CN117568482A - Molecular marker for prognosis of gastric cancer and application thereof - Google Patents
Molecular marker for prognosis of gastric cancer and application thereof Download PDFInfo
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- CN117568482A CN117568482A CN202311841357.8A CN202311841357A CN117568482A CN 117568482 A CN117568482 A CN 117568482A CN 202311841357 A CN202311841357 A CN 202311841357A CN 117568482 A CN117568482 A CN 117568482A
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- C12Q2600/00—Oligonucleotides characterized by their use
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Abstract
The invention provides a biomarker related to detection or prognosis of gastric cancer and prognosis of chemotherapy or immunotherapy applicability and application thereof. The biomarkers include IFT52, OSMR, SNX10, VCAN, which may be used for prognosis of gastric cancer, diagnosis and/or prediction of risk of suitability for chemotherapy or immunotherapy. The invention also provides kits suitable for prognosis of gastric cancer, diagnosis and/or prognosis of chemotherapeutic or immunotherapeutic applicability.
Description
Technical Field
The invention belongs to the field of biomedicine, and in particular relates to a molecular marker for prognosis of gastric cancer and application thereof.
Background
Gastric cancer is the most common cancer worldwide and is the third leading cause of cancer-related death, leading to death in nearly 80 tens of thousands worldwide. Recent data shows that gastric cancer incidence is highest in east asia, central europe and eastern europe, accounting for 87% of all newly registered cases worldwide. Even in areas of low incidence, they are generally diagnosed at an advanced stage, which greatly limits the treatment options for the patient and is a major cause of poor prognosis for the patient. Currently, the 5-year survival rate of this disease is only between 30% and 35%. Gastric cancer is a heterogeneous disease that is stratified by histopathological variation. Currently, widely accepted histopathological classification methods are the "intestinal" and "diffuse" types proposed by Lauren. Although these histopathological differences are associated with gastric cancer prognosis, they are not routinely used as a basis for determining gastric cancer treatment and management.
With the development of second generation sequencing technology, cancer genomic analysis reveals the relationship between various malignant tumors and genomic information. In 2014, some studies revealed the entire structure of gastric cancer genomics using whole-exome sequencing and whole-genome sequencing. These studies will help to identify new therapeutic targets for gastric cancer. The TCGA study group performed an integrated genomic analysis on gastric cancer, suggesting four molecular subtypes: EBV, microsatellite instability (MSI), chromosome Instability (CIN) and Genome Stable (GS) gastric cancer. These studies relate different molecular subtypes to a number of histological phenotypes, which lay the foundation for new therapeutic strategies and accurate medical treatment.
Recently, immune Checkpoint Inhibitors (ICIs) have been used for gastric cancer treatment. However, patients have a limited response rate to this treatment, and a significant proportion of patients suffer from long-term immune-related side effects. Therefore, a deep understanding of the biological mechanisms of tumor genesis is critical for the development of novel gastric cancer therapies. Along with the development of tumors, it accumulates more and more genetic and epigenetic changes, and although the epigenetic factors cannot directly influence gene sequences, the epigenetic factors can regulate the transcription efficiency of specific genes in cells, are beneficial to improving the immunogenicity of tumor cells and infiltration of immune cells, and play an important role in the formation and diffusion of gastric cancer.
Most cells in humans have cilia, an organelle protruding from the surface of vertebrate cells, which is the junction of various signal pathways, especially the sonic hedgehog (SHH) and the Width (WNT) signal pathways. Cilia are fine hair-like structures protruding from the surface of many types of cells, including cells of the stomach wall. These structures have several important functions, including facilitating movement of the cell surface and transporting substances. Studies have shown that cilia loss in the stomach may be related to the development of gastric cancer. This is because cilia play a role in regulating the secretion of gastric acid and other factors that may lead to the development of cancer. A study published in the journal of clinical research has found that the loss of cilia in the stomach results in an increased production of a protein called IL-1. Beta. Which promotes the growth and survival of cancer cells. This study also found that restoring cilia in gastric cells reduced IL-1 beta production and inhibited cancer cell growth. Another study published in the Oncogene journal found that the loss of cilia in the stomach resulted in activation of a protein called YAP, which is known to promote cell proliferation and cancer progression. It was found that restoring cilia in gastric cells inhibited YAP activation and reduced cancer cell growth. Overall, these studies suggest that cilia may have a protective effect on the development of gastric cancer by modulating key signaling pathways that contribute to cancer growth and survival. Due to the very close link between cilia and the occurrence of cancer, scientists have found that blocking cilia growth in drug resistant cancer cell lines or restoring sensitivity of cancer cells to therapy.
Therefore, there is a need to find biomarkers based on the expression values of cilia-related genes that can identify prognosis and predictability of populations with high risk of recurrence or high progression, and further to investigate whether therapies targeting cilia structures can be used as cancer therapies.
Disclosure of Invention
The invention aims to provide a biomarker related to detection or prognosis of gastric cancer and prognosis of chemotherapy or immunotherapy applicability and application thereof. The biomarkers include IFT52, OSMR, SNX10, VCAN, which may be used for prognosis of gastric cancer, diagnosis and/or prediction of risk of suitability for chemotherapy or immunotherapy. The invention also provides kits suitable for prognosis of gastric cancer, diagnosis and/or prognosis of chemotherapeutic or immunotherapeutic applicability.
In a first aspect of the invention, there is provided the use of a molecular marker in the manufacture of a detection system for the detection or prognosis of gastric cancer, the prognosis of the suitability for chemotherapy or immunotherapy; the molecular marker is selected from IFT52, OSMR, SNX10, VCAN, or a combination thereof.
In one or more embodiments, the molecular marker combination is selected from the group consisting of:
(1) IFT52, OSMR, SNX10, and VCAN;
(2) OSMR, SNX10 and VCAN.
In one or more embodiments, the detecting or prognosticating comprises: according to the expression of the molecular marker:
(a) Analyzing the sensitivity of the gastric cancer patient to adjuvant chemotherapy, neoadjuvant chemotherapy or immunotherapy; preferably further comprising: formulating a treatment/medication regimen;
(b) Analyzing the major pathological remission, and/or survival of gastric cancer patients; or (b)
(c) A risk analysis or scoring of gastric cancer progression in gastric cancer patients is performed.
In one or more embodiments, the molecular marker is a gene associated with gastric cancer.
In one or more embodiments, the gastric cancer-associated gene is a gene associated with cilia growth, and/or cilia structure.
In one or more embodiments, a method of prognosis of gastric cancer, chemotherapy or immunotherapy suitability prognosis comprises: an auxiliary disease analysis method that does not have the direct purpose of obtaining a diagnosis result of a disease, but provides only auxiliary analysis/evaluation/scoring.
In one or more embodiments, the chemotherapy comprises: adjuvant (postoperative) chemotherapy and neoadjuvant (preoperative) chemotherapy.
In one or more embodiments, the immune therapy is an immune checkpoint inhibitor therapy.
In one or more embodiments, the immunotherapy is PD-1 immunotherapy.
In one or more embodiments, the PD-1 immunotherapy is an immunotherapy using a PD-1 antibody or an anti-PD-L1 antibody.
In one or more embodiments, the anti-PD-1 antibody is, for example, but not limited to: nal Wu Liyou mab, pamil mab (pembrolizumab), cimapril Li Shan antibody, terlipressin Li Shan antibody, singdi Li Shan antibody, orelizumab, tirelizumab, pe An Puli mab, and sirolimumab.
In one or more embodiments, the anti-PD-L1 antibody is, for example, but not limited to: rivaroxaglib You Shan antibody, atilizumab, en Wo Lishan antibody, shu Geli mab.
In one or more embodiments, the detection system includes: a detection reagent, kit or detection device; preferably, the detection reagent includes: PCR detection reagent and sequencing reagent.
In one or more embodiments, the detection reagent comprises: and a primer for specifically amplifying the molecular marker gene and a probe for specifically recognizing the molecular marker gene.
In one or more embodiments, the detection reagent is included in the kit.
In one or more embodiments, the detection device includes: a gene sequencing instrument, a chip, a probe set, a primer probe set or an electrophoresis device.
In one or more embodiments, the method of detecting comprises:
(a) For a test sample, the expression of IFT52, OSMR, SNX10 and/or VCAN is determined and risk analysis or scoring is performed using any risk scoring formula selected from the group consisting of:
risk score 1= (Coef IFT52 X IFT52 expression) + (Coef OSMR X OSMR expression) + (Coef SNX10 X SNX10 expression) + (Coef VCAN X VCAN expression);
risk score 2= (Coef OSMR X OSMR expression) + (Coef SNX10 X SNX10 expression) + (Coef VCAN X VCAN expression);
(b) Determining a threshold value (cut-off value);
(c) Comparing the result of (a) with the threshold value of (b) to obtain a detection result.
In one or more embodiments, in (a), the risk factor for the gene is:
Coef IFT52 :0 to 1, preferably 0.496.+ -. 0.05, more preferably 0.496.+ -. 0.02;
Coef OSMR :0 to 1, preferably 0.398.+ -. 0.05, more preferably 0.398.+ -. 0.02;
Coef SNX10 : -1 to 0, preferably-0.653±0.05, more preferably-0.653±0.02;
Coef VCAN :0 to 1, preferably 0.668.+ -. 0.05, more preferably 0.668.+ -. 0.02.
In one or more embodiments, in the 0 to 1 or-1 to 0, the Coef includes 0, 1 or-1.
In one or more embodiments, in (c), if the score value > the threshold value, the evaluation is: poor prognosis, high risk of recurrence, insensitivity to chemotherapy, or insensitivity to immune checkpoint inhibitor treatment; if the grading value is less than or equal to the threshold value, the grading value is evaluated as follows: good prognosis, low risk of recurrence, sensitivity to chemotherapy, or sensitivity to immune checkpoint inhibitor treatment.
In one or more embodiments, the threshold value of risk score 1 is 6.92; the threshold for risk score 2 is 3.43.
In a second aspect of the present invention, there is provided a kit or test device for prognosis of gastric cancer, suitability for chemotherapy or immunotherapy, comprising a test agent for prognosis of gastric cancer, suitability for chemotherapy or immunotherapy, comprising: a detection reagent for a molecular marker selected from IFT52, OSMR, SNX10, VCAN, or a combination thereof.
In one or more embodiments, the molecular marker combination is selected from the group consisting of: (1) IFT52, OSMR, SNX10, and VCAN; (2) OSMR, SNX10 and VCAN.
In one or more embodiments, the detection reagent comprises: PCR detection reagent and sequencing reagent.
In one or more embodiments, the detection reagent comprises: and a primer for specifically amplifying the molecular marker gene and a probe for specifically recognizing the molecular marker gene.
In one or more embodiments, the detection device further comprises: a gene sequencing instrument, a chip, a probe set, a primer probe set or an electrophoresis device.
In one or more embodiments, the molecular marker is a gene associated with gastric cancer.
In one or more embodiments, the gastric cancer-associated gene is a gene associated with cilia growth, and/or cilia structure.
In a third aspect of the present invention, there is provided a system for prognosis of gastric cancer, suitability for chemotherapy or immunotherapy, comprising a detection unit and a data analysis unit;
the detection unit includes: a detection reagent for measuring the expression level of a molecular marker, or a kit or a detection device containing the detection reagent; the molecular marker is selected from IFT52, OSMR, SNX10, VCAN, or a combination thereof;
the data analysis unit includes: and the processing unit is used for analyzing and processing the detection result of the detection unit to obtain gastric cancer prognosis or immune therapy applicability result.
In one or more embodiments, the molecular marker is a gene associated with gastric cancer.
In one or more embodiments, the gastric cancer-associated gene is a gene associated with cilia growth, and/or cilia structure.
In one or more embodiments, the molecular marker combination is selected from the group consisting of: (1) IFT52, OSMR, SNX10, and VCAN; (2) OSMR, SNX10 and VCAN.
In a fourth aspect of the invention, there is provided a method of prognosis of gastric cancer, or suitability for immunotherapy, the method comprising: detecting the molecular marker by using a detection system for specifically detecting the molecular marker; the molecular marker is selected from IFT52, OSMR, SNX10, VCAN, or a combination thereof; the detection system comprises a detection reagent, a kit or a detection device.
In one or more embodiments, the molecular marker is a gene associated with gastric cancer.
In one or more embodiments, the gastric cancer-associated gene is a gene associated with cilia growth, and/or cilia structure.
In one or more embodiments, the molecular marker combination is selected from the group consisting of: (1) IFT52, OSMR, SNX10, and VCAN; (2) OSMR, SNX10 and VCAN.
In one or more embodiments, the method comprises: according to the expression of the molecular marker; analyzing the sensitivity of the gastric cancer patient to adjuvant chemotherapy, neoadjuvant chemotherapy or immunotherapy; preferably further comprising: formulating a treatment/medication regimen; (b) Analyzing the major pathological remission, and/or survival of gastric cancer patients; or, (c) performing a risk analysis or scoring of gastric cancer progression in the gastric cancer patient.
In one or more embodiments, the immunotherapy is an anti-PD-1/anti-CTLA-4 immune checkpoint inhibitor therapy.
In one or more embodiments, the method comprises:
(a) For a test sample, the expression of IFT52, OSMR, SNX10 and/or VCAN is determined and risk analysis or scoring is performed using a risk scoring formula selected from the group consisting of:
risk score 1= (Coef IFT52 X IFT52 expression) + (Coef OSMR X OSMR expression) + (Coef SNX10 X SNX10 expression) + (Coef VCAN X VCAN expression);
risk score 2= (Coef OSMR X OSMR expression) + (Coef SNX10 X SNX10 expression) + (Coef VCAN X VCAN expression);
(b) Determining a threshold value (cut-off value);
(c) Comparing the result of (a) with the threshold value of (b) to obtain a detection result.
In one or more embodiments, in (a), the risk factor for the gene is:
Coef IFT52 :0 to 1, more preferably 0.496.+ -. 0.05, most preferably 0.496.+ -. 0.02;
Coef OSMR :0 to 1, more preferably 0.398.+ -. 0.05, most preferably 0.398.+ -. 0.02;
Coef SNX10 : -1 to 0, more preferably-0.653±0.05, most preferably-0.653±0.02;
Coef VCAN :0 to 1, more preferably 0.668.+ -. 0.05, most preferably 0.668.+ -. 0.02.
In one or more embodiments, in (c), if the score value > the threshold value, the evaluation is: poor prognosis, high risk of recurrence, insensitivity to chemotherapy, or insensitivity to immune checkpoint inhibitor treatment; if the grading value is less than or equal to the threshold value, the grading value is evaluated as follows: good prognosis, low risk of recurrence, sensitivity to chemotherapy, or sensitivity to immune checkpoint inhibitor treatment.
In one or more embodiments, the threshold value of risk score 1 is 6.92; the threshold for risk score 2 is 3.43.
Other aspects of the invention will be apparent to those skilled in the art in view of the disclosure herein.
Drawings
FIG. 1, survival analysis charts of 16 candidate cilia-associated genes (CTHRC 1, CENPF, VCAN, OSMR, SNX10, POC1A, CEP, EHD3, IDO1, LYAR, SPA17, CEP164, ASAP1, IFT52, GLIS2, ARMC 9)
FIG. 2, LASSO regression analysis.
FIG. 3, model genes IFT52, OSMR, SNX10, VCAN and risk factors (Coef).
FIG. 4 is a graph of Kaplan-Meier survival analysis of patients in different risk groups in the training set.
FIG. 5, a graph of time dependent ROC analysis of patients with different risk scores in the training set; wherein AUC represents area under the curve and numerals 1, 3, 5 represent ROC curves of subjects receiving immunotherapy for 1 year, 3 years, 5 years, respectively. Wherein, the true positive rate of 1 year = the number of low risk people who do not relapse within 1 year/the total number of people who do not relapse within 1 year; true positive rate for 3 years = number of low risk people without recurrence in 3 years/total number of non-recurrence people in 3 years; true positive rate for 5 years = number of low risk people without recurrence in 5 years/total number of non-recurrence people in 5 years; false positive rate = 1-true positive rate.
FIG. 6, kaplan-Meier survival analysis of different risk groups of patients in the test set.
FIG. 7, a graph of time dependent ROC analysis of patients with different risk scores in the test set; wherein AUC represents area under the curve and numerals 1, 3, 5 represent ROC curves of subjects receiving immunotherapy for 1 year, 3 years, 5 years, respectively. Wherein, the true positive rate of 1 year = the number of low risk people who do not relapse within 1 year/the total number of people who do not relapse within 1 year; true positive rate for 3 years = number of low risk people without recurrence in 3 years/total number of non-recurrence people in 3 years; true positive rate for 5 years = number of low risk people without recurrence in 5 years/total number of non-recurrence people in 5 years; false positive rate = 1-true positive rate.
FIG. 8, validation results of the 4 gene models (IFT 52, OSMR, SNX10, VCAN) in GSE 26899.
FIG. 9, validation of the gene models (IFT 52, OSMR, SNX10, VCAN) in GSE26901 dataset.
FIG. 10, validation results of the 4 gene models (IFT 52, OSMR, SNX10, VCAN) in GSE28541 dataset.
FIG. 11, validation results of the 4 gene models (IFT 52, OSMR, SNX10, VCAN) in GSE26253 dataset.
FIG. 12, validation results of the 4 gene models (IFT 52, OSMR, SNX10, VCAN) in the TCGA dataset.
FIG. 13, validation results of the gene models (IFT 52, OSMR, SNX10, VCAN) in PRJEB25780 (Cohort 1) dataset.
FIG. 14, validation results of the 4 gene models (IFT 52, OSMR, SNX10, VCAN) in PRJEB40416 dataset.
Fig. 15, validation results of gene models (IFT 52, OSMR, SNX10, VCAN) in tumor tissue samples of patients with 151 outpatients diagnosed with gastric cancer.
Figure 16, immune characteristics of different populations in training set.
Figure 17, immune profile for different populations in the test set.
Figure 18, GSE26899 dataset immune profile for different populations.
Figure 19, GSE26901 dataset immune profile for different populations.
Figure 20, GSE28541 dataset, immune profile for different populations.
Figure 21, GSE26253 dataset immune profile for different populations.
Figure 22, TCGA dataset immune profile for different populations.
FIG. 23, PRJEB25780 (Cohort 1) dataset, immune profile of different populations.
Figure 24, PRJEB40416 dataset immune profile for different populations.
FIG. 25, validation results of the gene models (OSMR, SNX10, VCAN) in GSE 26899.
Fig. 26, 3 validation results of gene models (OSMR, SNX10, VCAN) in GSE26901 dataset.
FIG. 27, validation results of the 3 gene models (OSMR, SNX10, VCAN) in GSE28541 dataset.
Fig. 28, 3 validation results of gene models (OSMR, SNX10, VCAN) in GSE26253 dataset.
FIG. 29, validation results of the 3 gene models (OSMR, SNX10, VCAN) in the TCGA dataset.
FIGS. 30, 3 validation results of gene models (OSMR, SNX10, VCAN) in PRJEB25780 (Cohort 1) dataset.
FIG. 31, validation results of the gene models (OSMR, SNX10, VCAN) in PRJEB40416 dataset.
Fig. 32, 3 results of validation of gene models (OSMR, SNX10, VCAN) in tumor tissue samples of patients with 151 out-patient diagnoses considered to suffer from gastric cancer.
Detailed Description
Aiming at the defect of lacking gastric cancer prognosis, especially lacking of the biomarker of the gastric cancer related to cilia in the prior art, the inventor provides a biomarker related to detection or prognosis of gastric cancer, auxiliary chemotherapy, novel auxiliary chemotherapy or immunotherapy applicability prognosis and application thereof through intensive researches. The biomarkers include IFT52, OSMR, SNX10, VCAN, which may be used for diagnosis and/or prediction of gastric cancer prognosis risk, adjuvant chemotherapy, neoadjuvant chemotherapy or immunotherapy suitability. The invention also provides a kit suitable for prognosis of gastric cancer, diagnosis and/or prediction of suitability for adjuvant chemotherapy, neoadjuvant chemotherapy or immunotherapy. The invention provides a new scheme for diagnosing and treating gastric cancer (especially cilia-related gastric cancer) clinically.
Terminology
As used herein, "molecular marker (marker)" refers to a biomolecule or fragment of a biomolecule, the change and/or detection of which may be associated with a particular physical condition or state. The terms "marker", "molecular marker" or "biomarker" are used interchangeably throughout the disclosure. In the present invention, the "molecular marker" means "cancer (tumor) marker" unless otherwise specified. In the present invention, the cancer is gastric cancer unless otherwise specified. In some specific embodiments, the "gene" refers to a gene associated with cilia growth, and/or cilia structure.
As used herein, the term "detecting" includes "assessing," determining, "" analyzing, "" predicting, "" evaluating; the term "evaluation" or "assessment" also includes "scoring".
As used herein, the "patient," "subject," or "individual" may refer to an organism, and in certain aspects, the subject may be a human. The subject providing the sample may include a population at risk of a potential disease or a population diagnosed with a disease. The disease in the present invention refers to gastric cancer.
As used herein, the term "gastric cancer" includes, but is not limited to: primary gastric cancer, progressive gastric cancer, gastroesophageal junction adenocarcinoma.
As used herein, the term "sample" is used interchangeably with "sample" and includes a substance obtained from an individual or isolated tissue, cell or body fluid that is suitable for tumor marker detection.
As used herein, "prognosis" refers to the prediction of the consequences that may be caused by a wound or disease (e.g., a tumor), and includes both recent and distant indications, including but not limited to ORR, DCR, OS, PFS, DFS, RFS. Wherein, "OS" refers to "over all survivinal" and total Survival time refers to the total time for Survival of all patients in the study; "PFS" refers to "Progression Free Survival", progression free survival, generally refers to the time that a patient has not progressed or is resistant to a disease after treatment; "DFS" refers to "Disease Free Survival", disease-free survival, generally refers to the time from the onset of randomization to disease recurrence or death (for any reason); "RFS" refers to "Recurrence Free Survival" and relapse free survival, generally refers to the patient's date of relapse or last follow-up date calculated from the date of initial treatment. Clinically, median OS, median PFS, median RFS, median DFS are generally expressed as the time to survival/progression free survival/relapse free survival/disease free survival achieved in 50% of patients.
As used herein, the "ROC curve" refers to a subject's working characteristic curve (Receiver Operating Characteristic curve). In certain embodiments of the invention, the ROC curve refers to a ROC curve between a true positive rate and a false positive rate.
As used herein, the "expression level" may refer to the concentration or amount of the gene/protein of the marker/indicator of the invention in a sample.
As used herein, the terms "high expression," "high expression level," and the like are interchangeable and shall mean at least a 5%, 10% or 20%, preferably at least 30% or 50%, more preferably at least 80% or 100% or more significant improvement as compared to a "control" or "threshold" in the sense of use. For example, the presence of at least one gene multiplex Student's T-test whose expression intensity exceeds a threshold value may be tested to determine significance.
As used herein, the terms "low expression", "low expression level", etc. are interchangeable and shall mean a reduction of at least 5%, 10% or 20%, preferably at least 30% or 50%, more preferably at least 80% or 100% or more significantly compared to a "control" or "threshold" in the sense of application. For example, the presence of at least one gene multiplex Student's T-test with an expression intensity below a threshold may be tested to determine significance.
As used herein, the setting of a "control" or "threshold" for gene or protein expression is readily set by one of skill in the art based on the teachings of the present invention. Selection of an appropriate "control" or "threshold" is a routine part of the design of an experiment, for example, the expression level of the corresponding gene/protein may first be analyzed statistically based on a sample of a subject (patient) whose prognosis/therapeutic efficacy is clear, and the obtained expression value is referred to as "control" or "threshold".
As used herein, the term "kit" may refer to a system of materials or reagents for performing the methods disclosed herein.
As used herein, definition of the stage of cancer may be performed with definition criteria already in the art.
As used herein, "/" may mean "and", or may also be denoted "or".
Biomarkers of the invention
In the invention, through deep analysis of samples of clinical patients with gastric cancer, a module related to gastric cancer progress is identified, and further screening is carried out to obtain a core gene: OSMR, SNX10, VCAN, and/or IFT52. The OSMR, SNX10, VCANI and IFT52 are closely related to the development and progression of gastric cancer, and play an important role in the process of cilia formation in particular, and the protein encoded by IFT52 is essential for the integrity of the IFT-B core complex, as well as for the biosynthesis and maintenance of cilia. Mutations in this gene are associated with cilia disease affecting the skeleton. The VCAN is a member of the agglecan/verscan proteoglycan family. The encoded protein is a large chondroitin sulfate proteoglycan, which is the main component of extracellular matrix. The protein is involved in cell adhesion, proliferation, diffusion, migration and angiogenesis, and plays a central role in tissue morphogenesis and maintenance.
Based on the new findings of the invention, a group of markers with diagnostic significance for gastric cancer are disclosed: OSMR, SNX10, VCAN and/or IFT52 genes. The invention also discloses a marker, a kit and a method for prognosis of gastric cancer and evaluation of therapeutic effect of the therapy.
The sequence position of the IFT52 gene can be referred to as a colorime 20, NC_000020.11 (43590937.. 43647299), the sequence information can be referred to as https:// www.ncbi.nlm.nih.gov/gene/51098# reference-sequences, and the invention can also cover the sequence variants in organisms.
The sequence position of the OSMR gene can be referred to as a colorime 5, NC_000005.10 (38846012.. 38945579), the sequence information can be referred to as https:// www.ncbi.nlm.nih.gov/gene/9180# reference-sequences, and the invention can also cover the sequence variants in organisms.
The sequence position of the SNX10 gene can be referred to as chromoname 7, NC_000007.14 (26291862.. 26374383), the sequence information can be referred to as https:// www.ncbi.nlm.nih.gov/gene/29887#reference-sequences, and the invention can also cover the sequence variants in organisms.
The sequence position of the VCAN gene can be referred to as a colorime 5, NC_000005.10 (83471744.. 83582302), the sequence information can be referred to as https:// www.ncbi.nlm.nih.gov/gene/1462#reference-sequences, and the invention can also cover the sequence variants thereof in organisms.
The molecular markers disclosed by the invention can be used as judgment markers (markers) for evaluating the development of gastric cancer and the curative effect of a therapy. Thus, can be used to understand what disease state an individual is in, to evaluate or predict the risk of a prognostic disease, and to formulate a treatment/dosing regimen. Such treatment/administration regimens include, but are not limited to: adjuvant chemotherapy (e.g., single dose of 5-fluorouracil, combination of 5-fluorouracil with cisplatin/oxaliplatin, doxorubicin or paclitaxel, combination of 5-fluorouracil with folinic acid), neoadjuvant chemotherapy, immunotherapy (e.g., PD-1 antibody immunotherapy).
As one mode, the method for predicting gastric cancer using the molecular marker comprises: (1) Detecting the expression level of OSMR, SNX10, VCAN and/or IFT52 genes in a gastric cancer patient sample; (2) based on the expression level obtained in (1): when the OSMR, VCAN and/or IFT52 genes are expressed in high, the gastric cancer patient is prompted to have bad prognosis and short survival time; when SNX10 is highly expressed, the gastric cancer patient is prompted to have good prognosis and long survival time.
In some embodiments, the risk score value (risk value) can be obtained by substituting the expression levels of OSMR, SNX10, VCAN, and IFT52 genes into the risk score formula at the time of detection. Comparing the risk value with a preset threshold value, a predicted outcome of the disease prognosis risk can be obtained. The higher the risk number, the higher the risk of disease prognosis. As used in the present invention, when the risk value is higher than a preset threshold, it means that the disease prognosis risk is high, belonging to a high risk group; and when the risk value is lower than a preset threshold value, the disease prognosis risk is low, and the disease prognosis risk belongs to a low risk group.
As used herein, the "Risk Score" (Risk Score) is calculated as: risk Score = amount of gene expression 1 ×Coef 1 + Gene expression level 2 ×Coef 2 +..+ Gene expression level n ×Coef n (Coef: regression coefficient of genes in a multifactor Cox regression analysis, n: total number of genes related to prognosis).
The level of expression of the molecular markers of the present invention can be determined according to established standard procedures (references) well known in the art. The assay may be performed at the RNA level or cDNA levels may be detected after reverse transcription of the RNA, for example by real-time fluorescent quantitative PCR techniques. At the protein level, ELISA is for example performed by immunohistochemical techniques.
As an alternative, depending on the protein of the molecular marker, detection may be achieved using antibodies that specifically bind to the protein, which may be performed by immunohistochemical staining of clinical samples. Common immunohistochemical methods include, but are not limited to, immunofluorescence, immunoenzyme-labeling, immunocolloidal gold, and the like. Immunofluorescence method uses the principle of antigen-antibody specific binding, firstly, the known antibody is marked with fluorescein, and the fluorescein is used as probe to check the correspondent antigen in cell or tissue, and then the cell or tissue is observed under the fluorescence microscope; when the fluorescein in the antigen-antibody complex is excited to emit light, the fluorescein emits fluorescence with a certain wavelength, so that the positioning of a certain antigen in the tissue can be determined, and further quantitative analysis can be performed. In the immune enzyme labeling method, an enzyme-labeled antibody acts on tissues or cells, then enzyme substrates are added to generate colored insoluble products or particles with certain electron density, and various antigen components on the surfaces and in the cells are subjected to localization research through a light mirror or an electron microscope. In the immune colloidal gold method, colloidal gold (gold hydrosol) which is a special metal particle is used as a marker, and the colloidal gold can rapidly and stably adsorb protein without obvious influence on the biological activity of the protein; the colloidal gold is used for marking primary antibody, secondary antibody or other molecules capable of specifically combining with immunoglobulin, etc. as probes, and can be used for qualitatively, positionally and quantitatively researching antigens in tissues or cells.
Alternatively, primers can be designed to specifically amplify the molecular markers based on their sequence for detection. Polymerase Chain Reaction (PCR) technology is a technique well known to those skilled in the art, the basic principle of which is a method of enzymatic synthesis of specific DNA fragments in vitro. The method of the present invention can be performed using conventional PCR techniques. For one molecular marker, the arrangement of one or more pairs of primers is possible, and the arrangement of multiple pairs of primers can obtain multiple sets of amplification products, which may be more beneficial for the confirmation of the results.
As an alternative, suitable probes can be designed based on the sequence of the molecular marker, immobilized on a microarray (microarray) or a gene chip. The gene chip generally comprises a solid carrier and oligonucleotide probes orderly fixed on the solid carrier, wherein the oligonucleotide probes consist of continuous nucleotides. In order to enhance the intensity of the detection signal and improve the accuracy of the detection result, the hybridization related site is preferably located in the middle of the probe. The solid phase carrier can be made of various common materials in the field of gene chips, such as but not limited to nylon membranes, glass slides or silicon wafers modified by active groups (such as aldehyde groups, amino groups, isothiocyanates and the like), unmodified glass slides, plastic sheets and the like. The probe may also comprise a stretch of amino-modified 1-30 poly-polydT (poly dT) at its 5' end. The gene chip comprises probes for at least one molecular marker of the invention; more preferably, the gene chip comprises probes for two or more than two molecular markers; most preferably, probes for all of the molecular markers of the invention are contained on one or more gene chips. For a molecular marker, the arrangement of one or more probes is possible, and the arrangement of a plurality of probes may be more advantageous for the confirmation of the result.
As an alternative, a method of binding the probe by the primer may be utilized, thereby making the qualitative and quantitative detection more sensitive and rapid. For example, taqman real-time fluorescent PCR detection techniques may be employed: in PCR amplification, a pair of primers is added, and a specific fluorescein-labeled Taqman probe is added, wherein the probe is an oligonucleotide, and a reporter fluorescent group and a quenching fluorescent group are respectively labeled at two ends of the oligonucleotide. When the probe is complete, the fluorescent signal emitted by the reporter group is absorbed by the quencher group; during PCR amplification, the 5 '. Fwdarw.3' exonuclease activity of Taq enzyme is used for carrying out enzyme digestion degradation on a probe, so that a report fluorescent group and a quenching fluorescent group are separated, fluorescein is dissociated in a reaction system, and emits fluorescence under specific light excitation, and along with the increase of the cycle times, the amplified target gene fragment grows exponentially, and a Ct (cycle threshold, ct) value is obtained by detecting the corresponding fluorescence signal intensity which changes along with the amplification in real time. The Ct value, i.e. the number of amplification cycles passed when the fluorescence signal of the amplified product reaches a set threshold in the PCR amplification process, has a linear relationship with the logarithm of the initial copy number of the template, and the more the template DNA amount, the fewer the cycle number when the fluorescence reaches the threshold, i.e. the smaller the Ct value, thereby realizing quantitative and qualitative analysis of the initial template.
Methods for amplifying specific fragments of genes by PCR are well known in the art and are not particularly limited in the present invention. Labeling of the amplified product may be accomplished by amplification using primers with a 5' labeling group, including but not limited to: digoxin molecules (DIG), biotin molecules (Bio), fluorescein and its derivative biomolecules (FITC, etc.), other fluorescent molecules (e.g., cy3, cy5, etc.), alkaline Phosphatase (AP), horseradish peroxidase (HRP), etc.
The invention also provides a kit for detection that may include a system for storing, transporting, or delivering reaction reagents or devices (e.g., primers, probes, etc. in appropriate containers) and/or cooperating materials (e.g., buffers, written instructions to perform an assessment, etc.) from one location to another. For example, the kit may include one or more housings (e.g., cassettes) containing the relevant reagents and/or cooperating materials. These contents may be delivered to the intended recipient simultaneously or separately.
In addition, various reagents required for DNA extraction, PCR, hybridization, color development, etc., may be included in the kit, including but not limited to: extract, amplification solution, hybridization solution, enzyme, control solution, color development solution, washing solution, antibody, etc.
In addition, the kit can also comprise instructions for use, chip image analysis software and the like.
The invention also provides a system for assessing the development of gastric cancer and the efficacy of therapy, comprising a detection unit and a data analysis unit; the detection unit includes: a detection reagent for determining the expression level of the molecular marker, or a reagent or device of a kit or detection device containing the detection reagent; the data analysis unit includes: and the processing unit is used for analyzing and processing the detection result of the detection unit to obtain the detection or prognosis result of the gastric cancer. The detection reagent includes (but is not limited to): an antibody specifically binding to a protein encoded by the molecular marker, a primer specifically amplifying the molecular marker gene, a probe specifically recognizing the molecular marker gene, and the like. Devices specific for detection may include, but are not limited to: immunohistochemical devices (e.g., ELISA detection kit/module/device), gene sequencing instruments, chips, probe sets (modules), primer probe sets (modules), or electrophoresis devices, and the like. The detection result comprises: diagnostic results, or risk assessment/scoring (e.g., grading) results.
As an alternative, the data analysis unit that can be used for analyzing the detection result of the detection unit includes: a calculation or scoring unit; preferably, the unit is provided with a detection result of gastric cancer prognosis risk, wherein the prediction value calculated by a risk scoring formula for OSMR, SNX10, VCAN and IFT52 genes is compared with a preset threshold value.
The invention has the following beneficial effects:
1) The invention finds the marker related to gastric cancer prognosis through the technology of bioinformatics for the first time: IFT52, OSMR, SNX10, VCAN; the risk of gastric cancer prognosis can be predicted by the expression of the gene;
2) The invention firstly proposes a prognosis mode of a predicted gastric cancer patient, which is formed by using 4 cilia gene combinations and 3 cilia gene combinations as molecular markers.
The invention will be further illustrated with reference to specific examples. It is to be understood that these examples are illustrative of the present invention and are not intended to limit the scope of the present invention. The experimental procedures, which are not specifically noted in the examples below, are generally carried out according to conventional conditions such as those described in J.Sam Brookfield et al, molecular cloning guidelines, third edition, scientific Press, or according to the manufacturer's recommendations.
Experimental method
1. Data collection and arrangement
The inventor performs feature screening by searching a document and collecting 1 set of GSE27342 data sets containing 80 stomach cancers and 80 gastric cancer side samples in total; 1 set of self-test data containing 98 samples and a data set containing 398 samples in total of 300 samples in GSE66229 are combined, 200 samples are randomly screened out of the 398 samples to be used as a training set for training a model, and the rest 198 samples are used as a test set; in addition, 8 sets of RNA high throughput sequencing (RNA-seq) data received adjuvant chemotherapy, neoadjuvant chemotherapy, or immunotherapy (anti-PD-1 antibody treatment) independently validated the robustness of the model (table 1).
TABLE 1 dataset of the invention
Note that: OS: total lifetime; PFS: progression free survival; RFS: no recurrence life time; DFS: disease-free survival time; the PRJEB25780 and PRJEB40416 datasets were both evaluated for immunotherapy efficacy using the solid tumor response evaluation criteria (Response Evaluation Criteria in Solid Tumors, RECIST). The response information includes: CR (complete response), PR (partial response), SD (disease stabilization), PD (disease progression). The adjuvant chemotherapy is postoperative chemotherapy, the new adjuvant chemotherapy is preoperative chemotherapy, and the immunotherapy is treatment with pembrolizumab.
Chemotherapy drugs: single dose of 5-fluorouracil; a combination of 5-fluorouracil with cisplatin/oxaliplatin, doxorubicin or paclitaxel; 5-fluorouracil is combined with folinic acid. Wherein, the specific chemotherapeutic drugs, the dosage and the administration frequency are adjusted by doctors according to the actual condition of patients.
Self-test data 1: after informed consent, 98 patient tumor tissue samples from the diagnosis of the clinic in the hospital, which are considered to be suffering from gastric cancer, are collected, and survival information such as OS, PFS and the like of the patient is obtained through follow-up visit in the clinic.
Self-test data 2: after informed consent, 151 samples of tumor tissues of patients from hospital outpatient diagnosis considered to suffer from gastric cancer are collected, and survival information such as PFS of the patients is obtained through outpatient follow-up.
2. Gene set enrichment analysis
In the tumor microenvironment, tumor tissues are infiltrated by various cells, and immune cells infiltrating the tumor can deeply influence the progress of the tumor. In order to accurately assess the composition of immune cells in the tumor microenvironment, the inventors have been able to quantify tumor-infiltrating immune cells from RNA sequencing data by a number of methods. Gene set enrichment (GSVA) analysis is a well-established method, the main idea being to concentrate information in the gene expression profile into one pathway or feature. Advantages of this approach include reduced noise and dimension reduction, and higher biological interpretation capabilities compared to single-gene analysis. The inventors utilized two gene sets of gene set FGE (derived from DOI:10.1016/j. Ccoll. 2021.04.014) and danaher (derived from DOI:10.1186/s 40425-017-0215-8) as background, combined with R package (R language kit) GSVA to convert gene expression profile to enrichment score of sample in various immune characteristics.
3. LASSO regression
LASSO regression is an example of regularization of a regression algorithm. Regularization is a method that solves the over-fitting problem by adding additional parameters, thereby reducing the parameters of the model, limiting complexity. LASSO regression is an L1 penalty model, and the inventor only needs to add an L1 norm to a least squares cost function, so that the regularization strength of the model is enhanced and the weight of the model is reduced by increasing the value of the super parameter alpha. By adjusting the strength of regularization, certain weights can be made zero, which makes the LASSO method a very powerful dimension reduction technique. The invention optimizes the characteristics and constructs a corresponding COX proportional risk model by using the R package glmnet.
4. Survival analysis
The survival analysis is a statistical analysis method for researching the distribution rule of survival time and the relation between the survival time and related factors. Survival analysis involves time-dependent indicators of healing, death, or growth and development of organs associated with the disease. The Kaplan-Meier survival curve is used for estimating survival rate and median survival time according to survival time distribution and displaying in a survival curve mode, so that survival characteristics are analyzed. The Log-rank test investigates the differences between groups by comparing the survival curves, typically the survival rates and their standard errors, between two or more groups. The COX risk ratio model was used to analyze the effect of two or more variables on survival. According to the invention, a Kaplan-Meier (KM) method is utilized to estimate survival rate, a survival curve is prepared, whether the survival curves among multiple groups have obvious differences is analyzed according to Log-rank test, and finally a COX risk proportion model is used for researching the influence of a certain factor on survival. The above methods were all based on R-packets surviviner and survivinal.
5. Software package
The glrnet, surviviner, survivinal, GSVA, tidyverse, timeROC, ggplot2, caret, stats, MCPcounter, clusterif iotaer, limma, and endrich plot packages referred to in the examples below are all prior art, and are derived from https:// cran. R-project. Org or http:// www.bioconductor.org/, and are run in R software after loading.
Example 1 screening candidate cilia characteristics
2636 gastric cancer tissue high expression genes (logFC >0.5, p < 0.05) were screened from GSE27342 dataset (80 vs. cancer side normal sample) using Limma function. Then intersection is carried out on 901 cilia-related genes in a GO database (http:// geneontologiy. Org /), so as to obtain 46 cilia-related genes with obvious high expression of gastric cancer tissues. From the survival analysis, 16 genes significantly correlated with prognosis, i.e., CTHRC1, CENPF, VCAN, OSMR, SNX10, POC1A, CEP, EHD3, IDO1, LYAR, SPA17, CEP164, ASAP1, IFT52, GLIS2, ARMC9, were screened out of 46 cilia-related genes significantly highly expressed in gastric cancer tissue, and subsequent model construction was performed with these 16 genes as the selection features (fig. 1).
Example 2 construction of risk models by LASSO
200 samples were randomly selected from the training/test model data of 398 sample data as training set in which LASSO (the Least Absolute Shrinkage and Selection Operator) regression was used to screen the optimal gene set (fig. 1). The 11 best gene sets (ARMC 9, CENPF, CEP72, CTHRC1, GLIS2, IFT52, OSMR, POC1A, SNX, SPA17, VCAN) were obtained among the 16 candidate cilia features. The 11 genes were combined with COX regression to construct a risk prognosis model.
The method is circulated for 500 times by stepwise regression, and finally a risk model (figure 3), abbreviated as 4 gene model, consisting of four genes (IFT 52, OSMR, SNX10, VCAN) and risk coefficients thereof shown in Table 2 is obtained.
TABLE 2 model genes and their risk factors
And obtaining a risk scoring formula according to regression coefficients of COX and the optimized 4 genes:
risk score = (0.496×ift52) + (0.398×osmr) +(-0.653×snx10) + (0.668×vcan).
The threshold was determined from the survivin_cut point in the R-language kit (the best cut point with survival data was calculated as the threshold using the maximum selection rank statistics method according to the arithmetic logic of the R-package survivin_cut point function, the final threshold was determined to be 6.92), and the patients in the training set were divided into a high risk group (> 6.92, rated as bad prognosis, high risk of recurrence and insensitive to immunotherapy) and a low risk group (.ltoreq.6.92, rated as good prognosis, low risk of recurrence and sensitive to immunotherapy). Kaplan-Meier survival analysis showed that the prognosis for the high risk group patients was worse than for the low risk group (FIG. 4), the difference was extremely significant, p.ltoreq.0.0001.
For patients with different risk scores in the training set, the time-dependent ROC curves showed area under the curve (AUC) values of 0.623, 0.685 and 0.694 for 1 year, 3 years and 5 years, respectively (fig. 5), indicating that the risk scores can predict patient survival with higher accuracy.
Similarly, the prognosis for the low risk group was also observed to be significantly better than for the high risk group (FIG. 6), the difference was extremely pronounced, p.ltoreq.0.0001 in the test set.
Time-dependent ROC curves for different risk scoring patients in the test set showed area under the curve (AUC) values of 0.607, 0.661 and 0.656 for 1, 3 and 5 years, respectively (fig. 7).
Example 3 function of IFT52, OSMR, SNX10, VCAN Gene
The 4 genes in the model are all related to cilia composition and their signal transmission, details are given in table 3.
TABLE 3 functional information of genes
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Example 4 robustness of 4 Gene model and adjuvant chemotherapy, neoadjuvant chemotherapy, immunotherapy applicability
The independent validation set data in table 1 was used as a validation sample, and the following risk scoring formulas were used to validate the robustness of the model and suitability of gastric cancer adjuvant chemotherapy, neoadjuvant chemotherapy, immunotherapy (anti-PD-1 immunotherapy):
risk score = (0.496×ift52) + (0.398×osmr) + (-0.653×snx10) + (0.668×vcan);
Wherein, if the score value is >6.92, the prognosis is poor and the recurrence is high; and if the grading value is less than or equal to 6.92, the prognosis is good, and the recurrence risk is low. And carrying out Kaplan-Meier survival analysis (Log-Rank) and Wilcox Rank sum test (Wilcox test) by combining the risk scoring result, and evaluating the suitability of the model for gastric cancer adjuvant chemotherapy, neoadjuvant chemotherapy and immunotherapy by using the verification data set of the table 1.
The response rate was further calculated, response rate = ratio of complete remission/partial remission.
The results are shown in Table 4.
Tables 4, 4 verification results of Gene combination model and comparison with Single/double Gene combination model
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The inventors respectively validated 93 samples in the GSE26899 set of data, and found that the prognosis of the low risk group screened by the model was significantly higher than that of the high risk group (fig. 8, log_rank=0.0092). Similar results were also observed in GSE26901 dataset (fig. 9, log_rank=0.00072), GSE28541 dataset (fig. 10, log_rank=0.037), GSE26253 dataset (fig. 11, log_rank=0.03) and TCGA dataset (fig. 12, log_rank=0.025) as well as in tumor tissue samples of patients diagnosed with gastric cancer in 151 outpatients (fig. 15,Log_rank p<0.001). Prognosis of outpatient specimens is a comparison of model predictions with follow-up results 10 years after clinical treatment.
In addition, it was found that the response rate (full/partial remission ratio) was significantly higher in the low risk group (62% low risk group, 29% high risk group) than in the high risk group, approximately 2-3 times higher in the PRJEB25780 dataset (fig. 13, wilcox_test p=0.036) and the PRJEB40416 dataset (fig. 14, wilcox_test p=0.059, only 15 samples). The non-response rate is obviously lower than that of the high-risk group, and the non-response rate is 14% -20% less than that of the high-risk group.
These results all show that the model has strong robustness and immune therapy applicability, and can be seen in multiple sets of gastric cancer related data to screen out low risk populations with significantly good prognosis, so that the model is also robust after actual disease prognosis analysis.
Example 5 immunofeature analysis of data
Bioinformatic analysis of the immune profile of the dataset of table 1, found that the low risk group population was mainly enriched on immune activation related pathways, showing immune activation status, suggesting that this part of patients may be suitable for immunotherapy; while the high risk group population is mainly enriched in immune-suppressive-related pathways such as EMT, and is shown to be in an immune-suppressive state (as shown in fig. 16-24), indicating that it is unsuitable for immunotherapy.
The bioinformatics analysis results again confirm the applicability of the 4-gene model to immunotherapy.
Example 6 clinical application of markers
Informed consent was collected samples of tumor tissue from 151 patients diagnosed with clinic for whom the clinical diagnosis was considered to be gastric cancer. These patient samples were scored in the same 4-gene model and analysis as in example 2.
TABLE 5 prediction of the 4 Gene models of the invention on clinical patients
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The results of model predictions according to the present invention are shown in table 4, with 37 out of 151 subjects with test results above the threshold, and the population of patients expected to be high risk patients and also the population not responding to chemotherapy/immunotherapy. The 114 subjects tested below the threshold, and the population of patients was expected to be low risk patients, and also a population effective for chemotherapy/immunotherapy. The results are substantially consistent with the clinical follow-up/follow-up results for the batch of patients.
Examples 7, 3 predictive model and predictive results for Gene combinations
Next, the inventors examined whether or not a prediction model with accurate prediction results as in the model in example 2 could be built, and constructed a 3-gene prediction model as shown in table 6.
Tables 6, 3 predictive model for Gene (OSMR, SNX10, VCAN) combinations
Further, kaplan-Meier survival analysis and Wilcox rank sum test were performed using the dataset in table 1 in combination with risk scoring results to explore the predictive accuracy of the model. The results are shown in Table 7.
Tables 7 and 3 verification results of Gene combination model
| Gene combination | Validating a data set | P value |
| OSMR、SNX10、VCAN | GSE26899 | 0.006 |
| OSMR、SNX10、VCAN | GSE26901 | 0 |
| OSMR、SNX10、VCAN | GSE28541 | 0.037 |
| OSMR、SNX10、VCAN | GSE26253 | 0.026 |
| OSMR、SNX10、VCAN | TCGA | 0.005 |
| OSMR、SNX10、VCAN | Self-test data 2 | 4.59e-06 |
Note that: 0 in the table indicates that the P value is less than 0.001.
The results show that the prognosis for the low risk group population is significantly better than the high risk population (fig. 25p=0.0059, fig. 26p=0.00025, fig. 27p=0.037, fig. 28p=0.026, fig. 29 p= 0.00051, fig. 32p+.0.0001), both in the individual data sets of table 1 and in the 151 clinical samples. Also, the response rate of low risk population against PD-1 immunotherapy is approximately twice that of the high risk group, and the non-response rate will be significantly lower than that of the high risk group (fig. 30, 31).
The above results demonstrate that, although the evaluation effect is not ideal after single or double gene modeling (table 4), the 3-gene model after reducing the base factor is also significant and can be used as a biomarker for prognosis of gastric cancer and related immunotherapy.
The foregoing examples illustrate only a few embodiments of the invention and are described in detail herein without thereby limiting the scope of the invention. It should be noted that it is possible for a person skilled in the art to make several variants and modifications without departing from the inventive concept, which fall within the scope of protection of the present invention. Accordingly, the scope of protection of the present invention is to be determined by the appended claims. All documents referred to in this application are incorporated by reference herein as if each was individually incorporated by reference.
Claims (10)
1. The application of the molecular marker in preparing a detection system for detecting or prognosing gastric cancer, and detecting the prognosis of chemotherapy or immunotherapy applicability; the molecular marker is selected from IFT52, OSMR, SNX10, VCAN, or a combination thereof.
2. The use of claim 1, wherein the combination of molecular markers is selected from the group consisting of:
(1) IFT52, OSMR, SNX10, and VCAN;
(2) OSMR, SNX10 and VCAN.
3. The use of claim 1, wherein the detecting or prognosticating comprises: according to the expression of the molecular marker:
(a) Analyzing the sensitivity of the gastric cancer patient to adjuvant chemotherapy, neoadjuvant chemotherapy or immunotherapy; preferably further comprising: formulating a treatment/medication regimen; preferably, the immunotherapy is an immune checkpoint inhibitor therapy; preferably, the immunotherapy is PD-1 immunotherapy;
(b) Analyzing the major pathological remission, and/or survival of gastric cancer patients; or (b)
(c) Performing a risk analysis or scoring of gastric cancer progression in a gastric cancer patient;
preferably, the molecular marker is a gene associated with gastric cancer; more preferably, the gastric cancer-associated gene is a gene associated with cilia growth, and/or cilia structure.
4. A use according to any one of claims 1-3, wherein the detection system comprises: a detection reagent, kit or detection device; preferably, the detection reagent includes: PCR detection reagent and sequencing reagent; more preferably, the detection reagent comprises: a primer for specifically amplifying the molecular marker gene and a probe for specifically recognizing the molecular marker gene; preferably, the kit comprises the detection reagent; preferably, the detecting device includes: a gene sequencing instrument, a chip, a probe set, a primer probe set or an electrophoresis device.
5. The use according to any one of claims 1 to 4, wherein the method of detection comprises:
(a) For a test sample, the expression of IFT52, OSMR, SNX10 and/or VCAN is determined and risk analysis or scoring is performed using any risk scoring formula selected from the group consisting of:
risk score 1= (Coef IFT52 X IFT52 expression) + (Coef OSMR X OSMR expression) + (Coef SNX10 X SNX10 expression) + (Coef VCAN X VCAN expression);
risk score 2= (Coef OSMR X OSMR expression) + (Coef SNX10 X SNX10 expression) + (Coef VCAN X VCAN expression);
(b) Determining a threshold value (cut-off value);
(c) Comparing the result of (a) with the threshold value of (b) to obtain a detection result.
6. The use of claim 5, wherein in (a) the risk factor for the gene is:
Coef IFT52 :0 to 1, preferably 0.496.+ -. 0.05, more preferably 0.496.+ -. 0.02;
Coef OSMR :0 to 1, preferably 0.398.+ -. 0.05, more preferably 0.398.+ -. 0.02;
Coef SNX10 : -1 to 0, preferably-0.653±0.05, more preferably-0.653±0.02;
Coef VCAN :0 to 1, preferably 0.668.+ -. 0.05, more preferably 0.668.+ -. 0.02.
7. The use according to claim 5 or 6, wherein in (c) if the score value > the threshold value, the evaluation is: poor prognosis, high risk of recurrence, insensitivity to chemotherapy, or insensitivity to immune checkpoint inhibitor treatment; if the grading value is less than or equal to the threshold value, the grading value is evaluated as follows: good prognosis, low risk of recurrence, sensitivity to chemotherapy, or sensitivity to immune checkpoint inhibitor treatment;
Preferably, the risk score 1 has a threshold of 6.92; the threshold for risk score 2 is 3.43.
8. A kit or test device for prognosis of gastric cancer, suitability for chemotherapy or immunotherapy, comprising a test agent for prognosis of gastric cancer, suitability for chemotherapy or immunotherapy, comprising: a detection reagent for a molecular marker selected from IFT52, OSMR, SNX10, VCAN, or a combination thereof;
preferably, the molecular marker combination is selected from: (1) IFT52, OSMR, SNX10, and VCAN; (2) OSMR, SNX10, and VCAN;
preferably, the detection reagent includes: PCR detection reagent and sequencing reagent; more preferably, the detection reagent comprises: a primer for specifically amplifying the molecular marker gene and a probe for specifically recognizing the molecular marker gene;
preferably, the detection device further includes: a gene sequencing instrument, a chip, a probe set, a primer probe set or an electrophoresis device;
preferably, the molecular marker is a gene associated with gastric cancer; more preferably, the gastric cancer-associated gene is a gene associated with cilia growth, and/or cilia structure.
9. A system for prognosis of gastric cancer, suitability for chemotherapy or immunotherapy, comprising a detection unit and a data analysis unit;
The detection unit includes: a detection reagent for measuring the expression level of a molecular marker, or a kit or a detection device containing the detection reagent; the molecular marker is selected from IFT52, OSMR, SNX10, VCAN, or a combination thereof;
the data analysis unit includes: the processing unit is used for analyzing and processing the detection result of the detection unit to obtain a gastric cancer prognosis or immunotherapy applicability result;
preferably, the molecular marker is a gene associated with gastric cancer; more preferably, the gastric cancer-associated gene is a gene associated with cilia growth, and/or cilia structure;
preferably, the molecular marker combination is selected from: (1) IFT52, OSMR, SNX10, and VCAN; (2) OSMR, SNX10 and VCAN.
10. A method of predicting prognosis of gastric cancer, or suitability for immunotherapy, comprising: detecting the molecular marker by using a detection system for specifically detecting the molecular marker; the molecular marker is selected from IFT52, OSMR, SNX10, VCAN, or a combination thereof; the detection system comprises a detection reagent, a kit or a detection device;
preferably, the molecular marker is a gene associated with gastric cancer; more preferably, the gastric cancer-associated gene is a gene associated with cilia growth, and/or cilia structure;
Preferably, the molecular marker combination is selected from: (1) IFT52, OSMR, SNX10, and VCAN; (2) OSMR, SNX10, and VCAN;
preferably, the method comprises: according to the expression of the molecular marker; (a) analyzing the sensitivity of a gastric cancer patient to immunotherapy; preferably further comprising: formulating a treatment/medication regimen; preferably, the immunotherapy is an anti-PD-1/anti-CTLA-4 immune checkpoint inhibitor therapy; (b) Analyzing the major pathological remission, and/or survival of gastric cancer patients; or, (c) performing a risk analysis or scoring of gastric cancer progression in the gastric cancer patient;
preferably, the method comprises:
(a) For a test sample, the expression of IFT52, OSMR, SNX10 and/or VCAN is determined and risk analysis or scoring is performed using a risk scoring formula selected from the group consisting of:
risk score 1= (Coef IFT52 X IFT52 expression) + (Coef OSMR X OSMR expression) + (Coef SNX10 X SNX10 expression) + (Coef VCAN X VCAN expression);
risk score 2= (Coef OSMR X OSMR expression) + (Coef SNX10 X SNX10 expression) + (Coef VCAN X VCAN expression);
(b) Determining a threshold value (cut-off value);
(c) Comparing the result of (a) with the threshold value of (b) to obtain a detection result;
more preferably, in (a), the risk factor of the gene is:
Coef IFT52 :0 to 1, more preferably 0.496.+ -. 0.05, most preferably 0.496.+ -. 0.02;
Coef OSMR :0 to 1, more preferably 0.398.+ -. 0.05, most preferably 0.398.+ -. 0.02;
Coef SNX10 : -1 to 0, more preferably-0.653±0.05, most preferably-0.653±0.02;
Coef VCAN :0 to 1, more preferably 0.668.+ -. 0.05, most preferably 0.668.+ -. 0.02;
more preferably, in (c), if the score value > the threshold value, the evaluation is: poor prognosis, high risk of recurrence, insensitivity to chemotherapy, or insensitivity to immune checkpoint inhibitor treatment; if the grading value is less than or equal to the threshold value, the grading value is evaluated as follows: good prognosis, low risk of recurrence, sensitivity to chemotherapy, or sensitivity to immune checkpoint inhibitor treatment; optimally, the threshold for risk score 1 is 6.92; the threshold for risk score 2 is 3.43.
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