WO2021036620A1 - Application of a group of genes related to ovarian cancer prognosis - Google Patents

Abstract

Based on the discovery of a group of 11 prognostic related gene profiles in the serous ovarian cancer and the detection of the expression level of the 11 prognostic related gene profiles in clinical tumor samples, the prognostic score of a patient is calculated according to the prognostic correlation coefficient of a group of the genes to evaluate the clinical prognosis of ovarian cancer patients and related application of the clinical prognosis.

Description

Application of a set of prognostic genes related to ovarian cancer Technical field

The invention belongs to the technical field of tumor gene detection, and specifically relates to a group of serous ovarian cancer-related genes and applications thereof.

Background technique

Ovarian cancer is a major disease that seriously threatens women’s health. Its incidence ranks second among malignant tumors of the female reproductive system, and its fatality rate ranks fifth among malignant tumors, and ranks first among gynecological tumors. According to the latest annual report of the well-known journal CA: a cancer journal for clinicians, there are more than 295,000 new cases of ovarian cancer and more than 185,000 deaths worldwide each year, and the number of patients and deaths is on the rise. The anatomical location of the ovary is hidden, and the early symptoms of ovarian cancer are not obvious. At present, there is no effective and beneficial method for early diagnosis or extensive screening in the population. Therefore, the early diagnosis rate of ovarian cancer is very low, as high as 70%-80% Of patients are in the advanced stage when they see a doctor. Among many human tumors, ovarian cancer is called the "silent killer" of women, which shows its significant clinical and social importance. Although most patients with ovarian cancer can achieve remission through cytoreductive surgery and initial therapy, the recurrence rate is still as high as 75%. In addition, according to statistics from the American Association of Obstetricians and Gynecologists (ACOG), the current treatment effect and prognosis of ovarian cancer are both poor, and the 5-year survival rate is only about 20%. Therefore, establishing early diagnosis methods, finding effective treatment targets, and accurately assessing prognosis to guide clinical intervention are important challenges for improving the survival rate of ovarian cancer patients.

In recent years, ovarian cancer surgery technology has been continuously improved, new chemotherapy drugs have been continuously put into use, and chemotherapy regimens have been continuously optimized. Specifically, it mainly includes PARP inhibitor therapy for patients with BRCA mutations, molecular targeted therapy such as folate receptors, immunotherapy, anti-angiogenesis drug therapy, and weekly paclitaxel chemotherapy regimens.

According to the 2014 World Health Organization (WHO) standard, ovarian tumors are histologically divided into epithelial tumors, sex cord-stromal tumors, germ cell tumors, etc., and each category is subdivided into many subcategories, such as epithelial tumors. Ovarian cancer is divided into serous carcinoma, mucinous carcinoma, endometrioid carcinoma, clear cell carcinoma, serous-mucinous carcinoma and so on. Serous ovarian cancer (Serous Ovarian Carcinoma, SOC) is the most important of epithelial ovarian cancer, accounting for 70-80% of ovarian cancer mortality. Based on the level of gene overexpression specific to each subtype, most SOCs can be divided into four subtypes: mesenchymal, immunoreactive, differentiated, and proliferative. Serous ovarian cancer is characterized by genetic changes, including hereditary BRCA gene mutations, TP53 mutations, DNA damage, chromosomal instability, and changes in RNA and miRNA expression and methylation status.

Ovarian cancer, like other tumors, is a "systemic and systemic disease with multi-factor origin and multi-step development". Even the same type of ovarian cancer, there is a high degree of heterogeneity at the molecular level among different individuals. Therefore, simple histological classification has limited guiding value for clinical intervention. Different tumor patients, even if their pathological types, stages, grades, and even the treatments they receive are the same, their survival times will still show differences. Tumor heterogeneity includes the heterogeneity between different patients of the same type of tumor and the heterogeneity between cells in individual tumors. There is a relatively consistent view that each patient’s tumor is unique, and this uniqueness can extend from the macroscopic to the microscopic to the different cell clones in individual tumors, and this is present in almost all types of tumors. unique. As we know, different genetic backgrounds, internal factors and environmental factors constitute the macroscopic tumor heterogeneity among patients. With the widespread application of high-throughput technology and single-cell sequencing technology, intratumoral heterogeneity has become a research hotspot in the field of tumors. It is the inevitable trend of future medical development to provide individualized precision treatment for the difference in the molecular level of each individual tumor. At present, the most successful work in this area is breast cancer. Not only can it be classified according to the different expression of related molecules (ER, Her2, etc.), and targeted operations and adjuvant treatments can be performed on different types of breast cancer patients. Oncotype DX (Genomic Health Company in the United States) and MammaPrint (Agendia Company in Norway) can be used to detect the expression of multiple genes to evaluate the prognosis of breast cancer recurrence and metastasis and guide treatment. The above methods have been maturely applied to the clinic and have obtained significant curative effects. At present, these two tests have been approved by the US FDA for marketing. In addition, Oncotype DX has also been recommended by the NCCN guidelines and is covered by the US medical insurance, which has completely realized the transformation from basic research to clinical application. In addition, genetic testing projects similar to Oncotype DX have also been developed for prostate cancer and colon cancer. But at present, there is no similar genetic test for ovarian cancer that can be used to guide the selection of treatment options. We know that ovarian cancer is a type of tumor with genetic factors that account for a larger cause of disease, and it is a huge challenge for patients and doctors. Therefore, the development of a similar genetic testing and scoring system for ovarian cancer for clinical guidance not only has extensive and solid technical conditions and knowledge base, but also is necessary and has the prospect and hope of obtaining good results.

Summary of the invention

The purpose of the present invention is to perform genetic diagnosis such as prognosis prediction and treatment plan evaluation for patients with ovarian cancer.

The sample in the present invention is the tumor tissue of a patient with ovarian cancer. mRNA is isolated from the tumor tissue sample of the patient, the expression level of ovarian cancer-related genes is determined, and the prognosis of serous ovarian cancer is evaluated by the multi-gene expression profile and scoring system for evaluating the prognosis of ovarian cancer. The expression level of related genes in clinical samples is detected, and the clinical prognosis of the patient is predicted by calculating the prognostic score.

In order to achieve the above purpose, the present invention provides a set of 11 ovarian cancer-related genes in the preparation of ovarian cancer prognostic prediction reagents, the above-mentioned 11 ovarian cancer-related genes are (1) homologous recombination repair genes: RAD51AP1; (2) ) Genes encoding biological enzymes: MTHFD2, GALNT10, PYCR1; (3) Signal transduction-related genes: CADPS2, DSE, ITGB8, PDE10A, SNX1; (4) Other genes: C9orf16, ARL4; and (5) Control genes: GAPDH , ACTB, GUSB and TFRC.

In some embodiments, the ovarian cancer is serous ovarian cancer.

In another embodiment, the present invention provides a set of gene probes or primers designed for the application of the 11 ovarian cancer-related genes of claim 1 in preparing reagents for predicting the prognosis of ovarian cancer. The gene probes can pass Molecular hybridization combines with the 11 ovarian cancer-related genes to generate hybridization signals, and the primers can amplify the 11 ovarian cancer-related genes through PCR-based technology.

In another embodiment, the present invention provides a composition for predicting the prognosis of ovarian cancer, which includes the above-mentioned gene probe or primer.

In another embodiment, the present invention provides a diagnostic kit for predicting the prognosis of ovarian cancer, which comprises the above-mentioned composition.

In some embodiments, the above 11 ovarian cancer-related genes can be detected by real-time fluorescent quantitative PCR, gene chip, second-generation high-throughput sequencing, Panomics or Nanostring technology.

In some embodiments, real-time fluorescent quantitative PCR, gene chip, second-generation high-throughput sequencing, Panomics or Nanostring technology can be used to detect the mRNA expression levels of the above 11 related genes in ovarian cancer.

The terms "gene probe" and "primer" used in this specification refer to oligonucleotides, preferably single-stranded deoxyribonucleotides, including naturally occurring dNMP (dAMP, dGMP, dCMP and dTMP), and variants. Nucleotides or non-natural nucleotides, in addition, ribonucleotides may also be included.

The gene probes and primers used in the present invention contain hybridizing nucleotide sequences complementary to the target position of the target nucleic acid. The term "complementary" means that under hybridization conditions, primers or probes are fully complementary to the target nucleic acid sequence selectively hybridizing, including substantially complementary and perfectly complementary. The meaning of is preferably completely complementary. The term "substantially complementary sequence" used in this specification includes not only completely identical sequences, but also sequences that can function as primers on a specific target sequence and are partially inconsistent with the sequence to be compared.

The sequences of gene probes and primers do not need to have sequences that are completely complementary to a part of the template sequence, as long as they have sufficient complementarity within a range that can hybridize to the template and exert its inherent functions. Therefore, the gene probes and primers in the present invention do not need to have a sequence that is completely complementary to the aforementioned nucleotide sequence as a template, as long as they have sufficient complementarity within a range that can hybridize to the template to exert its inherent function. The design of primers and gene probes is a skill mastered by those skilled in the art. For example, a program for primer design (for example, the PRIMER 3 program) can be used.

The mRNA expression level of the present invention can be determined by methods known in the art, including but not limited to real-time fluorescent quantitative PCR, gene chip, second-generation high-throughput sequencing, Panomics or Nanostring technology.

The invention provides a diagnostic reagent and kit containing the gene probe or primer of the invention for predicting the prognosis of ovarian cancer. In addition to the primers or gene probes for the above-mentioned 11-gene markers, the reagents and kits of the present invention may additionally contain tools known in the art for PCR reactions, RNA isolation of samples, and cDNA synthesis. And/or reagents. The kit of the present invention may additionally contain a tube for mixing the components, a microplate, and instruction materials describing the method of use, etc., as necessary.

Beneficial effects:

Although some molecular characterization studies have been carried out in ovarian cancer, few studies have attempted to identify genetic markers related to the prognosis of ovarian cancer, and there have been no reports on the clinical application of the prognostic scoring system. The present invention successfully finds a group of 11 important biomarker genes for predicting the overall survival of patients with ovarian cancer by using multiple omics data, and establishes a prognostic scoring system based on 11 gene markers for the first time. We have also confirmed that the prediction score of the system can accurately distinguish the different clinical prognosis of patients with ovarian cancer. This invention can be used to assist in predicting the response of patients with ovarian cancer to therapeutic interventions, to determine whether the patients benefit from chemotherapy and targeted therapy, to make treatment choices, to avoid excessive medical treatment, and to achieve the purpose of individualized medical treatment.

Description of the drawings

Figure 1: Kaplan-Meier survival curve example of representative genes in serous ovarian cancer gene tags. The p value is obtained by comparing the log-rank test between the two groups.

Figure 2: Co-expression network diagram of 232 overall survival related genes used in ovarian cancer of the present invention.

Figure 3: The first 20 highly enriched gene function groups in ovarian cancer of the present invention.

Figure 4A: Kaplan-Meier survival curves of two representative test sets in which ovarian cancer patients with different prognosis are divided into three groups: good, middle and poor according to the present invention.

Figure 4B: Hazard ratio (HR) in 100 test sets and its 95% HR confidence interval.

Figure 5: Kaplan-Meier survival curve shows that the prognostic score of the present invention can accurately predict the prognosis of ovarian cancer patients in multiple databases.

Figure 6: The present invention's assessment of the prognosis of patients with ovarian cancer is significantly better than the reported prognostic signatures of 5 genes.

detailed description

The following describes the technical means adopted by the present invention to achieve the intended purpose of the invention in conjunction with the drawings and the preferred embodiments of the present invention.

The present invention applies the international general tumor database and adopts a multi-step bioinformatics analysis method to firstly confirm 488 genes with significant differences in expression in normal ovarian tissue and serous ovarian cancer tissue. Through survival analysis, 232 genes are found Significantly related to overall survival (OS). The biological functions of these 232 genes involve processes such as cell division, epithelial cell differentiation, p53 signaling pathway, blood vessel formation, and drug metabolism. Using the patient's clinical information and related gene expression in the TCGA (The Cancer Genome Atlas) database, a typical discriminant analysis method was used to determine a set of prognostic-related gene markers for serous ovarian cancer, and a prognostic scoring system was established and verified on this basis Its evaluation value and accuracy.

Specifically, we used 6 publicly available international tumor data sets (Affymetrix chip [HG-U133_Plus_2] Affymetrix Human Genome U133 Plus 2.0 Array and [HG-U133A] Affymetrix Human Genome U133A Array):

GSE18520(https://www.ncbi.nlm.nih.gov/geo/geo2r/?acc=GSE18520)

GSE26712(https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE26712)

GSE40595(https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE40595)

GSE38666(https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE38666)

GSE27651(https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE27651)

GSE6008(https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE6008)

First, a multi-step bioinformatics analysis method was used to compare the gene expression between normal ovarian tissue and ovarian cancer tissue to determine the significantly differentially expressed genes in each data set. A total of 397 samples (349 cases of ovarian cancer, 48 cases of normal ovarian) Organization) included in the analysis. These 6 data sets have 24049, 11262, 37330, 17903, 11959, and 6733 differentially expressed genes (probe ID) that meet our standard (fold change (FC) ≥ 1.5, corrected p value <0.05), and then The intersection of these 6 groups of differentially expressed genes, a total of 590 genes (probe ID) were selected. Among them, 562 genes (probe ID) have the same trend of expression changes in the 6 data sets (the expressions are all up-regulated or all down-regulated in the 6 data sets), of which 260 genes (probe ID) are down-regulated, and 302 genes ( probe ID) expression is up-regulated; according to the chip gene probe, the corresponding 488 genes were found. The expression trend of these genes in the 6 data sets is the same (all up-regulated or all down-regulated in the 6 data sets), of which 222 The expression of two genes was down-regulated, and the expression of 266 genes was up-regulated.

To further evaluate the value of the differential expression of the above-mentioned 488 genes in the prognosis of ovarian cancer, we use the Kaplan-Meier curve (http://kmplot.com/analysis/index.php?p=service&cancer=ovar), an online tool for survival and prognosis, and The large public clinical microarray chip database analyzes the relationship between gene expression levels and the overall survival of patients with ovarian cancer. First, the above-mentioned 488 genes were divided into two groups (high or low expression) according to their expression levels, and Cox regression analysis, Kaplan-Meier survival curve and log-rank test were used to evaluate the high or low expression levels of these genes. The impact of overall survival (OS). It was found that there was a significant correlation between the expression levels of 232 genes out of 488 genes and the overall survival of patients with ovarian cancer (adjusted p-value≤0.05). Figure 1 shows the effect of 4 representative gene expression levels on the prognosis and survival of patients. Among the 232 genes, 82 genes have a hazard ratio (HR)<1 (higher gene expression is associated with a good prognosis), which are called protective genes; while the other 150 genes have HR>1 (higher gene expression is associated with poor prognosis). Prognosis-related), called risk genes. These 232 genes will be the objects of our next research and analysis, and the prognostic genetic markers of ovarian cancer will be screened from them. Finally, these genes were ranked according to the p value of the log-rank test generated by the above single factor analysis (see Table 1).

We used the gene function annotation analysis tool Metascape to perform Gene Ontology (GO) analysis of the above 232 genes and found that these genes were significantly enriched in many cellular processes, such as cell division, epithelial cell differentiation, p53 signaling pathway, blood vessel development, etc. These biological processes are all related to the occurrence and development of cancer. Based on this, the co-expression network diagram of the genes related to the overall survival of serous ovarian cancer was constructed (as shown in Figure 2 and Figure 3).

Based on the above results, we developed a prognostic scoring system for serous ovarian cancer. We applied stepwise canonical discriminant analysis to identify genetic markers that can identify the prognosis of patients with 100% accuracy, and finally determined a set of scoring systems composed of 11 specific serous ovarian cancer prognostic genes. Obtained a 100% prognostic prediction accuracy rate. These genes include: (1) homologous recombination repair gene: RAD51AP1; (2) encoding biological enzyme genes: MTHFD2, GALNT10, PYCR1; (3) signal transduction related genes: CADPS2, DSE, ITGB8, PDE10A, SNX1; ( 4) Other genes: C9orf16, ARL4. Each gene is a sequence of each gene known in the art or a synonym sequence of each gene, preferably a sequence of each gene derived from humans, more preferably RAD51AP1 is the sequence described in Genbank accession number NM_006479, and MTHFD2 is Genbank The sequence described in accession number NM_006636, GALNT10 is the sequence described in Genbank accession number NM_198321, PYCR1 is the sequence described in Genbank accession number NM_006907, CADPS2 is the sequence described in Genbank accession number NM_017954, DSE is the sequence described in Genbank accession number NM_013352 Sequence, ITGB8 is the sequence described in Genbank registration number NM_002214, PDE10A is the sequence described in Genbank registration number NM_001130690, SNX1 is the sequence described in Genbank registration number NM_003099, C9orf16 is the sequence described in Genbank registration number NM_024112, ARL4 is the sequence recorded in Genbank registration No. NM_005737 recorded sequence. Synonyms and sequences of each gene can be searched through Genbank or Swissprot.

The above-mentioned serous ovarian cancer prognosis scoring system uses predictive scores to calculate the survival probability of patients. The scoring system is defined as a linear combination of gene expression levels based on a canonical discriminant function. Calculated as follows:

Note: See Table 2.

The prognostic score of each patient can be used to assess their overall survival and risk of death. Sort the patients in the training group according to their prognostic scores, and divide the patients into three groups of the same number according to their scores, record the prognostic scores corresponding to the corresponding cut-off points, and take the average of the scores of each cut-off point as the true cut-off point According to the scores of these two cut-off points, patients in the test group are divided into three groups (prognosis) of "good", "medium" and "poor". Perform Kaplan-Meier analysis and Log-Rank test on three groups of patients in 100 test sets, compare the "medium" and "poor" groups with the "good" group respectively, calculate the hazard ratios (HR), and determine the three groups The difference in overall survival (OS) of patients (Figure 4A is two Kaplan-Meier survival curves representing the test set). In 99% of the test sets, the overall survival of patients in the "poor" group was significantly lower than that of patients in the "good" group (the low value of the 95% confidence interval> 1) (the upper part of Figure 4B); in the test above 60% Centrally, the overall survival of patients in the "medium" group was significantly lower than that of patients in the "good" group (lower value of the 95% confidence interval> 1) (the lower half of Figure 4B). These results strongly verify the ability of this 11-gene marker and the prognosis scoring system to distinguish patients with different prognosis, and can distinguish patients with good or poor prognosis.

In addition to the internal verification using the same set of data described above, we used 9 independent external ovarian cancer patient data sets to further verify the 11-gene marker and scoring system. We calculated the prognostic scores of all patients in each data set according to the 11-gene markers and scoring system, and ranked the patients according to the scores. After Kaplan-Meier analysis, in all 9 data sets, the "good" group and the "poor" group were well distinguished, and the overall survival of patients with high prognosis scores was significantly less than that of patients with low scores (p<0.05) , The overall survival difference between the groups is significant and statistically significant, and the HR value is between 1.54-4.76 (Figure 5 shows the Kaplan-Meier survival curves of 4 representative data sets, and see Table 3). So far we can conclude that the 11-gene markers and prognostic scoring system can independently predict the overall survival of patients with ovarian cancer, and it has good reproducibility.

In the present invention, the sample is a tumor tissue of an ovarian cancer patient. The above-mentioned ovarian cancer patient sometimes contains a part of normal cells, including but not limited to fresh biopsy tissue, postoperative tissue, fixed tissue, and paraffin-embedded tissue. The diagnostic tags for ovarian cancer prognosis prediction of the present invention can be detected by different detection technology platforms, including but not limited to real-time fluorescent quantitative PCR, gene chips, second-generation high-throughput sequencing, Panomics and Nanostring technologies, and are aimed at different technology platforms. Designed corresponding gene primers (real-time fluorescent quantitative PCR) and probes (gene chip, second-generation high-throughput sequencing, Panomics and Nanostring technology). A preferred solution is the detection of the expression level of the target gene, and more preferably the quantitative detection of the expression level of the target gene. In order to detect the expression level, RNA needs to be isolated from the sample tissue, and methods known in the art for isolating RNA from the sample can be used. The calculation method of the forecast score we defined is as above, but the absolute value of the forecast score and the demarcation of the score can be different according to different technology platforms and need to be determined separately.

On the other hand, the present invention provides a composition for predicting the prognosis of ovarian cancer, which contains a gene probe or primer as an effective ingredient, and the gene probe or primer is directed against the above-mentioned 11-gene markers (RAD51AP1, MTHFD2, GALNT10, PYCR1, CADPS2, DSE, ITGB8, PDE10A, SNX1, C9orf16, ARL4).

In another aspect, the present invention provides a diagnostic kit for predicting the prognosis of ovarian cancer, which includes the above-mentioned composition.

In the following, the present invention will be further clarified with reference to the accompanying drawings and specific embodiments. It should be understood that these embodiments are only used to illustrate the present invention and not to limit the scope of the present invention. After reading the present invention, those skilled in the art will understand various aspects of the present invention. Modifications in equivalent forms fall within the scope defined by the appended claims of this application.

Example 1

The prior art literature has used expression differences to show the correlation between genes or polygenomes and the prognosis of ovarian cancer. We compared the 11 gene scoring system with the existing single gene or genome system. We first performed univariate Cox regression analysis and showed that the 232 single genes from GSE described above had only weak overall survival for ovarian cancer. Associated. Then we use the previously reported 5-gene ovarian cancer prognostic label (Liu LW, etc., A Five-Gene Expression Signature Predicts Clinical Outcome of Ovarian Serous Cystadenocarcinoma. Biomed Res Int. 2016:6945304.) to calculate the prediction score. We use the multivariate Cox regression analysis method to analyze its 5 genes in the previous 100 training sets, use the same method to calculate the corresponding coefficients of each gene and take the average value, and use the same formula to calculate TCGA 307 ovarian cancer patients The prognostic score. Sort the patients in the training group according to their prognostic scores, and divide the patients into three groups of the same number according to the scores, record the prognostic scores corresponding to the corresponding cut-off points, and take the average of the scores corresponding to each cut-off point as the true cut-off review According to these two cut-off points, patients in the test group are divided into three groups (prognosis) "good", "medium" and "poor". Perform Kaplan-Meier analysis and Log-Rank test on the three groups of patients in all test sets, compare the "medium" and "poor" groups with the "good" group respectively, calculate the hazard ratios (HR), and determine the three groups The difference in overall survival (OS) of patients. In 90% of the test set, the overall survival of patients in the "poor" group was significantly lower than that of patients in the "good" group (the low value of the 95% confidence interval> 1); in 12% of the test sets, the total survival of patients in the "medium" group The survival time was significantly lower than that of the "good" group (lower value of 95% confidence interval> 1). In our 11-gene markers, these two percentages are 99% and 61%, respectively. In addition, the median HR of our 11-gene markers was 1.46 times higher than the median HR of the five prognostic markers ("medium" group vs. "good" group) and 1.73 times ("poor" group vs. "Good" group). Figure 6 shows the HR of the "medium" group and "poor" group compared with the "good" group in 100 test sets, respectively, and compares our 11-gene markers and 5-gene prognostic markers published by other researchers. Compared. These results indicate that the 11-gene markers of the present invention can distinguish ovarian cancer patients with different prognosis (overall survival) significantly better than the 5 gene prognostic markers.

Example 2

Predict the prognostic effect of patients with clinical serous ovarian cancer:

Collect the tumor tissues of ovarian cancer patients received clinically and extract RNA. The tumor tissues can include fresh biopsy tissues, postoperative tissues, fixed tissues and paraffin-embedded tissues. Then use the kit developed by the present invention and the corresponding instrument to quantitatively detect the expression levels of 11 genes in the prognosis scoring system. Input the expression level of 11 genes into the prognostic scoring formula established in the present invention:

After calculating the patient's prediction score, the doctor predicts the patient's prognosis based on the score value, such as the 5-year survival rate. At present, we have established the model through retrospective research and successfully verified it in different databases (Table 3). By calculating the prognostic scores of all patients in the database, the patients are ranked according to their scores. Kaplan-Meier was used to analyze significant differences between patient groups with "good" and "poor" prognosis. We found that patients with high prognostic scores had significantly shorter OS than those with low scores (p<0.05) (Figure 5). The HR value ranges from 1.54 to .9.76, confirming that the 11-gene prognostic scoring system can repeatedly predict the overall survival rate of patients with ovarian cancer. We have also begun to implement prospective studies to further improve the scoring system.

Example 3

Predict the response of clinical ovarian cancer patients to targeted therapies such as PARP inhibitors (such as but not limited to olaparib, rucaparib and niraparib);

As a prognostic and predictive biomarker, BRCA1/BRCA2 mutations are a larger risk factor for ovarian cancer (lifetime risk 40%). The response rate of olaparib in people without germ cell BRCA mutations is 30%. 50% of platinum-based drug-sensitive populations. In order to reduce the ineffective or excessive use of targeted drugs and reduce medical costs, the present invention predicts that patients with clinical ovarian cancer will respond to PARP inhibitors (such as but not limited to olaparib) through the following implementations. ), the reaction of rucaparib and niraparib); tumor tissues are collected from clinically accepted ovarian cancer patients and RNA is extracted. The tumor tissues can include fresh biopsy tissue, postoperative tissue, and fixed Tissue and paraffin-embedded tissue. Then use the kit developed by the present invention and the corresponding instrument to quantitatively detect the expression level of the 11 genes of the prognosis scoring system. Input the expression level of 11 genes into the prognostic scoring formula established in the present invention:

After calculating the predictive score of the patient, the doctor considers whether the patient should receive targeted therapy with PARP inhibitors based on the score value. For patients whose predicted scores indicate a good prognosis, doctors can be advised to consider the necessity of PARP inhibitor targeted therapy as appropriate, so as to avoid excessive medication, reduce medical costs, and finally achieve the purpose of precise or individualized medical treatment.

Example 4

Predicting the response of clinical ovarian cancer patients to the chemotherapy drug paclitaxel: At present, the recurrence rate of ovarian cancer chemotherapy is high, and the effective rate is low. In order to reduce ineffective or overuse of drugs and reduce medical costs, the present invention uses the following schemes to predict clinical ovarian cancer patients’ response to chemotherapy drugs Paclitaxel response: Collect tumor tissue and extract RNA from clinically accepted ovarian cancer patients. The tumor tissue can include fresh biopsy tissue, postoperative tissue, fixed tissue and paraffin-embedded tissue. Then use the kit developed by the present invention and the corresponding instrument to quantitatively detect the expression level of the 11 genes of the prognosis scoring system. Input the expression level of 11 genes into the prognostic scoring formula established in the present invention:

After calculating the predictive score of the patient, the doctor considers whether the patient should receive paclitaxel chemotherapy based on the score value. For patients whose predicted scores indicate a good prognosis, doctors can be advised to consider the necessity of paclitaxel treatment as appropriate, and for patients whose predicted scores indicate a poor prognosis, doctors can be advised to increase the treatment intensity of paclitaxel or other chemotherapeutics as appropriate.

Table 1. Summary of K-M drawing analysis results

The impact of differentially expressed genes on the overall survival of patients with ovarian cancer (genes that are significantly related to overall survival are highlighted with darkened fonts/numbers); if the gene has multiple Affymetrix probes, the most significant results are listed in this table.

Table 2. Typical discriminant function coefficients

Table 3. Nine independent public database verification results

The above descriptions are only preferred embodiments of the present invention, and do not limit the present invention in any form. Although the present invention has been disclosed in the preferred embodiments as above, it is not intended to limit the present invention. Anyone skilled in the art, Without departing from the scope of the technical solution of the present invention, when the technical content disclosed above can be used to make slight changes or modification into equivalent embodiments with equivalent changes, but any content that does not deviate from the technical solution of the present invention, according to the technology of the present invention Essentially, any simple modifications, equivalent changes and modifications made to the above embodiments still fall within the scope of the technical solutions of the present invention.

Claims (10)

The application of a set of 11 ovarian cancer-related genes in preparing reagents for predicting the prognosis of ovarian cancer, characterized in that the 11 ovarian cancer-related genes are (1) homologous recombination repair genes: RAD51AP1; (2) coding organisms Enzyme genes: MTHFD2, GALNT10, PYCR1; (3) Signal transduction related genes: CADPS2, DSE, ITGB8, PDE10A, SNX1; (4) Other genes: C9orf16, ARL4; and (5) Control genes: GAPDH, ACTB, GUSB and TFRC. The use according to claim 1, wherein the ovarian cancer is serous ovarian cancer. The application according to claim 1 or 2, wherein the 11 ovarian cancer-related genes can be detected by real-time fluorescent quantitative PCR, gene chip, second-generation high-throughput sequencing, Panomics or Nanostring technology. The application according to claim 3, wherein the mRNA expression level of the 11 related genes in ovarian cancer can be detected by real-time fluorescent quantitative PCR, gene chip, second-generation high-throughput sequencing, Panomics or Nanostring technology . A set of gene probes designed for the application of the 11 ovarian cancer-related genes of claim 1 or 2 in preparing reagents for predicting the prognosis of ovarian cancer, the gene probes can be hybridized with the 11 ovarian cancers Related genes combine to produce hybridization signals. The set of gene probes according to claim 5, wherein the set of gene probes can be detected by real-time fluorescent quantitative PCR, gene chip, second-generation high-throughput sequencing, Panomics or Nanostring technology. MRNA expression levels of three related genes in ovarian cancer. A set of primers designed for the application of the 11 ovarian cancer-related genes of claim 1 or 2 in preparing reagents for predicting the prognosis of ovarian cancer. The primers can be used to analyze the 11 ovarian cancer-related genes by PCR-based technology. Perform amplification. The set of primers according to claim 7, wherein the set of primers can be used to detect the presence of the 11 related genes by real-time fluorescent quantitative PCR, gene chip, second-generation high-throughput sequencing, Panomics or Nanostring technology MRNA expression level in ovarian cancer. A composition for predicting the prognosis of ovarian cancer, which is characterized in that it comprises the gene probe of claim 5 or the primer of claim 7. A kit for predicting the prognosis of ovarian cancer, characterized in that it comprises the composition of claim 9.

Images