GENE COMBINATIONS FOR HUMAN TUMOR CLASSIFICATION, AND ITS USE

Abstract

The present invention relates to the field of tumor grade detection. Specifically disclosed are a gene combination for human tumor grading, and a use thereof. The gene combination for human tumor grading consists of a gene set A and a gene fragment set B. The gene combination for human tumor grading is obtained from high-throughput sequencing data of an actual renal cancer case of the Peking University First Hospital by means of specific pairing and clustering, is derived from real data, and can be used for malignancy degree grading and prognosis prediction for renal cancer and pan-cancer.

Description

A gene combination for human tumor grading and its use

[0001] Cross-references

[0002] This application claims priority to the Chinese patent application filed with the China Patent Office on March 2, 2021, with application number 202110232095X, and invention name “A gene combination for human tumor grading and its use”, the entire contents of which are incorporated by reference into this application; this application also claims priority to the Chinese patent application filed with the China Patent Office on March 26, 2021, with application number 2021103327509, and invention name “A gene combination for human tumor grading and its use”, the entire contents of which are incorporated by reference into this application; this application also claims priority to the Chinese patent application filed with the China Patent Office on July 6, 2021, with application number 2021107623535, and invention name “A gene combination for human tumor grading and its use”, the entire contents of which are incorporated by reference into this application. Technical Field

[0003] The present invention relates to the field of tumor grading detection, and in particular to a gene combination for human tumor grading and its use. Background Art

[0004] Kidney cancer is a common malignant tumor of the urinary system. In recent years, the incidence of kidney cancer has been increasing annually, accounting for 2%-3% of all malignant tumors in adults, and is rising at an annual rate of approximately 2.5%. Renal cancer lacks typical clinical manifestations in its early stages, and by the time clinical symptoms appear, the disease is often in its advanced stages. Treatment for kidney cancer is a comprehensive, surgically based approach. During treatment, the malignancy of the renal cancer tissue is often graded to assist physicians in assessing disease progression and prognosis, and to formulate further treatment plans. The Fuhrman nuclear grade is currently the most widely used grading system for kidney cancer malignancy.

[0005] The Fuhrman nuclear grading system, proposed in 1982, assesses the malignancy and risk of tumors. According to the Fuhrman grading criteria, renal cell carcinoma is divided into four grades: G1 (well differentiated), G2 (moderately well differentiated), G3 (moderately differentiated), and G4 (poorly differentiated or undifferentiated). As the grade increases, the malignancy of the renal cell carcinoma increases, and the risk of recurrence and metastasis after treatment also increases. However, the Fuhrman grading system is a purely pathological image classification system and has the following drawbacks: 1. It relies on the pathologist's personal experience, which is subjective and can vary significantly between pathologists; 2. The distinction between G2 and G3 pathological images is limited, making it difficult to categorize them. These drawbacks can easily lead to inaccurate classification of malignancy, errors in disease progression and prognosis, and compromised diagnosis and treatment.

[0006] With the maturity and widespread adoption of second-generation sequencing technology, the use of genetic testing to diagnose diseases has garnered widespread attention. For example, whole-exome sequencing can be used to detect mutations and copy number variations in marker genes, thereby diagnosing tumors or assessing their progression. This approach overcomes the subjectivity and difficulty inherent in traditional tumor grading, and is of significant significance for the early diagnosis, treatment, and prognosis of tumors. However, existing gene panels and methods for grading the malignancy of renal cancer based on second-generation sequencing lack external validation, resulting in unreliable malignancy grading and impractical clinical application. Therefore, a novel tumor malignancy and risk grading system based on specific genetic testing is urgently needed.

[0007] Summary of the Invention

[0008] Therefore, the technical problem to be solved by the present invention is to provide a gene combination for grading renal cancer and its use, which can grade the malignancy of renal cancer and can be used to predict the prognosis of renal cancer patients. To achieve this goal, the present invention uses whole-exome sequencing technology to screen specially screened and grouped renal cancer patient data from Peking University First Hospital, and finally obtains this gene combination for grading the malignancy of renal cancer and predicting the prognosis, providing clinicians and patients with more accurate renal cancer malignancy information and disease prediction information.

[0009] According to one aspect of the present invention, the present invention provides a gene combination for grading human tumors, wherein the gene combination consists of a gene set A and a gene fragment set B;

[0010] 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, at least one of PDGFRA, 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;

[0011] The gene segment set B includes: 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, chr4:8579501-85 90249, chr4:9319501-9330249, chr5:150899501-150940249, chr6:1478 19501-147840249, chr6:157089501-157110249, chr6:164889501-16490 0249, chr6:20399501-20410249, chr6:26519501-26530249, chr6:71659 501-71670249, chr6:73329501-73340249, chr7:100539501-100560249, c hr8:1939501-1960249, chr8:21999501-22070249, chr8:29189501-2920 0249, chr9:91789501-91800249, chr10:99419501-99440249, chr11:177 39501-17760249, chr11:63329501-63350249, chr12:169501-250249, ch r12:54329501-54350249, 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 segments in the gene segment set B are annotated based on GRCh37. In GRCh38 or future versions of the human reference genome, the numbers may change, but the objective segment positions pointed to and the genes that can be used for detection will not change;

[0012] Optionally, the detailed genes included in the gene fragment set B are as follows:

[0013] Table 1 Gene fragment set B

[0014]

[0015]

[0016] Optionally, the gene set A includes at least one of: ASAH1, CCDC136, FAM71E2, IFITM3, KRT9, PRB2, PROSER3, TCEAL5, U2AF2, USP35, WDR44 and ZNF700;

[0017] Optionally, the gene segment set B includes: chr2:179479501-179610249, chr2:207989501-208000249, chr2:219719501-219840249, chr3:126249501-126270249, chr3:129319501-1293 30249, chr3:138659501-138770249, chr3:183999501-184020249, chr5:150899501-150940249, chr7:100539501-100560249 and at least one of chr13:114519501-114530249.

[0018] According to another aspect of the present invention, there is provided use of the above gene combination in preparing a product for human tumor grading detection.

[0019] Optionally, the tumor is a urinary system tumor or pan-cancer;

[0020] Optionally, the urinary system tumor is a malignant tumor of the urinary system;

[0021] Optionally, the urinary system tumor is renal cancer;

[0022] Optionally, the pan-cancer refers to cancer types in the TCGA pan-cancer data.

[0023] Optionally, the tumor grading refers to the determination of tumor malignancy and the prediction of tumor prognosis;

[0024] Optionally, the tumor grade is divided into a high-risk group and a low-risk group.

[0025] Optionally, the product includes primers, probes, reagents, kits, gene chips or detection systems for detecting the gene types of genes in the gene combination.

[0026] Optionally, the product is for detecting exons and related intron regions of genes in gene set A and gene fragment set B.

[0027] According to another aspect of the present invention, a method for grading the above-mentioned tumor is provided, comprising the following steps:

[0028] Step S1: evaluating gene mutations and gene copy number variations of genes included in gene set A in the cancer cell tissue, and evaluating gene copy number variations of gene fragment set B in the cancer cell tissue;

[0029] Step S2: Based on the evaluation results of step S1, the malignancy of the cancer is determined and the tumor prognosis is predicted.

[0030] Optionally, the gene mutation includes base substitution mutation, deletion mutation, insertion mutation and / or fusion mutation, and the gene copy number variation includes gene copy number increase and / or gene copy number decrease.

[0031] Optionally, in step S1, the sequencing data of the tumor tissue and normal tissue are compared to evaluate the gene mutations and copy number variations of the genes included in the gene set A, and the gene copy number variations of the gene fragment set B are simultaneously evaluated.

[0032] Optionally, in step S2, if at least one gene in gene set A has gene mutation or copy number variation, or at least one fragment in gene fragment set B has gene copy number increase, the tumor is classified as a high-risk group; conversely, if no gene in gene set A has gene mutation or copy number variation, and no fragment in gene fragment set B has gene copy number increase, the tumor is classified as a low-risk group.

[0033] Optionally, any gene fragments are selected from the gene combination and combined to form a new gene combination, and the same tumor grading method is used to grade the tumor malignancy and predict the tumor prognosis, thereby guiding clinical diagnosis and treatment.

[0034] The technical solution of the present invention has the following advantages:

[0035] 1. The detection gene panel of the present invention is derived from high-throughput sequencing data of actual renal cancer cases at Peking University First Hospital through specific paired cluster analysis. This real-world data has higher reliability and credibility, and can accurately grade the malignancy and predict the prognosis of renal cancer and pan-cancer.

[0036] 2. The gene combination described in the present invention includes a plurality of gene combinations, from which multiple gene combinations can be selected for judging the malignancy of renal cancer and pan-cancer, and for different clinical situations.

[0037] 3. Compared with whole-exome sequencing, the present invention performs targeted sequencing analysis on specific genes and DNA fragments, which can significantly improve sequencing depth and accuracy under the premise of the same cost. Under the premise of the same sequencing depth and accuracy, it can significantly save costs and has wide universality.

[0038] 4. Compared with the traditional Fuhrman pathological grading system, the present invention is completely unaffected by the subjective impression of the pathologist and has excellent objectivity and credibility. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0040] FIG1 is a Kaplan-Meier survival analysis diagram of Experimental Example 1 of the present invention using the gene combination in Example 1 of the present invention to perform renal cancer malignancy grading according to the grading standard, with tumor-specific survival as the primary endpoint;

[0041] FIG2 is a Kaplan-Meier survival analysis graph of experimental example 1 of the present invention using the gene combination in Example 1 of the present invention according to the renal cancer malignancy grading standard, with tumor progression-free survival as the primary endpoint;

[0042] FIG3 is a Kaplan-Meier survival analysis graph of Experimental Example 1 of the present invention using the gene combination in Example 1 of the present invention according to the renal cancer malignancy grading standard, with overall survival as the primary endpoint;

[0043] FIG4 is a Kaplan-Meier survival analysis chart of the present invention using tumor-specific survival as the primary endpoint after using gene combination 1 to perform renal cancer malignancy grading according to Experimental Example 2;

[0044] FIG5 is a Kaplan-Meier survival analysis diagram of experimental example 2 of the present invention using the gene combination 1 of the present invention for grading the malignancy of renal cancer according to the grading standard, with tumor progression-free survival as the primary endpoint.

[0045] FIG6 is a Kaplan-Meier survival analysis diagram of Experimental Example 2 of the present invention using the gene combination 1 of the present invention for grading the malignancy of renal cancer according to the grading standard, with overall survival as the primary endpoint.

[0046] FIG7 is a Kaplan-Meier survival analysis chart of Experimental Example 3 of the present invention using the gene combination in Example 1 of the present invention according to the pan-cancer malignancy grading standard, with tumor-specific survival as the primary endpoint;

[0047] FIG8 is a Kaplan-Meier survival analysis chart of Experimental Example 3 of the present invention using the gene combination in Example 1 of the present invention according to the pan-cancer malignancy grading standard, with tumor progression-free survival as the primary endpoint;

[0048] FIG9 is a Kaplan-Meier survival analysis chart of experimental example 3 of the present invention using the gene combination in Example 1 of the present invention according to the pan-cancer malignancy grading standard, with tumor disease-free survival as the primary endpoint;

[0049] FIG10 is a Kaplan-Meier survival analysis chart of Experimental Example 3 of the present invention using the gene combination in Example 1 of the present invention according to the pan-cancer malignancy grading standard, with overall tumor survival as the primary endpoint;

[0050] FIG11 is a Kaplan-Meier survival analysis chart of Experimental Example 4 of the present invention using the gene combination 1 in Experimental Example 2 of the present invention according to the pan-cancer malignancy grading standard, with tumor-specific survival as the primary endpoint;

[0051] FIG12 is a Kaplan-Meier survival analysis chart of Experimental Example 4 of the present invention using the gene combination 1 in Experimental Example 2 of the present invention according to the pan-cancer malignancy grading standard, with tumor progression-free survival as the primary endpoint;

[0052] FIG13 is a Kaplan-Meier survival analysis diagram of Experimental Example 4 of the present invention using the gene combination 1 in Experimental Example 2 of the present invention for pan-cancer malignancy grading, with overall tumor survival as the primary endpoint. DETAILED DESCRIPTION

[0053] The following examples are provided for a better understanding of the present invention and are not intended to limit the best mode of implementation. They do not limit the content and scope of protection of the present invention. Any product identical or similar to the present invention obtained by anyone under the guidance of the present invention or by combining the features of the present invention with other prior arts shall fall within the scope of protection of the present invention.

[0054] If no specific experimental steps or conditions are specified in the examples, the conventional experimental steps or conditions described in the literature in this field can be used. If the manufacturer of the reagents or instruments is not specified, they are all commercially available conventional reagents.

[0055] Example 1 Gene Panel for Human Tumor Grading

[0056] The inventors mainly used the high-throughput database of kidney cancer exome sequencing at Peking University First Hospital for screening and identified a gene panel for human tumor grading, which includes gene set A and gene fragment set B;

[0057] 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, 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;

[0058] The gene segment set B includes: 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, chr4:8579501-85 90249, chr4:9319501-9330249, chr5:150899501-150940249, chr6:1478 19501-147840249, chr6:157089501-157110249, chr6:164889501-16490 0249, chr6:20399501-20410249, chr6:26519501-26530249, chr6:71659 501-71670249, chr6:73329501-73340249, chr7:100539501-100560249, c hr8:1939501-1960249, chr8:21999501-22070249, chr8:29189501-2920 0249, chr9:91789501-91800249, chr10:99419501-99440249, chr11:177 39501-17760249, chr11:63329501-63350249, chr12:169501-250249, ch r12:54329501-54350249, 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 segments in the gene segment set B are annotated based on GRCh37. In GRCh38 or future versions of the human reference genome, the numbers may change, but the objective segment positions they point to and the genes that can be used for detection will not change.

[0059] Optionally, the detailed genes included in the gene fragment set B are as follows:

[0060] Table 1 Gene fragment set B

[0061]

[0062]

[0063] Example 2 A method for grading the malignancy and predicting the prognosis of human renal cancer

[0064] This embodiment provides a method for detecting the grade of human tumors, including using the gene panel in Example 1 to grade the malignancy of human renal cancer and predict the prognosis. The specific steps are as follows:

[0065] (1) Take renal cancer tissue and healthy control tissue specimens, wherein the renal cancer tissue specimens can be renal cancer cell lines, fresh renal cancer specimens, frozen renal cancer specimens or paraffin-embedded renal cancer specimens; the healthy control tissue can be tissue from a known healthy person, or it can be adjacent tissue of the renal cancer patient himself. In this embodiment, paraffin-embedded renal cancer specimens are selected, and the healthy control tissue uses adjacent normal tissue. DNA is extracted by conventional methods, and a library is constructed by conventional methods. Finally, targeted high-throughput sequencing is performed using the gene panel in Example 1, and the sequencing data of renal cancer tissue and healthy tissue are compared to obtain the gene mutation (Mutation) and copy number variation (CNV) of each gene in gene set A in the gene panel of the renal cancer tissue, as well as the copy number variation (CNV) of each gene in gene fragment set B.

[0066] The gene mutation includes base substitution mutation, deletion mutation, insertion mutation and fusion mutation, and the gene copy number variation includes gene copy number increase and gene copy number decrease.

[0067] (2) Based on the mutation and / or variation of each gene in the gene combination of the renal cancer tissue obtained in step (1), the following are judged:

[0068] If there is a gene mutation or copy number variation 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 renal cancer patient is in the high-risk group and has a worse tumor prognosis; conversely, if no gene in gene set A has a gene mutation or copy number variation, and no gene copy number increase in any fragment in gene fragment set B, the renal cancer patient is in the low-risk group and has a better tumor prognosis.

[0069] Example 3

[0070] As an alternative embodiment of Example 2, in the present invention, it is allowed to select and recombine the genes in the gene combination (panel) in Example 1 to form a new gene combination. The evaluation criteria are: among the genes selected from gene set A, if at least one gene has a gene mutation or copy number variation, it indicates that the renal cancer patient is in the high-risk group; if the gene fragments selected from gene fragment set B have an increase in gene copy number in at least one region, it indicates that the renal cancer patient is in the high-risk group; conversely, if there is no gene mutation or copy number variation in the genes selected from gene set A and no copy number increase in the fragments selected from gene fragment set B, the renal cancer patient is in the low-risk group.

[0071] Example 4 A method for pan-cancer malignancy grading and prognosis prediction

[0072] This embodiment provides a method for human tumor grade detection, including using the gene panel in Example 1 to perform pan-cancer malignancy grade and prognosis prediction. The specific steps are as follows:

[0073] (1) Take pan-cancer (here pan-cancer is defined as all cancer types in TCGA pan-cancer data, including adrenal cancer, urothelial carcinoma, breast cancer, cervical cancer, bile duct cancer, colon cancer, lymphoma, esophageal cancer, glioblastoma, head and neck squamous cell carcinoma, chromophobe renal cell carcinoma, clear cell renal cell carcinoma, papillary renal cell carcinoma, leukemia, glioma, hepatocellular carcinoma, lung adenocarcinoma, lung squamous cell carcinoma, mesothelioma, ovarian serous cystadenocarcinoma, pancreatic cancer, pheochromocytoma and paraganglioma, prostate cancer, Rectal cancer, sarcoma, skin melanoma, gastric cancer, testicular cancer, thyroid cancer, thymic cancer, endometrial cancer, uterine sarcoma, uveal melanoma, the pan-cancer mentioned in this embodiment is defined as such and will not be repeated) tissue and healthy control tissue specimens, the pan-cancer tissue specimens can be pan-cancer cell lines, fresh pan-cancer specimens, frozen pan-cancer specimens or paraffin-embedded pan-cancer specimens; the healthy control tissue can be tissue from a known healthy person or adjacent tissue of the pan-cancer patient himself. In this embodiment, paraffin-embedded pan-cancer specimens were selected, and the healthy control tissue used was adjacent normal tissue. DNA was extracted by conventional methods, and a library was constructed by conventional methods. Finally, targeted high-throughput sequencing was performed using the gene panel in Example 1. The sequencing data of the pan-cancer tissue and the healthy tissue were compared to obtain the gene mutation (Mutation) and copy number variation (CNV) of each gene in the gene set A in the gene panel of the pan-cancer tissue, as well as the copy number variation (CNV) of each gene in the gene fragment set B.

[0074] The gene mutation includes base substitution mutation, deletion mutation, insertion mutation and fusion mutation, and the gene copy number variation includes gene copy number increase and gene copy number decrease.

[0075] (2) Based on the mutation and / or variation of each gene in the pan-cancer tissue gene combination obtained in step (1), the following are judged:

[0076] If there is a gene mutation or copy number variation 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 pan-cancer patient is in the high-risk group and has a worse tumor prognosis; conversely, if no gene in gene set A has a gene mutation or copy number variation, and no fragment in gene fragment set B has an increase in gene copy number, the pan-cancer patient is in the low-risk group and has a better tumor prognosis.

[0077] Example 5

[0078] As an alternative embodiment of Example 4, the present invention allows the genes in the gene panel in Example 1 to be selected and recombined to form a new gene panel. The evaluation criteria are: if at least one gene in the genes selected from gene set A has a gene mutation or copy number variation, the pan-cancer patient is in the high-risk group; if at least one region of the gene fragments selected from gene fragment set B has a gene copy number increase, the pan-cancer patient is in the high-risk group; conversely, if no gene mutation or copy number variation 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 the low-risk group.

[0079] Experimental Example 1: Feasibility Validation of Gene Combinations and Detection Methods for Human Tumor Grading in Evaluating the Malignancy Grade and Prognosis Prediction of Human Clear Cell Renal Cell Carcinoma

[0080] Clear cell carcinoma is the most common pathological type of renal cancer, accounting for more than 70% of all renal cancers. The TCGA (PanCancer Atlas) renal clear cell carcinoma database is a globally recognized renal cancer database that can be used to test the feasibility and credibility of the present invention for evaluating the malignancy grade and prognosis prediction of renal cancer.

[0081] The TCGA (PanCancer Atlas) renal clear cell carcinoma data contains a total of 512 patients, of which 354 patients have complete gene mutation and copy number variation data, which are suitable for the application conditions of the present invention.

[0082] According to the method described in Example 2, all genes of the gene set A and all fragments of the gene fragment set B in Example 1 were selected for implementation in this experimental example. The genes actually used for detection in the gene fragment set B are shown in Table 2. The above 354 patients were graded for malignancy and successfully 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%. By Kaplan-Meier survival analysis, it can be seen that the tumor-specific survival (Figure 1), tumor progression-free survival (Figure 2) and overall survival (Figure 3) of the high-risk group and the low-risk group were statistically different and in line with the grouping expectations 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 overall survival (Log-rank p value = 4.523e-3). Therefore, the gene combination of the present invention is accurate and reliable for malignancy grading and prognosis prediction of renal cancer patients.

[0083] Table 2 Genes actually detected for gene fragment set B in Experimental Example 1

[0084] Gene fragment location Genes used for detection in the experimental example chr2:179479501-179610249TTNchr2:207989501-208000249KLF7chr2:219719501-219840249WNT6chr2:3679501-3700249COLEC11chr3:126249501-126270249 CHST13chr3:129319501-129330249PLXND1chr3:138659501-138770249FOXL2chr3:18399 9501-184020249PSMD2chr4:1189501-1230249CTBP1chr4:8579501-8590249GPR78chr4:93 19501-9330249USP17L5chr5:150899501-150940249FAT2chr6:147819501-147840249SAM D5chr6:157089501-157110249ARID1Bchr6:164889501-164900249C6orf118chr6:2039950 1-20410249E2F3chr6:26519501-26530249HCG11chr6:71659501-71670249B3GAT2chr6:7 3329501-73340249KCNQ5chr7:100539501-100560249ACHEchr8:1939501-1960249KBTBD11

[0085] chr8:21999501-22070249BMP1chr8:29189501-29200249DUSP4chr9:91789501-91800249SHC3chr10:9 9419501-99440249PI4K2Achr11:17739501-17760249MYOD1chr11:63329501-63350249PLAAT2chr12:16 9501-250249IQSEC3chr12:54329501-54350249HOXC13chr12:63179501-63550249AVPR1Achr12:72695 01-7310249CLSTN3chr13:114519501-114530249GAS6chr15:73649501-73670249HCN4chr15:74209501- 74220249LOXL1chr15:78409501-78430249CIB2chr15:83859501-83880249HDGFL3chr18:8809501-882 0249MTCL1chr19:24059501-24070249ZNF726chr19:4229501-4250249EBI3chr19:46879501-46900249P PP5Cchr20:22559501-22570249FOXA2chr20:62189501-62200249HELZ2chr21:45949501-46110249TSPE ARchr22:19499501-19760249SEPTIN5chr22:36649501-38700249MYH9chr22:46309501-47080249WNT7B

[0086] Experimental Example 2: Feasibility Verification of Optimal Gene Combinations and Detection Methods in Evaluating the Malignancy Grade and Prognosis Prediction of Human Renal Clear Cell Carcinoma

[0087] The present invention allows for the selection of arbitrary gene segments from a gene panel to form a new gene panel, using the same criteria for grading renal cancer malignancy and predicting tumor prognosis. Here, gene set A1 was selected from gene set A of the gene panel, and gene set B1 was selected from gene set B to form gene panel 1 (panel 1), which was used for grading renal cancer malignancy and predicting prognosis. A feasibility analysis was performed using the TCGA (PanCancer Atlas) renal clear cell carcinoma database. Similarly, the criteria are: if there is a gene mutation or copy number variation in at least one gene in gene set A1, or if there is an increase in gene copy number in at least one region of gene set B1, the renal cancer patient is in the high-risk group and has a worse tumor prognosis. Conversely, if no genes in gene set A1 have gene mutations or copy number variations, and no gene copy number increases in any segments of gene set B1, the renal cancer patient is in the low-risk group and has a better tumor prognosis. It should be noted that in this experimental example, gene combination 1 (panel 1) was selected based on the gene combination (panel) and has higher accuracy (higher specificity). Compared with gene combination 1 (panel 1), gene combination (panel) has a wider range of applicability (higher sensitivity).

[0088] Table 3 Gene set A1

[0089]

[0090] Table 4 Gene fragment set B1

[0091] Gene fragment location Genes used for detection in the experimental example chr2:179479501-179610249TTNchr2:207989501-208000249KLF7chr2:219719501-219840249WNT6chr3:126249501-126270249CHST13chr3:129319501-129330 249PLXND1chr3:138659501-138770249FOXL2chr3:183999501-184020249PSMD2chr5:1508 99501-150940249FAT2chr7:100539501-100560249ACHEchr13:114519501-114530249GAS6

[0092] According to the method described in Example 2, gene panel 1 (panel 1) was used to grade the malignancy of the above 354 patients and successfully divided them 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%. By Kaplan-Meier survival analysis, it can be seen that the tumor-specific survival (Figure 4), tumor progression-free survival (Figure 5) and overall survival (Figure 6) of the high-risk group and the low-risk group were statistically different and in line with the grouping expectations 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 overall survival (Log-rank p value = 1.703e-3). Therefore, other gene combinations selected from the gene panel of the present invention can still be used to grade the malignancy and predict the prognosis of renal cancer patients.

[0093] Experimental Example 3: Feasibility Verification of Gene Combinations and Detection Methods for Human Tumor Grading in Evaluating Pancancer Malignancy Grading and Prognosis Prediction

[0094] The TCGA (PanCancer Atlas) pan-cancer database is a globally recognized pan-cancer database that can be used to test the feasibility and credibility of the present invention for evaluating pan-cancer malignancy grading and prognosis prediction.

[0095] The TCGA (PanCancer Atlas) pan-cancer data contains a total of 10,967 cases of pan-cancer data, of which 9,896 cases have complete gene mutation and copy number variation data, which are suitable for the application conditions of the present invention.

[0096] The method described in Example 4 was used. In this experimental example, all genes in gene set A and all fragments in gene fragment set B in Example 1 were selected for implementation. The genes actually tested in gene fragment set B were the same as those in Table 2 of Experimental Example 1. The 9896 patients were classified by malignancy grade and successfully divided into a high-risk group and a low-risk group. The high-risk group accounted for 77.0% and the low-risk group accounted for 23.0%. Kaplan-Meier survival analysis showed that the high-risk and low-risk groups had statistically significant differences in tumor-specific survival (Figure 7), tumor progression-free survival (Figure 8), tumor disease-free survival (Figure 9), and overall survival (Figure 10), which were consistent with the grouping expectations 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 is accurate and reliable in grading the malignancy degree and predicting the prognosis of pan-cancer patients.

[0097] Experimental Example 4: Feasibility Verification of Optimal Gene Combinations and Detection Methods in Evaluating Pan-Cancer Malignancy Grading and Prognosis Prediction

[0098] The present invention allows for the selection of any gene fragments from a gene panel to form a new gene panel, using the same judgment criteria to grade pan-cancer malignancy and predict tumor prognosis. Here, according to the method described in Example 5, gene panel 1 (panel 1) in Experimental Example 2 was selected and used for pan-cancer malignancy grading and prognosis prediction, and a feasibility analysis was performed using the TCGA (PanCancer Atlas) pan-cancer database. Similarly, the judgment criteria are: if there is a gene mutation or copy number variation in at least one gene in gene set A1, or there is an increase in gene copy number in at least one region in gene fragment set B1, it indicates that the pan-cancer patient is in the high-risk group and has a worse tumor prognosis; conversely, if no gene in gene set A1 has a gene mutation or copy number variation, and no gene copy number increase in any fragment in gene fragment set B1, then the pan-cancer patient is in the low-risk group and has a better tumor prognosis. It should be noted that, in this experimental example, gene panel 1 (panel 1) was selected based on the gene panel. Since it has fewer detection sites, it has a significantly lower implementation cost.

[0099] According to the method described in Example 4, gene panel 1 was used to grade the malignancy of 9,896 patients in the pan-cancer database and successfully divided them into high-risk and low-risk groups. The high-risk group accounted for 26.8% and the low-risk group accounted for 73.2%. Kaplan-Meier survival analysis showed that the high-risk and low-risk groups had statistically significant differences in tumor-specific survival (Figure 11), tumor progression-free survival (Figure 12), and overall survival (Figure 13), which were consistent with the grouping expectations 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 overall survival (Log-rank p value = 5.302e-5). Therefore, other gene combinations selected from the gene panel of the present invention can still be used to grade the malignancy and predict the prognosis of pan-cancer patients.

[0100] Obviously, the above embodiments are merely examples for clarity of explanation and are not intended to limit the implementation methods. Those skilled in the art will readily appreciate that other variations or modifications based on the above descriptions are possible. It is not necessary and impossible to enumerate all implementation methods here. Obvious variations or modifications arising therefrom remain within the scope of protection of the present invention.

Claims

1. A gene combination for human tumor grading, characterized in that, the gene combination consists of gene set A and gene fragment set B; the gene set A includes at least one of 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, PDGFRA, 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 described gene fragment set B includes: 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, chr4:8579501-8590249, chr4:9319501-9330249, chr5:150899501-150940249, chr6:147819501-147840249, chr6:157089501-157110249, chr6:164889501-164900249, chr6:20399501-20410249, chr6:26519501-26530249, chr6:71659501-71670249, chr6:73329501-73340249, chr7:100539501-100560249, chr8:1939501-1960249, chr8:21999501-22070249, chr8:29189501-29200249, chr9:91789501-91800249, chr10:99419501-99440249, chr11:17739501-17760249, chr11:63329501-63350249, chr12:169501-250249, chr12:54329501-54350249, chr12:63179501-63550249, chr12:7269501-7310249, chr13:114519501-114530249, chr15:73649501-73670249, chr15:74209501-74220249, chr15:78409501-78430249, chr15:83859501-83880249, chr18:8809501-8820249, chr19:24059501-24070249, chr19:4229501-4250249, 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 the gene fragment set B are annotated based on GRCh37.

2. A gene combination for human tumor grading according to claim 1, characterized in that, the detailed genes included in the gene fragment set B are as follows in the table: Table 1 Gene fragment set B Optionally, the gene set A includes at least one of ASAH1, CCDC136, FAM71E2, IFITM3, KRT9, PRB2, PROSER3, TCEAL5, U2AF2, USP35, WDR44 and ZNF700; the gene fragment set B includes at least one of chr2:179479501-179610249, chr2:207989501-208000249, chr2:219719501-219840249, chr3:126249501-126270249, chr3:129319501-129330249, chr3:138659501-138770249, chr3:183999501-184020249, chr5:150899501-150940249, chr7:100539501-100560249 and chr13:114519501-114530249.

3. Use of the gene combination according to claim 1 or 2 in the preparation of a product for human tumor grading detection.

4. Use according to claim 3, characterized in that, the tumor is a urinary system tumor or pan-cancer; Optionally, the urinary system tumor is a malignant urinary system tumor; Optionally, the urinary system tumor is renal cancer; Optionally, the pan-cancer is a cancer type in the TCGA pan-cancer data.

5. The use according to claim 3 or 4, characterized in that the tumor grading refers to the judgment of tumor malignancy degree and the prediction of tumor prognosis, and is used to guide clinical diagnosis and treatment; Optionally, the tumor grading is divided into a high-risk group and a low-risk group.

6. The use according to any one of claims 3-5, characterized in that the product includes primers, probes, reagents, kits, gene chips or detection systems for detecting the gene types of the genes in the gene combination.

7. The application according to claim 6, characterized in that the product detects the exons and related intron regions of the genes in gene set A and gene fragment set B.

8. The application according to claim 5 or 6 or 7, characterized in that the method for tumor grading includes the following steps: Step S1: Evaluate the gene mutations and gene copy number variations of the genes included in gene set A in the cancer cell tissue, and evaluate the gene copy number variations of gene fragment set B in the cancer cell tissue; Step S2: Based on the evaluation results of Step S1, judge the cancer malignancy degree and predict the tumor prognosis.

9. The application according to claim 8, characterized in that the gene mutations include base substitution mutations, deletion mutations, insertion mutations and / or fusion mutations, and the gene copy number variations include gene copy number increases and / or gene copy number decreases.

10. The application according to claim 8 or 9, characterized in that In Step S1, by comparing the sequencing data of the tumor tissue and the normal tissue, it is used to evaluate the gene mutations and copy number variations of the genes included in gene set A, and at the same time evaluate the gene copy number variations of gene fragment set B.

11. The application according to claim 8 or 9 or 10, characterized in that In Step S2, if at least one gene in gene set A has a gene mutation or copy number variation, or at least one fragment in gene fragment set B has a gene copy number increase, the tumor grading is the high-risk group; otherwise, that is, no gene in gene set A has a gene mutation or copy number variation, and at the same time no fragment in gene fragment set B has a gene copy number increase, the tumor grading is the low-risk group.

12. The application according to any one of claims 1-11, characterized in that Select any gene fragment from the gene combination for combination to form a new gene combination, and use the same method for tumor grading to grade the tumor malignancy degree and predict the tumor prognosis, so as to guide clinical diagnosis and treatment.