CN112458171A - Marker for predicting cervical squamous carcinoma chemotherapy curative effect and screening method and application thereof - Google Patents
Marker for predicting cervical squamous carcinoma chemotherapy curative effect and screening method and application thereof Download PDFInfo
- Publication number
- CN112458171A CN112458171A CN202011353407.4A CN202011353407A CN112458171A CN 112458171 A CN112458171 A CN 112458171A CN 202011353407 A CN202011353407 A CN 202011353407A CN 112458171 A CN112458171 A CN 112458171A
- Authority
- CN
- China
- Prior art keywords
- genes
- cervical squamous
- snrpa
- pqbp1
- snrpb
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 206010041823 squamous cell carcinoma Diseases 0.000 title claims abstract description 41
- 238000002512 chemotherapy Methods 0.000 title claims abstract description 32
- 230000000694 effects Effects 0.000 title claims abstract description 25
- 238000012216 screening Methods 0.000 title claims abstract description 20
- 239000003550 marker Substances 0.000 title claims abstract description 16
- 238000000034 method Methods 0.000 title claims abstract description 12
- 108090000623 proteins and genes Proteins 0.000 claims abstract description 121
- 230000014509 gene expression Effects 0.000 claims abstract description 38
- 102100022013 U1 small nuclear ribonucleoprotein A Human genes 0.000 claims abstract description 27
- 101000617779 Homo sapiens U1 small nuclear ribonucleoprotein A Proteins 0.000 claims abstract description 25
- 102100034683 Small nuclear ribonucleoprotein-associated proteins B and B' Human genes 0.000 claims abstract description 25
- 101000611427 Homo sapiens Polyglutamine-binding protein 1 Proteins 0.000 claims abstract description 23
- 101000657845 Homo sapiens Small nuclear ribonucleoprotein-associated proteins B and B' Proteins 0.000 claims abstract description 23
- 102100029287 Serine/arginine-rich splicing factor 7 Human genes 0.000 claims abstract description 20
- 101000700735 Homo sapiens Serine/arginine-rich splicing factor 7 Proteins 0.000 claims abstract description 19
- 238000001959 radiotherapy Methods 0.000 claims abstract description 17
- 102100031712 Splicing factor 3A subunit 2 Human genes 0.000 claims abstract description 15
- 101000707561 Homo sapiens Splicing factor 3A subunit 2 Proteins 0.000 claims abstract description 14
- 239000003153 chemical reaction reagent Substances 0.000 claims abstract description 13
- 238000006243 chemical reaction Methods 0.000 claims abstract description 6
- DQLATGHUWYMOKM-UHFFFAOYSA-L cisplatin Chemical compound N[Pt](N)(Cl)Cl DQLATGHUWYMOKM-UHFFFAOYSA-L 0.000 claims abstract description 3
- 229960004316 cisplatin Drugs 0.000 claims abstract description 3
- 208000034254 Squamous cell carcinoma of the cervix uteri Diseases 0.000 claims description 12
- 201000006612 cervical squamous cell carcinoma Diseases 0.000 claims description 12
- 238000004393 prognosis Methods 0.000 claims description 9
- 238000010201 enrichment analysis Methods 0.000 claims description 4
- 102000004169 proteins and genes Human genes 0.000 claims description 4
- 101150032214 Pqbp1 gene Proteins 0.000 claims description 3
- 238000000540 analysis of variance Methods 0.000 claims description 3
- 238000003745 diagnosis Methods 0.000 claims description 3
- 239000000523 sample Substances 0.000 claims description 3
- 230000001225 therapeutic effect Effects 0.000 claims description 3
- 102100031181 Glyceraldehyde-3-phosphate dehydrogenase Human genes 0.000 claims description 2
- 238000002123 RNA extraction Methods 0.000 claims description 2
- 101150089378 SNRPA gene Proteins 0.000 claims description 2
- 101150036381 SNRPB gene Proteins 0.000 claims description 2
- 239000003795 chemical substances by application Substances 0.000 claims description 2
- 230000004186 co-expression Effects 0.000 claims description 2
- 108020004445 glyceraldehyde-3-phosphate dehydrogenase Proteins 0.000 claims description 2
- 230000002055 immunohistochemical effect Effects 0.000 claims description 2
- 239000003446 ligand Substances 0.000 claims description 2
- 239000013642 negative control Substances 0.000 claims description 2
- 238000010606 normalization Methods 0.000 claims description 2
- 239000013641 positive control Substances 0.000 claims description 2
- 238000012545 processing Methods 0.000 claims description 2
- 238000010839 reverse transcription Methods 0.000 claims description 2
- 238000012163 sequencing technique Methods 0.000 claims description 2
- 101150078450 Sf3a2 gene Proteins 0.000 claims 1
- 101150074406 Srsf7 gene Proteins 0.000 claims 1
- 230000003993 interaction Effects 0.000 claims 1
- 238000011127 radiochemotherapy Methods 0.000 abstract description 21
- 238000011282 treatment Methods 0.000 abstract description 15
- 230000000973 chemotherapeutic effect Effects 0.000 abstract description 2
- 230000001105 regulatory effect Effects 0.000 description 21
- 238000004458 analytical method Methods 0.000 description 14
- 102100040748 Polyglutamine-binding protein 1 Human genes 0.000 description 12
- 108020004414 DNA Proteins 0.000 description 11
- 101000881764 Homo sapiens Transcription elongation factor 1 homolog Proteins 0.000 description 11
- 108091023040 Transcription factor Proteins 0.000 description 11
- 102000040945 Transcription factor Human genes 0.000 description 11
- 101000914063 Eucalyptus globulus Leafy/floricaula homolog FL1 Proteins 0.000 description 8
- 101000877395 Homo sapiens ETS-related transcription factor Elf-1 Proteins 0.000 description 8
- 102100037116 Transcription elongation factor 1 homolog Human genes 0.000 description 8
- 230000027455 binding Effects 0.000 description 5
- 206010008342 Cervix carcinoma Diseases 0.000 description 4
- 208000006105 Uterine Cervical Neoplasms Diseases 0.000 description 4
- 201000010881 cervical cancer Diseases 0.000 description 4
- 230000006916 protein interaction Effects 0.000 description 4
- 230000019491 signal transduction Effects 0.000 description 4
- 206010028980 Neoplasm Diseases 0.000 description 3
- 108010009460 RNA Polymerase II Proteins 0.000 description 3
- 102000009572 RNA Polymerase II Human genes 0.000 description 3
- -1 SF3a2 Proteins 0.000 description 3
- 201000011510 cancer Diseases 0.000 description 3
- 210000004027 cell Anatomy 0.000 description 3
- 238000010586 diagram Methods 0.000 description 3
- 108020004999 messenger RNA Proteins 0.000 description 3
- 238000003012 network analysis Methods 0.000 description 3
- 238000013518 transcription Methods 0.000 description 3
- 230000035897 transcription Effects 0.000 description 3
- 241000282414 Homo sapiens Species 0.000 description 2
- 101000759547 Homo sapiens Zinc finger and BTB domain-containing protein 7A Proteins 0.000 description 2
- 108090001097 Transcription Factor DP1 Proteins 0.000 description 2
- 102000004853 Transcription Factor DP1 Human genes 0.000 description 2
- 102100023264 Zinc finger and BTB domain-containing protein 7A Human genes 0.000 description 2
- 230000031018 biological processes and functions Effects 0.000 description 2
- 238000010195 expression analysis Methods 0.000 description 2
- 230000004879 molecular function Effects 0.000 description 2
- 238000010837 poor prognosis Methods 0.000 description 2
- 230000005855 radiation Effects 0.000 description 2
- 230000035945 sensitivity Effects 0.000 description 2
- 241000894007 species Species 0.000 description 2
- 230000004083 survival effect Effects 0.000 description 2
- 102100030871 Cleavage and polyadenylation specificity factor subunit 5 Human genes 0.000 description 1
- 102100032254 DNA-directed RNA polymerases I, II, and III subunit RPABC1 Human genes 0.000 description 1
- 102000010834 Extracellular Matrix Proteins Human genes 0.000 description 1
- 108010037362 Extracellular Matrix Proteins Proteins 0.000 description 1
- 102100023434 Heterogeneous nuclear ribonucleoprotein A0 Human genes 0.000 description 1
- 241000282412 Homo Species 0.000 description 1
- 101000957603 Homo sapiens Cleavage and polyadenylation specificity factor subunit 1 Proteins 0.000 description 1
- 101000727072 Homo sapiens Cleavage and polyadenylation specificity factor subunit 5 Proteins 0.000 description 1
- 101001088179 Homo sapiens DNA-directed RNA polymerases I, II, and III subunit RPABC1 Proteins 0.000 description 1
- 101000685879 Homo sapiens Heterogeneous nuclear ribonucleoprotein A0 Proteins 0.000 description 1
- 101001122811 Homo sapiens Pre-mRNA-splicing factor ATP-dependent RNA helicase PRP16 Proteins 0.000 description 1
- 108091028043 Nucleic acid sequence Proteins 0.000 description 1
- 102000043276 Oncogene Human genes 0.000 description 1
- 108700020796 Oncogene Proteins 0.000 description 1
- 102100028729 Pre-mRNA-splicing factor ATP-dependent RNA helicase PRP16 Human genes 0.000 description 1
- 108091034057 RNA (poly(A)) Proteins 0.000 description 1
- 230000004570 RNA-binding Effects 0.000 description 1
- 238000000692 Student's t-test Methods 0.000 description 1
- 102000044209 Tumor Suppressor Genes Human genes 0.000 description 1
- 108700025716 Tumor Suppressor Genes Proteins 0.000 description 1
- 230000009471 action Effects 0.000 description 1
- 208000009956 adenocarcinoma Diseases 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000003766 bioinformatics method Methods 0.000 description 1
- 230000001413 cellular effect Effects 0.000 description 1
- 210000003850 cellular structure Anatomy 0.000 description 1
- 238000009096 combination chemotherapy Methods 0.000 description 1
- 238000002648 combination therapy Methods 0.000 description 1
- 238000011443 conventional therapy Methods 0.000 description 1
- 230000002596 correlated effect Effects 0.000 description 1
- 238000010219 correlation analysis Methods 0.000 description 1
- 230000000875 corresponding effect Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 210000002744 extracellular matrix Anatomy 0.000 description 1
- 238000012482 interaction analysis Methods 0.000 description 1
- 238000007477 logistic regression Methods 0.000 description 1
- 230000001575 pathological effect Effects 0.000 description 1
- 238000003068 pathway analysis Methods 0.000 description 1
- 230000008092 positive effect Effects 0.000 description 1
- 238000002360 preparation method Methods 0.000 description 1
- 230000001737 promoting effect Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 210000003660 reticulum Anatomy 0.000 description 1
- 238000012502 risk assessment Methods 0.000 description 1
- 238000007619 statistical method Methods 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
- 238000001356 surgical procedure Methods 0.000 description 1
- 230000009897 systematic effect Effects 0.000 description 1
- 238000012353 t test Methods 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
- 229940126585 therapeutic drug Drugs 0.000 description 1
- 238000013519 translation Methods 0.000 description 1
- 238000011269 treatment regimen Methods 0.000 description 1
- 238000011144 upstream manufacturing Methods 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
Images
Classifications
-
- 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
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
- G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
- G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
- G01N33/574—Immunoassay; Biospecific binding assay; Materials therefor for cancer
- G01N33/57407—Specifically defined cancers
- G01N33/57411—Specifically defined cancers of cervix
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
- G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
- G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
- G01N33/574—Immunoassay; Biospecific binding assay; Materials therefor for cancer
- G01N33/57484—Immunoassay; Biospecific binding assay; Materials therefor for cancer involving compounds serving as markers for tumor, cancer, neoplasia, e.g. cellular determinants, receptors, heat shock/stress proteins, A-protein, oligosaccharides, metabolites
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
- G16B15/00—ICT specially adapted for analysing two-dimensional or three-dimensional molecular structures, e.g. structural or functional relations or structure alignment
- G16B15/30—Drug targeting using structural data; Docking or binding prediction
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
- G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
- G16B20/30—Detection of binding sites or motifs
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
- G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
- G16B40/20—Supervised data analysis
-
- 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
- C12Q2600/106—Pharmacogenomics, i.e. genetic variability in individual responses to drugs and drug metabolism
-
- 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
- C12Q2600/158—Expression markers
-
- 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
- C12Q2600/166—Oligonucleotides used as internal standards, controls or normalisation probes
Abstract
The invention discloses a marker for predicting cervical squamous carcinoma chemotherapy curative effect and a screening method and application thereof. The marker is at least one of SRSF7, SNRPB, SNRPA, SF3A2 and PQBP1 genes. The application of a reagent for detecting a marker for predicting the chemotherapeutic effect of cervical squamous carcinoma in preparing a product for predicting the effect of cervical squamous carcinoma based on the combination of cisplatin and radiotherapy and chemotherapy is to detect the frequency or the expression level of SRSF7, SNRPB, SNRPA, SF3A2 or PQBP1 genes. The gene obtained by screening can accurately distinguish patients with chemoradiotherapy reactions from patients without chemoradiotherapy reactions, and the gene can be used for guiding the patients to select different treatment modes.
Description
Technical Field
The invention belongs to the technical field of biology, and particularly relates to a marker for predicting cervical squamous carcinoma chemotherapy curative effect and application of a reagent for detecting the marker for predicting the cervical squamous carcinoma chemotherapy curative effect in preparation of a product for predicting the combined chemotherapy effect of cervical squamous carcinoma.
Background
Cervical cancer is one of the leading causes of cancer death in women worldwide. Treatment of cervical cancer remains a significant challenge for researchers and physicians. Furthermore, mortality rates in less developed areas are significantly increased compared to industrialized countries, as most patients are already in the advanced stage at the time of diagnosis. The cervical cancer includes squamous cell carcinoma and adenocarcinoma, and the proportion of squamous cell carcinoma is the largest. Currently, the treatment of cervical squamous carcinoma mainly comprises surgery, chemotherapy, radiotherapy or combination therapy. Combined radiotherapy and chemotherapy is the main treatment method for advanced cervical squamous carcinoma.
In past work, it was found that combined chemoradiotherapy is ineffective and shows therapeutic resistance in some patients with locally advanced cervical squamous carcinoma. Approximately half of patients relapse or metastasize within the first two years after treatment, up to 40% do not respond to conventional therapy, and the reason why current combination chemotherapy treatments for locally advanced cervical squamous cell carcinoma are not known. For patients who are not effective, other effective treatment regimens need to be employed. Genomic imbalances can lead to uncontrolled expression of oncogenes and tumor suppressor genes in cancer cells. Therefore, we need to find genes related to treatment of locally advanced cervical squamous carcinoma patients to distinguish patients with good prognosis from patients with poor prognosis.
Clinically, personalized differences of patients are not considered for the treatment of the local advanced cervical squamous carcinoma chemoradiotherapy, so that the establishment of a prediction model for detecting gene expression profile data and screening patients insensitive to the local advanced cervical squamous carcinoma chemoradiotherapy is very important, and a positive effect on the prognosis of the patients can be generated to a certain extent. In treatment, the transcription factor is a protein which is combined with a specific DNA sequence to control the rate of information transcription from DNA to mRNA, and genes which are influenced by the transcription factor and are related to the curative effect of the chemoradiotherapy can be regulated by changing the transcription factor, so that the signal path of the action of the transcription factor is influenced, the sensitivity of the chemoradiotherapy is improved, and more patients can respond to the chemoradiotherapy treatment.
In summary, screening of new sensitivity genes for chemoradiotherapy of the local advanced cervical squamous cell carcinoma is helpful for improving the curative effect of chemoradiotherapy, realizing more accurate risk assessment, developing effective targeted therapeutic drugs and promoting the development of personalized treatment of the local advanced cervical squamous cell carcinoma.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a marker for predicting the curative effect of cervical squamous cell carcinoma chemotherapy, and patients with good prognosis and patients with poor prognosis can be distinguished by detecting the high or low expression level of related genes.
In order to achieve the purpose, the technical scheme adopted by the invention for solving the technical problems is as follows:
a marker for predicting the curative effect of cervical squamous carcinoma chemotherapy, which is at least one of SRSF7, SNRPB, SNRPA, SF3A2 and PQBP1 genes.
The application of a reagent for detecting a marker for predicting the chemotherapeutic effect of cervical squamous carcinoma in preparing a product for predicting the effect of cervical squamous carcinoma based on the combination of cisplatin and radiotherapy and chemotherapy is to detect the frequency or the expression level of SRSF7, SNRPB, SNRPA, SF3A2 or PQBP1 genes.
Further, the reagent for gene frequency includes a reagent for detecting gene frequency of SRSF7, SNRPB, SNRPA, SF3a2 or PQBP1 using an immunohistochemical library sequencing method.
Further, the agent for gene expression level includes a probe, primer, antibody or ligand against SRSF7, SNRPB, SNRPA, SF3a2 or PQBP1 gene.
Further, the product also comprises an RNA extraction reagent, a GAPDH internal reference primer, a PCR reaction solution, a reverse transcription reagent, a negative control and a positive control.
A kit for auxiliary diagnosis or curative effect prediction of cervical squamous carcinoma comprises primers for detecting the expression level of SRSF7, SNRPB, SNRPA, SF3A2 or PQBP1 genes;
the primer sequence is as follows:
SRSF7-F：5’-CTATGAGTGTGGCGAAAAGGGAC-3’；(SEQ ID NO.1)
SRSF7-R：5’-GAGTATCGCCTTCCTCTGGATC-3’；(SEQ ID NO.2)
SNRPB-F：5’-TTGGCACCTTCAAGGCTTTTGAC-3’；(SEQ ID NO.3)
SNRPB-R：5’-AGACCGAGGACTCGCTTCTCTT-3’；(SEQ ID NO.4)
SNRPA-F：5’-CTGGTATCACGGAGCCTGAAGA-3’；(SEQ ID NO.5)
SNRPA-R：5’-TGAGTCGGTCTTGGCATACTGG-3’；(SEQ ID NO.6)
SF3A2-F：5’-GAAGAACCACCTGGGCTCCTAT-3’；(SEQ ID NO.7)
SF3A2-R：5’-CAGGTTGGTCTGGTGCTTCTTC-3’；(SEQ ID NO.8)
PQBP1-F：5’-ACTCCGTGGTTACCAAATCGGC-3’；(SEQ ID NO.9)
PQBP1-R：5’-CCCTGTCTAGTTTCTCATGGCTG-3’。(SEQ ID NO.10)
a screening method for predicting a therapeutic effect marker of cervical squamous carcinoma chemotherapy according to claim 1, which comprises the following steps:
(1) retrieving a dataset of expression profiles of advanced chemo-radiotherapy treatment of locally advanced cervical squamous carcinoma in a GEO database (GEO, http:// www.ncbi.nlm.nih.gov/GEO /), for a total of 3 datasets, GSE 5603-GPL 10191, GSE 560303-GPL 16025 and GSE56363, respectively;
(2) based on the retrieved data set, the data is normalized by RMA software packages contained in R software (https:// www.r-project. org /), the data is grouped according to the effectiveness and the ineffectiveness of the radiotherapy and chemotherapy, and the different genes are screened by limma software packages, wherein the screening standard is as follows: p <0.05 and log | FC | > 0.5;
(3) taking intersection genes from the difference genes obtained from each data set in the step (2) in pairs respectively to obtain difference genes with common expression tendency;
(4) GO (gene ontology) and KEGG signal pathway enrichment analysis is carried out on the co-upregulated genes obtained in the step (3) by using DAVID (https:// DAVID. ncifcrf. gov /) tool, and ANOVA is adopted to test the difference between the data of the gene ontology and the KEGG signal pathway.
(5) The method comprises the following steps of constructing a protein interaction relation in an STRING (https:// STRING-db.org /) database, and finding out a functional module and an important gene by using a Cytoscape plug-in MCODE;
(6) downloading pathological samples of cervical squamous cell carcinoma patients in a TCGA database (https:// cancer. nih. gov /), analyzing the expression quantity and clinical data of key genes, judging the prognosis of important genes and obtaining target genes related to the prognosis. Analyzing intersection genes of the important genes and the database thereof by using HPA (https:// www.proteinatlas.org /);
(7) the transcription factor of the target gene is predicted by using NetworkAnalyst (http:// www.networkanalyst.ca), and then the binding site of JASPAR transcription factor and the target gene is used.
Further, in the step (1), the search keywords are "(local advanced systematic cell carcima) and (chemical thermal OR radio radiation)", the restriction study type is expression profiling by array, and the restriction species is Homo sapiens.
Further, the difference of the co-expression trend in step (3) is due to significant co-up-regulation and co-down-regulation of genes compared to non-responders in radiotherapy and chemotherapy.
Further, the difference genes with the common expression trend in the step (4) are common up-regulated genes.
Further, the target genes obtained in step (6) include SRSF7, SNRPB, SNRPA, SF3a2 and PQBP1 genes.
The invention has the beneficial effects that:
according to the method, differential genes related to local advanced cervical squamous cell carcinoma chemoradiotherapy are obtained by screening according to a responder and a non-responder in chemoradiotherapy, and GO and KEGG signal paths are subjected to enrichment analysis to obtain corresponding signal paths. And carrying out protein interaction network analysis on the commonly up-regulated differential genes, analyzing the functional modules to obtain important genes, carrying out clinical prognosis analysis on the important genes, and using the screened genes as markers for predicting the treatment effect of the local advanced cervical squamous cell carcinoma radiotherapy and chemotherapy. Through analysis of receiver operating characteristic curve (ROC curve), the gene obtained by screening can accurately distinguish a reaction patient from a non-reaction patient in radiotherapy and chemotherapy, and the gene can be used for guiding the patient to select different treatment modes.
Drawings
FIG. 1 is a schematic flow diagram of the present invention;
FIG. 2 is a graph showing the differential gene recognition between responders and non-responders in advanced cervical squamous carcinoma chemotherapy; (A) (ii) are differential gene volcanograms between responders and non-responders, in each volcanogram, genes in the left box represent down-regulated genes and genes in the right box represent up-regulated genes;
(B) 427 up-regulated differential genes and 58 down-regulated differential genes are screened out for the Venn diagram of the differential genes in 3 data sets;
FIG. 3 is a graph of GO and KEGG signal pathway analysis results of up-regulated differential genes; (A) GO analysis for 427 up-regulated differential genes; (B) is a signal path for differential gene enrichment.
FIG. 4 is a protein interaction network analysis of differential genes; identifying the top 3 functional modules in the PPI network through the MCODE plug-in of the Cytoscape, wherein the score of the functional module I is 10, the score of the functional module II is 9.6, and the score of the functional module III is 9.294;
FIG. 5 is the analysis of 10 important genes in functional module one; (A) BP/MF/CC analysis for GO based on 10 important genes; (B) the correlation among 10 key genes, the wider the line, the stronger the correlation; (C) the heatmap shows the correlation between genes.
FIG. 6 is the clinical significance of 10 important genes and the target genes obtained; (A) the total survival time of the expression levels of SRSF7, SNRPB, SNRPA, SF3A2 and PQBP1 is related, namely the target gene required by us is High in the High expression group and Low in the Low expression group, and P <0.05 shows that the target gene has significant difference; (B) venn shows 10 key genes and common genes of HAP database genes, and SNRPA, PQBP1 and SF3A2 have good prognosis on cervical squamous cell carcinoma;
FIG. 7 is a graph of potential transcription factors for predicting SRSF7, SNRPB, SNRPA, SF3A2, and PQBP 1; (A) the schematic diagram of a network of transcription factors and the screened 5 up-regulated genes is shown, wherein a round node represents a target gene, and a quadrangle represents a potential transcription factor; (B) expression levels of transcription factors ELF1, ZBTB7A and TFDP1 in cervical squamous cell carcinoma patients and normal humans; (C) is the correlation of ELF1 with SNRPB, SNRPA, SF3a2, and PQBP1 expression; (D) is a sequence mark of a transcription factor ELF1, the higher the total height of the sequence mark indicates that the conservative type of the base at the position is larger, the larger the letter is, the higher the probability of the occurrence of the base is; (E) prediction of binding sites for the transcription factor ELF1 in the SNRPA, SNRPB and PQBP1 gene sequences;
FIG. 8 is a ROC curve for the classification prediction of 3 data sets (GSE 5603-GPL 10191, GSE 5603-GPL 16025, GSE56363) for target genes of interest (SRSF7, SNRPB, SNRPA, SF3A2, PQBP1), where the AUC of the target genes of interest to GSE 560303-GPL 16025 is 0.935, the AUC of the target genes of interest to GSE 5603-GPL 10191 is 0.771, and the AUC of the target genes of interest to GSE56363 is 0.935.
Detailed Description
The following description of the embodiments of the present invention is provided to facilitate the understanding of the present invention by those skilled in the art, but it should be understood that the present invention is not limited to the scope of the embodiments, and it will be apparent to those skilled in the art that various changes may be made without departing from the spirit and scope of the invention as defined and defined in the appended claims, and all matters produced by the invention using the inventive concept are protected.
Example 1 screening to obtain differential genes related to the curative effects of radiotherapy and chemotherapy on cervical squamous cell carcinoma in advanced local stage
1. Searching an expression profile data set related to the radiotherapy and chemotherapy of the local advanced cervical squamous carcinoma in a GEO database, wherein the search keywords are as follows: "(localized advanced scientific cell carcinosoma) and (chemoradiotherpy OR radiotherpy OR radiation)", limiting the study types to: expression profiling by array, the limiting species are: homo sapiens. The data selected is such that (1) the data set must be expression mRNA chip data for the entire cervical cancer genome; (2) the data must be a control study of the effectiveness and ineffectiveness of combined chemoradiotherapy on locally advanced cervical squamous carcinoma; (3) the case (reponders) -control (non-reponders) group of datasets must contain or exceed 3 samples; (4) information on the processing result of each sample must be provided. Data sets meeting the above criteria are included in this study. Finally, there are 3 data sets that meet our criteria, GSE 563203-GPL 10191, GSE 563203-GPL 16025 and GSE 56363. As shown in table 1:
TABLE 1 data set relating to chemoradiotherapy of locally advanced cervical squamous carcinoma
2. And respectively carrying out data normalization by adopting an RMA software package under an R analysis platform based on the retrieved data set. The samples were then divided into chemoradiotherapy responders (experimental group) and non-responders (control group) in each data set, and differential gene analysis was performed using limma software package. The standard for selecting the difference genes is p-value <0.05, | log2FC | >0.5, 690 significant difference genes in GSE 5603-GPL 10191, wherein 482 up-regulated genes and 208 down-regulated genes are selected. There are 3763 differential genes in the GSE 563203-GPL 16025 genes, 2301 of which are up-regulated genes and 1462 of which are down-regulated genes. 4273 genes are differentially expressed in GSE56363, wherein 2132 genes are up-regulated and 2141 genes are down-regulated.
3. Taking intersection sets of every two of the difference genes obtained in the step B, wherein 485 common difference genes are obtained, 427 expression genes are up-regulated, and 58 expression genes are down-regulated, as shown in FIG. 2;
4. and D, performing GO and KEGG signal path enrichment analysis on the up-regulated gene obtained in the step C by using a DAVID (https:// DAVID. ncifcrf. gov /) tool, and checking the difference between the GO and KEGG signal path data by using ANOVA. GO analysis of 427 up-regulated differential genes obtains 21 classes of biological process annotation information, mainly focusing on positive alignment of transcription from RNA polymerase II promoter and negative alignment of transcription from RNA polymerase II promoter; the obtained cell component annotation information mainly comprises extracellular matrix, Golgi apapratus, endoplastic reticulum and the like; the results obtained 21 kinds of molecular function annotation information, mainly focusing on protein binding and identified protein binding, as shown in FIG. 3.
5. And C, carrying out protein interaction analysis on the up-regulated gene obtained in the step C by using a String tool, and obtaining 3 functional modules by using an MCODE plug-in, wherein the score of the module I is 10, the score of the module II is 9.6, and the score of the module III is 9.294 (figure 4).
6. And performing GO function analysis and KEGG signal path analysis on 10 genes of the functional module I with the highest score, and then verifying the significance of the genes on clinic. The biological processes of these 10 genes CPSF1, DHX38, HNRNPA0, SNRPB, SRSF7, SNRPA, NUDT21, SF3a2, PQBP1, POLR2E) were found to be mainly enriched in mRNA spicing, via spidiosomes, termination of RNA polymerase II translation; analysis of cellular composition shows that most of these genes are enriched in nucleoplasm, nucleous. In terms of molecular function, these genes were significantly associated with poly (a) RNA binding (fig. 5A). Analysis of these 10 important genes revealed that they had a positive correlation (FIGS. 5B-C). To further validate the clinical value of key genes, we evaluated the relationship between gene expression levels and clinical profiles. We found that high expression of SRSF7, SNRPB, SNRPA, SF3a2, PQBP1 was associated with high survival (fig. 6A), and that high expression of SNRPA, SF3a2 and PQBP1 in HPA databases showed good prognosis in cervical squamous cell carcinoma patients (fig. 6B).
Example 2 prediction of transcription factors of target genes
1. Transcription factors capable of modulating the expression of SRSF7, SNRPB, SNRPA, SF3a2 and PQBP1 were further predicted using the networkanalysis tool. We found that ZBTB7A, TFDP1 and ELF1 could regulate the expression of these 5 genes (fig. 7A).
2. Correlation analysis showed that ELF1 was negatively correlated with SNRPB, SNRPA, PQBP1 (fig. 7C).
3. To find the ELF1 sequence, we used the JASPAR analysis. As can be seen from the sequence tag of ELF1, the ELF1 sequence was higher in the conserved versions 7-10, and the frequency of occurrence of G and A bases was high (FIG. 7D).
4. We predicted the binding sequence of ELF1 to SNRPB, SNRPA and PQBP1 by JASPAR, and showed that ELF1 binds upstream of these genes. The study was scored using a 90% threshold, with a relative score of 0.9066 for SNRPB, 0.9384 for SNRPA, and 0.9038 for PQBP1 (fig. 7E).
Example 3 verification of the correlation of target genes with the efficacy of chemoradiotherapy for locally advanced cervical squamous carcinoma
1. The expression level difference of target genes in patients sensitive to and insensitive to local advanced cervical squamous carcinoma radiotherapy and chemotherapy is as follows:
(1) screening the expression level of the target gene in 3 data sets (GSE 563203-GPL 10191, GSE 563203-GPL 16025 and GSE 56363);
(2) dividing the samples in 3 data sets into a chemoradiotherapy sensitive group and an insensitive progenitor group respectively; (ii) a
(3) Statistical analysis was performed and the difference between the two sets of data was determined by t-test, p-value <0.05 and log2| FC | >0.5 was considered statistically significant.
(4) The results of the difference of the expression levels of the target genes in the patients with sensitive and insensitive chemoradiotherapy of the local advanced cervical squamous carcinoma are shown in the table 2:
TABLE 2 expression level differences of target genes in patients with sensitive and insensitive chemoradiotherapy of cervical squamous carcinoma in late local stage
2. Evaluation and analysis of target gene expression level on treatment effect of local advanced cervical squamous carcinoma chemoradiotherapy: an operating characteristic curve (ROC) can be used for judging the classification effect, and the Area (AUC) under the ROC curve is used for evaluating the quality of the classifier.
(1) Firstly, screening the expression quantity of a target gene in 3 data sets (GSE 563203-GPL 10191, GSE 563203-GPL 16025 and GSE 56363);
(2) establishing a Logistic regression model by utilizing a glmnet software package of the R platform, and predicting data;
(3) and (3) utilizing an ROCR software package to draw an ROC curve of the prediction result, and calculating an area AUC value under the curve, wherein the result is shown in FIG. 8.
In conclusion, the application collects a data set related to the curative effect of the local advanced cervical squamous carcinoma radiotherapy and chemotherapy, 5 target genes are obtained through bioinformatics analysis, and the functional annotation of the genes and the enriched signal transduction pathway are found to be related to the curative effect of the local advanced cervical squamous carcinoma radiotherapy and chemotherapy, so that a theoretical basis is provided for the research of the difference of the curative effect of the local advanced cervical squamous carcinoma radiotherapy and chemotherapy.
Sequence listing
<110> southwest university of medical science
<120> marker for predicting cervical squamous carcinoma chemotherapy curative effect and screening method and application thereof
<160> 10
<170> SIPOSequenceListing 1.0
<210> 1
<211> 23
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 1
ctatgagtgt ggcgaaaagg gac 23
<210> 2
<211> 22
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 2
gagtatcgcc ttcctctgga tc 22
<210> 3
<211> 23
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 3
ttggcacctt caaggctttt gac 23
<210> 4
<211> 22
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 4
agaccgagga ctcgcttctc tt 22
<210> 5
<211> 22
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 5
ctggtatcac ggagcctgaa ga 22
<210> 6
<211> 22
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 6
tgagtcggtc ttggcatact gg 22
<210> 7
<211> 22
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 7
gaagaaccac ctgggctcct at 22
<210> 8
<211> 22
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 8
caggttggtc tggtgcttct tc 22
<210> 9
<211> 22
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 9
actccgtggt taccaaatcg gc 22
<210> 10
<211> 23
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 10
ccctgtctag tttctcatgg ctg 23
Claims (10)
1. A marker for predicting the curative effect of cervical squamous carcinoma chemotherapy, wherein the marker is at least one of SRSF7, SNRPB, SNRPA, SF3A2 and PQBP1 genes.
2. The application of the reagent for detecting the marker for predicting the chemotherapy curative effect of the cervical squamous carcinoma in preparing the product for predicting the effect of the cervical squamous carcinoma based on the combination of cisplatin and radiotherapy and chemotherapy is characterized in that the reagent is used for detecting the frequency or the expression level of SRSF7, SNRPB, SNRPA, SF3A2 or PQBP1 genes.
3. The use of claim 2, wherein the reagents for detecting the gene frequency comprise reagents for detecting gene frequency of the SRSF7, SNRPB, SNRPA, SF3A2 or PQBP1 genes using an immunohistochemical library sequencing method.
4. The use of claim 2, wherein the agent for detecting the level of expression of said gene comprises a probe, primer, antibody or ligand directed against the SRSF7, SNRPB, SNRPA, SF3A2 or PQBP1 gene.
5. The use of claim 2, wherein the product further comprises an RNA extraction reagent, GAPDH internal reference primer, PCR reaction, reverse transcription reagent, negative control and positive control.
6. A kit for auxiliary diagnosis or curative effect prediction of cervical squamous carcinoma is characterized by comprising a primer for detecting the expression level of SRSF7, SNRPB, SNRPA, SF3A2 or PQBP1 genes, wherein the primer sequence is as follows:
SRSF7-F：5’-CTATGAGTGTGGCGAAAAGGGAC-3’；
SRSF7-R：5’-GAGTATCGCCTTCCTCTGGATC-3’；
SNRPB-F：5’-TTGGCACCTTCAAGGCTTTTGAC-3’；
SNRPB-R：5’-AGACCGAGGACTCGCTTCTCTT-3’；
SNRPA-F：5’-CTGGTATCACGGAGCCTGAAGA-3’；
SNRPA-R：5’-TGAGTCGGTCTTGGCATACTGG-3’；
SF3A2-F：5’-GAAGAACCACCTGGGCTCCTAT-3’；
SF3A2-R：5’-CAGGTTGGTCTGGTGCTTCTTC-3’；
PQBP1-F：5’-ACTCCGTGGTTACCAAATCGGC-3’；
PQBP1-R：5’-CCCTGTCTAGTTTCTCATGGCTG-3’。
7. the screening method for predicting the therapeutic effect marker of cervical squamous carcinoma chemotherapy according to claim 1, which comprises the following steps:
(1) searching gene expression profiles related to radiotherapy and chemotherapy of cervical squamous carcinoma in a GEO database, and searching GSE 5603-GPL 10191, GSE 560331-GPL 16025 and GSE56363 datasets;
(2) based on the retrieved data set, performing normalization processing on the data by adopting R software, grouping according to the effectiveness and the ineffectiveness of the radiotherapy and chemotherapy, and screening out differential genes by adopting limma software, wherein the screening standard is as follows: p <0.05 and log | FC | > 0.5;
(3) taking intersection genes from the difference genes obtained from each data set in the step (2) in pairs respectively to obtain difference genes with common expression tendency;
(4) carrying out GO and KEGG signal path enrichment analysis on the differential gene with the common expression trend obtained in the step (3) by using a DAVID tool, and detecting the difference between the data of the gene ontology and the data of the KEGG signal path by adopting ANOVA;
(5) obtaining an interaction network of the protein by using a STRING tool, and obtaining an important functional module and an important gene by using a Cytoscape plug MCODE;
(6) finding out the prognosis of important genes verified by cervical squamous cell carcinoma patients in TCGA database and HPA database, and obtaining target genes.
8. The screening method according to claim 7, wherein the difference in the co-expression trend in step (3) is due to significant co-up-regulation and co-down-regulation of genes compared to non-responders in chemotherapy.
9. The screening method according to claim 8, wherein the genes having different expression trends in common in step (4) are co-upregulated genes.
10. The screening method according to claim 9, wherein the target genes obtained in step (6) include SRSF7, SNRPB, SNRPA, SF3A2 and PQBP1 genes.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011353407.4A CN112458171A (en) | 2020-11-27 | 2020-11-27 | Marker for predicting cervical squamous carcinoma chemotherapy curative effect and screening method and application thereof |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011353407.4A CN112458171A (en) | 2020-11-27 | 2020-11-27 | Marker for predicting cervical squamous carcinoma chemotherapy curative effect and screening method and application thereof |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112458171A true CN112458171A (en) | 2021-03-09 |
Family
ID=74808721
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011353407.4A Pending CN112458171A (en) | 2020-11-27 | 2020-11-27 | Marker for predicting cervical squamous carcinoma chemotherapy curative effect and screening method and application thereof |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112458171A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113025720A (en) * | 2021-04-28 | 2021-06-25 | 深圳市人民医院 | Marker for detecting primary mammary diffuse large B cell lymphoma as well as kit and application thereof |
| CN116103403A (en) * | 2023-01-18 | 2023-05-12 | 山东大学 | Biomarker for diagnosis and prognosis of ovarian cancer and application thereof |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103409501A (en) * | 2013-05-10 | 2013-11-27 | 新疆医科大学 | Method for screening candidate plasma protein markers by cervical carcinoma specificity difference expression |
| US20160251648A1 (en) * | 2013-10-28 | 2016-09-01 | The Broad Institute Inc. | Functional genomics using crispr-cas systems, compositions, methods, screens and applications thereof |
| WO2019008415A1 (en) * | 2017-07-05 | 2019-01-10 | Datar Rajan | Exosome and pbmc based gene expression analysis for cancer management |
| CN109859801A (en) * | 2019-02-14 | 2019-06-07 | 辽宁省肿瘤医院 | A kind of model and method for building up containing seven genes as biomarker prediction lung squamous cancer prognosis |
| CN110331201A (en) * | 2019-07-26 | 2019-10-15 | 泗水县人民医院 | Cervical squamous cell carcinoma associated biomarkers and its application |
-
2020
- 2020-11-27 CN CN202011353407.4A patent/CN112458171A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103409501A (en) * | 2013-05-10 | 2013-11-27 | 新疆医科大学 | Method for screening candidate plasma protein markers by cervical carcinoma specificity difference expression |
| US20160251648A1 (en) * | 2013-10-28 | 2016-09-01 | The Broad Institute Inc. | Functional genomics using crispr-cas systems, compositions, methods, screens and applications thereof |
| WO2019008415A1 (en) * | 2017-07-05 | 2019-01-10 | Datar Rajan | Exosome and pbmc based gene expression analysis for cancer management |
| CN109859801A (en) * | 2019-02-14 | 2019-06-07 | 辽宁省肿瘤医院 | A kind of model and method for building up containing seven genes as biomarker prediction lung squamous cancer prognosis |
| CN110331201A (en) * | 2019-07-26 | 2019-10-15 | 泗水县人民医院 | Cervical squamous cell carcinoma associated biomarkers and its application |
Non-Patent Citations (2)
| Title |
|---|
| DONG OUYANG等: "Comprehensive analysis of prognostic alternative splicing signature in cervical cancer", 《CANCER CELL INTERNATIONAL》 * |
| 张振华: "局部中晚期宫颈鳞癌放化疗敏感性的临床及差异表达基因研究", 《中国博士学位论文全文数据库 医药卫生科技辑》 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113025720A (en) * | 2021-04-28 | 2021-06-25 | 深圳市人民医院 | Marker for detecting primary mammary diffuse large B cell lymphoma as well as kit and application thereof |
| CN113025720B (en) * | 2021-04-28 | 2022-12-27 | 深圳市人民医院 | Marker for detecting diffuse large B cell lymphoma of primary breast as well as kit and application thereof |
| CN116103403A (en) * | 2023-01-18 | 2023-05-12 | 山东大学 | Biomarker for diagnosis and prognosis of ovarian cancer and application thereof |
| CN116103403B (en) * | 2023-01-18 | 2023-08-11 | 山东大学 | Biomarker for diagnosis and prognosis of ovarian cancer and application thereof |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Cavalli et al. | Intertumoral heterogeneity within medulloblastoma subgroups | |
| Tamayo et al. | Predicting relapse in patients with medulloblastoma by integrating evidence from clinical and genomic features | |
| AU2017203060A1 (en) | Methods of using gene expression signatures to select a method of treatment, predict prognosis, survival, and/or predict response to treatment | |
| US20160222459A1 (en) | Molecular diagnostic test for lung cancer | |
| US20130332083A1 (en) | Gene Marker Sets And Methods For Classification Of Cancer Patients | |
| US20190085407A1 (en) | Methods and compositions for diagnosis of glioblastoma or a subtype thereof | |
| CN111128299B (en) | Construction method of ceRNA regulation and control network with significant correlation to colorectal cancer prognosis | |
| EP2419540B1 (en) | Methods and gene expression signature for assessing ras pathway activity | |
| US20160222460A1 (en) | Molecular diagnostic test for oesophageal cancer | |
| EP2982986B1 (en) | Method for manufacturing gastric cancer prognosis prediction model | |
| Munoz et al. | Molecular profiling and the reclassification of cancer: divide and conquer | |
| CN112458171A (en) | Marker for predicting cervical squamous carcinoma chemotherapy curative effect and screening method and application thereof | |
| Lim et al. | Prediction of prognostic signatures in triple-negative breast cancer based on the differential expression analysis via NanoString nCounter immune panel | |
| WO2020175903A1 (en) | Dna methylation marker for predicting recurrence of liver cancer, and use thereof | |
| US9580756B2 (en) | Stratification of left-side and right-side colon cancer | |
| US10240206B2 (en) | Biomarkers and methods for predicting benefit of adjuvant chemotherapy | |
| WO2021211057A1 (en) | Method of predicting the responsiveness to a cancer therapy | |
| Ren et al. | Prognostic role of NLGN2 and PTGDS in medulloblastoma based on gene expression omnibus | |
| US20230279502A1 (en) | Identification of estrogen receptor positive (er+) breast cancers that will not develop tamoxifen resistance | |
| EP4314832A1 (en) | Molecular subtyping of colorectal liver metastases to personalize treatment approaches | |
| Yao et al. | A Framework to Predict the Applicability of Gene Signatures for Improving Prognostic Prediction | |
| Liu et al. | CDHu40: a novel marker gene set of neuroendocrine prostate cancer (NEPC) | |
| WO2019152734A1 (en) | Biomarkers in de novo pyrimidine synthesis pathways and chemoresistance | |
| WO2021003176A1 (en) | Identification of patients that will respond to chemotherapy | |
| Byers | Molecular Profiling |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20210309 |
|
| RJ01 | Rejection of invention patent application after publication |