Gene Combinations for Classifying Human Tumors and Uses Thereof - Patent application

Abstract

The present invention relates to the field of tumor classification detection, and specifically discloses a gene combination for human tumor classification and its use, wherein the gene combination for human tumor classification comprises a gene set A and a gene fragment set B. The gene combination for human tumor classification is obtained from high-throughput sequencing data of actual kidney cancer cases from Peking University First Hospital through specific pairing and clustering analysis, and is derived from real data, and is useful for kidney cancer, pan-cancer malignancy classification and prognosis prediction.

Description

[Technical field]

[0001] [CROSS REFERENCE TO RELATED APPLICATIONS] This application claims priority to a Chinese patent application filed with the China Patent Office on March 2, 2021, with application number 202110232095X, and titled "Gene Combination for Human Tumor Classification and Use Thereof", the entire contents of which are incorporated herein by reference; this application also claims priority to a Chinese patent application filed with the China Patent Office on March 26, 2021, with application number 2021103327509, and titled "Gene Combination for Human Tumor Classification and Use Thereof", the entire contents of which are incorporated herein by reference; this application also claims priority to a Chinese patent application filed with the China Patent Office on July 6, 2021, with application number 2021107623535, and titled "Gene Combination for Human Tumor Classification and Use Thereof", the entire contents of which are incorporated herein by reference.

[0002] [Technical field] The present invention relates to the field of tumor classification detection, and in particular to gene combinations and their uses for human tumor classification. [Background technology]

[0003] Kidney cancer is a common malignant tumor of the urinary system. In recent years, the incidence rate of kidney cancer has been increasing year by year, accounting for 2%-3% of adult malignant tumors, and is increasing at a rate of about 2.5% per year. Kidney cancer does not have typical clinical signs in the early stages, and kidney cancer with clinical symptoms is often in the late stage. The treatment of kidney cancer is a comprehensive treatment based on surgery. In the treatment, it is necessary to classify the malignancy level of kidney cancer tissue in order to help doctors judge the progression and prognosis of the disease and further determine the treatment plan. The Fuhrman nuclear classification is currently the most widely used classification system for the malignancy level of kidney cancer.

[0004] The Fuhrman nuclear classification system was proposed in 1982 and is used to classify and evaluate the malignancy and risk of tumors. According to the Fuhrman classification criteria, kidney cancer is classified into four levels: G1 (well differentiated), G2 (moderately or well differentiated), G3 (moderately differentiated), and G4 (poorly or undifferentiated). The higher the level, the higher the malignancy and the higher the risk of recurrence and metastasis after treatment. However, the Fuhrman classification system is a pure pathology image classification system and has the following defects: 1. It requires the pathologist's personal experience to make a judgment, which is subjective and may vary greatly depending on the pathologist. 2. The distinction between G2 and G3 pathology images is small, making it difficult to classify the two. The above defects lead to inaccurate classification of the malignancy of the disease, which can cause errors in the judgment of disease progression and prognosis, affecting the diagnosis and treatment of the disease.

[0005] With the maturity and widespread use of second-generation sequencing technology, the method of diagnosing diseases using genetic testing has attracted widespread attention, such as detecting marker gene mutations and copy number variations through full exon sequencing to diagnose tumors and determine tumor progression. This method overcomes the drawbacks of traditional tumor classification, such as subjective effects and high classification difficulty, and is of great significance to the early diagnosis, treatment and prognosis of tumors. However, based on the existing second-generation sequencing technology, the gene combinations and methods used to classify the malignancy of kidney cancer all lack external validation, making the classification of malignancy unreliable and unable to be applied in clinical practice, so there is an urgent need to solve a new classification system for tumor malignancy and risk based on specific genetic testing. Summary of the Invention [Problem to be solved by the invention]

[0006] Therefore, the technical problem to be solved by the present invention is to provide a gene combination for classifying kidney cancer, which can be used for classifying the malignancy of kidney cancer and predicting the prognosis of kidney cancer patients, and its use. To achieve this objective, the present invention uses full-exon sequencing technology to screen kidney cancer patient data from Peking University First Hospital, which has been specially screened and grouped, and finally obtains the gene combination used for classifying the malignancy of kidney cancer and predicting the prognosis, thereby providing clinicians and patients with more accurate kidney cancer malignancy information and disease prediction information. [Means for solving the problem]

[0007] According to one aspect of the present invention, the present invention comprises: It consists of gene set A and gene fragment set B, The gene set A includes ASAH1, ASXL1, BCOR, BRAF, CALML6, CCDC136, CIDEC, COX18, CSF1R, CYP3A5, DEK, DNMT3A, EGR1, FAM71E2, FGFR1, FKBP7, FLT1, FLT3, FLT4, GLIS1, IDH2, IFITM3, IMMT, KDR, KIT, KMT2A, KNOP1, KRT76, KRT9, KRTAP10-10, KRTAP10-8, MAF, MECOM, MFRP, MLLT3, MNS1, MRTFA, MTOR, MYH11, NF1, NUP214, PDGFR at least one of A, PDGFRB, PML, PRB2, PROSER3, RAF1, RARA, RBM15, RET, REXO1, RPN1, RUNX1T1, SCYL1, SLC16A6, SRC, STAG2, TCEAL5, TET2, TMEM82, TP53, TRIM26, U2AF1, U2AF2, UGT1A1, USP35, VEGFA, WBP2NL, WDR44, ZNF20, ZNF700, and ZRSR2; The gene fragment set B is chr2: 179479501-179610249, chr2: 207989501-208000249, chr2: 219719501-219840249, chr2: 3679501-3700249, chr3: 126249501-126270249, chr3: 129319501-129330249, chr3: 138659501-138770249, chr3: 183999501-184020249, chr4: 1189501-1230249, chr4: 8579501-8 590249, chr4:9319501-9330249, chr5:150899501-150940249, chr6:147 819501-147840249, chr6:157089501-157110249, chr6:164889501-1649 00249, chr6:20399501-20410249, chr6:26519501-26530249, chr6:7165 9501-71670249, chr6:73329501-73340249, chr7:100539501-100560249, chr8:1939501-1960249, chr8:21999501-22070249, chr8:29189501-29200249, chr9:91789501-91800249, chr10:99419501-99440249, chr11:17 739501-17760249, chr11:63329501-63350249, chr12:169501-250249, c hr12:54329501-54350249, chr12:63179501-63550249, chr12:7269501-7 310249, chr13:114519501-114530249, chr15:73649501-73670249, chr1 5:74209501-74220249, chr15:78409501-78430249, chr15:83859501-83 880249, chr18:8809501-8820249, chr19:24059501-24070249, chr19:42 29501-4250249, chr19:46879501-46900249, chr20:22559501-22570249,The gene combination for human tumor classification includes at least one of chr20:62189501-62200249, chr21:45949501-46110249, chr22:19499501-19760249, chr22:36649501-38700249, and chr22:46309501-47080249, and the positions of the gene fragments in the gene fragment set B are annotated based on GRCh37, and the number may change in GRCh38 or a new human reference genome that will appear in the future, but the positions of the target fragments shown and the genes available for detection do not change.

[0008] Optionally, the genes included in the gene fragment set B are specifically shown in the following table: Table 1 Gene fragment set B JPEG0007667865000001.jpg210144 JPEG0007667865000002.jpg129145Optionally, the set of genes A includes at least one of ASAH1, CCDC136, FAM71E2, IFITM3, KRT9, PRB2, PROSER3, TCEAL5, U2AF2, USP35, WDR44, and ZNF700; Optionally, the gene fragment set B is selected from the group consisting of chr2:179479501-179610249, chr2:207989501-208000249, chr2:219719501-219840249, chr3:126249501-126270249, chr3:129319501-129330 249, chr3:138659501-138770249, chr3:183999501-184020249, chr5:150899501-150940249, chr7:100539501-100560249, and chr13:114519501-114530249.

[0009] According to another aspect of the present invention there is provided the use of the above gene combination in the manufacture of a product for the detection and classification of human tumors.

[0010] Optionally, the tumor is a urological tumor or a pan-cancer; Optionally, the urological tumor is a urological malignancy; Optionally, the urological tumor is renal cancer; Optionally, the pan-cancer is a cancer type in the TCGA pan-cancer data.

[0011] Optionally, the tumor classification is a determination of tumor grade and a prediction of tumor prognosis; Optionally, the tumor classification is divided into high-risk and low-risk groups.

[0012] Optionally, the article of manufacture comprises primers, probes, reagents, kits, gene chips or detection systems for detecting the genotypes of genes in the genetic combination.

[0013] Optionally, the product detects exons and associated intron regions of genes in gene set A and gene fragment set B.

[0014] According to another aspect of the present invention, Step S1 of evaluating gene mutations and gene copy number mutations of genes included in gene set A in the cancer cell tissue and evaluating gene copy number mutations of gene fragment set B in the cancer cell tissue; and step S2 of determining the malignancy of the cancer and predicting the tumor prognosis based on the evaluation result of step S1.

[0015] Optionally, the gene mutation comprises a base substitution mutation, a deletion mutation, an insertion mutation and / or a fusion mutation, and the gene copy number mutation comprises a gene copy number increase and / or a gene copy number decrease.

[0016] Optionally, in step S1, the sequencing data of the tumor tissue and the normal tissue are compared to evaluate gene mutations and copy number mutations of the genes included in the gene set A, and evaluate gene copy number mutations of the gene fragment set B.

[0017] Optionally, in step S2, if at least one gene in gene set A undergoes a gene mutation or copy number mutation, or at least one fragment in gene fragment set B undergoes a gene copy number increase, the tumor is classified as a high-risk group; conversely, i.e., if no gene in gene set A undergoes a gene mutation or copy number mutation, and no fragment in gene fragment set B undergoes a gene copy number increase, the tumor is classified as a low-risk group.

[0018] Optionally, any gene fragments are selected and combined from the gene combinations to form new gene combinations, and the same tumor classification method is used to classify tumor malignancy and predict tumor prognosis, to guide clinical practice. Effect of the Invention

[0019] The technical solution of the present invention has the following advantages: 1. The detection gene combination of the present invention is obtained by specific pairing and clustering analysis from high-throughput sequencing data of actual kidney cancer cases from Peking University First Hospital. Since it is derived from actual data, it is more reliable and certain, and can accurately classify the malignancy of kidney cancer and pan-cancer and predict the prognosis. 2. Since the gene combinations included in the gene combinations of the present invention are diverse, multiple gene combinations can be suitably selected from them and used to determine the malignancy of kidney cancer and pan-cancer, and can be adapted to various clinical situations. 3. Compared with whole-exon sequencing, the present invention performs targeted sequencing analysis on specific genes and DNA fragments, so that at the same cost, the sequencing depth and accuracy can be significantly improved; at the same sequencing depth and accuracy, the cost can be greatly reduced and it is highly versatile. 4. Compared with the conventional Fuhrman pathological classification system, the present invention does not affect the subjective judgment of pathologists at all, and is more objective and reliable. [Brief description of the drawings]

[0020] In order to make the technical solutions of certain embodiments of the present invention or the prior art clearer, the drawings necessary for describing the certain embodiments or the prior art will be briefly described below. Obviously, the drawings in the following description are only some embodiments of the present invention, and those skilled in the art can also obtain other drawings based on these drawings without requiring creative efforts.

[0021] [Figure 1] This is a Kaplan-Meier survival analysis diagram in which tumor-specific survival was the primary endpoint after classification according to the classification criteria for renal cancer malignancy using the gene combination in Example 1 of the present invention in Experimental Example 1 of the present invention. [Diagram 2] This is a Kaplan-Meier survival analysis diagram in which tumor progression-free survival was the primary endpoint after classification according to the classification criteria for renal cancer malignancy using the gene combination in Example 1 of the present invention in Experimental Example 1 of the present invention. [Diagram 3] FIG. 2 is a Kaplan-Meier survival analysis chart in which overall survival was the primary endpoint after classification according to the classification criteria for renal cancer malignancy using the gene combination in Example 1 of the present invention in Experimental Example 1 of the present invention. [Figure 4] FIG. 2 is a Kaplan-Meier survival analysis chart in which tumor-specific survival was the primary endpoint after classification according to the classification criteria for kidney cancer malignancy using gene combination 1 in Experimental Example 2 of the present invention. [Diagram 5]FIG. 2 is a Kaplan-Meier survival analysis chart in which tumor progression-free survival was the primary endpoint after classification according to the classification criteria for kidney cancer malignancy using gene combination 1 of the present invention in Experimental Example 2 of the present invention. [Figure 6] FIG. 2 is a Kaplan-Meier survival analysis chart in which overall survival was the primary endpoint after classification according to the classification criteria for renal cancer malignancy using gene combination 1 of the present invention in Experimental Example 2 of the present invention. [Figure 7] FIG. 13 is a Kaplan-Meier survival analysis chart in which tumor-specific survival was the primary endpoint after classification according to the pan-cancer malignancy classification criteria using the gene combination in Example 1 of the present invention in Experimental Example 3 of the present invention. [Figure 8] FIG. 13 is a Kaplan-Meier survival analysis chart in which tumor progression-free survival was the primary endpoint after classification according to the pan-cancer malignancy classification criteria using the gene combination in Example 1 of the present invention in Experimental Example 3 of the present invention. [Figure 9] FIG. 2 is a Kaplan-Meier survival analysis chart in which tumor-free survival was the primary endpoint after classification according to the pan-cancer malignancy classification criteria using the gene combination in Example 1 of the present invention in Experimental Example 3 of the present invention. [Figure 10] FIG. 13 is a Kaplan-Meier survival analysis chart in which overall tumor survival was the primary endpoint after classification according to the pan-cancer malignancy classification criteria using the gene combination in Example 1 of the present invention in Experimental Example 3 of the present invention. [Figure 11] This is a Kaplan-Meier survival analysis chart in which tumor-specific survival was the primary endpoint after classification according to the pan-cancer malignancy classification criteria using gene combination 1 in Experimental Example 2 of the present invention in Experimental Example 4 of the present invention. [Figure 12] This is a Kaplan-Meier survival analysis chart in which tumor progression-free survival was the primary endpoint after classification according to the pan-cancer malignancy classification criteria using gene combination 1 in Experimental Example 2 of the present invention in Experimental Example 4 of the present invention. [Figure 13]FIG. 13 is a Kaplan-Meier survival analysis chart in which overall tumor survival was the primary endpoint after classification according to the pan-cancer malignancy classification criteria using gene combination 1 in Experimental Example 2 of the present invention in Experimental Example 4 of the present invention. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0022] The following examples are provided for a better understanding of the present invention, and are not limited to the preferred embodiments described above, and are not intended to limit the content and scope of protection of the present invention. Any products identical or similar to those of the present invention obtained under the teachings of the present invention or in combination with other prior art features are included in the patent scope of the present invention.

[0023] If specific experimental steps or conditions are not specified in the examples, they may be performed according to the procedures or conditions of general experimental steps described in the literature in this field. If the manufacturer of the reagents or equipment used is not specified, general reagent products available on the market may be used.

[0024] Example 1 Gene Combinations for Classifying Human Tumors (Panel) The inventors mainly used the Peking University First Hospital's kidney cancer exon sequencing high-throughput database for screening to determine a gene combination (panel) for human tumor classification, which includes gene set A and gene fragment set B.

[0025] The gene set A includes ASAH1, ASXL1, BCOR, BRAF, CALML6, CCDC136, CIDEC, COX18, CSF1R, CYP3A5, DEK, DNMT3A, EGR1, FAM71E2, FGFR1, FKBP7, FLT1, FLT3, FLT4, GLIS1, IDH2, IFITM3, IMMT, KDR, KIT, KM T2A, KNOP1, KRT76, KRT9, KRTAP10-10, KRTAP10-8, MAF, MECOM, MFRP, MLLT3, MNS1, MRTFA, MTOR, MYH11, NF1, NUP214, PDGFRA, PDGFRB, PML, PRB2, PROSER3, RAF1, RARA, RBM15, RET, REXO1, RPN1, at least one of RUNX1T1, SCYL1, SLC16A6, SRC, STAG2, TCEAL5, TET2, TMEM82, TP53, TRIM26, U2AF1, U2AF2, UGT1A1, USP35, VEGFA, WBP2NL, WDR44, ZNF20, ZNF700, and ZRSR2; The gene fragment set B is chr2: 179479501-179610249, chr2: 207989501-208000249, chr2: 219719501-219840249, chr2: 3679501-3700249, chr3: 126249501-126270249, chr3: 129319501-129330249, chr3: 138659501-138770249, chr3: 183999501-184020249, chr4: 1189501-1230249, chr4: 8579501-8 590249, chr4:9319501-9330249, chr5:150899501-150940249, chr6:147 819501-147840249, chr6:157089501-157110249, chr6:164889501-1649 00249, chr6:20399501-20410249, chr6:26519501-26530249, chr6:7165 9501-71670249, chr6:73329501-73340249, chr7:100539501-100560249, chr8:1939501-1960249, chr8:21999501-22070249, chr8:29189501-29200249, chr9:91789501-91800249, chr10:99419501-99440249, chr11:17 739501-17760249, chr11:63329501-63350249, chr12:169501-250249, c hr12:54329501-54350249, chr12:63179501-63550249, chr12:7269501-7 310249, chr13:114519501-114530249, chr15:73649501-73670249, chr1 5:74209501-74220249, chr15:78409501-78430249, chr15:83859501-83 880249, chr18:8809501-8820249, chr19:24059501-24070249, chr19:42 29501-4250249, chr19:46879501-46900249, chr20:22559501-22570249,At least one of chr20:62189501-62200249, chr21:45949501-46110249, chr22:19499501-19760249, chr22:36649501-38700249 and chr22:46309501-47080249, The positions of the gene fragments in gene fragment set B are annotated based on GRCh37, and the number may change in GRCh38 or a new human reference genome that will be released in the future, but the positions of the target fragments shown and the genes available for detection will not change.

[0026] Optionally, the genes included in the gene fragment set B are specifically shown in the table below. Table 1 Gene fragment set B JPEG0007667865000003.jpg210143 JPEG0007667865000004.jpg129144

[0027] Example 2 Methods for grading and prognostic prediction of human kidney cancer This embodiment provides a method for classifying and detecting human tumors, including using the gene combination (panel) of Example 1 to classify the malignancy and predict the prognosis of human kidney cancer, and the steps are specifically as follows:

[0028] (1) Prepare a specimen of renal cancer tissue and a healthy control tissue, the renal cancer tissue specimen may be a renal cancer cell line, a fresh renal cancer specimen, a frozen renal cancer specimen or a paraffin-embedded renal cancer specimen, the healthy control tissue may be a tissue of a known healthy person, or a paratumor tissue of a renal cancer patient. In this embodiment, a paraffin-embedded renal cancer specimen is selected, and a paratumor normal tissue is used as the healthy control tissue, and DNA is extracted by a conventional method, and a library is constructed by a conventional method. Finally, the gene combination (panel) of Example 1 is used to perform targeted high-throughput sequencing, and the sequencing data of the renal cancer tissue and the healthy tissue are compared, and the gene mutation (Mutation) and copy number mutation (CNV) of each gene of the gene set A in the gene combination of the renal cancer tissue, and the copy number mutation (CNV) of each gene of the gene fragment set B are obtained.

[0029] The gene mutations include base substitution mutations, deletion mutations, insertion mutations and fusion mutations, and the gene copy number mutations include gene copy number increases and gene copy number decreases.

[0030] (2) A judgment is made based on the mutation and / or mutations of each gene in the gene combination of the kidney cancer tissue obtained in step (1).

[0031] If there is a gene mutation or copy number mutation in at least one gene in gene set A, or an increase in gene copy number in at least one region in gene fragment set B, the kidney cancer patient is in a high-risk group and has a poor tumor prognosis; conversely, if there is no gene mutation or copy number mutation in gene set A and no gene copy number increase in any fragment in gene fragment set B, the kidney cancer patient is in a low-risk group and has a good tumor prognosis.

[0032] Example 3 As an alternative embodiment of Example 2, the present invention can select and recombine genes in the gene combination (panel) of Example 1 to obtain a new gene combination. The evaluation criteria are as follows: if the genes are selected from gene set A, at least one of the genes has a gene mutation or copy number mutation, indicating that the kidney cancer patient is in a high-risk group; if the gene fragments are selected from gene fragment set B, at least one of the regions has an increase in gene copy number, indicating that the kidney cancer patient is in a high-risk group; and conversely, if no gene mutation or copy number mutation occurs in the genes selected from gene set A and no copy number increase occurs in the fragments selected from gene fragment set B, the kidney cancer patient is in a low-risk group.

[0033] Example 4 Method for grading and predicting prognosis of human pancancer This embodiment provides a method for classifying and detecting human tumors, which includes classifying the malignancy and predicting the prognosis of human pan-cancer using the gene combination (panel) of Example 1, and the steps are specifically as follows:

[0034] (1) Pan-cancer (here, pan-cancer is defined as all cancer types in the TCGA pan-cancer data, including adrenal gland cancer, urothelial carcinoma, breast cancer, cervical cancer, bile duct cancer, colon cancer, lymphoma, esophageal cancer, glioblastoma, head and neck squamous cell carcinoma, renal chromophobe cell carcinoma, renal clear cell carcinoma, papillary cell carcinoma of the kidney, leukemia, brain glioma, hepatocellular carcinoma, lung adenocarcinoma, lung squamous cell carcinoma, mesothelioma, ovarian serous cystadenocarcinoma, pancreatic cancer, pheochromocytoma and paraganglioma, prostate cancer, rectal cancer, sarcoma, cutaneous melanoma, gastric cancer, and testicular cancer) (including cancer, thyroid cancer, thymic cancer, endometrial cancer, uterine sarcoma, and uveal melanoma, and the pan-cancer described in this example is defined in the same way, so duplicated explanations will be omitted) tissue and healthy control tissue specimens are prepared, the pan-cancer tissue specimens may be pan-cancer cell lines, fresh pan-cancer specimens, frozen pan-cancer specimens, or paraffin-embedded pan-cancer specimens, and the healthy control tissues may be tissues of known healthy people, or para-cancer tissues of the pan-cancer patient himself. In this example, a paraffin-embedded pan-cancer specimen is selected, but para-cancer normal tissues are used as the healthy control tissues, DNA is extracted by a conventional method, a library is constructed by a conventional method, and finally, targeted high-throughput sequencing is performed using the gene combination (panel) of Example 1, and the sequencing data of the pan-cancer tissue and the healthy tissue are compared, and the gene mutation (Mutation) and copy number mutation (CNV) of each gene in gene set A in the gene combination of the pan-cancer tissue, and the copy number mutation (CNV) of each gene in gene fragment set B are obtained.

[0035] The gene mutations include base substitution mutations, deletion mutations, insertion mutations and fusion mutations, and the gene copy number mutations include gene copy number increases and gene copy number decreases.

[0036] (2) A judgment is made based on the mutations and / or mutations of each gene in the gene combination of the pan-cancer tissues obtained in step (1).

[0037] If there is a gene mutation or copy number mutation of at least one gene in gene set A, or at least one region gene copy number increase in gene fragment set B, the pan-cancer patient is a high-risk group with poor tumor prognosis; conversely, if there is no gene mutation or copy number mutation in gene set A and no gene copy number increase in any fragment in gene fragment set B, the pan-cancer patient is a low-risk group with good tumor prognosis.

[0038] Example 5 As an alternative embodiment of Example 4, the present invention can select and recombine genes in the gene combination (panel) of Example 1 to obtain a new gene combination, and the evaluation criteria are as follows: if the genes are selected from gene set A, at least one of the genes has a gene mutation or copy number mutation, which indicates that the pan-cancer patient is in a high-risk group; if the gene fragments are selected from gene fragment set B, at least one of the regions has a gene copy number increase, which indicates that the pan-cancer patient is in a high-risk group; and conversely, if no gene mutation or copy number mutation occurs in the genes selected from gene set A and no copy number increase occurs in the fragments selected from gene fragment set B, the pan-cancer patient is in a low-risk group.

[0039] Experimental Example 1: Verification of the feasibility of gene combinations and detection methods for human tumor classification in the evaluation of malignancy classification and prognosis prediction of human renal clear cell carcinoma Clear cell carcinoma is the most common pathological type of kidney cancer, accounting for more than 70% of all kidney cancers, and the TCGA (PanCancer Atlas) kidney clear cell carcinoma database is a globally recognized kidney cancer database, which can be used to evaluate the feasibility and reliability of the present invention in grading and predicting prognosis of kidney cancer.

[0040] The TCGA (PanCancer Atlas) renal clear cell carcinoma data includes data on a total of 512 patients, of which 354 patients have complete gene mutation and copy number variation data suitable for the application conditions of the present invention.

[0041] The method described in Example 2 was carried out. In this experimental example, all genes in gene set A of Example 1 and all fragments of gene set B can be selected and carried out. The genes actually used for detection in gene set B are shown in Table 2. The above 354 patients were classified according to the malignancy level and smoothly divided into high-risk and low-risk groups, of which the high-risk group accounted for 46.6% and the low-risk group accounted for 53.4%. Kaplan-Meier survival analysis showed that the tumor-specific survival (Figure 1), tumor progression-free survival (Figure 2), and total survival (Figure 3) of the high-risk group and the low-risk group all had statistical differences, which were in line with the prediction of the grouping of the present invention. The low-risk group had significantly better tumor-specific survival (Log-rank p-value = 7.800e-4), tumor progression-free survival (Log-rank p-value = 3.060e-4), and total survival (Log-rank p-value = 4.523e-3). Therefore, by using the gene combination of the present invention, the malignancy classification and prognosis prediction of kidney cancer patients can be performed accurately and reliably. Table 2. Genes actually detected for gene fragment set B in Experimental Example 1 TIFF0007667865000005.tif245136

[0042] Experimental Example 2: Verification of the feasibility of the preferred gene combination and detection method in evaluating the malignancy grading and prognosis prediction of human renal clear cell carcinoma In the present invention, it is possible to select and combine any gene fragments from the gene combination (panel) to form a new gene combination, and classify the malignancy of kidney cancer and predict the tumor prognosis according to the same judgment criteria. Here, gene set A1 is selected from gene set A of the gene combination (panel), and gene fragment set B1 is selected from gene fragment set B to form gene combination 1 (panel 1), which is used for classification of the malignancy and prediction of prognosis of kidney cancer, and the feasibility is analyzed using TCGA (PanCancer Atlas) kidney clear cell carcinoma data bake. Similarly, the judgment criteria are that if there is a gene mutation or copy number mutation of at least one gene in gene set A1, or an increase in gene copy number of at least one region in gene fragment set B1, the kidney cancer patient is a high-risk group and has a poor tumor prognosis; conversely, if there is no gene with a gene mutation or copy number mutation in gene set A1 and no increase in gene copy number occurs in any fragment in gene fragment set B1, the kidney cancer patient is a low-risk group and has a good tumor prognosis. In this experimental example, gene combination 1 (panel 1) was optimized based on gene combination (panel), and has higher accuracy (higher specificity) and a wider range of application of gene combination (panel) than gene combination 1 (panel 1) (higher sensitivity).

[0043] Table 3 Gene set A1 JPEG0007667865000006.jpg28147

[0044] Table 4 Gene fragment set B1 JPEG0007667865000007.jpg81147

[0045] In the gene combination 1 (panel 1) performed by the method of Example 2, the above 354 patients were classified according to the malignancy grade and smoothly divided into a high-risk group and a low-risk group, of which the high-risk group accounted for 14.4% and the low-risk group accounted for 85.6%. Kaplan-Meier survival analysis showed that the tumor-specific survival (Figure 4), tumor progression-free survival (Figure 5), and total survival (Figure 6) of the high-risk group and the low-risk group all had statistical differences, which were consistent with the prediction of the grouping of the present invention. The low-risk group had significantly better tumor-specific survival (Log-rank p value = 3.458e-4), tumor progression-free survival (Log-rank p value = 2.559e-4), and total survival (Log-rank p value = 1.703e-3). Therefore, malignancy grade classification and prognosis prediction of kidney cancer patients can also be performed using other gene combinations selected from the gene combinations (panel) of the present invention.

[0046] Experimental Example 3: Verification of the feasibility of gene combinations and detection methods for human tumor classification in the evaluation of malignancy classification and prognosis prediction of human pancancer The TCGA (PanCancer Atlas) pan-cancer database is a globally recognized pan-cancer database and can be used to assess the feasibility and reliability of the present invention in assessing pan-cancer grading and prognosis.

[0047] The TCGA (PanCancer Atlas) pan-cancer data includes a total of 10,967 pan-cancer data, of which 9,896 cases have complete gene mutation and copy number mutation data suitable for the application conditions of the present invention.

[0048] The method of Example 4 was carried out. In this experimental example, all genes in gene set A and all fragments in gene fragment set B in Example 1 can be selected and carried out. The genes actually used for detection in gene fragment set B are shown in Table 2 of Experimental Example 1. The above 9896 patients were classified according to the malignancy level and smoothly divided into high-risk and low-risk groups, of which the high-risk group accounted for 77.0% and the low-risk group accounted for 23.0%. Kaplan-Meier survival analysis showed that the tumor-specific survival (Figure 7), tumor progression-free survival (Figure 8), tumor disease-free survival (Figure 9) and overall survival (Figure 10) of the high-risk group and the low-risk group all had statistical differences, which was in line with the prediction of the grouping of the present invention. The low-risk group had significantly better tumor-specific survival (Log-rank p-value<1.000e-10), tumor progression-free survival (Log-rank p-value<1.000e-10), tumor disease-free survival (Log-rank p-value<1.000e-10), and overall survival (Log-rank p-value<1.000e-10). Therefore, the gene combination of the present invention can be used to accurately and reliably grade and predict the prognosis of pan-cancer patients.

[0049] Experimental Example 4: Verification of the feasibility of the preferred gene combination and detection method in the evaluation of the malignancy grading and prognosis prediction of human pan-cancer In the present invention, it is possible to select and combine any gene fragments from the gene combination (panel) to form a new gene combination, and classify the malignancy of pan-cancer and predict tumor prognosis according to the same judgment criteria. Here, according to the method of Example 5, gene combination 1 (panel 1) of Experimental Example 2 is selected to be used for pan-cancer malignancy classification and prognosis prediction, and the feasibility is analyzed using the TCGA (PanCancer Atlas) pan-cancer database. Similarly, the judgment criteria are that if there is a gene mutation or copy number mutation of at least one gene in gene set A1, or a gene copy number increase of at least one region in gene fragment set B1, the pan-cancer patient is a high-risk group and has a worse tumor prognosis; conversely, if there is no gene with a gene mutation or copy number mutation in gene set A1 and no gene copy number increase in any fragment in gene fragment set B1, the pan-cancer patient is a low-risk group and has a good tumor prognosis. It should be noted that in this experimental example, gene combination 1 (panel 1) is the preferred gene combination (panel) based, and since it has fewer detection sites, the implementation cost is significantly reduced.

[0050] Using the method of Example 4, gene combination 1 (panel 1) classified 9896 patients in the pan-cancer database according to the malignancy level, smoothly dividing them into high-risk and low-risk groups, of which the high-risk group accounted for 26.8% and the low-risk group accounted for 73.2%. Kaplan-Meier survival analysis showed that the tumor-specific survival (Figure 11), tumor progression-free survival (Figure 12), and total survival (Figure 13) of the high-risk and low-risk groups all had statistical differences, which were consistent with the prediction of the grouping of the present invention. The low-risk group had significantly better tumor-specific survival (Log-rank p-value = 1.536e-4), tumor progression-free survival (Log-rank p-value = 2.353e-3), and total survival (Log-rank p-value = 5.302e-5). Therefore, the malignancy level classification and prognosis prediction of pan-cancer patients can also be performed using other gene combinations selected from the gene combinations (panel) of the present invention.

[0051] Of course, the above examples are merely examples for clarifying the description, and are not intended to limit the embodiments. Those skilled in the art can make various other changes and variations based on the above description. It is not necessary or possible to comprehensively list all the embodiments here. Any obvious changes or variations derived from these are also included in the scope of the patent created by the present invention.

Claims

1. It consists of gene set A and gene set B, The gene set A includes ASAH1, CCDC136, FAM71E2, IFITM3, KRT9, PRB2, PROSER3, TCEAL5, U2AF2, USP35, WDR44, and ZNF700; and The gene set B is a gene combination for classifying human tumors, characterized in that it includes TTN, KLF7, WNT6, CHST13, PLXND1, FOXL2, PSMD2, FAT2, ACHE and GAS6.

2. The gene set A also includes ASXL1, BCOR, BRAF, CALML6, CIDEC, COX18, CSF1R, CYP3A5, DEK, DNMT3A, EGR1, FGFR1, FKBP7, FLT1, FLT3, FLT4, GLIS1, IDH2, IMMT, KDR, KIT, KMT2A, KNOP1, KRT76, KRTAP10-10, KRTAP10-8, MAF, MECOM, MFRP, MLLT3, MNS1 , MRTFA, MTOR, MYH11, NF1, NUP214, PDGFRA, PDGFRB, PML, RAF1, RARA, RBM15, RET, REXO1, RPN1, RUNX1T1, SCYL1, SLC16A6, SRC, STAG2, TET2, TMEM82, TP53, TRIM26, U2AF1, UGT1A1, VEGFA, WBP2NL, ZNF20, and ZRSR2; The gene combination for classifying human tumors according to claim 1, characterized in that the gene set B includes at least one selected from COLEC11, CTBP1, GPR78, USP17L5, SAMD5, ARID1B, C6orf118, E2F3, HCG11, B3GAT2, KCNQ5, KBTBD11, BMP1, DUSP4, SHC3, PI4K2A, MYOD1, PLAAT2, IQSEC3, HOXC13, AVPR1A, CLSTN3, HCN4, LOXL1, CIB2, HDGFL3, MTCL1, ZNF726, EBI3, PPP5C, FOXA2, HELZ2, TSPEAR, SEPTIN5, MYH9, and WNT7B.

3. 3. Use of the gene combination according to claim 1 or 2 in the manufacture of a product for the classification and detection of human tumors.

4. The use according to claim 3, characterized in that the tumor is a urological tumor or a pan-cancer.

5. The use according to claim 4, characterized in that the urological tumor is a urological malignant tumor.

6. the urological tumor is renal cancer, or The use according to claim 4, characterized in that the pan-cancer is a cancer type in the TCGA pan-cancer data.

7. The use according to any one of claims 3 to 6, characterized in that the tumor classification is a determination of tumor malignancy and a prediction of tumor prognosis, which is used to guide clinical practice.

8. The use according to any one of claims 3 to 6, characterized in that the tumor classification is divided into a high-risk group and a low-risk group.

9. The use according to any one of claims 3 to 8, characterized in that the product comprises a primer, a probe, a reagent, a kit, a gene chip or a detection system for detecting the genotype of the genes in the gene combination.

10. The use according to claim 9, characterized in that the product detects exons and associated intron regions of genes in gene set A and gene set B.

11. The tumor classification is S1, evaluating gene mutations and gene copy number mutations of genes included in gene set A in tumor tissue, and evaluating gene copy number mutations of gene set B in the tumor tissue; The use according to any one of claims 7 to 10, further comprising a step S2 of determining the malignancy of the cancer and predicting the tumor prognosis based on the evaluation result of the step S1.

12. The use according to claim 11, characterized in that the gene mutation comprises a base substitution mutation, a deletion mutation, an insertion mutation and / or a fusion mutation, and the gene copy number mutation comprises a gene copy number increase and / or a gene copy number decrease.

13. The use according to claim 11 or 12, characterized in that in step S1, the gene mutations and copy number mutations of the genes included in the gene set A are evaluated by comparing the sequencing data of the tumor tissue and the normal tissue, and the gene copy number mutations of the genes included in the gene set B are evaluated.

14. The use according to any one of claims 11 to 13, characterized in that in step S2, if at least one gene in gene set A undergoes a gene mutation or copy number mutation, or at least one gene in gene set B undergoes a gene copy number increase, the tumor is classified as a high-risk group, and vice versa, i.e., if there is no gene in gene set A undergoes a gene mutation or copy number mutation, and no gene copy number increase occurs in gene set B, the tumor is classified as a low-risk group.

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