of RP11-572C15.6, FENDRR, ACTA2-AS1 and ZNF667-AS1 in a biological sample or cancer disease animal model treated with the candidate agent, It relates to a method of screening a drug for the prevention or treatment of cancer.
  • the agent for measuring the expression level of the RP11-572C15.6, FENDRR, ACTA2-AS1 or ZNF667-AS1 gene is at least one selected from the group consisting of primers, probes and antisense nucleotides that specifically bind to the gene may include
  • the RP11-572C15.6, FENDRR, ACTA2-AS1 or ZNF667-AS1 gene is a process for confirming the presence and expression level of the gene.
  • As an analysis method for measuring the amount of the gene reverse transcription polymerase reaction (RT-PCR) , Competitive RT-PCR, Real-time RT-PCR, RNase protection assay (RPA), Northern blotting, DNA chip, etc.
  • RT-PCR reverse transcription polymerase reaction
  • RPA RNase protection assay
  • Northern blotting DNA chip, etc.
  • the present invention is not limited thereto.
  • the candidate agent is used to prevent cancer or It may further include the step of determining the drug for treatment.
  • the method comprising: treating a candidate drug to a biological sample isolated from a cancer subject or an animal model of cancer; And measuring the expression level of one or more genes selected from the group consisting of RP11-572C15.6, FENDRR, ACTA2-AS1 and ZNF667-AS1 in a biological sample or cancer disease animal model treated with the candidate agent,
  • the present invention relates to a method for screening a substance that inhibits epithelial-mesenchymal cell metastasis (EMT) of cancer.
  • EMT epithelial-mesenchymal cell metastasis
  • the agent for measuring the expression level of the RP11-572C15.6, FENDRR, ACTA2-AS1 or ZNF667-AS1 gene is at least one selected from the group consisting of primers, probes and antisense nucleotides that specifically bind to the gene may include
  • the RP11-572C15.6, FENDRR, ACTA2-AS1 or ZNF667-AS1 gene is a process for confirming the presence and expression level of the gene.
  • As an analysis method for measuring the amount of the gene reverse transcription polymerase reaction (RT-PCR) , Competitive RTR, Real-time RT-PCR, RNase protection assay (RPA), Northern blotting, DNA chip, etc. It is not limited.
  • the candidate agent is administered to the epithelium of cancer- It may further comprise the step of determining as a mesenchymal cell metastasis inhibitor.
  • the present invention it is possible to accurately and conveniently diagnose gastric cancer, particularly among cancers, at an early stage, and furthermore, it is possible to predict the prognosis of cancer or the therapeutic responsiveness to an anticancer agent, so that the most appropriate treatment method for cancer patients can be selected.
  • Each data is presented in matrix form, with rows representing individual genes and columns representing each tissue.
  • each cell represents the expression level of a gene in an individual tissue, and red and green represent the relative high and low expression levels represented by scale bars (log2 conversion scale).
  • Fig. 2 shows the prognostic relevance of LNC6 subtypes
  • Figure 2b shows a phylogenetic tree in the predictive model, and the patients were classified into subtypes with high predictability.
  • OS overall survival
  • RFS recurrence-free survival
  • RFS recurrence-free survival period
  • Fig. 4 relates to a subset of L6C anticancer drug resistance and epithelial phenotype
  • This analysis included AJCC stage II, III, or IV patients without primary metastasis.
  • CTX relapse-free survival
  • Figure 5a shows the responsiveness to pembrolizumab according to the LNC6 subtype, and
  • Figures 5b and 5c show the therapeutic responsiveness
  • the predictability of L6C subtypes and L6F subtypes is shown in those who show and those who show non-reactivity.
  • Figure 5d shows the normalized enrichment score (NES) of the up-regulated lncRNA in the L6E subtype in the treatment-responsive and non-responsive subjects.
  • CR means complete response
  • PR means partial response
  • SD stable disease
  • PD means progressive disease.
  • Figure 6 shows the cell type components in each LNC6 subtype
  • Figures 6a and 6b are the averaged xCell scores of TCGA cohort samples in each LNC6 subtype
  • Figure 6a shows the 5 cell type family components in each LNC6 subtype.
  • Figure 6b shows the components of 64 cell types in each LNC6 subtype.
  • FIG. 7 shows in vitro demonstration of the relationship between lncRNA and stem-like features
  • Figure 7b is the result of analyzing the ZNF667-AS1 knockdown efficiency by qRT-PCR after siRNA transformation in the EMT subtype gastric cancer cell line
  • Figure 7f shows the results of Western blot analysis using the EMT marker protein shown in the siRNA-transformed EMT subtype gastric cancer cell line
  • Figure 7g shows the MTS assay of oxaliplatin or 5FU in the siRNA-transformed EMT subtype gastric cancer cell line. shows the half maximal inhibitory concentration (IC 50 ) from
  • X-axis is LNC6 subtype
  • Y-axis is arranged in hierarchical clustering.
  • the 725 gastric developmental TFs identified at the level of mRNA abundance the 723 TFs whose expression levels were available in the TCGA cohort were shown. included in the analysis. TFs highly expressed in the early embryonic stage were classified into Group 1, and TFs highly expressed in the late embryonic stage or maturation stage were classified into Group 2, and the Y-axis was arranged from top to bottom according to the standard deviation in the TF groups and samples.
  • FIG. 11 shows specific gene mutations and copy number changes associated with the LNC6 subtype
  • FIG. 12 relates to the differential methylation of miRNA and protein expression and DNA in the TCGA STAD cohort
  • Figures 12a to 12c are multiple 2 to confirm the subtype-specific methylation of miRNA and protein expression and DNA in the TCGA data set -
  • Rho Spearman correlation coefficients
  • biomarker composition for diagnosis of cancer comprising at least one selected from the group consisting of RP11-572C15.6, FENDRR and ACTA2-AS1.
  • a biomarker composition for predicting the prognosis of cancer comprising at least one selected from the group consisting of RP11-572C15.6, FENDRR and ACTA2-AS1.
  • a biomarker composition for predicting therapeutic responsiveness to an anticancer agent for cancer comprising at least one selected from the group consisting of RP11-572C15.6, FENDRR, ACTA2-AS1 and ZNF667-AS1, a biomarker composition for predicting therapeutic responsiveness to an anticancer agent for cancer would like to provide
  • cancer comprising at least one selected from the group consisting of RP11-572C15.6, FENDRR, ACTA2-AS1 and ZNF667-AS1, epithelial-mesenchymal transition (EMT) ) to provide a diagnostic biomarker composition.
  • group consisting of RP11-572C15.6, FENDRR, ACTA2-AS1 and ZNF667-AS1, epithelial-mesenchymal transition (EMT) to provide a diagnostic biomarker composition.
  • a total of 12,727 lncRNA expression profiles and mRNA expression profiles were downloaded from the TCGA gastric adenocarcinoma (STAD) cohort consisting of 258 tumors, and then converted to a log2 base. Somatic mutations, copy-number alteration (CNA) and clinical data of TCGA STAD were downloaded from cBioPortal for Cancer Genomics. DNA methylation, miRNA expression and protein expression data from reverse phase protein array (RPPA) were downloaded from the UCSC Xena platform.
  • STAD gastric adenocarcinoma
  • Cluster analysis and visualization of lncRNA data were performed through Gene Cluster 3.0 and Java TreeView.
  • 258 TCGA STAD patients were classified into 6 clusters: 25 as L6A, 66 as L6B, 51 as L6C, 51 as L6D, 14 as L6E, and 51 as L6F.
  • multiple two-class t tests were performed on all possible combinations of the six subtypes.
  • five 2-sample t-tests were performed (L6A vs. L6B, L6A vs. L6C, L6A vs. L6D, L6A vs.
  • L6E, and L6A vs. L6A vs. L6A. L6F comparison) (P ⁇ 0.05).
  • lncRNAs with significant differences in expression in the 5 possible comparisons corresponded to the following 262 subtype-specific lncRNAs: 24 L6A, 67 L6B, 20 L6C, 55 L6D , 30 were classified as L6E, and 66 as L6F.
  • the lncRNA subtype signature was converted into an mRNA subtype signature by identifying the mRNA whose expression is specific to the LNC6 subtype, and the mRNA signature was independently verified using the mRNA expression data. applied to the cohort. Subtype-specific mRNA expression signatures were confirmed by multiple two-class t tests. For subtype L6A selection, five two-sample t-tests were performed (L6A vs. L6B, L6A vs. L6C, L6A vs. L6D, L6A vs. L6E, and L6A vs. L6F comparisons) (P ⁇ 0.001). .
  • the top 200 mRNAs were selected for each subtype according to the log ratio. Genes with significant differences in 4 comparisons were considered subtype-specific if the number of genes with significantly different expression in 5 possible comparisons was less than 200.
  • BCCP Bayesian compound covariate predictor
  • BCCP model was constructed based on lncRNA expression data in TCGA cohort for LNC6 subtype prediction in immunotherapy cohort and cancer cell line. Due to differences in the reference genome annotation version in the dataset, only 241 of the 262 subtype-specific lncRNAs were used.
  • lncRNA expression was analyzed from raw RNA sequencing data.
  • Pembrolizumab and 29 DNA-fingerprint GC cell lines were performed on 45 samples obtained from patients with metastatic GC who participated in Phase 2 clinical trials. Reads were aligned with the reference human genome GRCh38 according to the methods of the International Cancer Genome Consortium using STAR 2.6.0c.
  • Uniquely mapped reads for each non-coding RNA were calculated using the Rsubread package (ver. 1.34.0) with Gencode annotation (Release 22). Fragments per kilobase of transcript per million mapped reads (FPKM) values were calculated according to its definition using R ( https://www.r-project.org/).
  • TCGA subtypes and microsatellite instability (MSI) status were defined.
  • the BCCP model was applied to predict the benefits of different molecular subtypes and immunotherapy.
  • GIST gastrointestinal stromal tumor
  • ACRG Asian Cancer Research Group
  • ssGSEA Single-sample GSEA
  • GSEA Gene Set Enrichment Analysis
  • IPA Ingenuity Pathway Analysis
  • TCGA cohort transcripts by a gene signature-based method (xCell).
  • the 64 cell types were grouped into 5 cell type families, and the score of each cell type family was calculated as the sum of the cell type scores it contained.
  • Stem cell ability for each subtype was evaluated from the expression level of transcription factors that are expressed differently in the developmental stage of the mouse stomach.
  • 725 gastric developmental transcription factors identified at the mRNA abundance level 723 of the available expression level values in the TCGA cohort were used for analysis.
  • Each subtype cell cycle phase was evaluated from the expression level of S-phase abundant lncRNA identified by initial RNA capture sequencing.
  • lncRNA-mediated transcriptional network changes identified in LncRNA Modulator Atlas in Pan-cancer LncMAP
  • altered lncRNA-transcription factor gene triplets for each cancer type were identified by integrating paired lncRNA and gene expression profiles with genome-wide transcriptional regulation. Thereafter, only triplets containing immune-related genes obtained from the ImmPort project were used for analysis (17,572 triplets in STAD). The degree of immune regulation was defined as the number of triplets composed of each lncRNA.
  • CNA genes from TCGA cohort data were filtered by Q-value ( ⁇ 0.25) and frequency (>5%).
  • DNA methylation data were filtered to standard deviation >0.15, and if there was a significant difference in ⁇ -values in all five possible comparisons (P ⁇ 0.01), it was considered subtype specific, and a total of 38,476 subtype specific probes were identified.
  • L6A was 451, L6B 773, L6C 84, L6D 10, L6E 28,803, and L6F 8,355.
  • miRNA data were filtered for missing values ( ⁇ 20% of cohorts) and were considered subtype-specific if there was a significant difference (P ⁇ 0.05) in all five possible comparisons, and 143 subtype-specific miRNAs were identified: 17 L6A, 4 L6B, 0 L6C, 4 L6D, 8 L6E, and 110 L6F.
  • Proteins with a significant difference (P ⁇ 0.05) in all five possible comparisons were considered subtype-specific, resulting in a total of 40 subtype-specific proteins: 3 for L6A, 1 for L6B, 0 for L6C, 1 L6D, 10 L6E, and 25 L6F.
  • HM450 probes were annotated to lncRNA genes. Gene fusion cases were downloaded from the TCGA cohort, and data on gene fusion among 258 patients were available for 183 patients.
  • the gastric cancer cell line was cultured in RPMI-1640 medium containing 10% fetal bovine serum and penicillin/streptomycin (100ug/L each), wherein the culture was performed in a humidified incubator containing 5% CO 2 37 It was carried out under temperature conditions of °C.
  • ZNF667-AS1 knockdown by siRNA (Thermofisher Scientific) shown in Table 1 was performed using mirus transformation reagent (Mirus bio).
  • Cancer cells transformed with si-non-target or si-ZNF667-AS1 were inoculated into 96-well plates at an amount of 2X10 3 cells/well and cultured overnight before drug treatment, and 20uL CellTiter 96 AQueous One solution per well (MTS, Promega ) was maintained in the presence of drug for 72 h before addition. After incubating the plate for 3 hours, absorbance was measured at 490 nm using an ELISA reader (Bio tek).
  • a 24-well plate containing an 8-um-pore size chamber insert (Corning Costar) was used. did 2 X 10 5 cells in 200uL FBS-free culture medium were loaded into each filter insert (upper chamber), 700uL of culture medium containing 10% FBS was added to each lower chamber, and cultured at 37°C for 16 hours. did After harvesting, the bottom of the insert was fixed and dyed with crystal violet. The number of migrating or infiltrating cells was measured using an EVOS M7000 imaging system (Thermofisher Scientific).
  • the primary antibodies used were: ZNF667 (1:2000, Abcam, ab106432), N-Cadherin (1:1000, Cell signaling, 4061S), E-Cadherin (1:1000, Cell signaling, 14472S), Vimentin (1:1000, Cell signaling, 5741) and GAPDH (1:1000, Sigma, G9545). After washing 5 times with TBS-T, the blots were incubated with mustard radish hydrogen peroxide-conjugated secondary antibody and visualized with enhanced chemi-luminescence detection (ECL plus kit, Pierce).
  • lncRNA genes with unique expression for each subtype were identified: a total of 262 lncRNAs with distinct expression in the six subtypes corresponded to a total of 262 (Fig. 1b; Table 3).
  • LNC6 subtypes in the TCGA cohort represent sex, ethnicity, tumor location and stage of cancer, histological grade, and Loren subtype (Table 4).
  • the majority of L6A and L6E patients were male (84% and 100%).
  • the majority of L6A and L6D patients were from Western countries (96% and 92%).
  • Such ethnic differences were not found in the molecular subtypes of gastric cancer.
  • L6A patients had the highest proportion of the proximal part of the tumor (36%), whereas L6F patients had the lowest proportion (10%).
  • gastric cancer is usually diagnosed after advanced stage in Western countries, L6A subtype tumors showed a relatively low cancer stage.
  • L6C tumors also showed relatively low cancer stage, but L6E and L6F tumors showed high stage and histological grade.
  • the L6F subtype was rich in the diffuse subtype in the Loren classification.
  • the L6C probability corresponds to a positive predictive marker of response to immunotherapy
  • the L6F probability corresponds to a negative predictive marker of response to immunotherapy.
  • the deviation corresponds to 0 or 1, so that the predicted probability of the L6E subtype does not stratify immunotherapy responders and non-responders, although it is specifically upregulated in the L6E subtype.
  • L6F tumors are predicted to consist of stromal cells, that is, lymphoid epithelial cells, fibroblasts, chondrocytes, pericytes, and adipocytes, suggesting that the immune response is limited in L6F tumors (Fig. 6b). ).
  • L6F subtype showed the most significant association with clinical outcomes such as poor prognosis, early recurrence, and resistance to chemotherapy.
  • L6F-like gastric cancer cell lines were identified by applying BCCP predictors for L6F subtype to lncRNA expression data from 29 gastric cancer cell lines.
  • L6F probability showed a high correlation with epithelial-mesenchymal transition (EMT) subtype (Fig. 7a).
  • EMT epithelial-mesenchymal transition
  • L6A subtype was expressed through activation of metabolic pathways such as glycosylation, oxidative phosphorylation and fatty acid metabolism.
  • Hepatocyte nuclear factor-4 ⁇ (HNF4 ⁇ ) was predicted to be the most important upstream regulator of L6A.
  • the L6C subtype is associated with activation of the G2M checkpoint, E2F target, DNA repair, MYC target and MTORC1 signal, while the L6D subtype is associated with protein release and activation of KRAS signaling.
  • the L6E subtype is associated with the activation of an interferon response that maintains the activated immunity of the L6E subtype.
  • the L6F subtype is associated with activation of Wnt/ ⁇ -catenin signaling, TGF- ⁇ signaling, EMT, and angiogenesis.
  • TGF- ⁇ and Twist1 which are major regulators of EMT, are known as upstream regulators of the L6F subtype.
  • the molecular characteristics of the LNC6 subtype were further investigated using genomic and proteome data from TCGA data.
  • the L6B subtype was defined as a high copy number change, and only some genes were expressed differently among the LNC6 subtypes (Fig. 11a).
  • the L6C subtype is characterized by a high mutation burden, and many genes were mutated differently among the LNC6 subtypes (Fig. 11b).
  • the L6E subtype was characterized by a hypermethylation pattern (Fig. 12a), and the L6F subtype was characterized by the most distinct expression pattern of miRNAs and proteins (Figs. 12b and 12c) and a recurrent (15.4%) CLDN18-ARHGAP fusion.
  • the epigenetic background of subtype-specific lncRNAs and the identified epigenetic regulated lncRNAs were identified ( FIG. 13 ).
  • L6A and L6B subtypes were abundant in chromosomal instability (CIN) and microsatellite stability (MSS) subtypes.
  • the L6C subtype was abundant in the microsatellite instability (MSI) subtype, and the L6D subtype was mixed with a number of other molecular subtypes.
  • the L6E subtype corresponded to 100% Epstein-Barr virus (EBV) subtype, and the L6F subtype was abundant in the genetically stable (GS) subtype, the MP subtype and the EMT subtype.
  • the present invention relates to a composition for predicting cancer prognosis, a kit comprising the same, and a method for predicting cancer prognosis.

Abstract

The present invention relates to a composition for cancer prognosis prediction, a kit comprising same, and a cancer prognosis prediction method.

Description

암 예후 예측을 위한 조성물 및 이를 포함하는 키트Composition for predicting cancer prognosis and kit comprising same
본 발명은 암 예후 예측을 위한 조성물, 이를 포함하는 키트 및 암 예후 예측 방법에 관한 것이다. The present invention relates to a composition for predicting cancer prognosis, a kit comprising the same, and a method for predicting cancer prognosis.
암이란 개체의 필요에 따라 규칙적이고 절제 있는 증식과 억제를 할 수 있는 정상세포와 달리 조직 내에서 필요한 상태를 무시하고 무제한의 증식을 하는 미분화 세포로 구성된 세포덩어리로서 종양이라고도 한다. 이러한 무제한의 증식을 하는 암 세포는 주위의 조직으로 침투하고 더 심각한 경우는 신체의 다른 기관으로 전이가 되어 심각한 고통을 수반하고 결국 죽음을 초래하는 난치병이다.Cancer is a cell mass composed of undifferentiated cells that proliferate indefinitely while ignoring the necessary condition in the tissue, unlike normal cells, which can proliferate and suppress regularly and in a controlled manner according to individual needs. Such unrestricted proliferation of cancer cells infiltrates into surrounding tissues and, in more severe cases, metastasizes to other organs of the body, causing severe pain and eventually death.
암은 혈액암과 고형암으로 크게 분류되며, 위암, 췌장암, 유방암, 구강암, 간암, 자궁암, 식도암, 피부암 등 신체의 거의 모든 부위에서 발생하며, 이들의 치료방법으로 최근 글리벡 또는 허셉틴과 같은 소수의 표적치료제가 특정 암의 치료에 이용되고 있으나 현재까지는 수술이나 방사선 요법 및 세포증식을 억제하는 화학요법제를 이용한 항암제 치료가 주된 방법이다. 그러나 표적치료제가 아니기 때문에 기존 화학요법제의 가장 큰 문제는 세포독성으로 인한 부작용과 약제 내성으로써, 항암제에 의한 초기의 성공적인 반응에도 불구하고 결국에는 치료가 실패하게 되는 주요 요인이다. 따라서, 이러한 화학요법제의 한계를 극복하기 위해서는 항암작용 기전이 명확한 표적 치료제 개발이 지속적으로 필요하다.Cancer is broadly classified into blood cancer and solid cancer, and occurs in almost all parts of the body, such as stomach cancer, pancreatic cancer, breast cancer, oral cancer, liver cancer, uterine cancer, esophageal cancer, and skin cancer. Although therapeutic agents are being used for the treatment of specific cancers, surgery, radiation therapy, and anticancer drug treatment using chemotherapeutic agents that inhibit cell proliferation are the main methods up to now. However, since it is not a targeted therapy, the biggest problem with existing chemotherapeutic agents is the side effects and drug resistance due to cytotoxicity, which is a major factor that ultimately causes the treatment to fail despite the initial successful response to the anticancer agent. Therefore, in order to overcome the limitations of these chemotherapeutic agents, there is a continuous need to develop targeted therapeutics with a clear anticancer mechanism.
위암은 암-관련 죽음의 3번째 원인에 해당하고, 가장 흔한 암 중에서도 4번째 순위를 차지한다. 개인 맞춤형 약제의 개발을 위하여 임상적으로 그리고 생물학적으로 면밀한 분자적 분석이 요구되고 있다. 지난 몇 년 동안, 전장 유전체 수준에서 위암에 대한 오믹스(omics) 연구를 통해 생물학적 복잡성에 대한 이해도를 높였고, 특히 mRNA 유전자 발현 데이터의 분석을 통해 바이오마커를 이용한 예후 및 보조 항암 화학 요법에 대한 반응성 예측에 큰 발전을 가져왔다. 위암에 있어서 다른 수준에서의 분자적 프로파일링은 개별 맞춤화된 치료 전략의 발전을 가져올 것으로 예측된다. Gastric cancer is the third leading cause of cancer-related death and ranks fourth among the most common cancers. For the development of personalized pharmaceuticals, clinically and biologically close molecular analysis is required. In the past few years, understanding of biological complexity has been improved through omics studies of gastric cancer at the whole genome level, and in particular, prognosis using biomarkers and responsiveness to adjuvant chemotherapy through analysis of mRNA gene expression data. Great advances have been made in forecasting. Molecular profiling at different levels in gastric cancer is expected to lead to the development of personalized treatment strategies.
최근에는 긴 비암호화 RNA(long non-coding RNA; lncRNA)를 암의 예후 예측 및 치료를 위한 타겟으로 연구가 진행되고 있다. 상기 lncRNA는 암의 경로에 중요한 물질로 작용하며, 암으로의 발전 및 진행에 관련이 있다. 상기 lncRNA는 단백질-코딩 잠재력 없이 200개 이상의 뉴클레오티드로 이루어진 전사체이다. 이들은 단백질을 코딩하는 유전자보다 구성 뉴클레오티드의 수가 많으며, mRNA 보다 큰 정도로 특정 세포/조직 유형으로 제한되며, 암-유형 특이적 치료 타겟으로 전망되고 있다. Recently, long non-coding RNA (lncRNA) has been studied as a target for prognosis and treatment of cancer. The lncRNA acts as an important substance in the path of cancer, and is related to the development and progression of cancer. The lncRNA is a transcript of 200 or more nucleotides without protein-coding potential. They have a higher number of constituent nucleotides than the protein-coding gene, are restricted to specific cell/tissue types to a greater extent than mRNA, and are promising as cancer-type specific therapeutic targets.
핵산 기반 (RNA-타겟팅) 치료제는 몇몇 질환에서 임상적 성공을 보였고, lncRNA를 타겟으로 하여 몇몇의 암에서도 임상 전 성공을 보였다. 하지만 위암에서는 lncRNA 타겟에 대하여 보고된 바가 없고, 이들의 임상적 연관성 및 생물학적 기능에 대하여 충분한 연구가 이루어지고 있지 않다. Nucleic acid-based (RNA-targeting) therapeutics have shown clinical success in several diseases and preclinical success in some cancers by targeting lncRNA. However, there have been no reports of lncRNA targets in gastric cancer, and sufficient studies have not been conducted on their clinical relevance and biological functions.
본 발명의 일 목적은 암의 진단, 예후 또는 치료 반응성을 예측하기 위한 바이오마커 조성물을 제공하고자 한다. One object of the present invention is to provide a biomarker composition for predicting the diagnosis, prognosis or therapeutic responsiveness of cancer.
본 발명의 다른 목적은 암의 진단, 예후 또는 치료 반응성을 예측하기 위한 조성물, 이를 포함하는 키트를 제공하고자 한다.Another object of the present invention is to provide a composition for diagnosing cancer, prognosis or predicting therapeutic responsiveness, and a kit comprising the same.
본 발명의 또 다른 목적은 암의 진단, 예후 또는 치료 반응성을 예측하기 위한 정보 제공 방법을 제공하고자 한다. Another object of the present invention is to provide a method for providing information for predicting diagnosis, prognosis, or treatment responsiveness of cancer.
그러나 본 발명이 이루고자 하는 기술적 과제는 이상에서 언급한 과제에 제한되지 않으며, 언급되지 않은 또 다른 과제들은 아래의 기재로부터 당업계에서 통상의 지식을 가진 자에게 명확하게 이해될 수 있을 것이다. However, the technical problem to be achieved by the present invention is not limited to the above-mentioned problems, and other problems not mentioned will be clearly understood by those of ordinary skill in the art from the following description.
본 명세서에서, 암은 위암, 난소암, 대장암, 유방암, 간암, 췌장암, 자궁경부암, 갑상선암, 부갑상선암, 비소세포성폐암, 전립선암, 담낭암, 담도암, 비호지킨 림프종, 호지킨 림프종, 혈액암, 방광암, 신장암, 흑색종, 결장암, 골암, 피부암, 두부암, 자궁암, 직장암, 뇌종양, 항문부근암, 나팔관암종, 자궁내막암종, 질암, 음문암종, 식도암, 소장암, 내분비선암, 부신암, 연조직 육종, 요도암, 음경암, 수뇨관암, 신장세포 암종, 신장골반 암종, 중추신경계(CNS central nervoussystem) 종양, 1차 CNS 림프종, 척수 종양, 뇌간 신경교종 또는 뇌하수체 선종일 수 있으나, 바람직하게는 위암일 수 있다. As used herein, cancer is gastric cancer, ovarian cancer, colorectal cancer, breast cancer, liver cancer, pancreatic cancer, cervical cancer, thyroid cancer, parathyroid cancer, non-small cell lung cancer, prostate cancer, gallbladder cancer, biliary tract cancer, non-Hodgkin's lymphoma, Hodgkin's lymphoma, blood Cancer, bladder cancer, kidney cancer, melanoma, colon cancer, bone cancer, skin cancer, head cancer, uterine cancer, rectal cancer, brain tumor, perianal cancer, fallopian tube carcinoma, endometrial carcinoma, vaginal cancer, vulvar carcinoma, esophageal cancer, small intestine cancer, endocrine adenocarcinoma, part It may be renal cancer, soft tissue sarcoma, urethral cancer, penile cancer, ureter cancer, renal cell carcinoma, renal pelvic carcinoma, CNS central nervoussystem tumor, primary CNS lymphoma, spinal cord tumor, brainstem glioma or pituitary adenoma, but preferred It could be stomach cancer.
본 명세서에서 ZNF667-AS1(ZNF667 Antisense RNA 1 (Head To Head)), RP11-572C15.6, FENDRR(FOXF1 Adjacent Non-Coding Developmental Regulatory RNA) 및 ACTA2-AS1(ACTA2 Antisense RNA 1) 각각은 긴-비암호화 RNA 유전자(long non-coding RNA; lncRNA)로, 상기 ZNF667-AS1는 서열번호 1로 표시될 수 있고, 상기 RP11-572C15.6는 서열번호 2로 표시될 수 있으며, 상기 FENDRR은 서열번호 3으로 표시될 수 있고, 상기 ACTA2-AS1은 서열번호 4로 표시될 수 있으나, 이에 제한되는 것은 아니다.In the present specification, ZNF667-AS1 (ZNF667 Antisense RNA 1 (Head To Head)), RP11-572C15.6, FENDRR (FOXF1 Adjacent Non-Coding Developmental Regulatory RNA) and ACTA2-AS1 (ACTA2 Antisense RNA 1) each are long-ratio As a coding RNA gene (long non-coding RNA; lncRNA), the ZNF667-AS1 may be represented by SEQ ID NO: 1, the RP11-572C15.6 may be represented by SEQ ID NO: 2, and the FENDRR is SEQ ID NO: 3 may be represented, and the ACTA2-AS1 may be represented by SEQ ID NO: 4, but is not limited thereto.
본 발명의 일 구현 예에 따르면, RP11-572C15.6, FENDRR 및 ACTA2-AS1로 이루어진 군에서 선택된 1종 이상을 포함하는, 암의 진단용 바이오마커 조성물을 제공하고자 한다. According to one embodiment of the present invention, it is to provide a biomarker composition for diagnosis of cancer, comprising at least one selected from the group consisting of RP11-572C15.6, FENDRR and ACTA2-AS1.
본 발명의 일 구체 예에서는 목적하는 개체로부터 분리된 생물학적 시료에 있어서 상기 RP11-572C15.6, FENDRR 또는 ACTA2-AS1의 발현 수준이 대조군에 비하여 증가된 경우, 암의 발병하였거나 발병 가능성이 높을 것으로 예측할 수 있다. In one embodiment of the present invention, when the expression level of RP11-572C15.6, FENDRR or ACTA2-AS1 is increased compared to the control in a biological sample isolated from a subject of interest, the onset of cancer or a high probability of occurrence is predicted. can
본 발명에서 상기 "목적하는 개체"란, 상기 암의 발병 여부가 불확실한 개체로, 발병 가능성이 높은 개체를 의미한다. In the present invention, the "target individual" refers to an individual who is uncertain whether or not the onset of the cancer has a high probability of onset.
본 발명에서 상기 "생물학적 시료"는 개체로부터 얻어지거나 개체로부터 유래된 임의의 물질, 생물학적 체액, 조직 또는 세포를 의미하는 것으로, 예를 들면, 전혈(whole blood), 백혈구(leukocytes), 말초혈액 단핵 세포(peripheral blood mononuclear cells), 백혈구 연층(buffy coat), 혈장(plasma), 혈청(serum), 객담(sputum), 눈물(tears), 점액(mucus), 세비액(nasal washes), 비강 흡인물(nasal aspirate), 호흡(breath), 소변(urine), 정액(semen), 침(saliva), 복강 세척액(peritoneal washings), 복수(ascites), 낭종액(cystic fluid), 뇌척수막 액(meningeal fluid), 양수(amniotic fluid), 선액(glandular fluid), 췌장액(pancreatic fluid), 림프액(lymph fluid), 흉수(pleural fluid), 유두 흡인물(nipple aspirate), 기관지 흡인물(bronchial aspirate), 활액(synovial fluid), 관절 흡인물(joint aspirate), 기관 분비물(organ secretions), 세포(cell), 세포 추출물(cell extract) 또는 뇌척수액(cerebrospinal fluid)을 포함할 수 있으나, 이에 제한되는 것은 아니다. In the present invention, the "biological sample" refers to any material, biological fluid, tissue or cell obtained from or derived from an individual, for example, whole blood, leukocytes, peripheral blood mononuclear peripheral blood mononuclear cells, buffy coat, plasma, serum, sputum, tears, mucus, nasal washes, nasal aspirate (nasal aspirate), breath, urine, semen, saliva, peritoneal washings, ascites, cystic fluid, meningeal fluid , amniotic fluid, glandular fluid, pancreatic fluid, lymph fluid, pleural fluid, nipple aspirate, bronchial aspirate, synovial fluid), joint aspirate, organ secretions, cells, cell extract, or cerebrospinal fluid, but is not limited thereto.
본 발명에서 상기 "대조군"이란 암이 발병하지 않은 정상 대조군일 수 있다. In the present invention, the "control group" may be a normal control group that does not develop cancer.
본 발명에서 상기 "진단"은 특정 질병 또는 질환에 대한 대상(subject)의 감수성(susceptibility)을 판정하는 것, 대상이 특정 질병 또는 질환을 현재 가지고 있는지 여부를 판정하는 것, 특정 질병 또는 질환에 걸린 대상의 예후(prognosis)(예컨대, 전-전이성 또는 전이성 암 상태의 동정, 암의 단계 결정 또는 치료에 대한 암의 반응성 결정)를 판정하는 것, 또는 테라메트릭스(therametrics)(예컨대, 치료 효능에 대한 정보를 제공하기 위하여 객체의 상태를 모니터링하는 것)을 포함한다. 본 발명의 목적상, 상기 진단은 상기한 암의 발병 여부 또는 발병 가능성(위험성)을 확인하는 것이다. In the present invention, the "diagnosis" refers to determining the susceptibility of a subject to a specific disease or disorder, determining whether the subject currently has a specific disease or disorder, or having a specific disease or disorder Determining a subject's prognosis (e.g., identifying a pre-metastatic or metastatic cancer state, staging the cancer, or determining the responsiveness of a cancer to treatment), or therametrics (e.g., for treatment efficacy); monitoring the state of an object to provide information). For the purpose of the present invention, the diagnosis is to determine whether or not the above-described cancer is onset or the possibility (risk) of the occurrence.
본 발명의 다른 구현 예에 따르면, RP11-572C15.6, FENDRR 및 ACTA2-AS1로 이루어진 군에서 선택된 1종 이상의 유전자의 발현 수준을 측정할 수 있는 제제를 포함하는 암의 진단용 조성물에 관한 것이다.According to another embodiment of the present invention, it relates to a composition for diagnosis of cancer comprising an agent capable of measuring the expression level of one or more genes selected from the group consisting of RP11-572C15.6, FENDRR and ACTA2-AS1.
본 발명에서 상기 RP11-572C15.6, FENDRR 또는 ACTA2-AS1의 발현 수준을 측정할 수 있는 제제는 상기 유전자에 특이적으로 결합하는 프라이머, 프로브 및 안티센스 뉴클레오티드로 이루어진 군에서 선택된 1종 이상을 포함할 수 있다. In the present invention, the agent capable of measuring the expression level of RP11-572C15.6, FENDRR or ACTA2-AS1 may include one or more selected from the group consisting of primers, probes and antisense nucleotides that specifically bind to the gene. can
본 발명에서 상기 "프라이머"는 표적 유전자 서열을 인지하는 단편으로서, 정방향 및 역방향의 프라이머 쌍을 포함하나, 바람직하게는, 특이성 및 민감성을 가지는 분석 결과를 제공하는 프라이머 쌍이다. 프라이머의 핵산 서열이 시료 내 존재하는 비-표적 서열과 불일치하는 서열이어서, 상보적인 프라이머 결합 부위를 함유하는 표적 유전자 서열만 증폭하고 비특이적 증폭을 유발하지 않는 프라이머일 때, 높은 특이성이 부여될 수 있다.In the present invention, the "primer" is a fragment recognizing a target gene sequence, including a pair of forward and reverse primers, but preferably, a primer pair that provides analysis results having specificity and sensitivity. High specificity can be conferred when the primer's nucleic acid sequence is a sequence that is inconsistent with a non-target sequence present in the sample, thus amplifying only the target gene sequence containing the complementary primer binding site and not causing non-specific amplification. .
본 발명에서 상기 "프로브"란 시료 내의 검출하고자 하는 표적 물질과 특이적으로 결합할 수 있는 물질을 의미하며, 상기 결합을 통하여 특이적으로 시료 내의 표적 물질의 존재를 확인할 수 있는 물질을 의미한다. 프로브의 종류는 당업계에서 통상적으로 사용되는 물질로서 제한은 없으나, 바람직하게는 PNA(peptide nucleic acid), LNA(locked nucleic acid), 펩타이드, 폴리펩타이드, 단백질, RNA 또는 DNA일 수 있으며, 가장 바람직하게는 PNA이다. 보다 구체적으로, 상기 프로브는 바이오 물질로서 생물에서 유래되거나 이와 유사한 것 또는 생체 외에서 제조된 것을 포함하는 것으로, 예를 들어, 효소, 단백질, 항체, 미생물, 동식물 세포 및 기관, 신경세포, DNA, 및 RNA일 수 있으며, DNA는 cDNA, 게놈 DNA, 올리고뉴클레오타이드를 포함하며, RNA는 게놈 RNA, mRNA, 올리고뉴클레오타이드를 포함하며, 단백질의 예로는 항체, 항원, 효소, 펩타이드 등을 포함할 수 있다.In the present invention, the "probe" refers to a substance capable of specifically binding to a target substance to be detected in a sample, and refers to a substance capable of specifically confirming the presence of a target substance in a sample through the binding. The type of probe is not limited as a material commonly used in the art, but preferably PNA (peptide nucleic acid), LNA (locked nucleic acid), peptide, polypeptide, protein, RNA or DNA, and most preferably It is PNA. More specifically, the probe is a biomaterial derived from or similar thereto, or manufactured in vitro, and includes, for example, enzymes, proteins, antibodies, microorganisms, animal and plant cells and organs, neurons, DNA, and It may be RNA, and DNA includes cDNA, genomic DNA, and oligonucleotides, RNA includes genomic RNA, mRNA, and oligonucleotides, and examples of proteins include antibodies, antigens, enzymes, peptides, and the like.
본 발명의 일 구체 예에서는 목적하는 개체로부터 분리된 생물학적 시료에 있어서 상기 RP11-572C15.6, FENDRR 또는 ACTA2-AS1의 발현 수준이 대조군에 비하여 증가된 경우, 암이 발병하였거나 발병 가능성이 높은 것으로 예측할 수 있다. In one embodiment of the present invention, when the expression level of RP11-572C15.6, FENDRR or ACTA2-AS1 is increased compared to the control in the biological sample isolated from the subject of interest, cancer has occurred or is highly likely to be predicted. can
본 발명의 또 다른 구현 예에 따르면, 본 발명에 따른 암의 진단용 조성물을 포함하는 암의 진단용 키트에 관한 것이다. According to another embodiment of the present invention, there is provided a kit for diagnosis of cancer comprising the composition for diagnosis of cancer according to the present invention.
본 발명의 키트에는 암의 진단을 위하여 선택적으로 마커를 인지하는 프라이머, 프로브 또는 안티센스 뉴클레오티드 뿐만 아니라, 분석 방법에 적합한 한 종류 또는 그 이상의 다른 구성성분 조성물, 용액, 또는 장치가 포함될 수 있다.The kit of the present invention may include a primer, a probe, or an antisense nucleotide selectively recognizing a marker for diagnosis of cancer, as well as one or more other component compositions, solutions, or devices suitable for an analysis method.
본 발명의 암의 진단용 키트는 마이크로어레이 칩 키트, 유전자 증폭 키트 또는 나노스트링 키트일 수 있으나, 이에 제한되는 것은 아니다.The cancer diagnosis kit of the present invention may be a microarray chip kit, a gene amplification kit, or a nanostring kit, but is not limited thereto.
본 발명의 또 다른 구현 예에 따르면, 목적하는 개체로부터 분리된 생물학적 시료에서 RP11-572C15.6, FENDRR 및 ACTA2-AS1로 이루어진 군에서 선택된 1종 이상의 유전자의 발현 수준을 측정하는 단계를 포함하는, 암의 진단을 위한 정보 제공 방법에 관한 것이다. According to another embodiment of the present invention, comprising measuring the expression level of one or more genes selected from the group consisting of RP11-572C15.6, FENDRR and ACTA2-AS1 in a biological sample isolated from a subject of interest, It relates to a method of providing information for diagnosis of cancer.
본 발명에서 상기 생물학적 시료는 개체로부터 얻어지거나 개체로부터 유래된 임의의 물질, 생물학적 체액, 조직 또는 세포를 의미하는 것으로, 예를 들면, 전혈(whole blood), 백혈구(leukocytes), 말초혈액 단핵 세포(peripheral blood mononuclear cells), 백혈구 연층(buffy coat), 혈장(plasma), 혈청(serum), 객담(sputum), 눈물(tears), 점액(mucus), 세비액(nasal washes), 비강 흡인물(nasal aspirate), 호흡(breath), 소변(urine), 정액(semen), 침(saliva), 복강 세척액(peritoneal washings), 복수(ascites), 낭종액(cystic fluid), 뇌척수막 액(meningeal fluid), 양수(amniotic fluid), 선액(glandular fluid), 췌장액(pancreatic fluid), 림프액(lymph fluid), 흉수(pleural fluid), 유두 흡인물(nipple aspirate), 기관지 흡인물(bronchial aspirate), 활액(synovial fluid), 관절 흡인물(joint aspirate), 기관 분비물(organ secretions), 세포(cell), 세포 추출물(cell extract) 또는 뇌척수액(cerebrospinal fluid)을 포함할 수 있으나, 이에 제한되는 것은 아니다. In the present invention, the biological sample refers to any material, biological fluid, tissue or cell obtained from or derived from an individual, for example, whole blood, leukocytes, peripheral blood mononuclear cells ( peripheral blood mononuclear cells, buffy coat, plasma, serum, sputum, tears, mucus, nasal washes, nasal aspirate aspirate, breath, urine, semen, saliva, peritoneal washings, ascites, cystic fluid, meningeal fluid, amniotic fluid (amniotic fluid), glandular fluid, pancreatic fluid, lymph fluid, pleural fluid, nipple aspirate, bronchial aspirate, synovial fluid , joint aspirate, organ secretions, cells, cell extracts, or cerebrospinal fluid, but are not limited thereto.
본 발명에서는 상기와 같이 분리된 생물학적 시료에서 RP11-572C15.6, FENDRR 및 ACTA2-AS1으로 이루어진 군에서 선택된 1종 이상의 유전자의 발현 수준을 측정하는 단계를 포함할 수 있다.The present invention may include measuring the expression level of one or more genes selected from the group consisting of RP11-572C15.6, FENDRR and ACTA2-AS1 in the biological sample isolated as described above.
본 발명에서 상기 RP11-572C15.6, FENDRR 또는 ACTA2-AS1 유전자의 발현 수준을 측정하는 제제는 상기 유전자에 특이적으로 결합하는 프라이머, 프로브 및 안티센스 뉴클레오티드로 이루어진 군에서 선택된 1종 이상을 포함할 수 있다. In the present invention, the agent for measuring the expression level of the RP11-572C15.6, FENDRR or ACTA2-AS1 gene may include one or more selected from the group consisting of primers, probes and antisense nucleotides that specifically bind to the gene. have.
본 발명에 상기 RP11-572C15.6, FENDRR 또는 ACTA2-AS1 유전자의 존재 여부와 발현 정도를 확인하는 과정으로 유전자의 양을 측정하는 분석 방법으로는 역전사 중합효소반응(RT-PCR), 경쟁적 역전사 중합효소반응(Competitive RT-PCR), 실시간 역전사 중합효소반응(Real-time RT-PCR), RNase 보호 분석법(RPA; RNase protection assay), 노던 블랏팅(Northern blotting), DNA 칩 등이 있으나 이에 제한되는 것은 아니다. In the present invention, as an analysis method for measuring the amount of the gene in the process of checking the presence and expression level of the RP11-572C15.6, FENDRR or ACTA2-AS1 gene, reverse transcription polymerase reaction (RT-PCR), competitive reverse transcription polymerization Competitive RT-PCR, Real-time RT-PCR, RNase protection assay (RPA), Northern blotting, DNA chip, etc., but are limited thereto it is not
본 발명의 일 구체 예에서 목적하는 개체의 생물학적 시료에 대하여 측정된 RP11-572C15.6, FENDRR 및 ACTA2-AS1으로 이루어진 군에서 선택된 1종 이상의 유전자의 발현 수준이 대조군에 비하여 증가한 경우, 암이 발병하였거나 발병 가능성이 높은 것으로 예측할 수 있다. In one embodiment of the present invention, when the expression level of one or more genes selected from the group consisting of RP11-572C15.6, FENDRR and ACTA2-AS1 measured with respect to a biological sample of a target individual increases compared to the control group, cancer occurs or can be predicted to have a high probability of developing the disease.
본 발명의 또 다른 구현 예에 따르면, RP11-572C15.6, FENDRR 및 ACTA2-AS1로 이루어진 군에서 선택된 1종 이상을 포함하는, 암의 예후 예측을 위한 바이오마커 조성물을 제공하고자 한다. According to another embodiment of the present invention, it is to provide a biomarker composition for predicting the prognosis of cancer, comprising at least one selected from the group consisting of RP11-572C15.6, FENDRR and ACTA2-AS1.
본 발명의 일 구체 예에서는 목적하는 개체로부터 분리된 생물학적 시료에 있어서 상기 RP11-572C15.6, FENDRR 또는 ACTA2-AS1의 발현 수준이 대조군에 비하여 증가된 경우, 예후가 나쁠 것으로 예측할 수 있다. In one embodiment of the present invention, when the expression level of RP11-572C15.6, FENDRR or ACTA2-AS1 is increased compared to the control in the biological sample isolated from the subject of interest, it can be predicted that the prognosis is poor.
본 발명에서 상기 "대조군"은 암이 발병하지 않은 정상 대조군이거나, 암이 발병된 환자 모집단의 중앙값(또는 해당 환자의 평균값)이거나, 암이 발병된 환자 중 치료 반응성이 높은 환자 모집단의 중앙값(또는 해당 환자의 평균값)일 수 있다.In the present invention, the "control group" is a normal control group without cancer, the median value of the patient population with cancer (or the average value of the patient), or the median value of the patient population with high treatment responsiveness among patients with cancer (or average value of the patient).
본 발명에서 상기 "예후 예측"이란, 병리 상태의 진행 상황 및 치료 과정의 데이터를 취합하여, 병리 상태의 치료 결과를 예측하는 과정을 의미한다. 본 발명의 목적상 상기 예후 예측은 암의 치료 후 사망 가능성, 또는 치료 후 재발 또는 전이로 인한 사망 가능성을 판단하는 것으로 해석될 수 있으나, 이에 제한되는 것은 아니다.In the present invention, the term “prediction of prognosis” refers to a process of predicting the treatment result of a pathological condition by collecting data on the progress of the pathological state and the treatment process. For the purpose of the present invention, the prognosis prediction may be interpreted as determining the probability of death after treatment of cancer, or the probability of death due to recurrence or metastasis after treatment, but is not limited thereto.
본 발명의 다른 구현 예에 따르면, RP11-572C15.6, FENDRR 및 ACTA2-AS1로 이루어진 군에서 선택된 1종 이상의 유전자의 발현 수준을 측정할 수 있는 제제를 포함하는 암의 예후 예측용 조성물에 관한 것이다.According to another embodiment of the present invention, it relates to a composition for predicting the prognosis of cancer comprising an agent capable of measuring the expression level of one or more genes selected from the group consisting of RP11-572C15.6, FENDRR and ACTA2-AS1 .
본 발명에서 상기 RP11-572C15.6, FENDRR 또는 ACTA2-AS1의 발현 수준을 측정할 수 있는 제제는 상기 유전자에 특이적으로 결합하는 프라이머, 프로브 및 안티센스 뉴클레오티드로 이루어진 군에서 선택된 1종 이상을 포함할 수 있다. In the present invention, the agent capable of measuring the expression level of RP11-572C15.6, FENDRR or ACTA2-AS1 may include one or more selected from the group consisting of primers, probes and antisense nucleotides that specifically bind to the gene. can
본 발명에서 상기 RP11-572C15.6, FENDRR 또는 ACTA2-AS1의 유전자의 존재 여부와 발현 수준을 확인하는 과정으로, 상기 유전자의 양을 측정하는 분석 방법으로는 역전사 중합효소반응(RT-PCR), 경쟁적 역전사 중합효소반응(Competitive RT-PCR), 실시간 역전사 중합효소반응(Real-time RT-PCR), RNase 보호 분석법(RPA; RNase protection assay), 노던 블랏팅(Northern blotting), DNA 칩 등이 있으나 이에 제한되는 것은 아니다. In the present invention, as a process of confirming the presence and expression level of the gene of RP11-572C15.6, FENDRR or ACTA2-AS1, the analysis method for measuring the amount of the gene includes reverse transcription polymerase reaction (RT-PCR), Competitive RT-PCR, Real-time RT-PCR, RNase protection assay (RPA), Northern blotting, DNA chip, etc. However, the present invention is not limited thereto.
본 발명의 일 구체 예에서는 목적하는 개체로부터 분리된 생물학적 시료에 있어서 상기 RP11-572C15.6, FENDRR 또는 ACTA2-AS1의 발현 수준이 대조군에 비하여 증가된 경우, 예후가 나쁠 것으로 예측할 수 있다. In one embodiment of the present invention, when the expression level of RP11-572C15.6, FENDRR or ACTA2-AS1 is increased compared to the control in the biological sample isolated from the subject of interest, it can be predicted that the prognosis is poor.
본 발명의 또 다른 구현 예에 따르면, 본 발명에 따른 암의 예후 예측용 조성물을 포함하는 암의 예후 예측용 키트에 관한 것이다. According to another embodiment of the present invention, it relates to a kit for predicting the prognosis of cancer comprising the composition for predicting the prognosis of cancer according to the present invention.
본 발명의 키트에는 암의 예후 예측을 위하여 선택적으로 마커를 인지하는 프라이머, 프로브 또는 안티센스 뉴클레오티드 뿐만 아니라, 분석 방법에 적합한 한 종류 또는 그 이상의 다른 구성성분 조성물, 용액, 또는 장치가 포함될 수 있다.The kit of the present invention may include a primer, a probe, or an antisense nucleotide that selectively recognizes a marker for predicting the prognosis of cancer, as well as one or more other component compositions, solutions, or devices suitable for the analysis method.
본 발명의 암의 예후 예측용 키트는 마이크로어레이 칩 키트, 유전자 증폭 키트 또는 나노스트링 키트일 수 있으나, 이에 제한되는 것은 아니다.The kit for predicting cancer prognosis of the present invention may be a microarray chip kit, a gene amplification kit, or a nanostring kit, but is not limited thereto.
본 발명의 또 다른 구현 예에 따르면, 목적하는 개체로부터 분리된 생물학적 시료에서 RP11-572C15.6, FENDRR 및 ACTA2-AS1로 이루어진 군에서 선택된 1종 이상의 유전자의 발현 수준을 측정하는 단계를 포함하는, 암의 예후 예측을 위한 정보 제공 방법에 관한 것이다. According to another embodiment of the present invention, comprising measuring the expression level of one or more genes selected from the group consisting of RP11-572C15.6, FENDRR and ACTA2-AS1 in a biological sample isolated from a subject of interest, It relates to a method of providing information for predicting the prognosis of cancer.
본 발명에서 상기 생물학적 시료는 전혈(whole blood), 백혈구(leukocytes), 말초혈액 단핵 세포(peripheral blood mononuclear cells), 백혈구 연층(buffy coat), 혈장(plasma), 혈청(serum), 객담(sputum), 눈물(tears), 점액(mucus), 세비액(nasal washes), 비강 흡인물(nasal aspirate), 호흡(breath), 소변(urine), 정액(semen), 침(saliva), 복강 세척액(peritoneal washings), 복수(ascites), 낭종액(cystic fluid), 뇌척수막 액(meningeal fluid), 양수(amniotic fluid), 선액(glandular fluid), 췌장액(pancreatic fluid), 림프액(lymph fluid), 흉수(pleural fluid), 유두 흡인물(nipple aspirate), 기관지 흡인물(bronchial aspirate), 활액(synovial fluid), 관절 흡인물(joint aspirate), 기관 분비물(organ secretions), 세포(cell), 세포 추출물(cell extract) 또는 뇌척수액(cerebrospinal fluid)을 포함할 수 있으나, 이에 제한되는 것은 아니다. In the present invention, the biological sample is whole blood, leukocytes, peripheral blood mononuclear cells, buffy coat, plasma, serum, sputum , tears, mucus, nasal washes, nasal aspirate, breath, urine, semen, saliva, peritoneal washings, ascites, cystic fluid, meningeal fluid, amniotic fluid, glandular fluid, pancreatic fluid, lymph fluid, pleural fluid ), nipple aspirate, bronchial aspirate, synovial fluid, joint aspirate, organ secretions, cell, cell extract or cerebrospinal fluid, but is not limited thereto.
본 발명에서는 상기와 같이 분리된 생물학적 시료에서 RP11-572C15.6, FENDRR 및 ACTA2-AS1으로 이루어진 군에서 선택된 1종 이상의 유전자의 발현 수준을 측정하는 단계를 포함할 수 있다.The present invention may include measuring the expression level of one or more genes selected from the group consisting of RP11-572C15.6, FENDRR and ACTA2-AS1 in the biological sample isolated as described above.
본 발명에서 상기 RP11-572C15.6, FENDRR 또는 ACTA2-AS1 유전자의 발현 수준을 측정하는 제제는 상기 유전자에 특이적으로 결합하는 프라이머, 프로브 및 안티센스 뉴클레오티드로 이루어진 군에서 선택된 1종 이상을 포함할 수 있다. In the present invention, the agent for measuring the expression level of the RP11-572C15.6, FENDRR or ACTA2-AS1 gene may include one or more selected from the group consisting of primers, probes and antisense nucleotides that specifically bind to the gene. have.
본 발명에 상기 RP11-572C15.6, FENDRR 또는 ACTA2-AS1 유전자의 존재 여부와 발현 정도를 확인하는 과정으로 유전자의 양을 측정하는 분석 방법으로는 역전사 중합효소반응(RT-PCR), 경쟁적 역전사 중합효소반응(Competitive RT-PCR), 실시간 역전사 중합효소반응(Real-time RT-PCR), RNase 보호 분석법(RPA; RNase protection assay), 노던 블랏팅(Northern blotting), DNA 칩 등이 있으나 이에 제한되는 것은 아니다. In the present invention, as an analysis method for measuring the amount of the gene in the process of checking the presence and expression level of the RP11-572C15.6, FENDRR or ACTA2-AS1 gene, reverse transcription polymerase reaction (RT-PCR), competitive reverse transcription polymerization Competitive RT-PCR, Real-time RT-PCR, RNase protection assay (RPA), Northern blotting, DNA chip, etc., but are limited thereto it is not
본 발명의 일 구체 예에서 목적하는 개체의 생물학적 시료에 대하여 측정된 RP11-572C15.6, FENDRR 및 ACTA2-AS1으로 이루어진 군에서 선택된 1종 이상의 유전자의 발현 수준이 대조군에 비하여 증가한 경우, 예후가 나쁠 것으로 예측할 수 있다. In one embodiment of the present invention, when the expression level of one or more genes selected from the group consisting of RP11-572C15.6, FENDRR and ACTA2-AS1 measured with respect to the biological sample of the subject of the present invention is increased compared to the control group, the prognosis is poor can be predicted that
본 발명의 또 다른 구현 예에 따르면, RP11-572C15.6, FENDRR, ACTA2-AS1 및 ZNF667-AS1로 이루어진 군에서 선택된 1종 이상을 포함하는, 암의 항암제에 대한 치료 반응성 예측용 바이오마커 조성물을 제공하고자 한다.According to another embodiment of the present invention, comprising at least one selected from the group consisting of RP11-572C15.6, FENDRR, ACTA2-AS1 and ZNF667-AS1, a biomarker composition for predicting therapeutic responsiveness to an anticancer agent for cancer would like to provide
본 발명의 일 구체 예에서는 목적하는 개체로부터 분리된 생물학적 시료에 있어서 상기 RP11-572C15.6, FENDRR, ACTA2-AS1 또는 ZNF667-AS1의 발현 수준이 대조군에 비하여 증가된 경우, 예후가 나쁠 것으로 예측할 수 있다.In one embodiment of the present invention, when the expression level of RP11-572C15.6, FENDRR, ACTA2-AS1 or ZNF667-AS1 is increased compared to the control in the biological sample isolated from the subject of interest, the prognosis can be predicted to be poor. have.
본 발명에서 상기 "대조군"이란 암이 발병하지 않은 정상 대조군이거나, 암이 발병된 환자 모집단의 중앙값(또는 해당 환자의 평균값)이거나, 암이 발병된 환자 중 치료 반응성이 높은 환자 모집단의 중앙값(또는 해당 환자의 평균값)일 수 있다.In the present invention, the "control group" means a normal control group without cancer, the median value of the patient population with cancer (or the average value of the patient), or the median value of the patient population with high treatment responsiveness among patients with cancer (or average value of the patient).
본 발명에서 상기 항암제는 면역 항암제 또는 면역 치료제일 수 있다. 본 발명에서 상기 면역 항암제 또는 면역 치료제로는 단클론성 항체, 키메라 항원 수용체(CAR) T-세포, NK-세포, 수지상 세포(DC), 입양 세포 전이(ACT), 면역 체크포인트 조정제, 사이토카인, 암 백신, 애쥬번트, 암살상 바이러스, 또는 이들의 조합을 포함할 수 있고, 여기서 상기 단클론성 항체는 PD-1, PD-L1, CTLA-4, IDO, TIM-3, LAG-3, 4-1BB, OX40, MERTK, CD27, GITR, B7.1, TGF-β, BTLA, VISTA, 아르기나아제, MICA, MICB, B7-H4, CD28, CD137, 및 HVEM으로 구성되는 군으로부터 선택된 신호화 분자를 조절하는 것일 수 있으며, 바람직한 예시로는 항-PD-1 항체 또는 항-PD-L1 항체일 수 있고, 구체적인 예를 들면, 이필리무맙, 트레멜리무맙, 니볼루맙, 펨브롤리주맙, 피딜리주맙(CT-011), AMP-224, AMP-514(MEDI0680-Medimmune), MPDL3280A(Genentech Roche), MEDI4736, MSB0010718C(Merck Serono), YW243.55.S70 및 MDX-1105로 이루어진 군에서 선택된 1종 이상일 수 있으나, 이에 제한되는 것은 아니다. In the present invention, the anticancer agent may be an immune anticancer agent or an immunotherapeutic agent. In the present invention, the immunotherapy or immunotherapeutic agent includes monoclonal antibodies, chimeric antigen receptor (CAR) T-cells, NK-cells, dendritic cells (DC), adoptive cell transfer (ACT), immune checkpoint modulators, cytokines, cancer vaccine, adjuvant, oncolytic virus, or a combination thereof, wherein said monoclonal antibody is PD-1, PD-L1, CTLA-4, IDO, TIM-3, LAG-3, 4- a signaling molecule selected from the group consisting of 1BB, OX40, MERTK, CD27, GITR, B7.1, TGF-β, BTLA, VISTA, arginase, MICA, MICB, B7-H4, CD28, CD137, and HVEM; It may be to modulate, and a preferred example may be an anti-PD-1 antibody or an anti-PD-L1 antibody, and specific examples, ipilimumab, tremelimumab, nivolumab, pembrolizumab, pidilizumab (CT-011), AMP-224, AMP-514 (MEDI0680-Medimmune), MPDL3280A (Genentech Roche), MEDI4736, MSB0010718C (Merck Serono), YW243.55.S70 and at least one selected from the group consisting of MDX-1105 However, the present invention is not limited thereto.
본 발명에서 사용되는 용어, "면역 치료"란, 면역시스템을 자극하여 질환을 치료하는 방법으로, 본원발명에서는 소화기암을 치료하는 것을 의미한다. 수동적 면역치료는 체외에서 다량으로 만들어진 면역반응 성분 예컨대, 면역세포, 항체, 사이토카인 등을 암 환자에게 주입하여 암 세포를 공격하는 치료방법이고, 능동적 면역치료는 개인의 항체와 면역세포들을 능동적으로 활성화 또는 생산시키게 하여 암 세포를 공격하는 치료 방법이다. As used herein, the term “immunotherapy” refers to a method of treating a disease by stimulating the immune system, and in the present invention, it means treating gastrointestinal cancer. Passive immunotherapy is a treatment method that attacks cancer cells by injecting immune response components, such as immune cells, antibodies, and cytokines, made in large amounts outside the body, into cancer patients. It is a therapeutic method that attacks cancer cells by activating or producing them.
본 발명에서 있어서 "치료 반응성 예측"이란, 환자가 면역 항암제에 대해 선호적으로 또는 비선호적으로 반응할지 여부를 예측하는 것, 또는 항암제에 대한 내성의 위험성을 예측하는 것, 면역치료 후 환자의 예후 즉, 재발, 전이, 생존, 또는 무병생존 등을 예측하는 것을 의미한다. In the present invention, "therapeutic responsiveness prediction" refers to predicting whether a patient will respond favorably or non-preferably to an immune anticancer agent, or predicting the risk of resistance to an anticancer agent, prognosis of a patient after immunotherapy That is, it means predicting recurrence, metastasis, survival, or disease-free survival.
본 발명의 다른 구현 예에 따르면, RP11-572C15.6, FENDRR, ACTA2-AS1 및 ZNF667-AS1로 이루어진 군에서 선택된 1종 이상의 유전자의 발현 수준을 측정할 수 있는 제제를 포함하는 암의 항암제에 대한 치료 반응성 예측용 조성물에 관한 것이다.According to another embodiment of the present invention, RP11-572C15.6, FENDRR, ACTA2-AS1 and ZNF667-AS1 for an anticancer agent for cancer comprising an agent capable of measuring the expression level of one or more genes selected from the group consisting of It relates to a composition for predicting therapeutic responsiveness.
본 발명에서 상기 RP11-572C15.6, FENDRR, ACTA2-AS1 또는 ZNF667-AS1의 발현 수준을 측정할 수 있는 제제는 상기 유전자에 특이적으로 결합하는 프라이머, 프로브 및 안티센스 뉴클레오티드로 이루어진 군에서 선택된 1종 이상을 포함할 수 있다. In the present invention, the agent capable of measuring the expression level of RP11-572C15.6, FENDRR, ACTA2-AS1 or ZNF667-AS1 is one selected from the group consisting of primers, probes and antisense nucleotides specifically binding to the gene. may include more than one.
본 발명에서 상기 RP11-572C15.6, FENDRR, ACTA2-AS1 또는 ZNF667-AS1의 유전자의 존재 여부와 발현 수준을 확인하는 과정으로, 상기 유전자의 양을 측정하는 분석 방법으로는 역전사 중합효소반응(RT-PCR), 경쟁적 역전사 중합효소반응(Competitive RT-PCR), 실시간 역전사 중합효소반응(Real-time RT-PCR), RNase 보호 분석법(RPA; RNase protection assay), 노던 블랏팅(Northern blotting), DNA 칩 등이 있으나 이에 제한되는 것은 아니다. In the present invention, it is a process of confirming the presence and expression level of the gene of RP11-572C15.6, FENDRR, ACTA2-AS1 or ZNF667-AS1. As an analysis method for measuring the amount of the gene, reverse transcription polymerase reaction (RT -PCR), Competitive RT-PCR, Real-time RT-PCR, RNase protection assay (RPA), Northern blotting, DNA Chip, etc., but is not limited thereto.
본 발명의 일 구체 예에서는 목적하는 개체로부터 분리된 생물학적 시료에 있어서 상기 RP11-572C15.6, FENDRR, ACTA2-AS1 또는 ZNF667-AS1의 발현 수준이 대조군에 비하여 증가된 경우, 치료 반응성이 낮을 것으로 예측할 수 있다. In one embodiment of the present invention, when the expression level of RP11-572C15.6, FENDRR, ACTA2-AS1 or ZNF667-AS1 is increased compared to the control in the biological sample isolated from the subject of interest, the treatment responsiveness is predicted to be low. can
본 발명의 또 다른 구현 예에 따르면, 본 발명에 따른 암의 예후 예측용 조성물을 포함하는 암의 항암제에 대한 치료 반응성 예측용 키트에 관한 것이다. According to another embodiment of the present invention, it relates to a kit for predicting therapeutic responsiveness to an anticancer agent for cancer, comprising the composition for predicting the prognosis of cancer according to the present invention.
본 발명의 키트에는 암의 예후 예측을 위하여 선택적으로 마커를 인지하는 프라이머, 프로브 또는 안티센스 뉴클레오티드 뿐만 아니라, 분석 방법에 적합한 한 종류 또는 그 이상의 다른 구성성분 조성물, 용액, 또는 장치가 포함될 수 있다.The kit of the present invention may include a primer, a probe, or an antisense nucleotide that selectively recognizes a marker for predicting the prognosis of cancer, as well as one or more other component compositions, solutions, or devices suitable for the analysis method.
본 발명의 암의 예후 예측용 키트는 마이크로어레이 칩 키트, 유전자 증폭 키트 또는 나노스트링 키트일 수 있으나, 이에 제한되는 것은 아니다.The kit for predicting cancer prognosis of the present invention may be a microarray chip kit, a gene amplification kit, or a nanostring kit, but is not limited thereto.
본 발명의 또 다른 구현 예에 따르면, 목적하는 개체로부터 분리된 생물학적 시료에서 RP11-572C15.6, FENDRR, ACTA2-AS1 및 ZNF667-AS1로 이루어진 군에서 선택된 1종 이상의 유전자의 발현 수준을 측정하는 단계를 포함하는, 암의 항암제에 대한 치료 반응성 예측을 위한 정보 제공 방법에 관한 것이다. According to another embodiment of the present invention, measuring the expression level of one or more genes selected from the group consisting of RP11-572C15.6, FENDRR, ACTA2-AS1 and ZNF667-AS1 in a biological sample isolated from a subject of interest It relates to a method of providing information for predicting therapeutic responsiveness to an anticancer agent of cancer, including.
본 발명에서 상기 생물학적 시료는 전혈(whole blood), 백혈구(leukocytes), 말초혈액 단핵 세포(peripheral blood mononuclear cells), 백혈구 연층(buffy coat), 혈장(plasma), 혈청(serum), 객담(sputum), 눈물(tears), 점액(mucus), 세비액(nasal washes), 비강 흡인물(nasal aspirate), 호흡(breath), 소변(urine), 정액(semen), 침(saliva), 복강 세척액(peritoneal washings), 복수(ascites), 낭종액(cystic fluid), 뇌척수막 액(meningeal fluid), 양수(amniotic fluid), 선액(glandular fluid), 췌장액(pancreatic fluid), 림프액(lymph fluid), 흉수(pleural fluid), 유두 흡인물(nipple aspirate), 기관지 흡인물(bronchial aspirate), 활액(synovial fluid), 관절 흡인물(joint aspirate), 기관 분비물(organ secretions), 세포(cell), 세포 추출물(cell extract) 또는 뇌척수액(cerebrospinal fluid)을 포함할 수 있으나, 이에 제한되는 것은 아니다. In the present invention, the biological sample is whole blood, leukocytes, peripheral blood mononuclear cells, buffy coat, plasma, serum, sputum , tears, mucus, nasal washes, nasal aspirate, breath, urine, semen, saliva, peritoneal washings, ascites, cystic fluid, meningeal fluid, amniotic fluid, glandular fluid, pancreatic fluid, lymph fluid, pleural fluid ), nipple aspirate, bronchial aspirate, synovial fluid, joint aspirate, organ secretions, cell, cell extract or cerebrospinal fluid, but is not limited thereto.
본 발명에서는 상기와 같이 분리된 생물학적 시료에서 RP11-572C15.6, FENDRR, ACTA2-AS1 및 ZNF667-AS1으로 이루어진 군에서 선택된 1종 이상의 유전자의 발현 수준을 측정하는 단계를 포함할 수 있다.The present invention may include measuring the expression level of one or more genes selected from the group consisting of RP11-572C15.6, FENDRR, ACTA2-AS1 and ZNF667-AS1 in the biological sample isolated as described above.
본 발명에서 상기 RP11-572C15.6, FENDRR, ACTA2-AS1 또는 ZNF667-AS1 유전자의 발현 수준을 측정하는 제제는 상기 유전자에 특이적으로 결합하는 프라이머, 프로브 및 안티센스 뉴클레오티드로 이루어진 군에서 선택된 1종 이상을 포함할 수 있다. In the present invention, the agent for measuring the expression level of the RP11-572C15.6, FENDRR, ACTA2-AS1 or ZNF667-AS1 gene is at least one selected from the group consisting of primers, probes and antisense nucleotides that specifically bind to the gene may include
본 발명에 상기 RP11-572C15.6, FENDRR, ACTA2-AS1 또는 ZNF667-AS1 유전자의 존재 여부와 발현 정도를 확인하는 과정으로 유전자의 양을 측정하는 분석 방법으로는 역전사 중합효소반응(RT-PCR), 경쟁적 역전사 중합효소반응(Competitive RT-PCR), 실시간 역전사 중합효소반응(Real-time RT-PCR), RNase 보호 분석법(RPA; RNase protection assay), 노던 블랏팅(Northern blotting), DNA 칩 등이 있으나 이에 제한되는 것은 아니다. In the present invention, the RP11-572C15.6, FENDRR, ACTA2-AS1 or ZNF667-AS1 gene is a process for confirming the presence and expression level of the gene. As an analysis method for measuring the amount of the gene, reverse transcription polymerase reaction (RT-PCR) , Competitive RT-PCR, Real-time RT-PCR, RNase protection assay (RPA), Northern blotting, DNA chip, etc. However, the present invention is not limited thereto.
본 발명의 일 구체 예에서 목적하는 개체의 생물학적 시료에 대하여 측정된 RP11-572C15.6, FENDRR, ACTA2-AS1 및 ZNF667-AS1으로 이루어진 군에서 선택된 1종 이상의 유전자의 발현 수준이 대조군에 비하여 증가한 경우, 치료 반응성이 낮을 것으로 예측할 수 있다. In one embodiment of the present invention, when the expression level of one or more genes selected from the group consisting of RP11-572C15.6, FENDRR, ACTA2-AS1 and ZNF667-AS1 measured with respect to a biological sample of a target individual is increased compared to the control group , it can be predicted that the treatment responsiveness will be low.
본 발명의 또 다른 구현 예에 따르면, RP11-572C15.6, FENDRR, ACTA2-AS1 및 ZNF667-AS1로 이루어진 군에서 선택된 1종 이상을 포함하는, 암의 상피-중간엽 전이(Epithelial mesenchymal transition; EMT) 진단용 바이오마커 조성물을 제공하고자 한다. According to another embodiment of the present invention, cancer comprising at least one selected from the group consisting of RP11-572C15.6, FENDRR, ACTA2-AS1 and ZNF667-AS1, epithelial-mesenchymal transition (EMT) ) to provide a diagnostic biomarker composition.
본 발명의 일 구체 예에서는 목적하는 개체로부터 분리된 생물학적 시료에 있어서 상기 RP11-572C15.6, FENDRR, ACTA2-AS1 또는 ZNF667-AS1의 발현 수준이 대조군에 비하여 증가된 경우, 상피 중간엽 전이 아형의 가능성이 높을 것으로 예측할 수 있다. In one embodiment of the present invention, when the expression level of RP11-572C15.6, FENDRR, ACTA2-AS1 or ZNF667-AS1 is increased compared to the control in the biological sample isolated from the subject of interest, epithelial mesenchymal metastasis subtype can be predicted to be highly probable.
본 발명에서 상기 "대조군"이란 암이 발병하지 않은 정상 대조군이거나, 암이 발병된 환자 모집단의 중앙값(또는 해당 환자의 평균값)이거나, 암이 발병된 환자 중 치료 반응성이 높은 환자 모집단의 중앙값(또는 해당 환자의 평균값)일 수 있다.In the present invention, the "control group" means a normal control group without cancer, the median value of the patient population with cancer (or the average value of the patient), or the median value of the patient population with high treatment responsiveness among patients with cancer (or average value of the patient).
단, 본 발명에서 상기 “상피 중간엽 전이(Epithelial mesenchymal transition; EMT)”란, 상피세포가 중간엽 세포로 변하는 과정을 말한다. 즉 상피세포의 모습을 잃어버리고 중간엽 세포의 특징을 가지게 되는 변이과정으로 개체 형성 발달에 중요한 과정으로 알려져 있으며, 암세포의 생장, 약물 저항성, 침윤 및 전이 등과 관련되어 있다.However, in the present invention, the "epithelial mesenchymal transition (EMT)" refers to a process in which epithelial cells are transformed into mesenchymal cells. That is, it is a mutational process that loses the appearance of epithelial cells and acquires the characteristics of mesenchymal cells, which is known as an important process for individual formation and development, and is related to cancer cell growth, drug resistance, invasion and metastasis.
본 발명의 다른 구현 예에 따르면, RP11-572C15.6, FENDRR, ACTA2-AS1 및 ZNF667-AS1로 이루어진 군에서 선택된 1종 이상의 유전자의 발현 수준을 측정할 수 있는 제제를 포함하는 암의 상피-중간엽 전이(EMT) 진단용 조성물에 관한 것이다.According to another embodiment of the present invention, epithelial-middle of cancer comprising an agent capable of measuring the expression level of one or more genes selected from the group consisting of RP11-572C15.6, FENDRR, ACTA2-AS1 and ZNF667-AS1 It relates to a composition for diagnosing lobe metastasis (EMT).
본 발명에서 상기 RP11-572C15.6, FENDRR, ACTA2-AS1 또는 ZNF667-AS1의 발현 수준을 측정할 수 있는 제제는 상기 유전자에 특이적으로 결합하는 프라이머, 프로브 및 안티센스 뉴클레오티드로 이루어진 군에서 선택된 1종 이상을 포함할 수 있다. In the present invention, the agent capable of measuring the expression level of RP11-572C15.6, FENDRR, ACTA2-AS1 or ZNF667-AS1 is one selected from the group consisting of primers, probes and antisense nucleotides specifically binding to the gene. may include more than one.
본 발명에서 상기 RP11-572C15.6, FENDRR, ACTA2-AS1 또는 ZNF667-AS1의 유전자의 존재 여부와 발현 수준을 확인하는 과정으로, 상기 유전자의 양을 측정하는 분석 방법으로는 역전사 중합효소반응(RT-PCR), 경쟁적 역전사 중합효소반응(Competitive RT-PCR), 실시간 역전사 중합효소반응(Real-time RT-PCR), RNase 보호 분석법(RPA; RNase protection assay), 노던 블랏팅(Northern blotting), DNA 칩 등이 있으나 이에 제한되는 것은 아니다. In the present invention, it is a process of confirming the presence and expression level of the gene of RP11-572C15.6, FENDRR, ACTA2-AS1 or ZNF667-AS1. As an analysis method for measuring the amount of the gene, reverse transcription polymerase reaction (RT -PCR), Competitive RT-PCR, Real-time RT-PCR, RNase protection assay (RPA), Northern blotting, DNA Chip, etc., but is not limited thereto.
본 발명의 일 구체 예에서는 목적하는 개체로부터 분리된 생물학적 시료에 있어서 상기 RP11-572C15.6, FENDRR, ACTA2-AS1 또는 ZNF667-AS1의 발현 수준이 대조군에 비하여 증가된 경우, 상피 중간엽 전이 아형의 가능성이 높을 것으로 예측할 수 있다.In one embodiment of the present invention, when the expression level of RP11-572C15.6, FENDRR, ACTA2-AS1 or ZNF667-AS1 is increased compared to the control in the biological sample isolated from the subject of interest, epithelial mesenchymal metastasis subtype can be predicted to be highly probable.
본 발명의 또 다른 구현 예에 따르면, 본 발명에 따른 암의 상피-중간엽 전이(EMT) 진단용 조성물을 포함하는 암의 상피-중간엽 전이(EMT) 진단용 키트에 관한 것이다. According to another embodiment of the present invention, it relates to a kit for diagnosing epithelial-mesenchymal metastasis (EMT) of cancer, comprising the composition for diagnosing epithelial-mesenchymal metastasis (EMT) of cancer according to the present invention.
본 발명의 키트에는 암의 예후 예측을 위하여 선택적으로 마커를 인지하는 프라이머, 프로브 또는 안티센스 뉴클레오티드 뿐만 아니라, 분석 방법에 적합한 한 종류 또는 그 이상의 다른 구성성분 조성물, 용액, 또는 장치가 포함될 수 있다.The kit of the present invention may include a primer, a probe, or an antisense nucleotide that selectively recognizes a marker for predicting the prognosis of cancer, as well as one or more other component compositions, solutions, or devices suitable for the analysis method.
본 발명의 암의 예후 예측용 키트는 마이크로어레이 칩 키트, 유전자 증폭 키트 또는 나노스트링 키트일 수 있으나, 이에 제한되는 것은 아니다.The kit for predicting cancer prognosis of the present invention may be a microarray chip kit, a gene amplification kit, or a nanostring kit, but is not limited thereto.
본 발명의 또 다른 구현 예에 따르면, 목적하는 개체로부터 분리된 생물학적 시료에서 RP11-572C15.6, FENDRR, ACTA2-AS1 및 ZNF667-AS1로 이루어진 군에서 선택된 1종 이상의 유전자의 발현 수준을 측정하는 단계를 포함하는, 암의 상피-중간엽 전이(EMT) 진단을 위한 정보 제공 방법에 관한 것이다. According to another embodiment of the present invention, measuring the expression level of one or more genes selected from the group consisting of RP11-572C15.6, FENDRR, ACTA2-AS1 and ZNF667-AS1 in a biological sample isolated from a subject of interest It relates to an information providing method for diagnosing epithelial-mesenchymal metastasis (EMT) of cancer, including.
본 발명에서 상기 생물학적 시료는 전혈(whole blood), 백혈구(leukocytes), 말초혈액 단핵 세포(peripheral blood mononuclear cells), 백혈구 연층(buffy coat), 혈장(plasma), 혈청(serum), 객담(sputum), 눈물(tears), 점액(mucus), 세비액(nasal washes), 비강 흡인물(nasal aspirate), 호흡(breath), 소변(urine), 정액(semen), 침(saliva), 복강 세척액(peritoneal washings), 복수(ascites), 낭종액(cystic fluid), 뇌척수막 액(meningeal fluid), 양수(amniotic fluid), 선액(glandular fluid), 췌장액(pancreatic fluid), 림프액(lymph fluid), 흉수(pleural fluid), 유두 흡인물(nipple aspirate), 기관지 흡인물(bronchial aspirate), 활액(synovial fluid), 관절 흡인물(joint aspirate), 기관 분비물(organ secretions), 세포(cell), 세포 추출물(cell extract) 또는 뇌척수액(cerebrospinal fluid)을 포함할 수 있으나, 이에 제한되는 것은 아니다. In the present invention, the biological sample is whole blood, leukocytes, peripheral blood mononuclear cells, buffy coat, plasma, serum, sputum , tears, mucus, nasal washes, nasal aspirate, breath, urine, semen, saliva, peritoneal washings, ascites, cystic fluid, meningeal fluid, amniotic fluid, glandular fluid, pancreatic fluid, lymph fluid, pleural fluid ), nipple aspirate, bronchial aspirate, synovial fluid, joint aspirate, organ secretions, cell, cell extract or cerebrospinal fluid, but is not limited thereto.
본 발명에서는 상기와 같이 분리된 생물학적 시료에서 RP11-572C15.6, FENDRR, ACTA2-AS1 및 ZNF667-AS1으로 이루어진 군에서 선택된 1종 이상의 유전자의 발현 수준을 측정하는 단계를 포함할 수 있다.The present invention may include measuring the expression level of one or more genes selected from the group consisting of RP11-572C15.6, FENDRR, ACTA2-AS1 and ZNF667-AS1 in the biological sample isolated as described above.
본 발명에서 상기 RP11-572C15.6, FENDRR, ACTA2-AS1 또는 ZNF667-AS1 유전자의 발현 수준을 측정하는 제제는 상기 유전자에 특이적으로 결합하는 프라이머, 프로브 및 안티센스 뉴클레오티드로 이루어진 군에서 선택된 1종 이상을 포함할 수 있다. In the present invention, the agent for measuring the expression level of the RP11-572C15.6, FENDRR, ACTA2-AS1 or ZNF667-AS1 gene is at least one selected from the group consisting of primers, probes and antisense nucleotides that specifically bind to the gene may include
본 발명에 상기 RP11-572C15.6, FENDRR, ACTA2-AS1 또는 ZNF667-AS1 유전자의 존재 여부와 발현 정도를 확인하는 과정으로 유전자의 양을 측정하는 분석 방법으로는 역전사 중합효소반응(RT-PCR), 경쟁적 역전사 중합효소반응(Competitive RT-PCR), 실시간 역전사 중합효소반응(Real-time RT-PCR), RNase 보호 분석법(RPA; RNase protection assay), 노던 블랏팅(Northern blotting), DNA 칩 등이 있으나 이에 제한되는 것은 아니다. In the present invention, the RP11-572C15.6, FENDRR, ACTA2-AS1 or ZNF667-AS1 gene is a process for confirming the presence and expression level of the gene. As an analysis method for measuring the amount of the gene, reverse transcription polymerase reaction (RT-PCR) , Competitive RT-PCR, Real-time RT-PCR, RNase protection assay (RPA), Northern blotting, DNA chip, etc. However, the present invention is not limited thereto.
본 발명의 일 구체 예에서 목적하는 개체의 생물학적 시료에 대하여 측정된 RP11-572C15.6, FENDRR, ACTA2-AS1 및 ZNF667-AS1으로 이루어진 군에서 선택된 1종 이상의 유전자의 발현 수준이 대조군에 비하여 증가한 경우, 암 세포가 상피-중간엽 전이 아형을 포함할 가능성이 높을 것으로 예측할 수 있다. In one embodiment of the present invention, when the expression level of one or more genes selected from the group consisting of RP11-572C15.6, FENDRR, ACTA2-AS1 and ZNF667-AS1 measured with respect to a biological sample of a target individual is increased compared to the control group , it can be predicted that cancer cells are highly likely to contain epithelial-mesenchymal transition subtypes.
본 발명의 또 다른 구현 예에 따르면, 암 개체로부터 분리한 생물학적 시료 또는 암 질환 동물 모델에 후보 약제를 처리하는 단계; 및 상기 후보 약제가 처리된 생물학적 시료 또는 암 질환 동물 모델에서 RP11-572C15.6, FENDRR, ACTA2-AS1 및 ZNF667-AS1로 이루어진 군에서 선택된 1종 이상의 유전자의 발현 수준을 측정하는 단계를 포함하는, 암의 예방 또는 치료용 약물을 스크리닝하는 방법에 관한 것이다. According to another embodiment of the present invention, the method comprising: treating a candidate drug to a biological sample isolated from a cancer subject or an animal model of cancer; And measuring the expression level of one or more genes selected from the group consisting of RP11-572C15.6, FENDRR, ACTA2-AS1 and ZNF667-AS1 in a biological sample or cancer disease animal model treated with the candidate agent, It relates to a method of screening a drug for the prevention or treatment of cancer.
본 발명에서 상기 RP11-572C15.6, FENDRR, ACTA2-AS1 또는 ZNF667-AS1 유전자의 발현 수준을 측정하는 제제는 상기 유전자에 특이적으로 결합하는 프라이머, 프로브 및 안티센스 뉴클레오티드로 이루어진 군에서 선택된 1종 이상을 포함할 수 있다. In the present invention, the agent for measuring the expression level of the RP11-572C15.6, FENDRR, ACTA2-AS1 or ZNF667-AS1 gene is at least one selected from the group consisting of primers, probes and antisense nucleotides that specifically bind to the gene may include
본 발명에 상기 RP11-572C15.6, FENDRR, ACTA2-AS1 또는 ZNF667-AS1 유전자의 존재 여부와 발현 정도를 확인하는 과정으로 유전자의 양을 측정하는 분석 방법으로는 역전사 중합효소반응(RT-PCR), 경쟁적 역전사 중합효소반응(Competitive RT-PCR), 실시간 역전사 중합효소반응(Real-time RT-PCR), RNase 보호 분석법(RPA; RNase protection assay), 노던 블랏팅(Northern blotting), DNA 칩 등이 있으나 이에 제한되는 것은 아니다. In the present invention, the RP11-572C15.6, FENDRR, ACTA2-AS1 or ZNF667-AS1 gene is a process for confirming the presence and expression level of the gene. As an analysis method for measuring the amount of the gene, reverse transcription polymerase reaction (RT-PCR) , Competitive RT-PCR, Real-time RT-PCR, RNase protection assay (RPA), Northern blotting, DNA chip, etc. However, the present invention is not limited thereto.
본 발명에서 측정된 RP11-572C15.6, FENDRR, ACTA2-AS1 및 ZNF667-AS1 로 이루어진 군에서 선택된 1종 이상의 유전자의 발현 수준이 후보 약제가 처리되기 전에 비하여 감소한 경우 상기 후보 약제를 암의 예방 또는 치료용 약제로 판단하는 단계를 추가로 포함할 수 있다. When the expression level of one or more genes selected from the group consisting of RP11-572C15.6, FENDRR, ACTA2-AS1 and ZNF667-AS1 measured in the present invention is reduced compared to before the candidate agent is treated, the candidate agent is used to prevent cancer or It may further include the step of determining the drug for treatment.
본 발명의 또 다른 구현 예에 따르면, 암 개체로부터 분리한 생물학적 시료 또는 암 질환 동물 모델에 후보 약제를 처리하는 단계; 및 상기 후보 약제가 처리된 생물학적 시료 또는 암 질환 동물 모델에서 RP11-572C15.6, FENDRR, ACTA2-AS1 및 ZNF667-AS1로 이루어진 군에서 선택된 1종 이상의 유전자의 발현 수준을 측정하는 단계를 포함하는, 암의 상피-중간엽 세포전이(EMT) 억제 물질을 스크리닝하는 방법에 관한 것이다. According to another embodiment of the present invention, the method comprising: treating a candidate drug to a biological sample isolated from a cancer subject or an animal model of cancer; And measuring the expression level of one or more genes selected from the group consisting of RP11-572C15.6, FENDRR, ACTA2-AS1 and ZNF667-AS1 in a biological sample or cancer disease animal model treated with the candidate agent, The present invention relates to a method for screening a substance that inhibits epithelial-mesenchymal cell metastasis (EMT) of cancer.
본 발명에서 상기 RP11-572C15.6, FENDRR, ACTA2-AS1 또는 ZNF667-AS1 유전자의 발현 수준을 측정하는 제제는 상기 유전자에 특이적으로 결합하는 프라이머, 프로브 및 안티센스 뉴클레오티드로 이루어진 군에서 선택된 1종 이상을 포함할 수 있다. In the present invention, the agent for measuring the expression level of the RP11-572C15.6, FENDRR, ACTA2-AS1 or ZNF667-AS1 gene is at least one selected from the group consisting of primers, probes and antisense nucleotides that specifically bind to the gene may include
본 발명에 상기 RP11-572C15.6, FENDRR, ACTA2-AS1 또는 ZNF667-AS1 유전자의 존재 여부와 발현 정도를 확인하는 과정으로 유전자의 양을 측정하는 분석 방법으로는 역전사 중합효소반응(RT-PCR), 경쟁적 역전사 중합효소반응(Competitive RTR), 실시간 역전사 중합효소반응(Real-time RT-PCR), RNase 보호 분석법(RPA; RNase protection assay), 노던 블랏팅(Northern blotting), DNA 칩 등이 있으나 이에 제한되는 것은 아니다. In the present invention, the RP11-572C15.6, FENDRR, ACTA2-AS1 or ZNF667-AS1 gene is a process for confirming the presence and expression level of the gene. As an analysis method for measuring the amount of the gene, reverse transcription polymerase reaction (RT-PCR) , Competitive RTR, Real-time RT-PCR, RNase protection assay (RPA), Northern blotting, DNA chip, etc. It is not limited.
본 발명에서 측정된 RP11-572C15.6, FENDRR, ACTA2-AS1 및 ZNF667-AS1 로 이루어진 군에서 선택된 1종 이상의 유전자의 발현 수준이 후보 약제가 처리되기 전에 비하여 감소한 경우 상기 후보 약제를 암의 상피-중간엽 세포전이 억제 물질로 판단하는 단계를 추가로 포함할 수 있다. When the expression level of one or more genes selected from the group consisting of RP11-572C15.6, FENDRR, ACTA2-AS1 and ZNF667-AS1 measured in the present invention is reduced compared to before the candidate agent is treated, the candidate agent is administered to the epithelium of cancer- It may further comprise the step of determining as a mesenchymal cell metastasis inhibitor.
본 발명에서는 암 중에서도 특히 위암을 조기에 정확하면서도 간편하게 진단할 수 있고, 더 나아가서는 암의 예후, 또는 항암제에 대한 치료 반응성을 예측할 수 있어, 암 환자에 대한 가장 적절한 치료 방식을 선택할 수 있도록 한다.In the present invention, it is possible to accurately and conveniently diagnose gastric cancer, particularly among cancers, at an early stage, and furthermore, it is possible to predict the prognosis of cancer or the therapeutic responsiveness to an anticancer agent, so that the most appropriate treatment method for cancer patients can be selected.
도 1은 TCGA 코호트(n = 258)에서 STAD 조직으로부터 얻어진 lncRNA 데이터의 계측적 클러스터링 및 구분되는 발현 분석한 결과를 나타낸 것으로, 도 1a는 계측적 클러스터링 분석을 위하여, 적어도 7개의 조직에서 조직 별 중앙 값에 대하여 적어도 0.5 차이(log2 값)를 보이는 유전자(1,001 lncRNAs)가 선별된 결과를 보이는 것이고, 도 1b는 TCGA 코호트에서 LNC6 아형에 대하여 특이적인 lncRNA 발현 시그니처를 나타낸 것이다. 각 데이터는 매트릭스 형태로 나타내었으며, 행은 개별 유전자를 나타내고, 열은 각 조직을 나타낸다. 각 매트릭스 데이터에서 각각의 셀은 개별 조직에서 유전자의 발현 수준을 나타내며, 적색과 녹색은 스케일 바(log2 전환 스케일)로 나타내어지는 상대적 높고 낮은 발현 수준을 나타낸다.1 shows the results of measurement clustering and differential expression analysis of lncRNA data obtained from STAD tissues in the TCGA cohort (n = 258). Genes (1,001 lncRNAs) showing at least a 0.5 difference (log2 value) with respect to the values were selected, and FIG. 1b shows the lncRNA expression signature specific for the LNC6 subtype in the TCGA cohort. Each data is presented in matrix form, with rows representing individual genes and columns representing each tissue. In each matrix data, each cell represents the expression level of a gene in an individual tissue, and red and green represent the relative high and low expression levels represented by scale bars (log2 conversion scale).
도 2는 LNC6 아형의 예후 관련도를 나타낸 것으로, 도 2a는 TCGA 코호트(n = 258)에서 LNC6 아형에 특이적인 mRNA 발현 시그니처로, TCGA 데이터 세트에서 아형 특이적인 mRNA를 규명하기 위하여 다중 2-시료 t-테스트를 수행하였다. 도 2b는 예측 모델에서 계통도를 나타낸 것으로, 환자를 큰 예측 가능성이 있는 아형으로 분류하였다. 도 2c는 실험 코호트(n = 1,933)에서 환자의 총 생존 기간(overall survival; OS)과 무재발 생존 기간(recurrence-free survival; RFS)의 카플란-마이어 플롯(Kaplan-Meier plots)을 나타낸 것이다. Fig. 2 shows the prognostic relevance of LNC6 subtypes, and Fig. 2a is an LNC6 subtype-specific mRNA expression signature in the TCGA cohort (n = 258). Multiple 2-samples to identify subtype-specific mRNA in the TCGA data set. A t-test was performed. Figure 2b shows a phylogenetic tree in the predictive model, and the patients were classified into subtypes with high predictability. Figure 2c shows Kaplan-Meier plots of patients' overall survival (OS) and recurrence-free survival (RFS) in an experimental cohort (n = 1,933).
도 3은 한국 코호트(n=180)에서 LNC6 아형과 보조 항암 화학 요법 사이 상관 관계를 나타낸 것으로, 각 LNC6 아형 별 보조 항암 화학 요법(CTX)를 받은 환자와 받지 않은 환자 사이 무재발 생존 기간(RFS)의 카플란-마이어 플롯을 나타낸 것이며, 이때 대상 환자로는 한국인 코호트에서 원발 전이가 없는 AJCC 암기 II, III 또는 IV 환자를 대상으로 하였다. 3 shows the correlation between LNC6 subtypes and adjuvant chemotherapy in a Korean cohort (n=180), and the recurrence-free survival period (RFS) between patients who received adjuvant chemotherapy (CTX) and patients who did not receive adjuvant chemotherapy (CTX) for each LNC6 subtype. ) is a Kaplan-Meier plot, and the target patients were AJCC stage II, III, or IV patients without primary metastasis in a Korean cohort.
도 4는 L6C의 항암제 내성과 상피 표현형의 서브세트에 관한 것으로, 도 4a는 한국인 코호트(n = 180)에서 LNC6 아형과 중간엽/상피 아형(MP/EP) 사이 코드표를 나타낸 것이다. 본 분석에는 원발 전이가 없는 AJCC 암기 II, III 또는 IV 환자를 대상으로 하였다. 도 4b는 보조 항암 화학 요법(CTX)를 받은 EP 아형 환자와 받지 않은 EP 아형 환자(n = 122) 사이 무재발 생존 기간(RFS)의 카플란-마이어 플롯을 나타낸 것으로, EP 아형 환자에 있어서 보조 항암 화학 요법과 L6C 아형 사이 상호 관계를 분석하였다. Fig. 4 relates to a subset of L6C anticancer drug resistance and epithelial phenotype, and Fig. 4a is a code table between LNC6 subtype and mesenchymal/epithelial subtype (MP/EP) in a Korean cohort (n = 180). This analysis included AJCC stage II, III, or IV patients without primary metastasis. 4B shows a Kaplan-Meier plot of relapse-free survival (RFS) between patients with EP subtypes who received adjuvant chemotherapy (CTX) and those who did not (n = 122) with adjuvant chemotherapy in patients with adjuvant chemotherapy (CTX). The correlation between chemotherapy and the L6C subtype was analyzed.
도 5는 면역 요법에 대한 치료 반응성과 LNC6 아형(n=45) 사이의 상호 관계를 나타낸 것으로, 도 5a는 LNC6 아형에 따른 펨브롤리주맙에 대한 반응성을 나타낸 것이고, 도 5b 및 5c는 치료 반응성을 보이는 자와 비-반응성을 보이는 자에서 L6C 아형과 L6F 아형의 예측 가능성을 나타낸 것이다. 도 5d는 치료 반응성을 보이는 자와 비-반응성을 보이는 자에서 L6E 아형에서 상향 조절된 lncRNA의 정규화된 풍부도(Normalized enrichment score; NES)를 나타낸 것이다. 여기서, CR은 완전 관해(complete response), PR은 부분적 반응성(partial response), SD는 안정한 질환(stable disease), PD는 진행중인 질환(progressive disease)를 의미한다. Figure 5 shows the correlation between therapeutic responsiveness to immunotherapy and LNC6 subtype (n=45), Figure 5a shows the responsiveness to pembrolizumab according to the LNC6 subtype, and Figures 5b and 5c show the therapeutic responsiveness The predictability of L6C subtypes and L6F subtypes is shown in those who show and those who show non-reactivity. Figure 5d shows the normalized enrichment score (NES) of the up-regulated lncRNA in the L6E subtype in the treatment-responsive and non-responsive subjects. Here, CR means complete response, PR means partial response, SD means stable disease, and PD means progressive disease.
도 6은 각 LNC6 아형에서 세포 유형 구성요소를 나타낸 것으로, 도 6a 및 6b는 각 LNC6 아형에서 TCGA 코호트 시료의 xCell 스코어를 평균화한 것으로, 도 6a는 각 LNC6 아형에서 5개의 세포 유형 패밀리 구성요소를 나타낸 것이고, 도 6b는 각 LNC6 아형에서 64개의 세포 유형의 구성요소를 나타낸 것이다. Figure 6 shows the cell type components in each LNC6 subtype, Figures 6a and 6b are the averaged xCell scores of TCGA cohort samples in each LNC6 subtype, and Figure 6a shows the 5 cell type family components in each LNC6 subtype. shown, and Figure 6b shows the components of 64 cell types in each LNC6 subtype.
도 7은 lncRNA 및 줄기 유사 특징 사이 관계의 생체 외 입증을 나타낸 것으로, 도 7a는 위암 세포주(n=29) 사이의 상피 중간엽 전이 아형(epithelial-to-mesenchymal transition (EMT) subtype)과 관련된 L6F 아형의 예측된 가능성을 나타낸 것이고, 도 7b는 EMT 아형 위암 세포주에서 siRNA 형질 전환 후 qRT-PCR로 ZNF667-AS1 넉다운 효율을 분석한 결과이며, 도 7c 내지 7e는 세포 이동 및 침윤 어쎄이로부터 이동된 및 침윤된 세포의 대표 이미지를 나타낸 것으로, 통계적 바 그래프는 3번의 독립된 실험에서 평균화 결과를 나타낸다(t-test, **P < 0.05; n = 3). 도 7f는 siRNA 형질 전환된 EMT 아형의 위암 세포주에서 나타낸 EMT 마커 단백질을 사용하여 웨스턴 블럿 분석을 수행한 결과를 나타낸 것이고, 도 7g는 siRNA 형질 전환된 EMT 아형의 위암 세포주에서 옥살리플라틴 또는 5FU의 MTS 어쎄이로부터 반 최고치 억제 농도(half maximal inhibitory concentration, IC50)를 나타낸 것이다. 7 shows in vitro demonstration of the relationship between lncRNA and stem-like features, and FIG. 7a shows L6F associated with epithelial-to-mesenchymal transition (EMT) subtype between gastric cancer cell lines (n=29). It shows the predicted possibility of the subtype, Figure 7b is the result of analyzing the ZNF667-AS1 knockdown efficiency by qRT-PCR after siRNA transformation in the EMT subtype gastric cancer cell line, Figures 7c to 7e are cell migration and invasion assays and Representative images of infiltrated cells are shown, and the statistical bar graph shows the averaged results from three independent experiments (t-test, **P <0.05; n = 3). Figure 7f shows the results of Western blot analysis using the EMT marker protein shown in the siRNA-transformed EMT subtype gastric cancer cell line, and Figure 7g shows the MTS assay of oxaliplatin or 5FU in the siRNA-transformed EMT subtype gastric cancer cell line. shows the half maximal inhibitory concentration (IC 50 ) from
도 8은 TCGA 코호트(n=258)에서 특징(hallmark) 유전자 세트의 유전자 세트 풍부 분석을 나타낸 것으로, 분석 전 Z 스코어에 의해 시료에서 유전자 발현 값을 정규화하였고, 순위 정규화 방법에 의해 ssGSEAProjection를 적용하였으며, X 축은 LNC6 아형, Y 축은 계층적 클러스터링으로 배열하였다. Figure 8 shows the gene set abundance analysis of the hallmark gene set in the TCGA cohort (n = 258), in which the gene expression values in the sample were normalized by the Z score before analysis, and ssGSEAProjection was applied by the rank normalization method. , X-axis is LNC6 subtype, and Y-axis is arranged in hierarchical clustering.
도 9는 TCGA 코호트(n=258)에서 위 발달 전사 인자의 발현 패턴을 나타낸 것으로, mRNA 풍부의 수준에서 규명된 725개의 위 발달 TF에 있어서, TCGA 코호트에서 발현 수준이 이용 가능한 723개의 TF를 본 분석에 포함시켰다. 초기 배아 단계에서 높게 발현된 TF를 그룹 1, 후기 배아 단계 또는 성숙 단계에서 높게 발현되는 TF를 그룹 2로 구분하였고, Y 축은 TF 그룹 및 시료에서 표준 편차에 따라 상부에서 하부로 정렬하였다. Figure 9 shows the expression patterns of gastric developmental transcription factors in the TCGA cohort (n=258). For the 725 gastric developmental TFs identified at the level of mRNA abundance, the 723 TFs whose expression levels were available in the TCGA cohort were shown. included in the analysis. TFs highly expressed in the early embryonic stage were classified into Group 1, and TFs highly expressed in the late embryonic stage or maturation stage were classified into Group 2, and the Y-axis was arranged from top to bottom according to the standard deviation in the TF groups and samples.
도 10은 TCGA 코호트(n=258)에서 S-기의 lncRNA의 발현 패턴을 나타낸 것으로, 일시적으로 발현된 S-기 풍부 lncRNA에서, TCGA 코호트 시료에서 표준 편차가 0.3 보다 큰 85개의 lncRNA를 나타내었다. 10 shows the expression pattern of S-phase lncRNAs in the TCGA cohort (n=258). In the transiently expressed S-phase rich lncRNAs, 85 lncRNAs with a standard deviation greater than 0.3 in the TCGA cohort sample were shown. .
도 11은 LNC6 아형과 관련된 특이적 유전자 돌연변이 및 복제 수 변화를 나타낸 것으로, 도 11a는 TCGA 코호트(n = 256)에서 특이적으로 돌연변이된 유전자를 나타낸 것이며, 바는 돌연변이의 수를 나타낸 것으로, MutSigCV에 의해 확인된 특이적으로 돌연변이된 유전자는 q 값으로 필터링되고, 빈도에 의해 순위를 매겼다(왼쪽). 돌연변이 색은 돌연변이의 부류를 나타내고, 카이 자승 검증(chi-square)으로 아형 특이적 유전자 돌연변이를 확인하였다. 도 11b는 TCGA 코호트(n=257)에서 복제 수 돌연변이를 나타낸 것으로, 바는 복제수가 변화된 게놈의 부분을 나타낸 것이며, 특이적으로 복제 수가 변화된 유전자는 q 값에 의해 필터링되고, 빈도에 의해 순위를 매겼다(왼쪽). 카이 자승 검증으로 아형 특이적 복제수 변화를 확인하였다.11 shows specific gene mutations and copy number changes associated with the LNC6 subtype, FIG. 11a shows the genes specifically mutated in the TCGA cohort (n = 256), the bars show the number of mutations, and MutSigCV Specific mutated genes identified by q were filtered by q value and ranked by frequency (left). Mutation color indicates the class of mutation, and subtype-specific gene mutations were confirmed by chi-square testing. 11B shows copy number mutations in the TCGA cohort (n=257), where the bars represent the portion of the genome with a changed copy number, and genes with a specific copy number change are filtered by q value, and ranked by frequency. assigned (left). Subtype-specific copy number changes were confirmed by chi-square verification.
도 12는 TCGA STAD 코호트에서 miRNA 및 단백질의 발현과 DNA의 구분되는 메틸화에 관한 것으로, 도 12a 내지 12c는 TCGA 데이터 세트에서 miRNA 및 단백질의 발현과 DNA의 아형-특이적 메틸화를 확인하기 위하여 다중 2-시료 t-테스트를 수행하였다. 데이터는 매트릭스 형식으로 나타내었고, 행은 각 유전자를 나타내며, 열은 각 조직을 나타낸다. 보다 상세하게, 도 12a는 TCGA 코호트(n=217)에서 LNC6 아형에 특이적인 DNA 메틸화 시그니처를 나타낸 것으로, 표준 편차가 0.15 미만인 경우 무시하였다. 도 12b는 TCGA 코호트(n=218)에서 LNC6 아형 특이적인 miRNA 발현 시그니처를 나타낸 것으로, miRNA의 결측 값이 코호트의 20% 보다 큰 경우 이를 무시하였다. TCGA 코호트(n=224)에서 LNC6 아형에 특이적인 단백질 발현(RPPA) 시그니처를 나타낸 것이다. Figure 12 relates to the differential methylation of miRNA and protein expression and DNA in the TCGA STAD cohort, Figures 12a to 12c are multiple 2 to confirm the subtype-specific methylation of miRNA and protein expression and DNA in the TCGA data set - A sample t-test was performed. Data are presented in a matrix format, with a row representing each gene and a column representing each tissue. More specifically, FIG. 12A shows the DNA methylation signature specific for the LNC6 subtype in the TCGA cohort (n=217), which was ignored if the standard deviation was less than 0.15. Figure 12b shows the LNC6 subtype-specific miRNA expression signature in the TCGA cohort (n=218), which was ignored when the missing value of miRNA was greater than 20% of the cohort. A protein expression (RPPA) signature specific for the LNC6 subtype in the TCGA cohort (n=224).
도 13은 TCGA 코호트(n=228)에서 LNC6 아형-특이적 lncRNA 유전자의 메틸화 패턴을 나타낸 것으로, 262개의 LNC6 아형 특이적 lncRNA 유전자에서, HM450 프로브에 주석을 단 184개의 유전자를 나타내었다. 각 lncRNA와 프로브 쌍에서 메틸화 변화 및 유전자 발현 사이의 스피어만 상관 계수(Spearman correlation coefficients; Rho)를 왼쪽에 나타내었다. 동일한 유전자 프로모터에 다중 프로브를 주석을 달았다면, lncRNA를 위해 가장 큰 절대적인 계수의 프로브를 선별하였다. 13 shows the methylation pattern of LNC6 subtype-specific lncRNA genes in the TCGA cohort (n=228). Among 262 LNC6 subtype-specific lncRNA genes, 184 genes annotated with HM450 probes are shown. Spearman correlation coefficients (Rho) between methylation changes and gene expression in each lncRNA and probe pair are shown on the left. If multiple probes were annotated in the same gene promoter, the probe with the largest absolute count was selected for lncRNA.
본 발명의 일 구현 예에 따르면, RP11-572C15.6, FENDRR 및 ACTA2-AS1로 이루어진 군에서 선택된 1종 이상을 포함하는, 암의 진단용 바이오마커 조성물을 제공하고자 한다. According to one embodiment of the present invention, it is to provide a biomarker composition for diagnosis of cancer, comprising at least one selected from the group consisting of RP11-572C15.6, FENDRR and ACTA2-AS1.
본 발명의 다른 구현 예에 따르면, RP11-572C15.6, FENDRR 및 ACTA2-AS1로 이루어진 군에서 선택된 1종 이상을 포함하는, 암의 예후 예측을 위한 바이오마커 조성물을 제공하고자 한다. According to another embodiment of the present invention, it is to provide a biomarker composition for predicting the prognosis of cancer, comprising at least one selected from the group consisting of RP11-572C15.6, FENDRR and ACTA2-AS1.
본 발명의 또 다른 구현 예에 따르면, RP11-572C15.6, FENDRR, ACTA2-AS1 및 ZNF667-AS1로 이루어진 군에서 선택된 1종 이상을 포함하는, 암의 항암제에 대한 치료 반응성 예측용 바이오마커 조성물을 제공하고자 한다.According to another embodiment of the present invention, comprising at least one selected from the group consisting of RP11-572C15.6, FENDRR, ACTA2-AS1 and ZNF667-AS1, a biomarker composition for predicting therapeutic responsiveness to an anticancer agent for cancer would like to provide
본 발명의 또 다른 구현 예에 따르면, RP11-572C15.6, FENDRR, ACTA2-AS1 및 ZNF667-AS1로 이루어진 군에서 선택된 1종 이상을 포함하는, 암의 상피-중간엽 전이(Epithelial mesenchymal transition; EMT) 진단용 바이오마커 조성물을 제공하고자 한다. According to another embodiment of the present invention, cancer comprising at least one selected from the group consisting of RP11-572C15.6, FENDRR, ACTA2-AS1 and ZNF667-AS1, epithelial-mesenchymal transition (EMT) ) to provide a diagnostic biomarker composition.
이하, 실시예를 통하여 본 발명을 더욱 상세히 설명하고자 한다. 이들 실시예는 오로지 본 발명을 보다 구체적으로 설명하기 위한 것으로서, 본 발명의 요지에 따라 본 발명의 범위가 이들 실시예에 의해 제한되지 않는다는 것은 당업계에서 통상의 지식을 가진 자에게 있어서 자명할 것이다.Hereinafter, the present invention will be described in more detail through examples. These examples are only for illustrating the present invention in more detail, and it will be apparent to those skilled in the art that the scope of the present invention is not limited by these examples according to the gist of the present invention. .
실시예Example
TCGA 위암 코호트의 게놈 및 임상적 데이터Genomic and clinical data of the TCGA gastric cancer cohort
258개의 종양으로 이루어진 TCGA 위선암(stomach adenocarcinoma; STAD) 코호트로부터 총 12,727 lncRNA의 발현 프로파일과 mRNA 발현 프로파일을 다운받은 뒤, log2 베이스로 전환시켰다. TCGA STAD의 체세포 돌연변이, 복제 수 변화(copy-number alteration; CNA) 및 임상적 데이터는 cBioPortal for Cancer Genomics로부터 다운받았다. 역상 단백질 어레이(reverse phase protein array; RPPA)로부터 DNA 메틸화, miRNA 발현 및 단백질 발현 데이터는 UCSC Xena 플랫폼으로부터 다운받았다. A total of 12,727 lncRNA expression profiles and mRNA expression profiles were downloaded from the TCGA gastric adenocarcinoma (STAD) cohort consisting of 258 tumors, and then converted to a log2 base. Somatic mutations, copy-number alteration (CNA) and clinical data of TCGA STAD were downloaded from cBioPortal for Cancer Genomics. DNA methylation, miRNA expression and protein expression data from reverse phase protein array (RPPA) were downloaded from the UCSC Xena platform.
아형-특이적 lncRNA의 아형 분류 및 동정Subtype classification and identification of subtype-specific lncRNAs
Gene Cluster 3.0 및 Java TreeView를 통하여 클러스터 분석 및 lncRNA 데이터의 시각화를 수행하였다. 계층적 클러스터링 결과, 258 TCGA STAD 환자를 6개의 클러스터로 분류하였다: 25명은 L6A로, 66명은 L6B로, 51명은 L6C로, 51명은 L6D로, 14명은 L6E로, 그리고 51명은 L6F로 분류하였다. 그 후, 아형-특이적 lncRNA를 확인하기 위하여, 6개 아형의 가능한 모든 조합에 대해 다중 2-클래스 t 테스트(multiple two-class t tests)를 수행하였다. 아형 L6A의 선별을 위하여 5번의 2-시료 t-테스트(2-sample t-tests)를 수행하였다(L6A vs. L6B, L6A vs. L6C, L6A vs. L6D, L6A vs. L6E, and L6A vs. L6F 비교) (P<0.05). 5개의 가능한 비교에 있어서 발현에 유의적 차이가 있는 lncRNA는 다음의 262개의 아형-특이적 lncRNA에 해당한다: 24개는 L6A로, 67개는 L6B로, 20개는 L6C로, 55개는 L6D로, 30개는 L6E로, 그리고 66개는 L6F로 분류되었다.Cluster analysis and visualization of lncRNA data were performed through Gene Cluster 3.0 and Java TreeView. As a result of hierarchical clustering, 258 TCGA STAD patients were classified into 6 clusters: 25 as L6A, 66 as L6B, 51 as L6C, 51 as L6D, 14 as L6E, and 51 as L6F. Then, to identify subtype-specific lncRNAs, multiple two-class t tests were performed on all possible combinations of the six subtypes. For the screening of subtype L6A, five 2-sample t-tests were performed (L6A vs. L6B, L6A vs. L6C, L6A vs. L6D, L6A vs. L6E, and L6A vs. L6A vs. L6A. L6F comparison) (P<0.05). lncRNAs with significant differences in expression in the 5 possible comparisons corresponded to the following 262 subtype-specific lncRNAs: 24 L6A, 67 L6B, 20 L6C, 55 L6D , 30 were classified as L6E, and 66 as L6F.
LNC6 아형의 예측 모델Predictive models of LNC6 subtypes
lncRNA 발현 데이터는 대규모 코호트 데이터세트 중 TCGA 코호트에서만 이용 가능하므로, 그 발현이 LNC6 아형에 특이적인 mRNA를 규명함으로써 lncRNA 아형 시그니처를 mRNA 아형 시그니처로 전환하였고, mRNA 발현 데이터를 이용하여 mRNA 시그니처를 독립 검증 코호트에 적용하였다. 아형-특이적 mRNA 발현 시그니처를 다중 2-클래스 t 테스트에 의해 확인하였다. 아형 L6A 선별을 위하여, 5개의 2-시료 t-테스트를 수행하였다(L6A vs. L6B, L6A vs. L6C, L6A vs. L6D, L6A vs. L6E, and L6A vs. L6F comparisons) (P<0.001). 로그 비율에 따라 각 아형에 대하여 상위 200 mRNA를 선별하였다. 5개의 가능한 비교에 있어서 유의적 차이가 있는 발현을 보이는 유전자의 수가 200개 미만인 경우, 4개의 비교에서 유의적 차이가 있는 유전자를 아형-특이적인 것으로 간주하였다. 아형 예측 모델을 제작하기 위하여, Bayesian compound covariate predictor (BCCP) 알고리즘을 사용하여 이전에 개발된 모델을 사용하였다. 간단히는 총 1,200개 유전자 시그니처(각 아형별 200개 유전자를 포함함)에 대한 유전자 발현 데이터를 사용하여 아형에 속하는 각 조직 시료의 베이즈 확률(Bayesian probability)을 측정하였다. 실험 코호트에서 시료를 베이즈 확률 스코어에 따라 6개 아형 중 하나로 분류하였다. 예측을 위해 BRB-Array Tools (National Institutes of Health)을 사용하였다. 면역 치료 코호트 및 암 세포주에서 LNC6 아형 예측을 위하여 TCGA 코호트에서 lncRNA 발현 데이터에 근거하여 BCCP 모델을 제작하였다. 데이터세트 내 레퍼런스 게놈 주석 버전(reference genome annotation version)의 차이로 인하여, 262 아형-특이적 lncRNA 중 241개만 사용하였다. Since lncRNA expression data is available only in the TCGA cohort among large cohort datasets, the lncRNA subtype signature was converted into an mRNA subtype signature by identifying the mRNA whose expression is specific to the LNC6 subtype, and the mRNA signature was independently verified using the mRNA expression data. applied to the cohort. Subtype-specific mRNA expression signatures were confirmed by multiple two-class t tests. For subtype L6A selection, five two-sample t-tests were performed (L6A vs. L6B, L6A vs. L6C, L6A vs. L6D, L6A vs. L6E, and L6A vs. L6F comparisons) (P<0.001). . The top 200 mRNAs were selected for each subtype according to the log ratio. Genes with significant differences in 4 comparisons were considered subtype-specific if the number of genes with significantly different expression in 5 possible comparisons was less than 200. To fabricate the subtype prediction model, a previously developed model was used using the Bayesian compound covariate predictor (BCCP) algorithm. Briefly, we measured the Bayesian probability of each tissue sample belonging to a subtype using gene expression data for a total of 1,200 gene signatures (including 200 genes for each subtype). In the experimental cohort, samples were classified into one of six subtypes according to Bayes probability scores. BRB-Array Tools (National Institutes of Health) were used for prediction. BCCP model was constructed based on lncRNA expression data in TCGA cohort for LNC6 subtype prediction in immunotherapy cohort and cancer cell line. Due to differences in the reference genome annotation version in the dataset, only 241 of the 262 subtype-specific lncRNAs were used.
실험 코호트의 게놈 및 임상적 데이터Genomic and clinical data of the experimental cohort
미국 국립생물공학 정보센터(NCBI; accession numbers GSE13861, GSE26942, GSE26253, GSE29272, GSE66229, GSE14209, GSE84437, GSE15459)의 유전자 발현 옴니버스 데이터베이스(Gene Expression Omnibus database)로부터 1,933 환자를 포함하는 7개 독립 GC 코호트에서의 mRNA 발현 및 생존데이터를 획득하였다. 한국 코호트의 267명 환자에서 155명은 표준 보조 항암 화학 요법(5-FU 또는 5-FU와 시스플라틴/옥살리플라틴, 독소루비신 또는 파클리탁셀의 조합)을 받았다. 이들 중, 미국 암연합회(American Joint Commission on Cancer (AJCC), 6th edition) II기, III기 또는 IV기 질환으로 원발 전이가 없는 환자 180명을 보조 항암 화학 요법의 이점을 평가하기 위한 분석 서브세트에 포함시켰다. 190명의 환자 중 132명의 환자가 보조 항암 화학 요법을 받았다. In 7 independent GC cohorts, including 1,933 patients, from the Gene Expression Omnibus database of the US National Center for Biotechnology Information (NCBI; accession numbers GSE13861, GSE26942, GSE26253, GSE29272, GSE66229, GSE14209, GSE84437, GSE15459). mRNA expression and survival data were obtained. Of the 267 patients in the Korean cohort, 155 received standard adjuvant chemotherapy (5-FU or a combination of 5-FU with cisplatin/oxaliplatin, doxorubicin, or paclitaxel). Of these, American Joint Commission on Cancer (AJCC), 6th edition, 180 patients with stage II, III, or IV disease without primary metastasis were evaluated in a subset of analyzes to evaluate the benefits of adjuvant chemotherapy. included in Of the 190 patients, 132 received adjuvant chemotherapy.
면역치료 코호트 및 GC 세포주에서의 lncRNA 발현 분석Analysis of lncRNA expression in immunotherapy cohorts and GC cell lines
로우 RNA 시퀀싱 데이터로부터 lncRNA 발현을 분석하였다. 펨브롤리주맙 및 29 DNA-핑거프린트 GC 세포주의 임상 2기에 참여한 전이성 GC 환자로부터 얻어진 45개의 시료에 대하여 수행하였다. STAR 2.6.0c를 이용하여 국제암유전체 협력단(International Cancer Genome Consortium)의 방법에 따라 리드를 레퍼런스 인간 게놈 GRCh38와 얼라인하였다. Gencode 주석(Release 22)과 함께 Rsubread 패키지(ver. 1.34.0)를 사용하여 각 비코딩 RNA에 대한 유니크 맵 리드(uniquely mapped read)를 계산하였다. R (https://www.r-project.org/)을 이용하여 그것의 정의에 따라 FPKM(Fragments per kilobase of transcript per million mapped reads) 값을 계산하였다. lncRNA expression was analyzed from raw RNA sequencing data. Pembrolizumab and 29 DNA-fingerprint GC cell lines were performed on 45 samples obtained from patients with metastatic GC who participated in Phase 2 clinical trials. Reads were aligned with the reference human genome GRCh38 according to the methods of the International Cancer Genome Consortium using STAR 2.6.0c. Uniquely mapped reads for each non-coding RNA were calculated using the Rsubread package (ver. 1.34.0) with Gencode annotation (Release 22). Fragments per kilobase of transcript per million mapped reads (FPKM) values were calculated according to its definition using R ( https://www.r-project.org/).
다른 분자 아형의 예측Prediction of different molecular subtypes
TCGA 아형 및 미세부수체 불안정성(microsatellite instability; MSI) 상태를 정의하였다. 다른 분자 아형 및 면역 치료의 이점을 예측하기 위하여 BCCP 모델을 적용하였다. 환자를 해당 분자 아형으로 분류하기 위하여 위장관 기질 종양(gastrointestinal stromal tumor; GIST) 및 아시아 암 연구 그룹(Asian Cancer Research Group; ACRG) 유전자 발현 시그니처를 적용하였다. TCGA subtypes and microsatellite instability (MSI) status were defined. The BCCP model was applied to predict the benefits of different molecular subtypes and immunotherapy. In order to classify the patient into the corresponding molecular subtype, gastrointestinal stromal tumor (GIST) and Asian Cancer Research Group (ACRG) gene expression signatures were applied.
통계적 분석statistical analysis
각 아형 별 총 생존 기간(overall survival; OS) 및 무재발 생존 기간(recurrence-free survival; RFS) 사이의 상관 관계를 분석하기 위하여 카플란-마이어 플롯(Kaplan-Meier plots) 및 로그 순위법(log-rank tests)을 사용하였다. 두 아형(EP∩L6C vs EP-L6C) 사이의 상호 관계 정도와, 보조 항암 화학 요법의 이점을 평가하기 위하여, 콕스 비례 위험 모델(Cox proportional hazards model)을 적용하였다. 모델은 세가지 공변량으로, 성별, 나이 및 미국 암연합회(AJCC) 암의 병기를 포함하였다. 모든 통계적 분석은 R 언어 환경(http://www.r-project.org)에서 수행하였다.To analyze the correlation between overall survival (OS) and recurrence-free survival (RFS) for each subtype, Kaplan-Meier plots and log-rank method (log- rank tests) were used. To evaluate the degree of correlation between the two subtypes (EP∩L6C vs EP-L6C) and the benefit of adjuvant chemotherapy, a Cox proportional hazards model was applied. The model included three covariates: sex, age and American Cancer Society (AJCC) stage of cancer. All statistical analyzes were performed in the R language environment ( http://www.r-project.org ).
생물 정보학 분석bioinformatics analysis
TCGA 코호트 및 면역 치료 코호트에서 GenePattern과 함께 Single-sample GSEA (ssGSEA), Gene Set Enrichment Analysis (GSEA)의 연장(extension)을 수행하였다. 간단히는, 시료 및 유전자 세트의 각 페어링의 분리된 풍부 점수(enrichment scores)를 계산하였고, 이는 유전자 세트에서 유전자가 시료 내에서 대등하게 상향- 또는 하향-조절되었는 지 정도를 나타낸다. 발현 값은, 시료에 걸쳐 유전자 당 Z-정규화를 수행하였고, 그 후 시료 당 Z 값에 의해 순위를 매겼다. TCGA 코호트 분석을 위하여 "hallmark" 유전자 세트를 사용하였다. 얻어진 풍부 점수는 Z 스코어에 의해 시료에 걸쳐 정규화되었다(NES - normalized enrichment score). Single-sample GSEA (ssGSEA) and Gene Set Enrichment Analysis (GSEA) extensions were performed with GenePattern in the TCGA cohort and immunotherapy cohort. Briefly, separate enrichment scores of each pairing of the sample and gene set were calculated, indicating the extent to which genes in the gene set were equally up- or down-regulated within the sample. Expression values were Z-normalized per gene across samples and then ranked by Z values per sample. The "hallmark" gene set was used for TCGA cohort analysis. The obtained enrichment score was normalized across samples by the Z score (NES - normalized enrichment score).
표준 경로(canonical pathways)와 각 아형의 상류 조절자(upstream regulators)를 규명하기 위하여 Ingenuity Pathway Analysis (IPA)을 사용하였다. IPA는 적정한 수의 유전자가 요구되므로, 5개의 가능한 비교에서 발현 상 유의적 차이(P<0.05)를 보이는 mRNA를 분석에 사용하였다. 절대적 로그 비율이 1 초과인 mRNAs만을 L6F의 분석에 사용하였다. 유전자의 결과적 수는 다음과 같았다: L6A는 1,576개, L6B는 404개, L6C는 427개, L6D는 1,756개, L6F는 2,391개에 해당하였다. 아형별 시료와 TCGA 코호트에서 시료 나머지 사이에서의 유전자의 발현 로그 비율을 분석하였다. Ingenuity Pathway Analysis (IPA) was used to characterize canonical pathways and upstream regulators of each subtype. Since IPA requires an appropriate number of genes, mRNAs showing significant differences in expression (P<0.05) in five possible comparisons were used for analysis. Only mRNAs with an absolute log ratio greater than 1 were used for the analysis of L6F. The resulting number of genes was: 1,576 for L6A, 404 for L6B, 427 for L6C, 1,756 for L6D, and 2,391 for L6F. The log ratio of gene expression between subtype samples and the rest of the samples in the TCGA cohort was analyzed.
다른 상물학적 특성 분석Analysis of other botanical properties
64개의 면역 및 기질 세포 타입을 포함하는 세포 이질성(cellular heterogeneity)은 유전자 시그니처-기반 방법(xCell)에 의한 TCGA 코호트 전사체로부터 추론되었다. 64개의 세포형은 5개의 세포형 패밀리로 그룹핑하였고, 각 세포형 패밀리의 스코어는 포함하는 세포형 스코어의 총체로 계산되었다. 각 아형 별 줄기세포능은 마우스 위 발달 단계에서 다르게 발현되는 전사 인자의 발현 수준으로부터 평가되었다. mRNA 풍부 수준에서 규명된 725개의 위 발달 전사인자 중 TCGA 코호트에서 발현 수준 값이 이용 가능한 723개를 분석에 사용하였다. 각 아형 별 세포 주기의 단계는 초기 RNA 캡쳐 시퀀싱에 의해 규명된 S-단계 풍부 lncRNA의 발현 정도로부터 평가하였다. 1,145개의 일시적으로 발현되는 S-단계 풍부 lncRNA 중, TCGA 코호트 시료에 걸쳐 표준 편차가 0.3 초과인 경우 분석에 사용하였다. 위암에서 면역 조절에 관여하는 lncRNA를 확인하기 위하여, LncRNA Modulator Atlas in Pan-cancer (LncMAP)에서 규명된 lncRNA-중재 전사 네트워크 변화들을 평가하였다. 간단히는, 게놈-와이드 전사 조절(genome-wide transcriptional regulation)로 페어링된 lncRNA 및 유전자 발현 프로파일을 통합함으로써 각 암 유형별 변화된 lncRNA-전사 인자 유전자 트리플렛(triplets)을 확인하였다. 이후, ImmPort 프로젝트로부터 얻어진 면역-관련 유전자를 포함한 트리플렛만을 분석에 사용하였다(STAD에서 17,572 트리플렛). 면역 조절의 정도는 각 lncRNA가 구성하는 트리플렛의 수로 정의하였다. Cellular heterogeneity involving 64 immune and stromal cell types was deduced from TCGA cohort transcripts by a gene signature-based method (xCell). The 64 cell types were grouped into 5 cell type families, and the score of each cell type family was calculated as the sum of the cell type scores it contained. Stem cell ability for each subtype was evaluated from the expression level of transcription factors that are expressed differently in the developmental stage of the mouse stomach. Among the 725 gastric developmental transcription factors identified at the mRNA abundance level, 723 of the available expression level values in the TCGA cohort were used for analysis. Each subtype cell cycle phase was evaluated from the expression level of S-phase abundant lncRNA identified by initial RNA capture sequencing. Of the 1,145 transiently expressed S-phase abundant lncRNAs, those with a standard deviation greater than 0.3 across the TCGA cohort samples were used for analysis. To identify lncRNAs involved in immune regulation in gastric cancer, lncRNA-mediated transcriptional network changes identified in LncRNA Modulator Atlas in Pan-cancer (LncMAP) were evaluated. Briefly, altered lncRNA-transcription factor gene triplets for each cancer type were identified by integrating paired lncRNA and gene expression profiles with genome-wide transcriptional regulation. Thereafter, only triplets containing immune-related genes obtained from the ImmPort project were used for analysis (17,572 triplets in STAD). The degree of immune regulation was defined as the number of triplets composed of each lncRNA.
다른 게놈 분석other genomic analysis
TCGA 코호트 데이터(395 프로파일 시료)로부터 Q-value (<0.1)에 의해 돌연변이된 유전자를 거른 뒤 상위 29 유전자를 TCGA 코호트로부터 돌연변이 빈도로 플롯팅하였다(n=258). TCGA 코호트 데이터로부터 CNA 유전자를 Q-값 (<0.25) 및 빈도(>5%)로 필터링 하였다. TCGA 코호트로부터 CNA 빈도로 상위 25개 유전자를 플로팅하였다(n=258). DNA 메틸화 데이터는 표준 편차 >0.15로 필터링하였고, 모든 5개의 가능한 비교에서 β-값에 유의적 차이가 있는 경우(P<0.01), 아형 특이적인 것으로 간주하였고, 총 38,476개의 아형 특이적 프로브를 확인하였다: L6A는 451개, L6B는 773개, L6C는 84개, L6D는 10개, L6E는 28,803개, 그리고 L6F는 8,355개에 해당한다. miRNA 데이터는 결측 값(코호트의 <20%)으로 필터링하였고, 모든 5개의 가능한 비교에서 유의적 차이를 보이는 경우(P<0.05) 아형 특이적인 것으로 간주하였고, 143개의 아형 특이적 miRNA를 확인하였다: L6A는 17개, L6B는 4개, L6C는 0개, L6D는 4개, L6E는 8개, L6F는 110개에 해당하였다. 모든 5개의 가능한 비교에서 유의적 차이(P<0.05)가 있는 단백질은 아형 특이적인 것으로 간주하였고, 총 40개의 아형 특이적 단백질을 얻었다: L6A는 3개, L6B는 1개, L6C는 0개, L6D는 1개, L6E는 10개, L6F는 25개에 해당하였다. lncRNA 유전자의 후성 조절의 분석을 위하여, HM450 프로브를 lncRNA 유전자에 주석으로 달았다. TCGA 코호트에서 유전자 융합 경우를 다운로드 하였고, 258명의 환자 중 유전자 융합이 발생한 데이터는 183명의 환자에게서 이용이 가능하였다. After filtering out the mutated genes by Q-value (<0.1) from the TCGA cohort data (395 profile samples), the top 29 genes were plotted as mutation frequencies from the TCGA cohort (n=258). CNA genes from TCGA cohort data were filtered by Q-value (<0.25) and frequency (>5%). The top 25 genes by CNA frequency from the TCGA cohort were plotted (n=258). DNA methylation data were filtered to standard deviation >0.15, and if there was a significant difference in β-values in all five possible comparisons (P<0.01), it was considered subtype specific, and a total of 38,476 subtype specific probes were identified. L6A was 451, L6B 773, L6C 84, L6D 10, L6E 28,803, and L6F 8,355. miRNA data were filtered for missing values (<20% of cohorts) and were considered subtype-specific if there was a significant difference (P<0.05) in all five possible comparisons, and 143 subtype-specific miRNAs were identified: 17 L6A, 4 L6B, 0 L6C, 4 L6D, 8 L6E, and 110 L6F. Proteins with a significant difference (P<0.05) in all five possible comparisons were considered subtype-specific, resulting in a total of 40 subtype-specific proteins: 3 for L6A, 1 for L6B, 0 for L6C, 1 L6D, 10 L6E, and 25 L6F. For analysis of epigenetic regulation of lncRNA genes, HM450 probes were annotated to lncRNA genes. Gene fusion cases were downloaded from the TCGA cohort, and data on gene fusion among 258 patients were available for 183 patients.
세포 배양 및 형질 전환Cell culture and transformation
위암 세포주를 10% 소태아 혈청이 첨가되었고, 페니실린/스트렙토마이신(100ug/L each)을 포함하는 RPMI-1640 배지에서 배양하였고, 이때 배양은 5% CO2를 포함하는 가습화된 배양기 내에서 37℃의 온도 조건 하에서 수행되었다. 표 1에 나타낸 siRNA (Thermofisher Scientific)에 의한 ZNF667-AS1 넉다운은 mirus 형질 전환 시약(Mirus bio)을 사용하여 수행되었다. The gastric cancer cell line was cultured in RPMI-1640 medium containing 10% fetal bovine serum and penicillin/streptomycin (100ug/L each), wherein the culture was performed in a humidified incubator containing 5% CO 2 37 It was carried out under temperature conditions of °C. ZNF667-AS1 knockdown by siRNA (Thermofisher Scientific) shown in Table 1 was performed using mirus transformation reagent (Mirus bio).
서열(5'->3')sequence (5'->3')
센스 가닥(Sense)Sense strand GCUCCUAGCAACCAACAUUTT(서열번호 5)GCUCCUAGCAACCAACAAUUTT (SEQ ID NO: 5)
안티센스 가닥(Antisense)Antisense Strand (Antisense) AAUGUUGGUUGCUAGGAGCTG(서열번호 6)AAUGUUGGUUGCUAGGAGCTG (SEQ ID NO: 6)
정량적 실시간 RT-PCRQuantitative real-time RT-PCR
암 세포에 대하여 TRI 시약을 이용해 총 RNA를 추출하였다. M-MLV 역전사효소(Enzynomics)를 사용하여 cDNA를 합성하였다. SYBR Green PCR 마스터 믹스 (Thermofisher Scientific) 및 하기 표 2의 프라이머를 이용하여 Eco 실시간 PCR 시스템(Illumina) 상에서 실시간 qPCR에 의해 유전자 발현을 측정하였다. GAPDH에 대하여 상대적 유전자 발현량을 정규화 하였다. Total RNA was extracted from cancer cells using TRI reagent. cDNA was synthesized using M-MLV reverse transcriptase (Enzynomics). Gene expression was measured by real-time qPCR on an Eco real-time PCR system (Illumina) using the SYBR Green PCR master mix (Thermofisher Scientific) and the primers in Table 2 below. Relative gene expression levels were normalized to GAPDH.
유전자gene 정방향 프라이머(Forward Primer)Forward Primer 역방향 프라이머(Reverse Primer)Reverse Primer
FENDRRFENDRR AGAGTGCTTCCACTGCCCTA (서열번호 7)AGAGTGCTTCCACTGCCCTA (SEQ ID NO: 7) CCCATTTGCAAAGGCTACAT (서열번호 8)CCCATTTGCAAAGGCTACAT (SEQ ID NO: 8)
MAGI2-AS3MAGI2-AS3 TGGGTCTGTGCAGAGTTGAG (서열번호 9)TGGGTCTGTGCAGAGTTGAG (SEQ ID NO: 9) GCTGGTTATGGCCAATGAGT (서열번호 10)GCTGGTTATGGCCAATGAGT (SEQ ID NO: 10)
ACTA2-AS1ACTA2-AS1 GTGGTTCTGGTTTGCCTGAT (서열번호 11)GTGGTTCTGGTTTGCCTGAT (SEQ ID NO: 11) CTGGCCCTGTAACACCAGAT (서열번호 12)CTGGCCCTGTAACACCAGAT (SEQ ID NO: 12)
ZNF667-AS1ZNF667-AS1 GGACACTGTGCAGGATGATG (서열번호 13)GGACACTGTGCAGGATGATG (SEQ ID NO: 13) GGCAAGAATGCTGTGTCTCA (서열번호 14)GGCAGAATGCTGTGTCTCA (SEQ ID NO: 14)
RP11-572C15.6RP11-572C15.6 TCATCCCTCTTCCTTGATGG (서열번호 15)TCATCCCTCTTCCTTGATGG (SEQ ID NO: 15) ATTGGCAACTTTGGGCTATG (서열번호 16)ATTGGCAACTTTGGGCTATG (SEQ ID NO: 16)
세포 생존율 어쎄이Cell viability assay
si-비 타겟 또는 si-ZNF667-AS1로 형질 전환된 암 세포를 96-웰 플레이트에 2X103 세포/웰의 양으로 접종하여 약물 처리 전 밤새 배양하고, 웰당 20uL CellTiter 96 AQueous One 용액(MTS, Promega)을 첨가하기 전에 72 시간 동안 약물의 존재 하에서 유지하였다. 플레이트를 3시간 동안 배양한 뒤 ELISA 리더(Bio tek)를 이용하여 490 nm에서 흡광도를 측정하였다. Cancer cells transformed with si-non-target or si-ZNF667-AS1 were inoculated into 96-well plates at an amount of 2X10 3 cells/well and cultured overnight before drug treatment, and 20uL CellTiter 96 AQueous One solution per well (MTS, Promega ) was maintained in the presence of drug for 72 h before addition. After incubating the plate for 3 hours, absorbance was measured at 490 nm using an ELISA reader (Bio tek).
세포 이동능 및 침윤능 어쎄이Cell migration and invasiveness assay
si-비 타겟 또는 si-ZNF667-AS1로 형질 전환된 암 세포의 이동능 및 침윤능을 평가하기 위하여, 8-um-기공 사이즈의 챔버 인저트(Corning Costar)를 포함하는 24-웰 플레이트를 사용하였다. 200uL FBS-프리 배양 배지 내 2 X 105 세포를 각 필터 인저트(상부 챔버)에 로딩하고, 각 하부 챔버에 10% FBS를 포함하는 배양 배지 700uL를 추가한 뒤, 37℃에서 16 시간 동안 배양하였다. 수확 후 인저트의 바닥을 고정시킨 뒤 크리스탈 바이올렛으로 염색하였다. EVOS M7000 이미징 시스템(Thermofisher Scientific)을 이용하여 이동 또는 침윤한 세포의 수를 측정하였다. To evaluate the migratory and invasive ability of cancer cells transformed with si-non-target or si-ZNF667-AS1, a 24-well plate containing an 8-um-pore size chamber insert (Corning Costar) was used. did 2 X 10 5 cells in 200uL FBS-free culture medium were loaded into each filter insert (upper chamber), 700uL of culture medium containing 10% FBS was added to each lower chamber, and cultured at 37°C for 16 hours. did After harvesting, the bottom of the insert was fixed and dyed with crystal violet. The number of migrating or infiltrating cells was measured using an EVOS M7000 imaging system (Thermofisher Scientific).
면역 블롯immunoblot
프로테아제 억제제 칵테일 및 포스파타제 억제제(Roche)로 보충된 RIPA 버퍼에서 세포를 용혈시켰다. BCA 단백질 어쎄이 키트(Thermofisher Scientific)를 이용하여 총 단백질 농도를 측정하였다. SDS-PAGE를 이용하여 단백질 10ug을 분리한 뒤 PVDF 막(Millipore) 상에서 이동시키고 1차 항체를 이용하여 4℃에서 밤새 배양하였다. 사용된 1차 항체는 다음과 같다: ZNF667 (1:2000, Abcam, ab106432), N-Cadherin (1:1000, Cell signaling, 4061S), E-Cadherin (1:1000, Cell signaling, 14472S), Vimentin (1:1000, Cell signaling, 5741) 및 GAPDH (1:1000, Sigma, G9545). TBS-T로 5회 세척하고, 블롯을 겨자무과산화수소-컨쥬게이트된 2차 항체로 배양한 뒤 향상된 케미-루미네센스(chemi-luminescence) 검출(ECL plus kit, Pierce)로 시각화 하였다.Cells were lysed in RIPA buffer supplemented with a protease inhibitor cocktail and a phosphatase inhibitor (Roche). Total protein concentration was determined using a BCA protein assay kit (Thermofisher Scientific). After separating 10ug of the protein using SDS-PAGE, it was transferred on a PVDF membrane (Millipore) and incubated overnight at 4°C using a primary antibody. The primary antibodies used were: ZNF667 (1:2000, Abcam, ab106432), N-Cadherin (1:1000, Cell signaling, 4061S), E-Cadherin (1:1000, Cell signaling, 14472S), Vimentin (1:1000, Cell signaling, 5741) and GAPDH (1:1000, Sigma, G9545). After washing 5 times with TBS-T, the blots were incubated with mustard radish hydrogen peroxide-conjugated secondary antibody and visualized with enhanced chemi-luminescence detection (ECL plus kit, Pierce).
위암에서 lncRNA의 영향을 확인하기 위한 시스템적 접근A Systematic Approach to Identify the Effect of lncRNA in Gastric Cancer
총 게놈 수준에서 무편파적 접근으로 위암에서 lncRNA의 영향을 확인하기 위하여, 비지도 클러스터링(unsupervised clustering)을 수행하였다. TCGA 코호트에서 lncRNA 발현의 계층적 클러스터링 분석을 통해 6개의 클러스터로 구분하였고, L6A, L6B, L6C, L6D, L6E, 및 L6F로 명명하였다(도 1a). 다음으로, 각 아형 별로 발현이 독특한 lncRNA 유전자를 규명하였다: 6개의 아형에서 구분되는 발현을 보이는 lncRNA는 총 262개에 해당하였다(도 1b; 표 3).To confirm the effect of lncRNA in gastric cancer with an unbiased approach at the total genome level, unsupervised clustering was performed. Through hierarchical clustering analysis of lncRNA expression in the TCGA cohort, it was divided into 6 clusters and named L6A, L6B, L6C, L6D, L6E, and L6F (FIG. 1a). Next, lncRNA genes with unique expression for each subtype were identified: a total of 262 lncRNAs with distinct expression in the six subtypes corresponded to a total of 262 (Fig. 1b; Table 3).
ENSEMBL IDENSEMBL ID 유전자 명
(Gencode v19)gene name
(Gencode v19) 		아형subtype 비율 (subtype/others)ratio (subtype/others)
ENSG00000269900.2ENSG00000269900.2 RMRPRMRP L6AL6A 2.012.01
ENSG00000242125.2ENSG00000242125.2 SNHG3SNHG3 L6AL6A 1.371.37
ENSG00000271824.1ENSG00000271824.1 AC009014.3AC009014.3 L6AL6A 1.161.16
ENSG00000126005.11ENSG00000126005.11 MMP24-AS1MMP24-AS1 L6AL6A 0.970.97
ENSG00000233621.1ENSG00000233621.1 RP11-422J8.1RP11-422J8.1 L6AL6A 0.890.89
ENSG00000229953.1ENSG000002229953.1 RP11-284F21.7RP11-284F21.7 L6AL6A 0.880.88
ENSG00000260704.1ENSG00000260704.1 LINC00543LINC00543 L6AL6A 0.790.79
ENSG00000175701.6ENSG00000175701.6 LINC00116LINC00116 L6AL6A 0.770.77
ENSG00000258940.2ENSG00000258940.2 RP11-407N17.5RP11-407N17.5 L6AL6A 0.640.64
ENSG00000226330.1ENSG00000226330.1 RP11-739N20.2RP11-739N20.2 L6AL6A 0.610.61
ENSG00000268516.1ENSG00000268516.1 CTD-3138B18.5CTD-3138B18.5 L6AL6A -0.26-0.26
ENSG00000272645.1ENSG00000272645.1 RP11-504P24.8RP11-504P24.8 L6AL6A -0.31-0.31
ENSG00000269958.1ENSG00000269958.1 RP11-73M18.8RP11-73M18.8 L6AL6A -0.49-0.49
ENSG00000267449.1ENSG00000267449.1 RP11-264B14.2RP11-264B14.2 L6AL6A -0.54-0.54
ENSG00000188206.5ENSG00000188206.5 HNRNPU-AS1HNRNPU-AS1 L6AL6A -0.58-0.58
ENSG00000257621.3ENSG00000257621.3 RP11-349A22.5RP11-349A22.5 L6AL6A -0.61-0.61
ENSG00000273014.1ENSG00000273014.1 RP11-225B17.2RP11-225B17.2 L6AL6A -0.77-0.77
ENSG00000229152.1ENSG00000229152.1 ANKRD10-IT1ANKRD10-IT1 L6AL6A -0.91-0.91
ENSG00000259865.1ENSG00000259865.1 RP11-488L18.10RP11-488L18.10 L6AL6A -0.96-0.96
ENSG00000268205.1ENSG00000268205.1 CTC-444N24.11CTC-444N24.11 L6AL6A -0.99-0.99
ENSG00000245532.4ENSG000000245532.4 NEAT1NEAT1 L6AL6A -1.14-1.14
ENSG00000267207.1ENSG00000267207.1 RP11-264B14.1RP11-264B14.1 L6AL6A -1.30-1.30
ENSG00000251562.3ENSG00000251562.3 MALAT1MALAT1 L6AL6A -1.39-1.39
ENSG00000264940.2ENSG00000264940.2 SNORD3CSNORD3C L6AL6A -1.50-1.50
ENSG00000130600.11ENSG000001130600.11 H19H19 L6BL6B 2.242.24
ENSG00000260032.1ENSG00000260032.1 LINC00657LINC00657 L6BL6B 1.581.58
ENSG00000269972.1ENSG00000269972.1 RP3-430N8.10RP3-430N8.10 L6BL6B 1.271.27
ENSG00000234678.1ENSG00000234678.1 RP11-465N4.4RP11-465N4.4 L6BL6B 1.241.24
ENSG00000269987.1ENSG00000269987.1 RP3-430N8.11RP3-430N8.11 L6BL6B 1.191.19
ENSG00000253352.4ENSG00000253352.4 TUG1TUG1 L6BL6B 1.151.15
ENSG00000232803.1ENSG00000232803.1 RP11-93B14.5RP11-93B14.5 L6BL6B 1.071.07
ENSG00000254635.1ENSG00000254635.1 WAC-AS1WAC-AS1 L6BL6B 1.001.00
ENSG00000234771.2ENSG000000234771.2 RP11-395P17.3RP11-395P17.3 L6BL6B 0.970.97
ENSG00000270580.1ENSG00000270580.1 RP11-1186N24.5RP11-1186N24.5 L6BL6B 0.860.86
ENSG00000234072.1ENSG00000234072.1 AC074117.10AC074117.10 L6BL6B 0.840.84
ENSG00000260766.1ENSG00000260766.1 RP11-226L15.5RP11-226L15.5 L6BL6B 0.810.81
ENSG00000228989.1ENSG00000228989.1 AC133528.2AC133528.2 L6BL6B 0.770.77
ENSG00000206195.6ENSG00000206195.6 AP000525.9AP000525.9 L6BL6B 0.740.74
ENSG00000238035.4ENSG00000238035.4 AC138035.2AC138035.2 L6BL6B 0.740.74
ENSG00000247228.2ENSG00000247228.2 RP11-296I10.3RP11-296I10.3 L6BL6B 0.730.73
ENSG00000267100.1ENSG00000267100.1 ILF3-AS1ILF3-AS1 L6BL6B 0.730.73
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ENSG00000245694.4ENSG00000245694.4 CRNDECRNDE L6CL6C 0.470.47
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ENSG00000228630.1ENSG00000228630.1 HOTAIRHOTAIR L6CL6C 0.360.36
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ENSG00000245060.2ENSG000000245060.2 LINC00847LINC00847 L6CL6C -0.44-0.44
ENSG00000249456.1ENSG00000249456.1 RP11-298J20.4RP11-298J20.4 L6CL6C -0.47-0.47
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ENSG00000214719.7ENSG00000214719.7 AC005562.1AC005562.1 L6DL6D -0.12-0.12
ENSG00000261801.1ENSG00000261801.1 LOXL1-AS1LOXL1-AS1 L6DL6D -0.15-0.15
ENSG00000233903.2ENSG00000233903.2 Z83851.4Z83851.4 L6DL6D -0.16-0.16
ENSG00000230513.1ENSG00000230513.1 THAP7-AS1THAP7-AS1 L6DL6D -0.18-0.18
ENSG00000235314.1ENSG000000235314.1 LINC00957LINC00957 L6DL6D -0.18-0.18
ENSG00000223813.2ENSG00000223813.2 AC007255.8AC007255.8 L6DL6D -0.20-0.20
ENSG00000273145.1ENSG00000273145.1 CITF22-92A6.1CITF22-92A6.1 L6DL6D -0.21-0.21
ENSG00000268087.1ENSG00000268087.1 CTC-429P9.2CTC-429P9.2 L6DL6D -0.22-0.22
ENSG00000267322.1ENSG00000267322.1 SCARNA17SCARNA17 L6DL6D -0.26-0.26
ENSG00000224914.2ENSG00000224914.2 LINC00863LINC00863 L6DL6D -0.26-0.26
ENSG00000267904.1ENSG00000267904.1 CTC-429P9.5CTC-429P9.5 L6DL6D -0.27-0.27
ENSG00000196810.4ENSG00000196810.4 CTBP1-AS2CTBP1-AS2 L6DL6D -0.27-0.27
ENSG00000259806.2ENSG00000259806.2 CTD-2196E14.4CTD-2196E14.4 L6DL6D -0.28-0.28
ENSG00000270017.1ENSG00000270017.1 CTD-2576F9.2CTD-2576F9.2 L6DL6D -0.29-0.29
ENSG00000263327.2ENSG00000263327.2 TAPT1-AS1TAPT1-AS1 L6DL6D -0.35-0.35
ENSG00000245937.3ENSG000000245937.3 CTC-228N24.3CTC-228N24.3 L6DL6D -0.36-0.36
ENSG00000175772.10ENSG00000175772.10 AC112229.7AC112229.7 L6DL6D -0.37-0.37
ENSG00000271359.1ENSG00000271359.1 RP11-84C13.1RP11-84C13.1 L6DL6D -0.41-0.41
ENSG00000269481.1ENSG00000269481.1 CTD-2521M24.6CTD-2521M24.6 L6DL6D -0.47-0.47
ENSG00000273015.1ENSG00000273015.1 LINC00938LINC00938 L6DL6D -0.49-0.49
ENSG00000272667.1ENSG00000272667.1 RP11-395A13.2RP11-395A13.2 L6DL6D -0.50-0.50
ENSG00000272447.1ENSG00000272447.1 RP11-182L21.6RP11-182L21.6 L6DL6D -0.53-0.53
ENSG00000267080.1ENSG00000267080.1 ASB16-AS1ASB16-AS1 L6DL6D -0.57-0.57
ENSG00000253982.1ENSG00000253982.1 CTD-2336O2.1CTD-2336O2.1 L6DL6D -0.57-0.57
ENSG00000239569.2ENSG000000239569.2 KMT2E-AS1KMT2E-AS1 L6DL6D -0.59-0.59
ENSG00000236618.2ENSG00000236618.2 PITPNA-AS1PITPNA-AS1 L6DL6D -0.60-0.60
ENSG00000163597.10ENSG00000163597.10 SNHG16SNHG16 L6DL6D -0.60-0.60
ENSG00000254911.2ENSG00000254911.2 SCARNA9SCARNA9 L6DL6D -0.61-0.61
ENSG00000270081.1ENSG00000270081.1 RP5-935K16.1RP5-935K16.1 L6DL6D -0.64-0.64
ENSG00000226137.3ENSG000000226137.3 BAIAP2-AS1BAIAP2-AS1 L6DL6D -0.66-0.66
ENSG00000233368.2ENSG000000233368.2 RP11-277L2.3RP11-277L2.3 L6DL6D -0.68-0.68
ENSG00000264575.1ENSG00000264575.1 LINC00526LINC00526 L6DL6D -0.68-0.68
ENSG00000265479.1ENSG00000265479.1 DTX2P1-UPK3BP1-PMS2P11DTX2P1-UPK3BP1-PMS2P11 L6DL6D -0.69-0.69
ENSG00000265091.1ENSG00000265091.1 RP11-835E18.5RP11-835E18.5 L6DL6D -0.69-0.69
ENSG00000257167.2ENSG00000257167.2 TMPO-AS1TMPO-AS1 L6DL6D -0.72-0.72
ENSG00000204054.7ENSG00000204054.7 LINC00963LINC00963 L6DL6D -0.72-0.72
ENSG00000236537.1ENSG00000236537.1 RP11-732M18.3RP11-732M18.3 L6DL6D -0.73-0.73
ENSG00000270103.2ENSG00000270103.2 RNU11RNU11 L6DL6D -0.73-0.73
ENSG00000270170.1ENSG00000270170.1 NCBP2-AS2NCBP2-AS2 L6DL6D -0.73-0.73
ENSG00000232533.1ENSG000000232533.1 AC093673.5AC093673.5 L6DL6D -0.74-0.74
ENSG00000262477.1ENSG00000262477.1 AC021224.1AC021224.1 L6DL6D -0.75-0.75
ENSG00000233223.2ENSG00000233223.2 AC113189.5AC113189.5 L6DL6D -0.75-0.75
ENSG00000255198.3ENSG00000255198.3 SNHG9SNHG9 L6DL6D -0.77-0.77
ENSG00000214783.5ENSG00000214783.5 POLR2J4POLR2J4 L6DL6D -0.79-0.79
ENSG00000270726.1ENSG00000270726.1 AJ271736.10AJ271736.10 L6DL6D -0.81-0.81
ENSG00000229874.2ENSG00000229874.2 RP11-312O7.2RP11-312O7.2 L6DL6D -0.88-0.88
ENSG00000273432.1ENSG00000273432.1 RP5-1165K10.2RP5-1165K10.2 L6DL6D -0.95-0.95
ENSG00000261512.2ENSG00000261512.2 RP11-46D6.1RP11-46D6.1 L6DL6D -1.01-1.01
ENSG00000270066.2ENSG00000270066.2 SCARNA2SCARNA2 L6DL6D -1.39-1.39
ENSG00000272933.1ENSG00000272933.1 RP11-47A8.5RP11-47A8.5 L6DL6D -1.62-1.62
ENSG00000258486.2ENSG00000258486.2 RN7SL1RN7SL1 L6DL6D -2.31-2.31
ENSG00000259001.2ENSG00000259001.2 RPPH1RPPH1 L6DL6D -2.48-2.48
ENSG00000250742.1ENSG00000250742.1 RP11-834C11.4RP11-834C11.4 L6EL6E 2.932.93
ENSG00000256039.1ENSG000000256039.1 RP11-291B21.2RP11-291B21.2 L6EL6E 1.041.04
ENSG00000235576.1ENSG000000235576.1 AC092580.4AC092580.4 L6EL6E 0.960.96
ENSG00000253838.1ENSG00000253838.1 RP11-44K6.2RP11-44K6.2 L6EL6E 0.900.90
ENSG00000261520.1ENSG00000261520.1 DLGAP1-AS5DLGAP1-AS5 L6EL6E 0.820.82
ENSG00000132832.5ENSG00000132832.5 RP11-445H22.3RP11-445H22.3 L6EL6E 0.740.74
ENSG00000261971.2ENSG00000261971.2 RP11-473M20.7RP11-473M20.7 L6EL6E 0.640.64
ENSG00000227619.1ENSG00000227619.1 RP11-492E3.2RP11-492E3.2 L6EL6E 0.570.57
ENSG00000273290.1ENSG00000273290.1 CTC-297N7.8CTC-297N7.8 L6EL6E 0.560.56
ENSG00000258521.1ENSG00000258521.1 RP11-638I2.9RP11-638I2.9 L6EL6E 0.550.55
ENSG00000239213.1ENSG00000239213.1 RP11-85F14.5RP11-85F14.5 L6EL6E 0.520.52
ENSG00000259834.1ENSG00000259834.1 RP11-284N8.3RP11-284N8.3 L6EL6E 0.460.46
ENSG00000229950.1ENSG000002229950.1 TFAP2A-AS1TFAP2A-AS1 L6EL6E 0.460.46
ENSG00000267745.1ENSG00000267745.1 RP11-686D22.8RP11-686D22.8 L6EL6E 0.440.44
ENSG00000262222.1ENSG00000262222.1 RP11-876N24.4RP11-876N24.4 L6EL6E 0.320.32
ENSG00000206028.1ENSG00000206028.1 CTA-373H7.7CTA-373H7.7 L6EL6E 0.290.29
ENSG00000254287.1ENSG00000254287.1 RP11-44K6.4RP11-44K6.4 L6EL6E 0.270.27
ENSG00000225783.2ENSG000000225783.2 MIATMIAT L6EL6E 0.260.26
ENSG00000263013.1ENSG00000263013.1 RP11-876N24.5RP11-876N24.5 L6EL6E 0.250.25
ENSG00000203362.2ENSG00000203362.2 RP3-337H4.8RP3-337H4.8 L6EL6E 0.220.22
ENSG00000267074.1ENSG00000267074.1 RP11-1094M14.5RP11-1094M14.5 L6EL6E 0.210.21
ENSG00000249746.1ENSG000000249746.1 RP11-254I22.3RP11-254I22.3 L6EL6E 0.160.16
ENSG00000261270.1ENSG00000261270.1 RP11-325K4.3RP11-325K4.3 L6EL6E 0.070.07
ENSG00000235437.3ENSG000000235437.3 RP11-357C3.3RP11-357C3.3 L6EL6E -0.79-0.79
ENSG00000272620.1ENSG00000272620.1 AFAP1-AS1AFAP1-AS1 L6EL6E -0.86-0.86
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ENSG00000238142.1ENSG00000238142.1 RP11-108M9.4RP11-108M9.4 L6EL6E -1.02-1.02
ENSG00000261123.1ENSG00000261123.1 RP11-304L19.3RP11-304L19.3 L6EL6E -1.29-1.29
ENSG00000259933.2ENSG000000259933.2 RP11-304L19.1RP11-304L19.1 L6EL6E -1.37-1.37
ENSG00000272763.1ENSG00000272763.1 RP11-357H14.17RP11-357H14.17 L6EL6E -1.43-1.43
ENSG00000272761.1ENSG00000272761.1 RP11-572C15.6RP11-572C15.6 L6FL6F 2.022.02
ENSG00000269936.2ENSG000000269936.2 MIR145MIR145 L6FL6F 1.721.72
ENSG00000203706.4ENSG00000203706.4 SERTAD4-AS1SERTAD4-AS1 L6FL6F 1.171.17
ENSG00000268388.1ENSG00000268388.1 FENDRRFENDRR L6FL6F 1.071.07
ENSG00000255248.2ENSG00000255248.2 RP11-166D19.1RP11-166D19.1 L6FL6F 1.041.04
ENSG00000249669.3ENSG00000249669.3 MIR143HGMIR143HG L6FL6F 0.920.92
ENSG00000237125.4ENSG00000237125.4 HAND2-AS1HAND2-AS1 L6FL6F 0.770.77
ENSG00000224958.1ENSG00000224958.1 PGM5-AS1PGM5-AS1 L6FL6F 0.760.76
ENSG00000267047.1ENSG00000267047.1 RP11-589P10.7RP11-589P10.7 L6FL6F 0.740.74
ENSG00000166770.6ENSG00000166770.6 ZNF667-AS1ZNF667-AS1 L6FL6F 0.660.66
ENSG00000261625.1ENSG00000261625.1 RP11-554A11.4RP11-554A11.4 L6FL6F 0.600.60
ENSG00000253864.1ENSG00000253864.1 AC131025.8AC131025.8 L6FL6F 0.590.59
ENSG00000250734.2ENSG00000250734.2 RP11-404E16.1RP11-404E16.1 L6FL6F 0.580.58
ENSG00000234638.1ENSG000000234638.1 AC053503.6AC053503.6 L6FL6F 0.470.47
ENSG00000180139.10ENSG00000180139.10 ACTA2-AS1ACTA2-AS1 L6FL6F 0.460.46
ENSG00000235501.1ENSG00000235501.1 RP4-639F20.1RP4-639F20.1 L6FL6F 0.440.44
ENSG00000269186.1ENSG00000269186.1 LINC01082LINC01082 L6FL6F 0.430.43
ENSG00000230148.4ENSG00000230148.4 HOXB-AS1HOXB-AS1 L6FL6F 0.420.42
ENSG00000230630.1ENSG00000230630.1 DNM3OSDNM3OS L6FL6F 0.390.39
ENSG00000272755.1ENSG00000272755.1 RP11-326G21.1RP11-326G21.1 L6FL6F 0.370.37
ENSG00000228221.1ENSG000000228221.1 LINC00578LINC00578 L6FL6F 0.370.37
ENSG00000225986.1ENSG000000225986.1 RP3-340N1.5RP3-340N1.5 L6FL6F 0.330.33
ENSG00000262879.1ENSG00000262879.1 RP11-156P1.3RP11-156P1.3 L6FL6F 0.320.32
ENSG00000259248.1ENSG00000259248.1 USP3-AS1USP3-AS1 L6FL6F 0.270.27
ENSG00000234456.3ENSG000000234456.3 MAGI2-AS3MAGI2-AS3 L6FL6F 0.270.27
ENSG00000258441.1ENSG00000258441.1 LINC00641LINC00641 L6FL6F 0.260.26
ENSG00000261534.1ENSG00000261534.1 RP11-244O19.1RP11-244O19.1 L6FL6F 0.260.26
ENSG00000269044.1ENSG00000269044.1 CTC-429P9.3CTC-429P9.3 L6FL6F 0.240.24
ENSG00000255455.2ENSG00000255455.2 RP11-890B15.3RP11-890B15.3 L6FL6F 0.230.23
ENSG00000267532.2ENSG000000267532.2 MIR497HGMIR497HG L6FL6F 0.210.21
ENSG00000250360.1ENSG00000250360.1 CTD-2089N3.1CTD-2089N3.1 L6FL6F 0.210.21
ENSG00000267082.1ENSG00000267082.1 CTC-510F12.2CTC-510F12.2 L6FL6F 0.180.18
ENSG00000224739.2ENSG00000224739.2 AC016735.1AC016735.1 L6FL6F -0.18-0.18
ENSG00000260711.1ENSG00000260711.1 RP11-747H7.3RP11-747H7.3 L6FL6F -0.18-0.18
ENSG00000234286.1ENSG000000234286.1 AC006026.13AC006026.13 L6FL6F -0.20-0.20
ENSG00000238164.2ENSG00000238164.2 RP3-395M20.8RP3-395M20.8 L6FL6F -0.21-0.21
ENSG00000267751.1ENSG00000267751.1 AC009005.2AC009005.2 L6FL6F -0.22-0.22
ENSG00000269609.1ENSG00000269609.1 RP11-18I14.10RP11-18I14.10 L6FL6F -0.22-0.22
ENSG00000260196.1ENSG00000260196.1 RP1-239B22.5RP1-239B22.5 L6FL6F -0.22-0.22
ENSG00000235823.1ENSG00000235823.1 LINC00263LINC00263 L6FL6F -0.23-0.23
ENSG00000253716.1ENSG00000253716.1 RP13-582O9.5RP13-582O9.5 L6FL6F -0.26-0.26
ENSG00000231770.1ENSG00000231770.1 TMEM44-AS1TMEM44-AS1 L6FL6F -0.27-0.27
ENSG00000214293.4ENSG000000214293.4 RSBN1L-AS1RSBN1L-AS1 L6FL6F -0.29-0.29
ENSG00000225138.3ENSG000000225138.3 CTD-2228K2.7CTD-2228K2.7 L6FL6F -0.32-0.32
ENSG00000234155.1ENSG000000234155.1 RP11-30P6.6RP11-30P6.6 L6FL6F -0.34-0.34
ENSG00000251603.1ENSG00000251603.1 RP11-164P12.4RP11-164P12.4 L6FL6F -0.34-0.34
ENSG00000254837.1ENSG00000254837.1 AP001372.2AP001372.2 L6FL6F -0.40-0.40
ENSG00000227036.2ENSG00000227036.2 LINC00511LINC00511 L6FL6F -0.43-0.43
ENSG00000234608.3ENSG000000234608.3 MAPKAPK5-AS1MAPKAPK5-AS1 L6FL6F -0.44-0.44
ENSG00000175061.13ENSG00000175061.13 C17orf76-AS1C17orf76-AS1 L6FL6F -0.45-0.45
ENSG00000177410.8ENSG00000177410.8 ZFAS1ZFAS1 L6FL6F -0.51-0.51
ENSG00000233834.2ENSG00000233834.2 AC005083.1AC005083.1 L6FL6F -0.52-0.52
ENSG00000268006.1ENSG00000268006.1 PTOV1-AS1PTOV1-AS1 L6FL6F -0.52-0.52
ENSG00000247271.2ENSG00000247271.2 ZBED5-AS1ZBED5-AS1 L6FL6F -0.55-0.55
ENSG00000232677.2ENSG00000232677.2 LINC00665LINC00665 L6FL6F -0.61-0.61
ENSG00000196696.8ENSG00000196696.8 PDXDC2PPDXDC2P L6FL6F -0.64-0.64
ENSG00000232445.1ENSG00000232445.1 RP11-132A1.4RP11-132A1.4 L6FL6F -0.67-0.67
ENSG00000232956.4ENSG00000232956.4 SNHG15SNHG15 L6FL6F -0.71-0.71
ENSG00000261183.1ENSG00000261183.1 RP11-532F12.5RP11-532F12.5 L6FL6F -0.77-0.77
ENSG00000272141.1ENSG00000272141.1 RP11-465B22.8RP11-465B22.8 L6FL6F -0.78-0.78
ENSG00000196756.7ENSG00000196756.7 SNHG17SNHG17 L6FL6F -0.82-0.82
ENSG00000226950.2ENSG00000226950.2 DANCRDANCR L6FL6F -0.82-0.82
ENSG00000261373.1ENSG00000261373.1 VPS9D1-AS1VPS9D1-AS1 L6FL6F -0.91-0.91
ENSG00000224259.1ENSG00000224259.1 RP11-48O20.4RP11-48O20.4 L6FL6F -1.05-1.05
ENSG00000203499.6ENSG00000203499.6 FAM83H-AS1FAM83H-AS1 L6FL6F -1.26-1.26
ENSG00000259187.1ENSG00000259187.1 CTD-2008A1.1CTD-2008A1.1 L6FL6F -1.55-1.55
LNC6의 인구학적 및 임상학적 연관성Demographic and Clinical Relevance of LNC6
TCGA 코호트에서 LNC6 아형은 성별, 민족, 종양 위치 및 암의 병기, 조직학적 등급 및 로렌 아형을 나타낸다(표 4). L6A 및 L6E 환자 대부분이 남성이었다(84% 및 100%). L6A 및 L6D 환자의 대부분은 서양 국가 환자이었다(96% and 92%). 민족에서의 그러한 차이는 위암의 분자적 아형에서는 발견할 수 없었다. L6A 환자가 종양의 근위부의 가장 높은 비율을 보였고 (36%), 반면 L6F 환자가 가장 낮은 비율을 보였다(10%). 흥미롭게도, 일반적으로 위암은 서양 국가에서 병기가 진행된 후 진단이 되지만, L6A 아형 종양은 상대적으로 낮은 암의 병기를 보였다. L6C 종양 또한 상대적으로 낮은 암의 병기를 보였으나, L6E 및 L6F 종양은 높은 병기와 조직학적 등급을 보였다. 마지막으로, L6F 아형은 로렌 분류에서 미만성 아형이 풍부하였다. LNC6 subtypes in the TCGA cohort represent sex, ethnicity, tumor location and stage of cancer, histological grade, and Loren subtype (Table 4). The majority of L6A and L6E patients were male (84% and 100%). The majority of L6A and L6D patients were from Western countries (96% and 92%). Such ethnic differences were not found in the molecular subtypes of gastric cancer. L6A patients had the highest proportion of the proximal part of the tumor (36%), whereas L6F patients had the lowest proportion (10%). Interestingly, although gastric cancer is usually diagnosed after advanced stage in Western countries, L6A subtype tumors showed a relatively low cancer stage. L6C tumors also showed relatively low cancer stage, but L6E and L6F tumors showed high stage and histological grade. Finally, the L6F subtype was rich in the diffuse subtype in the Loren classification.
LNC6LNC6 L6AL6A L6BL6B L6CL6C L6DL6D L6EL6E L6FL6F nn
나이, 평균 (SD)age, mean (SD) 66.2 (10.3)66.2 (10.3) 66.2 (9.7)66.2 (9.7) 68.5 (10.0)68.5 (10.0) 68.9 (9.9)68.9 (9.9) 62.6 (12.3)62.6 (12.3) 60.2 (10.5)60.2 (10.5) 258258
남성male 21/25
(84%)21/25
(84%) 		45/66
(68%)45/66
(68%) 		24/51
(47%)24/51
(47%) 		26/51
(51%)26/51
(51%) 		14/14
(100%)14/14
(100%) 		29/51
(57%)29/51
(57%) 		258258
서양 기원(Western origin)Western origin 24/25
(96%)24/25
(96%) 		47/66
(71%)47/66
(71%) 		29/51
(57%)29/51
(57%) 		47/51
(92%)47/51
(92%) 		9/14
(64%)9/14
(64%) 		37/51
(73%)37/51
(73%) 		258258
근위부(Proximal location) Proximal location 9/25
(36%)9/25
(36%) 		14/66
(21%)14/66
(21%) 		9/51
(18%)9/51
(18%) 		8/43
(19%)8/43
(19%) 		3/14
(21%)3/14
(21%) 		5/51
(10%)5/51
(10%) 		250250
T3/4T3/4 7/25
(28%)7/25
(28%) 		48/66
(73%)48/66
(73%) 		38/50
(76%)38/50
(76%) 		24/44
(55%)24/44
(55%) 		13/14
(93%)13/14
(93%) 		44/51
(86%)44/51
(86%) 		250250
N1-3N1-3 18/25
(72%)18/25
(72%) 		42/66
(64%)42/66
(64%) 		26/50
(52%)26/50
(52%) 		31/41
(76%)31/41
(76%) 		10/14
(71%)10/14
(71%) 		35/50
(70%)35/50
(70%) 		246246
M1 M1 0/25
(0%)0/25
(0%) 		4/62
(6%)4/62
(6%) 		1/48
(2%)1/48
(2%) 		7/48
(15%)7/48
(15%) 		1/12
(8%)1/12
(8%) 		3/51
(6%)3/51
(6%) 		246246
AJCC 암기 III/IVAJCC Memorization III/IV 8/25
(32%)8/25
(32%) 		30/66
(45%)30/66
(45%) 		16/51
(31%)16/51
(31%) 		21/51
(41%)21/51
(41%) 		8/14
(57%)8/14
(57%) 		31/51
(61%)31/51
(61%) 		240240
조직학적 등급 3 histological grade 3 12/25
(48%)12/25
(48%) 		34/64
(53%)34/64
(53%) 		32/51
(63%)32/51
(63%) 		31/51
(61%)31/51
(61%) 		14/14
(100%)14/14
(100%) 		43/48
(90%)43/48
(90%) 		253253
로렌 미만형(Lauren Diffuse type)Lauren Diffuse type 5/25
(20%)5/25
(20%) 		7/66
(11%)7/66
(11%) 		3/51
(6%)3/51
(6%) 		11/51
(23%)11/51
(23%) 		3/13
(23%)3/13
(23%) 		34/49
(69%)34/49
(69%) 		247247
LNC6 아형의 진단적 중요성Diagnostic significance of the LNC6 subtype
TCGA 코호트에서 후속 조치 기간의 단축으로, 독립적 일반 GC 코호트(Total n = 1,933)에서 LNC6 아형의 진단적 관계를 평가하였다. 먼저, TCGA 코호트로부터 LNC6 아형 특이적 mRNA 유전자 발현 시그니처를 추출하였다(도 2a). 이후 예측 모델을 설계하기 위하여 BCCP 알고리즘을 시행하였고(도 2b), 각 시그니처의 강도를 평가하였다. TCGA 코호트에서 각 시그니처의 베이지안 확률의 ROC 분석 결과 AUC는 0.9143 내지 0.9845에 해당하였다. 이러한 로버스트 예측 모델(robust prediction model)을 일반 GC 코호트 데이터세트에 적용하였다. 이때 상기 데이터세트에서 mRNA 유전자 발현 및 생존 데이터는 이용 가능하다. 상기한 예측 모델로 환자를 계층화 하였을 때, OS와 RFS에서 비슷한 생존 패턴을 보였다 (도 2c). L6A 및 L6F 아형은 나쁜 예후와 상관 관계가 있고, 다음으로 L6B 및 L6D 아형이 상관 관계가 있었다. L6C 및 L6E 아형은 좋은 예후와 상관 관계가 있었다. 이를 통해 위암의 임상적 결과는 lncRNA 발현 패턴을 반영한 전사적 특징에 기인한 것을 알 수 있었다.With a shorter follow-up period in the TCGA cohort, we evaluated the diagnostic relationship of LNC6 subtypes in an independent general GC cohort (Total n = 1,933). First, the LNC6 subtype-specific mRNA gene expression signature was extracted from the TCGA cohort (Fig. 2a). Afterwards, the BCCP algorithm was implemented to design a predictive model (FIG. 2b), and the strength of each signature was evaluated. As a result of ROC analysis of the Bayesian probability of each signature in the TCGA cohort, AUC was 0.9143 to 0.9845. This robust prediction model was applied to the general GC cohort dataset. In this case, mRNA gene expression and survival data in the dataset are available. When patients were stratified with the aforementioned predictive model, similar survival patterns were shown in OS and RFS (Fig. 2c). The L6A and L6F subtypes correlated with poor prognosis, followed by the L6B and L6D subtypes. L6C and L6E subtypes correlated with good prognosis. Through this, it was found that the clinical outcome of gastric cancer is due to the transcriptional characteristics reflecting the lncRNA expression pattern.
LNC6 아형 및 보조 항암 화학 요법LNC6 subtypes and adjuvant chemotherapy
반 이상의 환자가 표준 보조 항암 화학 요법을 받은 코호트에서 LNC6 아형과 보조 항암 화학 요법으로 인한 임상적 이점 사이 상관 관계를 조사하였다(도 3). L6A (P=0.025), L6B (P=0.03), 및 L6D (P=0.057) 아형이 보조 항암 화학 요법에 대하여 이점을 보이는 반면, L6C 아형(P=0.51)의 경우 보조 항암 화학 요법에 대하여 이점이 없는 것을 볼 수 있었다. 상피 및 중간엽 표현형의 두가지 구분되는 분자적 아형은 보조 항암 화학과의 상호 작용을 입증하므로, 이러한 두가지 분자적 아형과 LNC6 아형 사이 관련성을 분석하였다. 상피 표현형(epithelial phenotype; EP)은 L6B 및 L6C에서 풍부한 반면, 중간엽 표현형(mesenchymal phenotype; MP)은 L6F에서 풍부하였다(도 4a). EP 아형 종양을 가진 환자는 보조 항암 화학 요법에서 이점을 가지는 주된 아형이므로, EP 아형의 서브세트 분석을 수행하였다. L6C 아형 환자의 경우 보조 항암 화학 요법에 어떠한 이점도 보이지 않은 반면(P=0.76), L6C 아형에 속하지 않는 환자는 보조 항암 화학 요법에 우수한 이점을 보였다(P<0.0001) (도 4b). 콕스 회귀 모델에 적용하는 상호 작용 실험을 수행하여 두 군 사이에서 보조 항암 화학 요법의 영향의 차이점을 평가하였고, 상호 작용은 유의성을 보였다(P=0.01 by likelihood ratio test). 화학 반응에 민감한 것으로 알려진 EP 아형 종양의 서브세트인 L6C 아형 종양은 보조 항암 화학 요법에 이점을 보이지 않았는바, lncRNA 발현 패턴은 EP 아형을 임상적 관련된 아형으로 추가로 세분할 수 있음을 알 수 있었다. We investigated the correlation between the LNC6 subtype and the clinical benefit from adjuvant chemotherapy in a cohort where more than half of the patients received standard adjuvant chemotherapy (Figure 3). L6A (P=0.025), L6B (P=0.03), and L6D (P=0.057) subtypes show benefit over adjuvant chemotherapy, whereas L6C subtype (P=0.51) benefits over adjuvant chemotherapy I could see it was missing. As two distinct molecular subtypes of epithelial and mesenchymal phenotypes demonstrate interactions with adjuvant anticancer chemistries, we analyzed the association between these two molecular subtypes and the LNC6 subtype. The epithelial phenotype (EP) was abundant in L6B and L6C, whereas the mesenchymal phenotype (MP) was abundant in L6F (Fig. 4a). Since patients with EP subtype tumors are the predominant subtype that would benefit from adjuvant chemotherapy, a subset analysis of the EP subtype was performed. Patients with the L6C subtype did not show any benefit to adjuvant chemotherapy (P=0.76), whereas patients not belonging to the L6C subtype showed a superior benefit to adjuvant chemotherapy (P<0.0001) (Fig. 4b). An interaction experiment applied to the Cox regression model was performed to evaluate the difference in the effect of adjuvant chemotherapy between the two groups, and the interaction showed significance (P=0.01 by likelihood ratio test). L6C subtype tumors, a subset of EP subtype tumors known to be chemically sensitive, showed no benefit to adjuvant chemotherapy, suggesting that the lncRNA expression pattern could further subdivide the EP subtype into clinically relevant subtypes. .
LNC6 아형 및 면역 치료LNC6 Subtypes and Immunotherapy
펨브롤리주맙으로 처리된 전이성 위암 환자의 코호트로부터 lncRNA 발현데이터를 이용하여 LNC6 아형과 면역 치료에 대한 임상적 이점 사이 상관 관계를 분석하였다. TCGA 코호트에서 아형 특이적 lncRNA의 발현 데이터에 기반하여 예측 모델을 설계하였다. L6C (46%) 및 L6E (100%) 아형은 면역 치료에 대한 치료 반응성이 좋았으나, 다른 아형의 경우 치료 반응성이 나빴다(≤25%) (도 5a). 임상적 반응은 L6C (P=0.012) 및 L6F (P=0.016) 아형의 예측되는 확률과 매우 높은 상관 관계를 보였다(도 5b-c). L6C 확률은 면역 치료에 대한 반응의 긍정적 예측 표지에 해당하는 반면, L6F 확률은 면역 치료에 대한 반응의 부정적 예측 표지에 해당한다. 아마도 트레이닝 시료(training samples)에서 L6E 아형의 적은 수로 인하여 편차가 0 또는 1에 해당하여, L6E 아형의 예측되는 확률이 면역 치료 반응자와 비반응자를 계층화하지 못하였더라도, L6E 아형에서 특이적으로 상향 조절된 lncRNA의 GSEA가 성공적으로 면역 치료 반응자와 비반응자를 계층화하였다(P=0.00021) (도 5d). 따라서, lncRNA 발현 패턴을 고려함으로써 위암에서 면역 치료 반응성의 예측 값을 더할 수 있다. LNC6 아형에서 종양 미세 환경의 기본적인 면역적 특징을 보다 잘 이해하기 위하여, 유전자 시그니처-기반 방법에 의해 전사체로부터 세포 서브세트를 열거하였다. L6E 종양에서 골수성 및 림프성 면역 세포가 풍부한 것으로 예측되었는 바, 이로부터 L6E 종양에서 활동적인 면역 반응이 있음을 알 수 있었다(도 6a). 반면에, L6F 종양은 기질 세포즉, 림프성 상피 세포, 섬유아세포, 연골 세포, 혈관 주위 세포 및 지방 세포로 이루어지는 것으로 예측되는 바, L6F 종양에서 면역 반응이 제한되어 있는 것을 알 수 있었다(도 6b). We analyzed the correlation between LNC6 subtype and clinical benefit for immunotherapy using lncRNA expression data from a cohort of pembrolizumab-treated metastatic gastric cancer patients. A predictive model was designed based on the expression data of subtype-specific lncRNAs in the TCGA cohort. The L6C (46%) and L6E (100%) subtypes were highly responsive to immunotherapy, but the other subtypes were poorly responsive (≤25%) ( FIG. 5A ). The clinical response showed a very high correlation with the predicted probability of the L6C (P=0.012) and L6F (P=0.016) subtypes ( FIGS. 5b-c ). The L6C probability corresponds to a positive predictive marker of response to immunotherapy, whereas the L6F probability corresponds to a negative predictive marker of response to immunotherapy. Presumably due to the small number of L6E subtypes in the training samples, the deviation corresponds to 0 or 1, so that the predicted probability of the L6E subtype does not stratify immunotherapy responders and non-responders, although it is specifically upregulated in the L6E subtype. lncRNA GSEA successfully stratified immunotherapy responders and non-responders (P=0.00021) ( FIG. 5D ). Therefore, the predictive value of immunotherapy responsiveness in gastric cancer can be added by considering the lncRNA expression pattern. To better understand the basic immunological features of the tumor microenvironment in the LNC6 subtype, cell subsets were enumerated from the transcriptome by a gene signature-based method. As it was predicted that myeloid and lymphoid immune cells were abundant in L6E tumors, it was confirmed that there was an active immune response in L6E tumors (FIG. 6a). On the other hand, L6F tumors are predicted to consist of stromal cells, that is, lymphoid epithelial cells, fibroblasts, chondrocytes, pericytes, and adipocytes, suggesting that the immune response is limited in L6F tumors (Fig. 6b). ).
위암 세포주에서 lncRNA에 의해 중재되는 줄기세포 유사 특징들Stem cell-like features mediated by lncRNA in gastric cancer cell lines
L6F 아형이 나쁜 예후, 조기 재발 및 항암 화학 요법에 대한 내성과 같은 임상적 결과와 가장 유의적 연관성을 보이므로, LNC6 아형 중 특정 lncRNA가 임상적 관련된 표현형에 연관이 있는 지 확인하였다. 먼저, L6F 아형에 대한 BCCP 예측자를 29개 위암 세포주로부터의 lncRNA 발현 데이터에 적용함으로써 L6F-유사 위암 세포주를 규명하였다. L6F 확률은 상피-중간엽 전이 아형(epithelial-mesenchymal transition (EMT) subtype)과 높은 상관 관계를 보였고(도 7a). qRT-PCR을 이용하여 RNA 시퀀싱 데이터로부터 얻어진 결과에 의해 선별된 L6F 아형에서 특이적으로 상향 조절된 5개의 lncRNA의 발현을 확인하였다. 하기 표 5에 나타낸 이들 lncRNA의 발현은 비-EMT 아형 세포주(n=3)에 비하여 EMT 아형 세포주(n=3)에서 평균 발현 수준이 높았다. 특히, ZNF667-AS1 (ENSG00000166770.6)는 시료의 소수에서 통계적으로 유의한 차이를 보였다. EMT 아형 세포주에서 siRNA를 이용하여 ZNF667-AS1를 넉다운한 결과, 세포의 침윤성 및 이동능을 감소시켰다(도 7b 및 7c-7e). 더욱이, ZNF667-AS1 사일런싱(silencing)에 의해 E-카데린(E-cadherin)의 발현이 증가되고, N-카데린(N-cadherin) 및 비멘틴(vimentin)의 발현이 감소되었는 바, 상기 EMT 아형 세포들이 중간엽 특성을 잃고, 상피적 특성을 얻은 것을 알 수 있었다(도 7f). 이로써, ZNF667-AS1가 위암 세포주의 EMT 표현형에 관련성이 높은 것을 알 수 있었다. 이를 통해 ZNF667-AS1를 사일런싱함으로써 EMT 위암 세포주의 옥살리플라틴 및 5-FU에 대한 감수성을 증가시킬 수 있음을 알 수 있었다(도 7f 및 7g).Since the L6F subtype showed the most significant association with clinical outcomes such as poor prognosis, early recurrence, and resistance to chemotherapy, we checked whether a specific lncRNA among LNC6 subtypes was associated with a clinically relevant phenotype. First, L6F-like gastric cancer cell lines were identified by applying BCCP predictors for L6F subtype to lncRNA expression data from 29 gastric cancer cell lines. L6F probability showed a high correlation with epithelial-mesenchymal transition (EMT) subtype (Fig. 7a). The expression of five lncRNAs specifically upregulated in the L6F subtypes selected by the results obtained from the RNA sequencing data using qRT-PCR was confirmed. The expression of these lncRNAs shown in Table 5 below was higher in the mean expression level in the EMT subtype cell line (n=3) than in the non-EMT subtype cell line (n=3). In particular, ZNF667-AS1 (ENSG00000166770.6) showed a statistically significant difference in a small number of samples. As a result of knocking down ZNF667-AS1 using siRNA in the EMT subtype cell line, cell invasiveness and migration ability were reduced ( FIGS. 7b and 7c-7e ). Furthermore, the expression of E-cadherin was increased by ZNF667-AS1 silencing, and the expression of N-cadherin and vimentin was decreased. It was found that the EMT subtype cells lost mesenchymal characteristics and acquired epithelial characteristics (FIG. 7f). Accordingly, it was found that ZNF667-AS1 is highly related to the EMT phenotype of gastric cancer cell lines. Through this, it was found that the sensitivity to oxaliplatin and 5-FU of the EMT gastric cancer cell line could be increased by silencing ZNF667-AS1 ( FIGS. 7f and 7g ).
아형subtype 세포주cell line RP11-572C15.6RP11-572C15.6 ZNF667-AS1ZNF667-AS1 MAGI2-AS3MAGI2-AS3 FENDRRFENDRR ACTA2-AS1ACTA2-AS1
EMTEMT MKN1MKN1 -11.99-11.99 -8.29-8.29 -13.32-13.32 -9.90-9.90 -13.51-13.51
SNU1750SNU1750 -12.87-12.87 -6.46-6.46 -9.94-9.94 -13.52-13.52 -14.29-14.29
SNU484SNU484 -15.06-15.06 -2.56-2.56 -11.80-11.80 -16.18-16.18 -12.94-12.94
non-EMTnon-EMT YCC3YCC3 -22.26-22.26 -10.62-10.62 -7.66-7.66 -12.98-12.98 -13.92-13.92
SNU719SNU719 -15.00-15.00 -11.07-11.07 -13.27-13.27 -13.81-13.81 -14.92-14.92
MKN74MKN74 -16.68-16.68 -11.42-11.42 -16.44-16.44 -15.40-15.40 -14.38-14.38
평균 배수 변화average multiple change 4.674.67 5.275.27 0.770.77 0.860.86 0.830.83
P value (Student's t-test) P value (Student's t-test) 0.120.12 0.040.04 0.790.79 0.680.68 0.170.17
LNC6 아형의 생물학적 영향Biological effects of LNC6 subtypes
LNC6 아형의 생물학적 특성을 규명하기 위하여, GSEA(Gene set enrichment analysis) 및 IPA(ingenuity pathway analysis)를 수행하였다(도 8, 표 6 및 7). L6A 아형은, 당화, 산화적 인산화 및 지방산 대사와 같은 대사 경로의 활성화를 통해 나타내었다. 간 세포 핵 인자(Hepatocyte nuclear factor-4α; HNF4α)가 가장 중요한 L6A의 상류 조절자인 것으로 예측되었다. L6C 아형은 G2M 체크포인트(checkpoint), E2F 타겟, DNA 수선(repair), MYC 타겟 및 MTORC1 신호의 활성화와 관련되어 있고, L6D 아형은 단백질 방출 및 KRAS 신호의 활성화와 관련되어 있다. L6E 아형은 L6E 아형의 활성화된 면역을 유지시키는 인터페론 반응의 활성화와 관련되어 있다. 가장 특징적으로, L6F 아형은 Wnt/β-카테닌(Wnt/β-catenin) 신호, TGF-β 신호, EMT, 및 혈관 신생의 활성화와 관련된다. 또한, EMT의 주 조절자에 해당하는 TGF-β 및 Twist1는 L6F 아형의 상류 조절자로 알려져 있다. In order to identify the biological characteristics of the LNC6 subtype, GSEA (gene set enrichment analysis) and IPA (ingenuity pathway analysis) were performed ( FIGS. 8 , Tables 6 and 7 ). The L6A subtype was expressed through activation of metabolic pathways such as glycosylation, oxidative phosphorylation and fatty acid metabolism. Hepatocyte nuclear factor-4α (HNF4α) was predicted to be the most important upstream regulator of L6A. The L6C subtype is associated with activation of the G2M checkpoint, E2F target, DNA repair, MYC target and MTORC1 signal, while the L6D subtype is associated with protein release and activation of KRAS signaling. The L6E subtype is associated with the activation of an interferon response that maintains the activated immunity of the L6E subtype. Most characteristically, the L6F subtype is associated with activation of Wnt/β-catenin signaling, TGF-β signaling, EMT, and angiogenesis. In addition, TGF-β and Twist1, which are major regulators of EMT, are known as upstream regulators of the L6F subtype.
LNC6 아형의 줄기세포능을 평가하기 위하여, 마우스에서 위 발달 과정 중 다르게 발현되는 전사 인자의 발현 패턴을 확인하였다 (도 9). 초기 배아 단계에서 과발현되는 전사인자는 L6F 아형, 다음으로 L6B 아형에서 발현이 상향 조절되었는 바, 이를 통하여 이들 아형이 줄기세포능을 갖는 것을 알 수 있었다. 이를 통해 L6F 아형의 활성화된 생물학적 경로와 L6F 아형의 나쁜 예후 사이의 상관 관계를 알 수 있었다. 한편, 후기 배아 단계 또는 성숙 단계에서 과발현되는 전사인자는 LNC6 아형들 사이에서 다르게 발현되지 않았다. 또한, S-기에서 풍부 lncRNA의 발현 패턴을 조사한 결과, S-기 lncRNA는 L6D 및 L6F에서 그 발현이 하향 조절되었으나, L6B에서는 상향 조절된 것을 볼 수 있었다 (도 10). 이는 줄기세포의 휴지 특징(quiescent feature)과 EMT 시그니처와 증식 시그니처 사이의 음성 상관관계에 의한 것이다. In order to evaluate the stem cell ability of the LNC6 subtype, the expression pattern of transcription factors that are expressed differently during gastric development in mice was confirmed ( FIG. 9 ). Transcription factors overexpressed in the early embryonic stage were up-regulated in expression in the L6F subtype and then in the L6B subtype, suggesting that these subtypes have stem cell ability. This suggests a correlation between the activated biological pathways of the L6F subtype and the poor prognosis of the L6F subtype. On the other hand, transcription factors overexpressed in the late embryonic stage or maturation stage were not expressed differently among LNC6 subtypes. In addition, as a result of examining the expression pattern of abundant lncRNA in S-phase, it was found that the expression of S-phase lncRNA was down-regulated in L6D and L6F, but up-regulated in L6B (FIG. 10). This is due to the negative correlation between the quiescent feature of stem cells and the EMT signature and proliferation signature.
LNC6LNC6 Ingenuity Canonical PathwaysIngenuity Canonical Pathways -log(p-value) -log(p-value) zScorezScore RatioRatio
　L6A　L6A Sumoylation PathwaySumoylation Pathway 2.962.96 -1.94-1.94 15.6%15.6%
DNA Double-Strand Break Repair by Non-Homologous End JoiningDNA Double-Strand Break Repair by Non-Homologous End Joining 2.892.89 NaNNaN 35.7%35.7%
Stearate Biosynthesis I (Animals)Stearate Biosynthesis I (Animals) 2.802.80 1.671.67 20.5%20.5%
EIF2 SignalingEIF2 Signaling 2.502.50 -2.84-2.84 11.4%11.4%
Acyl-CoA HydrolysisAcyl-CoA Hydrolysis 2.272.27 0.000.00 33.3%33.3%
　L6B　L6B Type I Diabetes Mellitus SignalingType I Diabetes Mellitus Signaling 4.074.07 -1.63-1.63 8.1%8.1%
Cytotoxic T Lymphocyte-mediated Apoptosis of Target CellsCytotoxic T Lymphocyte-mediated Apoptosis of Target Cells 3.813.81 -2.00-2.00 15.6%15.6%
Antigen Presentation PathwayAntigen Presentation Pathway 3.453.45 NaNNaN 13.2%13.2%
CD28 Signaling in T Helper CellsCD28 Signaling in T Helper Cells 3.453.45 -2.00-2.00 6.7%6.7%
Nur77 Signaling in T LymphocytesNur77 Signaling in T Lymphocytes 3.413.41 NaNNaN 10.2%10.2%
　L6CL6C Regulation of Actin-based Motility by RhoRegulation of Actin-based Motility by Rho 4.324.32 1.001.00 10.1%10.1%
RhoGDI SignalingRhoGDI Signaling 3.883.88 0.300.30 6.9%6.9%
Glucocorticoid Receptor SignalingGlucocorticoid Receptor Signaling 2.992.99 NaNNaN 4.6%4.6%
Integrin SignalingIntegrin Signaling 2.962.96 -0.30-0.30 5.4%5.4%
GP6 Signaling PathwayGP6 Signaling Pathway 2.962.96 -2.33-2.33 6.7%6.7%
　L6D　L6D Protein Ubiquitination PathwayProtein Ubiquitination Pathway 5.435.43 NaNNaN 15.5%15.5%
Estrogen Receptor SignalingEstrogen Receptor Signaling 4.844.84 NaNNaN 18.7%18.7%
Sirtuin Signaling PathwaySirtuin Signaling Pathway 4.624.62 -1.33-1.33 14.4%14.4%
Cleavage and Polyadenylation of Pre-mRNACleavage and Polyadenylation of Pre-mRNA 4.004.00 NaNNaN 50.0%50.0%
mTOR SignalingmTOR Signaling 3.863.86 0.000.00 14.9%14.9%
　L6E　L6E MSP-RON Signaling PathwayMSP-RON Signaling Pathway 4.254.25 NaNNaN 17.6%17.6%
Calcium Transport ICalcium Transport I 4.244.24 0.450.45 50.0%50.0%
Sperm MotilitySperm Motility 3.763.76 -1.60-1.60 13.3%13.3%
Interferon SignalingInterferon Signaling 3.543.54 2.122.12 22.2%22.2%
Endothelin-1 SignalingEndothelin-1 Signaling 3.493.49 -1.09-1.09 11.1%11.1%
　L6FL6F Axonal Guidance SignalingAxonal Guidance Signaling 10.6010.60 NaNNaN 20.1%20.1%
cAMP-mediated signalingcAMP-mediated signaling 10.5010.50 5.535.53 25.6%25.6%
Hepatic Fibrosis / Hepatic Stellate Cell ActivationHepatic Fibrosis / Hepatic Stellate Cell Activation 9.489.48 NaNNaN 26.3%26.3%
G-Protein Coupled Receptor SignalingG-Protein Coupled Receptor Signaling 8.698.69 NaNNaN 22.3%22.3%
Gαi SignalingGαi Signaling 7.447.44 2.832.83 27.9%27.9%
LNC6LNC6 상류 조절자upstream regulator p-value of overlapp-value of overlap 예측 활성 상태Prediction Active Activation z-scoreActivation z-score
　L6AL6A HNF4AHNF4A 8.92E-088.92E-08 1.8441.844
CD24CD24 1.49E-061.49E-06 억제control -4.126-4.126
CST5CST5 1.21E-031.21E-03 1.7681.768
ESR1ESR1 1.41E-031.41E-03 억제control -5.532-5.532
TCOF1TCOF1 2.00E-032.00E-03
　L6BL6B SAFBSAFB 2.81E-052.81E-05 1.6731.673
interferon beta-1ainterferon beta-1a 3.47E-053.47E-05
SodSod 1.45E-041.45E-04 활성화Activation 22
EBI3EBI3 2.28E-042.28E-04 -0.685-0.685
CIITACIITA 4.81E-044.81E-04 -0.41-0.41
　L6CL6C ERBB2ERBB2 2.91E-052.91E-05 -1.327-1.327
Rhox4b (includes others)Rhox4b (includes others) 1.36E-041.36E-04
Histone h3Histone h3 2.59E-042.59E-04
Ctbpctbp 3.36E-043.36E-04
MM-401MM-401 3.70E-043.70E-04
　L6DL6D HNF4AHNF4A 3.68E-143.68E-14 -1.604-1.604
mir-149mir-149 4.33E-044.33E-04
miR-16-5p (and other miRNAs w/seed AGCAGCA)miR-16-5p (and other miRNAs w/seed AGCAGCA) 7.60E-047.60E-04 억제control -2.896-2.896
tunicamycintunicamycin 1.27E-031.27E-03 1.4671.467
ONECUT1ONECUT1 1.44E-031.44E-03
　L6EL6E PHF1PHF1 1.87E-081.87E-08 활성화Activation 2.2192.219
KAT6AKAT6A 3.16E-083.16E-08 억제control -3.231-3.231
CDX2CDX2 2.13E-072.13E-07 억제control -2.762-2.762
COMMD3-BMI1COMMD3-BMI1 4.93E-074.93E-07 활성화Activation 2.8912.891
STAT5ASTAT5A 8.44E-078.44E-07 -1.145-1.145
L6FL6F TGFB1TGFB1 4.10E-294.10E-29 활성화Activation 7.4287.428
ERBB2ERBB2 1.20E-221.20E-22 억제control -2.928-2.928
TGFB3TGFB3 1.15E-201.15E-20 활성화Activation 4.7954.795
beta-estradiolbeta-estradiol 2.69E-192.69E-19 활성화Activation 3.1393.139
TWIST1TWIST1 1.17E-181.17E-18 활성화Activation 4.5084.508
LNC6 아형의 게놈 배경Genomic background of the LNC6 subtype
TCGA 데이터로부터 게놈 및 프로테옴 데이터를 이용하여 LNC6 아형의 분자적 특성을 더욱 조사하였다. L6B 아형을 고복제수 변화로 정의하였고, 일부의 유전자만 LNC6 아형 사이에서 다르게 발현되었다(도 11a). L6C 아형은 높은 돌연변이 부하(high mutation burden)로 특징지어 지며, LNC6 아형 사이에서 많은 유전자가 다르게 돌연변이 되었다(도 11b). L6E 아형은 과메틸화 패턴에 의해 특징지어지며(도 12a), L6F 아형은 miRNA 및 단백질의 가장 구분되는 발현 패턴(도 12b 및 12c)과 재발성 (15.4%) CLDN18-ARHGAP 융합으로 특징지어 진다. 또한, 아형-특이적 lncRNA의 후생적 배경 및 규명된 후생적 조절된 lncRNAs에 대하여 확인하였다(도 13).The molecular characteristics of the LNC6 subtype were further investigated using genomic and proteome data from TCGA data. The L6B subtype was defined as a high copy number change, and only some genes were expressed differently among the LNC6 subtypes (Fig. 11a). The L6C subtype is characterized by a high mutation burden, and many genes were mutated differently among the LNC6 subtypes (Fig. 11b). The L6E subtype was characterized by a hypermethylation pattern (Fig. 12a), and the L6F subtype was characterized by the most distinct expression pattern of miRNAs and proteins (Figs. 12b and 12c) and a recurrent (15.4%) CLDN18-ARHGAP fusion. In addition, the epigenetic background of subtype-specific lncRNAs and the identified epigenetic regulated lncRNAs were identified ( FIG. 13 ).
다른 분자적 아형의 유사도Similarity of Different Molecular Subtypes
위암의 다른 분자적 아형에 대한 LNC6의 유사도를 확인하였다(도 6). L6A 및 L6B 아형은 염색체 불안정성(chromosomal instability; CIN) 및 미세부수체 안정성(microsatellite stability; MSS) 아형에서 풍부하였다. L6C 아형은 미세부수체 불안정성(microsatellite instability; MSI) 아형에서 풍부하였고, L6D 아형은 다수의 다른 분자적 아형과 혼합되어 있었다. L6E 아형은 100% 엡스타인-바 바이러스(Epstein-Barr virus; EBV) 아형에 해당하였고, L6F 아형은 유전적으로 안정(genomically stable; GS)한 아형, MP 아형 및 EMT 아형에서 풍부하였다.The similarity of LNC6 to other molecular subtypes of gastric cancer was confirmed (FIG. 6). L6A and L6B subtypes were abundant in chromosomal instability (CIN) and microsatellite stability (MSS) subtypes. The L6C subtype was abundant in the microsatellite instability (MSI) subtype, and the L6D subtype was mixed with a number of other molecular subtypes. The L6E subtype corresponded to 100% Epstein-Barr virus (EBV) subtype, and the L6F subtype was abundant in the genetically stable (GS) subtype, the MP subtype and the EMT subtype.
이상으로 본 발명의 특정한 부분을 상세히 기술하였는 바, 당업계의 통상의 지식을 가진 자에게 있어서 이러한 구체적인 기술은 단지 바람직한 구현예일 뿐이며, 이에 본 발명의 범위가 제한되는 것이 아닌 점은 명백하다. 따라서, 본 발명의 실질적인 범위는 첨부된 청구항과 그의 등가물에 의하여 정의된다고 할 것이다.As the specific parts of the present invention have been described in detail above, for those of ordinary skill in the art, these specific descriptions are only preferred embodiments, and it is clear that the scope of the present invention is not limited thereto. Accordingly, the substantial scope of the present invention will be defined by the appended claims and their equivalents.
본 발명은 암 예후 예측을 위한 조성물, 이를 포함하는 키트 및 암 예후 예측 방법에 관한 것이다. The present invention relates to a composition for predicting cancer prognosis, a kit comprising the same, and a method for predicting cancer prognosis.

Claims (25)

  1. RP11-572C15.6, FENDRR(FOXF1 Adjacent Non-Coding Developmental Regulatory RNA) 및 ACTA2-AS1(ACTA2 Antisense RNA 1)로 이루어진 군에서 선택된 1종 이상을 포함하는, 암의 진단 또는 예후 예측용 바이오마커 조성물.RP11-572C15.6, FENDRR (FOXF1 Adjacent Non-Coding Developmental Regulatory RNA) and ACTA2-AS1 (ACTA2 Antisense RNA 1) comprising at least one selected from the group consisting of, cancer diagnosis or prognosis prediction biomarker composition.
  2. RP11-572C15.6, FENDRR(FOXF1 Adjacent Non-Coding Developmental Regulatory RNA) 및 ACTA2-AS1(ACTA2 Antisense RNA 1)로 이루어진 군에서 선택된 1종 이상의 유전자의 발현 수준을 측정할 수 있는 제제를 포함하는 암의 진단 또는 예후 예측용 조성물.RP11-572C15.6, FENDRR (FOXF1 Adjacent Non-Coding Developmental Regulatory RNA) and ACTA2-AS1 (ACTA2 Antisense RNA 1) of cancer containing an agent capable of measuring the expression level of one or more genes selected from the group consisting of A composition for diagnosis or prognosis.
  3. 제2항에 있어서,3. The method of claim 2,
    상기 발현 수준을 측정할 수 있는 제제는 상기 유전자에 특이적으로 결합하는 프라이머, 프로브 및 안티센스 뉴클레오티드로 이루어진 군에서 선택된 1종 이상을 포함하는, 조성물. The agent capable of measuring the expression level comprises at least one selected from the group consisting of primers, probes and antisense nucleotides that specifically bind to the gene, composition.
  4. 제2항에 있어서,3. The method of claim 2,
    상기 암은 위암, 난소암, 대장암, 유방암, 간암, 췌장암, 자궁경부암, 갑상선암, 부갑상선암, 비소세포성폐암, 전립선암, 담낭암, 담도암, 비호지킨 림프종, 호지킨 림프종, 혈액암, 방광암, 신장암, 흑색종, 결장암, 골암, 피부암, 두부암, 자궁암, 직장암, 뇌종양, 항문부근암, 나팔관암종, 자궁내막암종, 질암, 음문암종, 식도암, 소장암, 내분비선암, 부신암, 연조직 육종, 요도암, 음경암, 수뇨관암, 신장세포 암종, 신장골반 암종, 중추신경계(CNS central nervoussystem) 종양, 1차 CNS 림프종, 척수 종양, 뇌간 신경교종 또는 뇌하수체 선종인, 조성물. The cancer is gastric cancer, ovarian cancer, colorectal cancer, breast cancer, liver cancer, pancreatic cancer, cervical cancer, thyroid cancer, parathyroid cancer, non-small cell lung cancer, prostate cancer, gallbladder cancer, biliary tract cancer, non-Hodgkin's lymphoma, Hodgkin's lymphoma, blood cancer, bladder cancer , kidney cancer, melanoma, colon cancer, bone cancer, skin cancer, head cancer, uterine cancer, rectal cancer, brain tumor, perianal cancer, fallopian tube carcinoma, endometrial carcinoma, vaginal cancer, vulvar carcinoma, esophageal cancer, small intestine cancer, endocrine adenocarcinoma, adrenal cancer, soft tissue sarcoma, urethral cancer, penile cancer, ureter cancer, renal cell carcinoma, renal pelvic carcinoma, CNS central nervoussystem tumor, primary CNS lymphoma, spinal cord tumor, brainstem glioma or pituitary adenoma.
  5. 제2항 내지 제4항 중 어느 한 항의 조성물을 포함하는 암의 예후 예측용 조성물을 포함하는 암의 진단 또는 예후 예측용 키트. A kit for diagnosing or predicting a prognosis of cancer comprising a composition for predicting the prognosis of cancer comprising the composition of any one of claims 2 to 4.
  6. 목적하는 개체로부터 분리된 생물학적 시료에서 RP11-572C15.6, FENDRR(FOXF1 Adjacent Non-Coding Developmental Regulatory RNA) 및 ACTA2-AS1(ACTA2 Antisense RNA 1)로 이루어진 군에서 선택된 1종 이상의 유전자의 발현 수준을 측정하는 단계를 포함하는, 암의 진단을 위한 정보 제공 방법.Measuring the expression level of one or more genes selected from the group consisting of RP11-572C15.6, FENDRR (FOXF1 Adjacent Non-Coding Developmental Regulatory RNA), and ACTA2-AS1 (ACTA2 Antisense RNA 1) in a biological sample isolated from a subject of interest A method of providing information for diagnosing cancer, comprising the step of:
  7. 제6항에 있어서,7. The method of claim 6,
    상기 생물학적 시료는 전혈(whole blood), 백혈구(leukocytes), 말초혈액 단핵 세포(peripheral blood mononuclear cells), 백혈구 연층(buffy coat), 혈장(plasma), 혈청(serum), 객담(sputum), 눈물(tears), 점액(mucus), 세비액(nasal washes), 비강 흡인물(nasal aspirate), 호흡(breath), 소변(urine), 정액(semen), 침(saliva), 복강 세척액(peritoneal washings), 복수(ascites), 낭종액(cystic fluid), 뇌척수막 액(meningeal fluid), 양수(amniotic fluid), 선액(glandular fluid), 췌장액(pancreatic fluid), 림프액(lymph fluid), 흉수(pleural fluid), 유두 흡인물(nipple aspirate), 기관지 흡인물(bronchial aspirate), 활액(synovial fluid), 관절 흡인물(joint aspirate), 기관 분비물(organ secretions), 세포(cell), 세포 추출물(cell extract) 또는 뇌척수액(cerebrospinal fluid)인, 암의 진단을 위한 정보 제공 방법.The biological sample includes whole blood, leukocytes, peripheral blood mononuclear cells, buffy coat, plasma, serum, sputum, tears ( tears), mucus, nasal washes, nasal aspirate, breath, urine, semen, saliva, peritoneal washings, Ascites, cystic fluid, meningeal fluid, amniotic fluid, glandular fluid, pancreatic fluid, lymph fluid, pleural fluid, nipple aspirate, bronchial aspirate, synovial fluid, joint aspirate, organ secretions, cell, cell extract or cerebrospinal fluid ( cerebrospinal fluid), a method of providing information for the diagnosis of cancer.
  8. 제6항에 있어서,7. The method of claim 6,
    측정된 RP11-572C15.6, FENDRR 및 ACTA2-AS1으로 이루어진 군에서 선택된 1종 이상의 유전자의 발현 수준이 대조군에 비하여 증가된 경우 암이 발병하였거나 발병 가능성이 높은 것으로 예측하는, 암의 진단을 위한 정보 제공 방법.When the expression level of one or more genes selected from the group consisting of measured RP11-572C15.6, FENDRR and ACTA2-AS1 is increased compared to the control group, cancer has occurred or is predicted to have a high probability of occurrence, information for diagnosis of cancer How to provide.
  9. 제6항에 있어서,7. The method of claim 6,
    상기 암은 위암, 난소암, 대장암, 유방암, 간암, 췌장암, 자궁경부암, 갑상선암, 부갑상선암, 비소세포성폐암, 전립선암, 담낭암, 담도암, 비호지킨 림프종, 호지킨 림프종, 혈액암, 방광암, 신장암, 흑색종, 결장암, 골암, 피부암, 두부암, 자궁암, 직장암, 뇌종양, 항문부근암, 나팔관암종, 자궁내막암종, 질암, 음문암종, 식도암, 소장암, 내분비선암, 부신암, 연조직 육종, 요도암, 음경암, 수뇨관암, 신장세포 암종, 신장골반 암종, 중추신경계(CNS central nervoussystem) 종양, 1차 CNS 림프종, 척수 종양, 뇌간 신경교종 또는 뇌하수체 선종인, 암의 진단을 위한 정보 제공 방법.The cancer is gastric cancer, ovarian cancer, colorectal cancer, breast cancer, liver cancer, pancreatic cancer, cervical cancer, thyroid cancer, parathyroid cancer, non-small cell lung cancer, prostate cancer, gallbladder cancer, biliary tract cancer, non-Hodgkin's lymphoma, Hodgkin's lymphoma, blood cancer, bladder cancer , kidney cancer, melanoma, colon cancer, bone cancer, skin cancer, head cancer, uterine cancer, rectal cancer, brain tumor, perianal cancer, fallopian tube carcinoma, endometrial carcinoma, vaginal cancer, vulvar carcinoma, esophageal cancer, small intestine cancer, endocrine adenocarcinoma, adrenal cancer, soft tissue Information for diagnosis of sarcoma, urethral cancer, penile cancer, ureter cancer, renal cell carcinoma, renal pelvic carcinoma, CNS central nervoussystem tumor, primary CNS lymphoma, spinal cord tumor, brainstem glioma or pituitary adenoma, cancer How to provide.
  10. 목적하는 개체로부터 분리된 생물학적 시료에서 RP11-572C15.6, FENDRR(FOXF1 Adjacent Non-Coding Developmental Regulatory RNA) 및 ACTA2-AS1(ACTA2 Antisense RNA 1)로 이루어진 군에서 선택된 1종 이상의 유전자의 발현 수준을 측정하는 단계를 포함하는, 암의 예후 예측을 위한 정보 제공 방법.Measuring the expression level of one or more genes selected from the group consisting of RP11-572C15.6, FENDRR (FOXF1 Adjacent Non-Coding Developmental Regulatory RNA), and ACTA2-AS1 (ACTA2 Antisense RNA 1) in a biological sample isolated from a subject of interest A method of providing information for predicting the prognosis of cancer, comprising the step of:
  11. 제10항에 있어서,11. The method of claim 10,
    상기 생물학적 시료는 전혈(whole blood), 백혈구(leukocytes), 말초혈액 단핵 세포(peripheral blood mononuclear cells), 백혈구 연층(buffy coat), 혈장(plasma), 혈청(serum), 객담(sputum), 눈물(tears), 점액(mucus), 세비액(nasal washes), 비강 흡인물(nasal aspirate), 호흡(breath), 소변(urine), 정액(semen), 침(saliva), 복강 세척액(peritoneal washings), 복수(ascites), 낭종액(cystic fluid), 뇌척수막 액(meningeal fluid), 양수(amniotic fluid), 선액(glandular fluid), 췌장액(pancreatic fluid), 림프액(lymph fluid), 흉수(pleural fluid), 유두 흡인물(nipple aspirate), 기관지 흡인물(bronchial aspirate), 활액(synovial fluid), 관절 흡인물(joint aspirate), 기관 분비물(organ secretions), 세포(cell), 세포 추출물(cell extract) 또는 뇌척수액(cerebrospinal fluid)인, 암의 예후 예측을 위한 정보 제공 방법.The biological sample includes whole blood, leukocytes, peripheral blood mononuclear cells, buffy coat, plasma, serum, sputum, tears ( tears), mucus, nasal washes, nasal aspirate, breath, urine, semen, saliva, peritoneal washings, Ascites, cystic fluid, meningeal fluid, amniotic fluid, glandular fluid, pancreatic fluid, lymph fluid, pleural fluid, nipple aspirate, bronchial aspirate, synovial fluid, joint aspirate, organ secretions, cells, cell extract or cerebrospinal fluid ( cerebrospinal fluid), a method of providing information for predicting the prognosis of cancer.
  12. 제10항에 있어서,11. The method of claim 10,
    측정된 RP11-572C15.6, FENDRR 및 ACTA2-AS1으로 이루어진 군에서 선택된 1종 이상의 유전자의 발현 수준이 대조군에 비하여 증가된 경우 예후가 나쁠 것으로 예측하는, 암의 예후 예측을 위한 정보 제공 방법.When the expression level of one or more genes selected from the group consisting of the measured RP11-572C15.6, FENDRR and ACTA2-AS1 is increased compared to the control group, the prognosis is predicted to be poor, the information providing method for predicting the prognosis of cancer.
  13. RP11-572C15.6, FENDRR(FOXF1 Adjacent Non-Coding Developmental Regulatory RNA), ACTA2-AS1(ACTA2 Antisense RNA 1) 및 ZNF667-AS1(ZNF667 Antisense RNA 1 (Head To Head))로 이루어진 군에서 선택된 1종 이상을 포함하는, 암의 항암제에 대한 치료 반응성 예측용 바이오마커 조성물.At least one selected from the group consisting of RP11-572C15.6, FENDRR (FOXF1 Adjacent Non-Coding Developmental Regulatory RNA), ACTA2-AS1 (ACTA2 Antisense RNA 1) and ZNF667-AS1 (ZNF667 Antisense RNA 1 (Head To Head)) A biomarker composition for predicting therapeutic responsiveness to an anticancer agent of cancer, comprising a.
  14. RP11-572C15.6, FENDRR(FOXF1 Adjacent Non-Coding Developmental Regulatory RNA), ACTA2-AS1(ACTA2 Antisense RNA 1) 및 ZNF667-AS1(ZNF667 Antisense RNA 1 (Head To Head))로 이루어진 군에서 선택된 1종 이상의 유전자의 발현 수준을 측정할 수 있는 제제를 포함하는 암의 항암제에 대한 치료 반응성 예측용 조성물.At least one selected from the group consisting of RP11-572C15.6, FENDRR (FOXF1 Adjacent Non-Coding Developmental Regulatory RNA), ACTA2-AS1 (ACTA2 Antisense RNA 1) and ZNF667-AS1 (ZNF667 Antisense RNA 1 (Head To Head)) A composition for predicting therapeutic responsiveness to an anticancer agent for cancer, comprising an agent capable of measuring the expression level of a gene.
  15. 제14항의 조성물을 포함하는 암의 항암제에 대한 치료 반응성 예측용 키트. A kit for predicting therapeutic responsiveness to an anticancer agent for cancer comprising the composition of claim 14 .
  16. 목적하는 개체로부터 분리된 생물학적 시료에서 RP11-572C15.6, RP11-572C15.6, FENDRR(FOXF1 Adjacent Non-Coding Developmental Regulatory RNA), ACTA2-AS1(ACTA2 Antisense RNA 1) 및 ZNF667-AS1(ZNF667 Antisense RNA 1 (Head To Head))로 이루어진 군에서 선택된 1종 이상의 유전자의 발현 수준을 측정하는 단계를 포함하는, 암의 항암제에 대한 치료 반응성 예측을 위한 정보 제공 방법. In biological samples isolated from the subject of interest, RP11-572C15.6, RP11-572C15.6, FENDRR (FOXF1 Adjacent Non-Coding Developmental Regulatory RNA), ACTA2-AS1 (ACTA2 Antisense RNA 1) and ZNF667-AS1 (ZNF667 Antisense RNA) 1 (Head To Head)) comprising the step of measuring the expression level of one or more genes selected from the group consisting of, information providing method for predicting treatment responsiveness to anticancer drugs of cancer.
  17. 제16항에 있어서,17. The method of claim 16,
    상기 항암제는 면역 항암제인, 암의 항암제에 대한 치료 반응성 예측을 위한 정보 제공 방법. The anti-cancer agent is an immune anti-cancer agent, a method of providing information for predicting treatment responsiveness to an anti-cancer agent of cancer.
  18. 제16항에 있어서,17. The method of claim 16,
    측정된 RP11-572C15.6, FENDRR, ACTA2-AS1 및 ZNF667-AS1으로 이루어진 군에서 선택된 1종 이상의 유전자의 발현 수준이 대조군에 비하여 증가한 경우, 치료 반응성이 낮을 것으로 예측하는, 암의 항암제에 대한 치료 반응성 예측을 위한 정보 제공 방법. When the expression level of one or more genes selected from the group consisting of the measured RP11-572C15.6, FENDRR, ACTA2-AS1 and ZNF667-AS1 increases compared to the control group, the treatment responsiveness is predicted to be low. An informative method for predicting responsiveness.
  19. RP11-572C15.6, RP11-572C15.6, FENDRR(FOXF1 Adjacent Non-Coding Developmental Regulatory RNA), ACTA2-AS1(ACTA2 Antisense RNA 1) 및 ZNF667-AS1(ZNF667 Antisense RNA 1 (Head To Head))로 이루어진 군에서 선택된 1종 이상을 포함하는, 암의 상피-중간엽 전이(Epithelial mesenchymal transition; EMT) 진단용 바이오마커 조성물.Consisting of RP11-572C15.6, RP11-572C15.6, FENDRR (FOXF1 Adjacent Non-Coding Developmental Regulatory RNA), ACTA2-AS1 (ACTA2 Antisense RNA 1) and ZNF667-AS1 (ZNF667 Antisense RNA 1 (Head To Head)) A biomarker composition for diagnosing epithelial mesenchymal transition (EMT) of cancer, comprising one or more selected from the group.
  20. RP11-572C15.6, RP11-572C15.6, FENDRR(FOXF1 Adjacent Non-Coding Developmental Regulatory RNA), ACTA2-AS1(ACTA2 Antisense RNA 1) 및 ZNF667-AS1(ZNF667 Antisense RNA 1 (Head To Head))로 이루어진 군에서 선택된 1종 이상의 유전자의 발현 수준을 측정할 수 있는 제제를 포함하는 암의 상피-중간엽 전이(Epithelial mesenchymal transition; EMT) 진단용 조성물. Consisting of RP11-572C15.6, RP11-572C15.6, FENDRR (FOXF1 Adjacent Non-Coding Developmental Regulatory RNA), ACTA2-AS1 (ACTA2 Antisense RNA 1) and ZNF667-AS1 (ZNF667 Antisense RNA 1 (Head To Head)) A composition for diagnosing epithelial mesenchymal transition (EMT) of cancer comprising an agent capable of measuring the expression level of one or more genes selected from the group.
  21. 제20항의 조성물을 포함하는 암의 상피-중간엽 전이(Epithelial mesenchymal transition; EMT) 진단용 키트. A kit for diagnosing epithelial-mesenchymal transition (EMT) of cancer comprising the composition of claim 20.
  22. 목적하는 개체로부터 분리된 생물학적 시료에서 RP11-572C15.6, RP11-572C15.6, FENDRR(FOXF1 Adjacent Non-Coding Developmental Regulatory RNA), ACTA2-AS1(ACTA2 Antisense RNA 1) 및 ZNF667-AS1(ZNF667 Antisense RNA 1 (Head To Head))로 이루어진 군에서 선택된 1종 이상의 유전자의 발현 수준을 측정하는 단계를 포함하는, 암의 상피-중간엽 전이(Epithelial mesenchymal transition; EMT) 진단을 위한 정보 제공 방법.In biological samples isolated from the subject of interest, RP11-572C15.6, RP11-572C15.6, FENDRR (FOXF1 Adjacent Non-Coding Developmental Regulatory RNA), ACTA2-AS1 (ACTA2 Antisense RNA 1) and ZNF667-AS1 (ZNF667 Antisense RNA) 1 (Head To Head)) comprising the step of measuring the expression level of one or more genes selected from the group consisting of, epithelial-mesenchymal transition of cancer (Epithelial mesenchymal transition; EMT) information providing method for diagnosis.
  23. 제22항에 있어서,23. The method of claim 22,
    측정된 RP11-572C15.6, FENDRR, ACTA2-AS1 및 ZNF667-AS1으로 이루어진 군에서 선택된 1종 이상의 유전자의 발현 수준이 대조군에 비하여 증가한 경우, 암 세포가 상피-중간엽 전이 아형을 포함할 가능성이 높을 것으로 예측하는, 암의 상피-중간엽 전이(Epithelial mesenchymal transition; EMT) 진단을 위한 정보 제공 방법.If the measured expression level of one or more genes selected from the group consisting of RP11-572C15.6, FENDRR, ACTA2-AS1 and ZNF667-AS1 is increased compared to the control group, there is a possibility that the cancer cells contain an epithelial-mesenchymal metastasis subtype. A method of providing information for diagnosing epithelial mesenchymal transition (EMT) of cancer, which is predicted to be high.
  24. 암 개체로부터 분리한 생물학적 시료 또는 암 질환 동물 모델에 후보 약제를 처리하는 단계; 및 상기 후보 약제가 처리된 생물학적 시료 또는 암 질환 동물 모델에서 RP11-572C15.6, RP11-572C15.6, FENDRR(FOXF1 Adjacent Non-Coding Developmental Regulatory RNA), ACTA2-AS1(ACTA2 Antisense RNA 1) 및 ZNF667-AS1(ZNF667 Antisense RNA 1 (Head To Head))로 이루어진 군에서 선택된 1종 이상의 유전자의 발현 수준을 측정하는 단계를 포함하는, 암의 예방 또는 치료용 약물을 스크리닝하는 방법.treating a candidate agent with a biological sample isolated from a cancer subject or an animal model of cancer; and RP11-572C15.6, RP11-572C15.6, FENDRR (FOXF1 Adjacent Non-Coding Developmental Regulatory RNA), ACTA2-AS1 (ACTA2 Antisense RNA 1) and ZNF667 in a biological sample treated with the candidate agent or in an animal model of cancer disease -AS1 (ZNF667 Antisense RNA 1 (Head To Head)) A method of screening a drug for preventing or treating cancer, comprising measuring the expression level of one or more genes selected from the group consisting of.
  25. 암 개체로부터 분리한 생물학적 시료 또는 암 질환 동물 모델에 후보 약제를 처리하는 단계; 및 상기 후보 약제가 처리된 생물학적 시료 또는 암 질환 동물 모델에서 RP11-572C15.6, RP11-572C15.6, FENDRR(FOXF1 Adjacent Non-Coding Developmental Regulatory RNA), ACTA2-AS1(ACTA2 Antisense RNA 1) 및 ZNF667-AS1(ZNF667 Antisense RNA 1 (Head To Head))로 이루어진 군에서 선택된 1종 이상의 유전자의 발현 수준을 측정하는 단계를 포함하는, 암의 상피-중간엽 세포전이(EMT) 억제 물질을 스크리닝하는 방법.treating a candidate agent with a biological sample isolated from a cancer subject or an animal model of cancer; and RP11-572C15.6, RP11-572C15.6, FENDRR (FOXF1 Adjacent Non-Coding Developmental Regulatory RNA), ACTA2-AS1 (ACTA2 Antisense RNA 1) and ZNF667 in a biological sample treated with the candidate agent or in an animal model of cancer disease -AS1 (ZNF667 Antisense RNA 1 (Head To Head)) comprising the step of measuring the expression level of one or more genes selected from the group consisting of, cancer epithelial-mesenchymal cell metastasis (EMT) inhibitory material screening method .
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