WO2022184073A1 - 一种用于人肿瘤分级的基因组合及其用途 - Google Patents

一种用于人肿瘤分级的基因组合及其用途 Download PDF

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WO2022184073A1
WO2022184073A1 PCT/CN2022/078709 CN2022078709W WO2022184073A1 WO 2022184073 A1 WO2022184073 A1 WO 2022184073A1 CN 2022078709 W CN2022078709 W CN 2022078709W WO 2022184073 A1 WO2022184073 A1 WO 2022184073A1
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gene
tumor
cancer
copy number
grading
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French (fr)
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熊耕砚
何世明
何志嵩
周利群
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北京大学第一医院
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Priority to EP22762537.3A priority patent/EP4265739A1/en
Priority to US18/273,213 priority patent/US20240084392A1/en
Publication of WO2022184073A1 publication Critical patent/WO2022184073A1/zh

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    • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
    • C12Q1/6883—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
    • C12Q1/6886—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
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    • C12Q2600/112—Disease subtyping, staging or classification
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  • the invention relates to the field of tumor grading detection, in particular to a gene combination used for human tumor grading and use thereof.
  • Kidney cancer is a common malignant tumor of the urinary system. In recent years, the incidence of renal cancer has increased year by year, accounting for 2%-3% of adult malignant tumors, and it is increasing at a rate of about 2.5% every year. Kidney cancer lacks typical clinical manifestations in the early stage, and kidney cancer with clinical symptoms is often at an advanced stage.
  • the treatment of kidney cancer is a comprehensive treatment based on surgery. During the treatment, it is often necessary to grade the malignant degree of kidney cancer tissue to assist doctors in judging the disease. The progression and prognosis of renal cell carcinoma, and the development of further treatment plans, the Fuhrman nuclear classification is currently the most widely used system for grading the malignant degree of renal cancer.
  • the Fuhrman nuclear grading system was proposed in 1982, and the malignancy and risk of the tumor were graded and evaluated by the Fuhrman grading system.
  • renal cancer is divided into four levels: G1 (well differentiated), G2 (moderately well differentiated), G3 (moderately differentiated), and G4 (poorly differentiated or undifferentiated).
  • G1 well differentiated
  • G2 moderately well differentiated
  • G3 moderately differentiated
  • G4 poorly differentiated or undifferentiated
  • the higher the degree the higher the risk of disease recurrence and metastasis after treatment.
  • the Fuhrman grading system is a pure pathological image classification system, which has the following defects: 1. It needs to be judged according to the personal experience of the pathologist, there is a certain degree of subjectivity, and there are huge differences between different pathologists; 2.
  • the pathological images of G2 grade and The degree of distinction of G3 pathological images is small, and it is difficult to classify the two.
  • the above defects can easily lead to inaccurate grading of the malignancy of the disease, error in the judgment of disease progression and prognosis, and affect the diagnosis and treatment of the disease.
  • next-generation sequencing technology With the maturity and promotion of next-generation sequencing technology, the use of genetic testing to diagnose diseases has attracted widespread attention. To determine tumor progression. This method overcomes the defects of subjectivity and difficulty in traditional tumor grading, and has great significance for early diagnosis, treatment and prognosis of tumors.
  • the existing gene combinations and methods based on next-generation sequencing technology for the classification of renal cancer malignancy lack external verification, the malignancy classification is unreliable, and cannot be used clinically. Therefore, it is urgent to find a new type of gene detection based on specific genes. tumor malignancy and risk grading system.
  • the technical problem to be solved by the present invention is to provide a gene combination for renal cancer grading and its use, which can grade the malignant degree of renal cancer and can be used for prognosis prediction of renal cancer patients.
  • the invention uses the whole exome sequencing technology to screen the renal cancer patient data of Peking University First Hospital specially screened and grouped, and finally obtains the gene combination, which is used for the malignant degree classification and prognosis prediction of renal cancer, and is useful for clinicians. and patients provide more accurate information on the malignancy of renal cancer and disease prediction.
  • the present invention provides a gene combination for human tumor grading, wherein the gene combination consists of a gene set A and a 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, PDGFRA, PDGFRB, PML, PRB2, PROSER3, RAF1, RARA, RBM15, RET, REXO1, RPN1, RUNX1T1, SCYL1, SLC16A6, SRC, STAG2, TCEAL5, TET2, TMEM82, TP53, TRIM26, U2AF1, U2AF
  • the detailed genes included in the described gene fragment set B are as follows:
  • the gene set A includes: at least one of ASAH1, CCDC136, FAM71E2, IFITM3, KRT9, PRB2, PROSER3, TCEAL5, U2AF2, USP35, WDR44 and ZNF700;
  • the tumor is a urinary system tumor or pan-cancer
  • the tumor of the urinary system is a malignant tumor of the urinary system
  • the urinary system tumor is renal cancer
  • the pan-cancer is a cancer type in the TCGA pan-cancer data.
  • the tumor grade refers to the judgment of tumor malignancy and the prediction of tumor prognosis
  • the tumor grades are divided into high-risk groups and low-risk groups.
  • the product includes primers, probes, reagents, kits, gene chips or detection systems for detecting the genotypes of genes in the gene combination.
  • the product is for detection of exons and related intron regions of genes in gene set A and gene fragment set B.
  • a tumor grading method for the above-mentioned tumor comprising the following steps:
  • Step S1 evaluating the gene mutation and gene copy number variation of the genes contained in the gene set A in the cancer cell tissue, and evaluating the gene copy number variation of the gene fragment set B in the cancer cell tissue;
  • Step S2 Based on the evaluation result of Step S1, determine the malignancy of the cancer and predict the prognosis of the tumor.
  • the gene mutation includes base substitution mutation, deletion mutation, insertion mutation and/or fusion mutation
  • the gene copy number variation includes gene copy number increase and/or gene copy number decrease.
  • step S1 by comparing the sequencing data of the tumor tissue and the normal tissue, it is used to evaluate the gene mutation and copy number variation of the genes contained in the gene set A, and simultaneously evaluate the gene fragment set B. gene copy number variation.
  • the tumor is classified as a high-risk group; On the contrary, that is, there is no gene mutation or copy number variation in gene set A and no gene copy number increase in gene segment set B, the tumor is classified as a low-risk group.
  • any gene fragment is selected from the gene combination to form a new gene combination, and the same tumor grading method is used to grade the tumor malignancy and predict the tumor prognosis, so as to guide clinical diagnosis and treatment.
  • the detection gene combination of the present invention is obtained from the high-throughput sequencing data of the actual renal cancer cases of Peking University First Hospital through specific paired cluster analysis, and the real data has higher reliability. It can accurately grade the malignancy and predict the prognosis of renal cancer and pan-cancer.
  • the gene combination of the present invention includes the diversity of gene combinations, from which a variety of gene combinations can be selected to be used for judging the degree of malignancy of renal cancer and pan-cancer, and for different clinical situations.
  • the present invention performs targeted sequencing analysis on specific genes and DNA fragments, which can significantly improve the sequencing depth and accuracy under the same cost premise, and under the premise of the same sequencing depth and accuracy It can significantly save costs and has wide applicability.
  • the present invention is completely unaffected by the subjective impression of pathologists, and has excellent objectivity and reliability.
  • Fig. 1 is a Kaplan-Meier survival analysis diagram with tumor-specific survival as the primary endpoint after the gene combination in Example 1 of the present invention is used to classify the malignant degree of renal cancer in Experimental Example 1 of the present invention;
  • Fig. 2 is a Kaplan-Meier survival analysis diagram with tumor progression-free survival as the primary endpoint after the gene combination in Example 1 of the present invention is used to classify the malignant degree of renal cancer in Experimental Example 1 of the present invention;
  • Fig. 3 is the Kaplan-Meier survival analysis chart with overall survival as the primary endpoint after using the gene combination in Example 1 of the present invention to carry out the classification of renal cancer malignancy in Experimental Example 1 of the present invention;
  • Fig. 4 is the Kaplan-Meier survival analysis chart with tumor-specific survival as the primary end point after using the gene combination 1 to carry out the grading standard of renal cancer malignancy in Experimental Example 2 of the present invention;
  • FIG. 5 is a Kaplan-Meier survival analysis diagram with tumor progression-free survival as the primary endpoint after the renal cancer malignancy grading standard grading using the gene combination 1 of the present invention in Experimental Example 2 of the present invention.
  • Fig. 6 is a Kaplan-Meier survival analysis chart with overall survival as the primary endpoint after the renal cancer malignancy grading standard grading using the gene combination 1 of the present invention in Experimental Example 2 of the present invention.
  • Fig. 7 is a Kaplan-Meier survival analysis chart with tumor-specific survival as the primary endpoint after the pan-cancer malignancy grading standard is performed using the gene combination in Example 1 of the present invention in Experimental Example 3 of the present invention;
  • FIG. 8 is a Kaplan-Meier survival analysis diagram with tumor progression-free survival as the primary endpoint after the pan-cancer malignancy grading standard is performed using the gene combination in Example 1 of the present invention in Experimental Example 3 of the present invention;
  • Fig. 9 is a Kaplan-Meier survival analysis chart with tumor disease-free survival as the primary endpoint after the pan-cancer malignancy grading standard is performed using the gene combination in Example 1 of the present invention in Experimental Example 3 of the present invention;
  • Fig. 10 is a Kaplan-Meier survival analysis chart with tumor overall survival as the primary endpoint after the pan-cancer malignancy grading standard is performed using the gene combination in Example 1 of the present invention in Experimental Example 3 of the present invention;
  • Figure 11 is a Kaplan-Meier survival analysis chart with tumor-specific survival as the primary endpoint after the pan-cancer malignancy grading standard is performed using the gene combination 1 in Experimental Example 2 of the present invention in Experimental Example 4 of the present invention;
  • Figure 12 is a Kaplan-Meier survival analysis chart with tumor progression-free survival as the primary endpoint after the pan-cancer malignancy grading standard grading using the gene combination 1 in Experimental Example 2 of the present invention in Experimental Example 4 of the present invention;
  • FIG. 13 is a Kaplan-Meier survival analysis chart with tumor overall survival as the primary endpoint after the pan-cancer malignancy grading standard is performed using the gene combination 1 in Experiment 2 of the present invention in Experimental Example 4 of the present invention.
  • the inventors mainly screened the renal cancer exome sequencing high-throughput database of Peking University First Hospital, and confirmed a gene panel (panel) for human tumor grading, which includes 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, PDGFRA, PDGFRB, PML, PRB2, PROSER3, RAF1, RARA, RBM15, RET, REXO1, RPN1, RUNX1T1, SCYL1, SLC16A6, SRC, STAG2, TCEAL5, TET2, TMEM82, TP53, TRIM26, U2AF1, U2AF
  • the detailed genes included in the described gene fragment set B are as follows:
  • Example 2 A method for grading the malignant degree of human renal cancer and predicting the prognosis
  • the present embodiment provides a method for the detection of human tumor grading, including, using the gene panel (panel) in Embodiment 1 to perform the malignant degree grading and prognosis prediction of human renal cancer, and the specific steps are as follows:
  • 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; healthy control tissues can be known
  • the tissue of a generally recognized healthy person can also be the adjacent tissue of the kidney cancer patient himself.
  • paraffin-embedded renal cancer specimens were selected, and the healthy control tissues were normal tissues adjacent to the cancer.
  • DNA was extracted by conventional methods, and libraries were constructed by conventional methods.
  • Example 1 was used for Targeted high-throughput sequencing, comparing the sequencing data of renal cancer tissue and healthy tissue, to obtain the gene mutation (Mutation) and copy number variation (CNV) of each gene of gene set A in the gene combination of the renal cancer tissue, and the gene Copy number variation (CNV) of each gene in fragment set B.
  • Metation gene mutation
  • CNV copy number variation
  • the gene mutation includes base substitution mutation, deletion mutation, insertion mutation and fusion mutation
  • the gene copy number variation includes gene copy number increase and gene copy number decrease.
  • the renal cancer patient is in a high-risk group and has a worse tumor prognosis; On the contrary, there is no gene mutation or copy number variation in gene set A, and no gene copy number increase in gene fragment set B, the renal cancer patients are in the low-risk group and have better tumor prognosis.
  • the genes in the gene panel in Example 1 are allowed to be selected and recombined to form a new gene panel.
  • the gene mutation or copy number variation of at least one of the genes indicates that the renal cancer patients are in the high-risk group; for the gene fragments selected from the gene fragment set B, at least one of the regional genes
  • the increased copy number indicates that the renal cancer patients are in the high-risk group; on the contrary, there is no gene mutation or copy number variation in the genes selected in the gene set A, and there is no copy in the selected fragments in the gene fragment set B.
  • the number of kidney cancer patients increased, and the renal cancer patients were in the low-risk group.
  • Example 4 A method for human pan-cancer (Pancancer) malignancy grading and prognosis prediction
  • the present embodiment provides a method for the detection of human tumor grading, comprising, using the gene combination (panel) in Embodiment 1 to carry out the grading and prognosis prediction of human pan-cancer (Pancancer) malignancy, and the specific steps are as follows:
  • pan-cancer is defined as all cancer types in the TCGA pan-cancer data, including adrenal cancer, urothelial cancer, breast cancer, cervical cancer, bile duct cancer, colon cancer, lymphoma, esophageal cancer, Plasmoblastoma, head and neck squamous cell carcinoma, renal chromophobe carcinoma, renal clear cell carcinoma, renal papillary 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 cancer Sarcoma, uveal melanoma,
  • Example 1 paraffin-embedded pan-cancer specimens were selected, and normal tissues adjacent to cancer were used as healthy control tissues. DNA was extracted by conventional methods, and libraries were constructed by conventional methods. Finally, the gene panel in Example 1 was used for Targeted high-throughput sequencing, comparing the sequencing data of the pan-cancer tissue and healthy tissue, to obtain the gene mutation (Mutation) and copy number variation (CNV) of each gene of gene set A in the gene combination of the pan-cancer tissue, and the gene Copy number variation (CNV) of each gene in fragment set B.
  • Metation gene mutation
  • CNV copy number variation
  • the gene mutation includes base substitution mutation, deletion mutation, insertion mutation and fusion mutation
  • the gene copy number variation includes gene copy number increase and gene copy number decrease.
  • pan-cancer patient If there is a gene mutation or copy number variation of at least one gene in gene set A, or there is an increase in gene copy number in at least one region of gene segment set B, the pan-cancer patient is in a high-risk group and has a worse tumor prognosis; On the contrary, there is no gene mutation or copy number variation in gene set A, and no gene copy number increase in gene segment set B, the pan-cancer patients are in the low-risk group and have better tumor prognosis.
  • the genes in the gene panel in Example 1 are allowed to be selected and recombined to form a new gene panel.
  • the gene mutation or copy number variation of at least one of the genes indicates that the pan-cancer patients are in the high-risk group;
  • the gene fragments selected from the gene fragment set B at least one of the regional genes
  • the increase in copy number indicates that the pan-cancer patients are in the high-risk group; on the contrary, there is no gene mutation or copy number variation in the selected gene in gene set A, and no copy appears in the selected segment in gene fragment set B. increased, and the pan-cancer patients were in the low-risk group.
  • Experimental Example 1 Feasibility verification of the gene combination and detection method for human tumor grading in evaluating the malignancy grading and prognosis prediction of human clear cell renal cell carcinoma
  • Clear cell carcinoma is the most common pathological type of renal carcinoma, accounting for more than 70% of all renal carcinomas.
  • the TCGA (PanCancer Atlas) renal clear cell carcinoma database is a globally recognized renal carcinoma database, which can be used to test the present invention for evaluating renal carcinoma. Feasibility and reliability of malignancy grading and prognostic prediction.
  • TCGA PanCancer Atlas renal clear cell carcinoma data has a total of 512 patient data, of which 354 patients have complete gene mutation and copy number variation data, which are suitable for the application conditions of the present invention.
  • the use of the gene combination of the present invention is accurate and reliable for grading the malignant degree and predicting the prognosis of renal cancer patients.
  • Experimental Example 2 Feasibility verification of the preferred gene combination and detection method in assessing the malignancy grading and prognosis prediction of human clear cell renal cell carcinoma
  • the present invention allows to select any gene segment from the gene panel for combination to form a new gene panel, and use the same judgment standard to grade the malignant degree of renal cancer and predict the tumor prognosis.
  • the gene set A1 is selected from the gene set A of the gene combination (panel)
  • the gene fragment set B1 is selected from the gene fragment set B to form the gene combination 1 (panel 1), which is used for the grading and prognosis of renal cancer.
  • the judgment criteria are: if there is a gene mutation or copy number variation of at least one gene in the gene set A1, or there is an increase in the copy number of at least one region in the gene segment set B1, it indicates that the renal cancer patient is at high risk. On the other hand, if there is no gene mutation or copy number variation in the gene set A1, and there is no gene copy number increase in any segment in the gene segment set B1, this type of renal cancer patients is a low-risk group , with better tumor prognosis. It should be noted that, in this experimental example, the gene combination 1 (panel 1) is optimized on the basis of the gene combination (panel), and has higher accuracy (higher specificity) than the gene combination. 1 (panel 1), the gene combination (panel) has a wider application range (higher sensitivity).
  • gene panel 1 panel 1 graded the above-mentioned 354 patients on the degree of malignancy, 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 14.4%. The proportion was 85.6%.
  • Kaplan-Meier survival analysis there were statistically significant differences in tumor-specific survival (Fig. 4), tumor progression-free survival (Fig. 5) and overall survival (Fig. 6) between the high-risk group and the low-risk group.
  • TCGA PanCancer Atlas pan-cancer database is a globally recognized pan-cancer database, which can be used to test the feasibility and reliability of the present invention for evaluating pan-cancer malignancy grading and prognosis prediction.
  • TCGA PanCancer Atlas pan-cancer data has 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.
  • 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), and tumor disease-free survival (Log-rank p value ⁇ 1.000e-10).
  • p-value ⁇ 1.000e-10) and overall survival Log-rank p-value ⁇ 1.000e-10. Therefore, the use of the gene combination of the present invention is accurate and reliable for malignancy grading and prognosis prediction of pan-cancer patients.
  • Experimental Example 4 Feasibility verification of the preferred gene combination and detection method in assessing the grading of human pan-cancer malignancy and prognosis prediction
  • the present invention allows to select any gene segment from the gene panel for combination to form a new gene panel, and to use the same judgment standard to grade pan-cancer malignancy and predict tumor prognosis.
  • the gene combination 1 (panel 1) in Experimental Example 2 was selected and used for pan-cancer malignancy grading and prognosis prediction, and the TCGA (PanCancer Atlas) pan-cancer database was used for feasibility. analyze.
  • the judgment criteria are: if there is a gene mutation or copy number variation of at least one gene in gene set A1, or there is an increase in gene copy number in at least one region of gene segment set B1, it indicates that the pan-cancer patient is at high risk.
  • gene combination 1 panel 1 is selected on the basis of gene combination (panel), and has significantly lower implementation costs due to fewer detection sites.
  • gene panel 1 panel 1 graded 9896 patients in the above-mentioned pan-cancer database for the degree of malignancy, and successfully divided them into a high-risk group and a low-risk group, of which the high-risk group accounted for 26.8%. , the low-risk group accounted for 73.2%.
  • the tumor-specific survival Fig. 11
  • tumor progression-free survival Fig. 12
  • overall survival Fig.

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Abstract

本发明涉及肿瘤分级检测领域，具体公开了一种用于人肿瘤分级的基因组合及其用途，所述的用于人肿瘤分级的基因组合由基因集A和基因片段集B组成，所述用于人肿瘤分级的基因组合是从北京大学第一医院的实际肾癌病例的高通量测序数据中，通过特定的配对聚类分析得来，来源于真实的数据度，可以针对肾癌、泛癌进行恶性程度分级和预后预测。

Description

一种用于人肿瘤分级的基因组合及其用途
交叉引用
本申请要求在2021年3月2日提交中国专利局、申请号为202110232095X、发明名称为“一种用于人肿瘤分级的基因组合及其用途”的中国专利申请的优先权，其全部内容通过引用结合在本申请中；本申请还要求在2021年3月26日提交中国专利局、申请号为2021103327509、发明名称为“一种用于人肿瘤分级的基因组合及其用途”的中国专利申请的优先权，其全部内容通过引用结合在本申请中；本申请还要求在2021年7月6日提交中国专利局、申请号为2021107623535、发明名称为“一种用于人肿瘤分级的基因组合及其用途”的中国专利申请的优先权，其全部内容通过引用结合在本申请中。
技术领域
本发明涉及肿瘤分级检测领域，具体涉及一种用于人肿瘤分级的基因组合及其用途。
背景技术
肾癌是常见的泌尿系统恶性肿瘤。近年来，肾癌的发病率逐年升高，占成人恶性肿瘤的2％-3％，且每年以2.5％左右的速度上升。肾癌早期缺乏典型的临床表现，出现临床症状的肾癌往往已处于晚期，肾癌的治疗是以手术为基础的综合治疗，治疗中往往需要对肾癌组织进行恶性程度分级以协助医生判断疾病的进展和预后，同时制定进一步治疗方案，Fuhrman核分级是目前应用最广泛的肾癌恶性程度分级系统。
Fuhrman核分级系统于1982年被提出，通过Fuhrman分级系统对该肿瘤的恶性程度及危险度进行分级评价。依据Fuhrman分级标准，将肾癌分为G1(高分化),G2(中高分化),G3(中分化),G4(低分化或未分化)四个层级，随着层级的增高，肾癌的恶性程度就越高，疾病治疗后出现复发、转移的风险也随之升高。然而，Fuhrman分级系统是纯病理图像分型系统，存在如下缺陷：1、需要根据病理医生个人的经验进行判断，存在一定的主观性，不同病理医生之间差别巨大；2、G2级病理图像和G3级病理图像的区分度小，将两者分级难度大。以上的缺陷容易导致疾病的恶性程度分级不准确，对疾病进展和预后判断发生误差，影响疾病的诊疗。
随着二代测序技术的成熟和推广，利用基因检测来诊断疾病的方法受到了广泛的瞩目，如通过全外显子测序，检测标记物基因的突变及拷贝数变异情况，以此诊断肿瘤或判断肿瘤的进展。此方法克服了传统的肿瘤分级中存在的主观性影响和分级难度大的缺陷，对于肿瘤的早期诊断、治疗和预后有重大的意义。然而，现存基于二代测序技术且用于肾癌恶性程度分级的基因组合和方法，均缺乏外部验证，恶性程度分级不可靠，临床无法应用，因此，亟需找到一种基于特定基因检测的新型的肿瘤恶性程度及危险度分级系统。
发明内容
因此，本发明要解决的技术问题在于提供一种用于肾癌分级的基因组合及其用途，它能够对肾癌恶性程度进行分级并可用于肾癌患者的预后预测，为了实现这一目的，本发明使用全外显子测序技术，针对特殊筛选和分组的北京大学第一医院的肾癌患者数据进行筛选，最终得到本基因组合，用于肾癌的恶性程度分级和预后预测，为临床医生和患者提供了更为准确的肾癌恶性程度信息和疾病预测信息。
根据本发明的一个方面，本发明提供了一种用于人肿瘤分级的基因组合，所述的基因组合由基因集A和基因片段集B组成；
所述的基因集A包括：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和ZRSR2中至少一个；
所述的基因片段集B包括：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、chr20:62189501-62200249、chr21:45949501-46110249、chr22:19499501-19760249、chr22:36649501-38700249和chr22:46309501-47080249中至少一个；所述基因片段集B中基因片段位置以GRCh37为标准进行注释，在GRCh38或未来出现的新版人类参考基因组中，其数字可能发生改变，但指向的客观片段位置和可用于检测的基因不会发生改变；
可选的，所述的基因片段集B中包括的详细基因如下表：
表1基因片段集B
Figure PCTCN2022078709-appb-000001
Figure PCTCN2022078709-appb-000002
可选的，所述的基因集A包括：ASAH1、CCDC136、FAM71E2、IFITM3、KRT9、PRB2、PROSER3、TCEAL5、U2AF2、USP35、WDR44和ZNF700至少一个；
可选的，所述的基因片段集B包括：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和chr13:114519501-114530249至少一个。
根据本发明的另一个方面，提供了上述基因组合在制备用于人肿瘤分级检测的产品中的用途。
可选的，所述的肿瘤为泌尿系统肿瘤或泛癌；
可选的，所述泌尿系统肿瘤为泌尿系统恶性肿瘤；
可选的，所述泌尿系统肿瘤为肾癌；
可选的，所述泛癌为TCGA泛癌数据中的癌种。
可选的，所述的肿瘤分级是指肿瘤恶性程度判断和肿瘤预后的预测；
可选的，所述肿瘤分级分为高风险组和低风险组。
可选的，所述产品包括用于检测所述基因组合中基因的基因类型的引物、探针、试剂、试剂盒、基因芯片或检测系统。
可选的，所述产品为针对基因集A和基因片段集B中基因的外显子和相关内含子区域进行检测。
根据本发明的另一个方面，提供了上述肿瘤的肿瘤分级方法，包括如下步骤：
步骤S1:评估所述癌细胞组织中的基因集A中所包含基因的基因突变和基因拷贝数变异，评估癌细胞组织中的基因片段集B的基因拷贝数变异；
步骤S2：基于步骤S1的评估结果，判断癌症恶性程度并进行肿瘤预后预测。
可选的，所述的基因突变包括碱基置换突变、缺失突变、插入突变和/或融合突变，所述基因拷贝数变异包括基因拷贝数增加和/或基因拷贝数减少。
可选的，所述步骤S1中，通过比较所述肿瘤组织与正常组织的测序数据，用于评估所述基因集A中包含基因的基因突变和拷贝数变异，同时评估所述基因片段集B的基因拷贝数变异。
可选的，所述步骤S2中，如果基因集A中至少一个基因出现基因突变或拷贝数变异，或基因片段集B中至少一个片段出现基因拷贝数增加，所述肿瘤分级为高风险组；反之，即基因集A中没有基因出现基因突变或拷贝数变异，同时基因片段集B中没有任何片段出现基因拷贝数增加，所述肿瘤分级为低风险组。
可选的，从所述基因组合中选择任意基因片段进行组合，形成新的基因组合，使用相同的肿瘤分级的方法对肿瘤恶性程度进行分级和肿瘤预后预测，从而指导临床诊疗。
本发明技术方案，具有如下优点：
1.本发明的所述检测基因组合是从北京大学第一医院的实际肾癌病例的高通量测序数据中，通过特定的配对聚类分析得来，来源于真实的数据具有更高的可靠性和可信度，可以准确针对肾癌、泛癌进行恶性程度分级和预后预测。
2.本发明所述基因组合包括基因组合具有多元性，可以从中优选出多种基因组合用于肾癌、泛癌恶性程度的判断，用于不同的临床情况。
3.相比于全外显子测序，本发明针对特定的基因和DNA片段进行靶向测序分析，在相同的成本前提下可以明显提高测序深度和精准度，在相同测序深度和精准度的前提下，可以明显节约成本，普适性广。
4.相比于传统Fuhrman病理分级系统，本发明完全不受病理医生的主观印象影响，具有极佳的客观性和可信度。
附图说明
为了更清楚地说明本发明具体实施方式或现有技术中的技术方案，下面将对具体实施方式或现 有技术描述中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图是本发明的一些实施方式，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其他的附图。
图1是本发明实验例1中利用本发明实施例1中的基因组合进行肾癌恶性程度分级标准分级后，将肿瘤特异性生存作为主要终点的Kaplan-Meier生存分析图；
图2是本发明实验例1中利用本发明实施例1中的基因组合进行肾癌恶性程度分级标准分级后，将肿瘤无进展生存作为主要终点的Kaplan-Meier生存分析图；
图3是本发明实验例1中利用本发明实施例1中的基因组合进行肾癌恶性程度分级标准分级后，将总生存作为主要终点的Kaplan-Meier生存分析图；
图4是本发明实验例2中利用基因组合1进行肾癌恶性程度分级标准分级后，将肿瘤特异性生存作为主要终点的Kaplan-Meier生存分析图；
图5是本发明实验例2中利用本发明基因组合1进行肾癌恶性程度分级标准分级后，将肿瘤无进展生存作为主要终点的Kaplan-Meier生存分析图。
图6是本发明实验例2中利用本发明基因组合1进行肾癌恶性程度分级标准分级后，将总生存作为主要终点的Kaplan-Meier生存分析图。
图7是本发明实验例3中利用本发明实施例1中的基因组合进行泛癌恶性程度分级标准分级后，将肿瘤特异性生存作为主要终点的Kaplan-Meier生存分析图；
图8是本发明实验例3中利用本发明实施例1中的基因组合进行泛癌恶性程度分级标准分级后，将肿瘤无进展生存作为主要终点的Kaplan-Meier生存分析图；
图9是本发明实验例3中利用本发明实施例1中的基因组合进行泛癌恶性程度分级标准分级后，将肿瘤无病生存作为主要终点的Kaplan-Meier生存分析图；
图10是本发明实验例3中利用本发明实施例1中的基因组合进行泛癌恶性程度分级标准分级后，将肿瘤总生存作为主要终点的Kaplan-Meier生存分析图；
图11是本发明实验例4中利用本发明实验例2中的基因组合1进行泛癌恶性程度分级标准分级后，将肿瘤特异性生存作为主要终点的Kaplan-Meier生存分析图；
图12是本发明实验例4中利用本发明实验例2中的基因组合1进行泛癌恶性程度分级标准分级后，将肿瘤无进展生存作为主要终点的Kaplan-Meier生存分析图；
图13是本发明实验例4中利用本发明实验例2中的基因组合1进行泛癌恶性程度分级标准分级后，将肿瘤总生存作为主要终点的Kaplan-Meier生存分析图。
具体实施方式
提供下述实施例是为了更好地进一步理解本发明，并不局限于所述最佳实施方式，不对本发明的内容和保护范围构成限制，任何人在本发明的启示下或是将本发明与其他现有技术的特征进行组合而得出的任何与本发明相同或相近似的产品，均落在本发明的保护范围之内。
实施例中未注明具体实验步骤或条件者，按照本领域内的文献所描述的常规实验步骤的操作或条件即可进行。所用试剂或仪器未注明生产厂商者，均为可以通过市购获得的常规试剂产品。
实施例1 用于人肿瘤分级的基因组合(Panel)
发明人主要利用北京大学第一医院肾癌外显子测序高通量数据库进行筛选，确认了一种用于人肿瘤分级的基因组合(panel)，该基因组合包括基因集A和基因片段集B；
所述的基因集A包括：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和ZRSR2中至少一个；
所述的基因片段集B包括：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、chr20:62189501-62200249、chr21:45949501-46110249、chr22:19499501-19760249、chr22:36649501-38700249和chr22:46309501-47080249中至少一个；所述基因片段集B中基因片段位置以GRCh37为标准进行注释，在GRCh38或未来出现的新版人类参考基因组中，其数字可能发生改变，但指向的客观片段位置和可用于检测的基因不会发生改变。
可选的，所述的基因片段集B中包括的详细基因如下表：
表1基因片段集B
Figure PCTCN2022078709-appb-000003
Figure PCTCN2022078709-appb-000004
实施例2 一种用于人肾癌恶性程度分级和预后预测的方法
本实施例提供了一种用于人肿瘤分级检测的的方法，包括，利用实施例1中的基因组合(panel)进行人肾癌恶性程度分级和预后预测，具体步骤如下：
(1)取肾癌组织和健康对照组织标本，所述肾癌组织标本可以是肾癌细胞系、新鲜肾癌标本、冰冻肾癌标本或石蜡包埋肾癌标本；健康对照组织可以是已知公认健康人的组织，也可以是肾癌患者本人的癌旁组织。在本实施例中选择石蜡包埋的肾癌标本，健康对照组织使用的是癌旁正常组织，通过常规方法提取DNA，通过常规方法构建文库，最终使用实施例1中的基因组合(panel)进行靶向高通量测序，比较肾癌组织与健康组织的测序数据，得到所述肾癌组织的基因组合中基因集A各个基因的基因突变(Mutation)和拷贝数变异(CNV)情况，以及基因片段集B中各个基因的拷贝数变异(CNV)情况。
所述的基因突变包括碱基置换突变、缺失突变、插入突变和融合突变，所述基因拷贝数变异包括基因拷贝数增加和基因拷贝数减少。
(2)基于步骤(1)中获得的肾癌组织的基因组合中各基因的突变和/或变异情况进行判断：
如果存在基因集A中至少一个基因的基因突变或拷贝数变异，或存在基因片段集B中至少一个区域基因拷贝数增加，所述的肾癌患者为高风险组，具有更差的肿瘤预后；反之，基因集A中没有基因出现基因突变或拷贝数变异，同时基因片段集B中没有任何片段出现基因拷贝数增加，所述的肾癌患者为低风险组，具有较好的肿瘤预后。
实施例3
作为实施例2的可替换的实施方式，在本发明中，允许对实施例1中的基因组合(panel)中的基因进行挑选并重新组合，形成新的基因组合，评判标准为，从基因集A中挑选出来的基因，则其中至少一个基因的基因突变或拷贝数变异，表明所述的肾癌患者为高风险组；从基因片段集B中挑选出来的基因片段，则其中至少一个区域基因拷贝数增加，表明所述的肾癌患者为高风险组；反之，基因集A中挑选出的基因中没有出现基因突变或拷贝数变异，同时基因片段集B中挑选出来的片段中没有出现拷贝数增加，所述肾癌患者为低风险组。
实施例4 一种用于人泛癌(Pancancer)恶性程度分级和预后预测的方法
本实施例提供了一种用于人肿瘤分级检测的的方法，包括，利用实施例1中的基因组合(panel)进行人泛癌(Pancancer)恶性程度分级和预后预测，具体步骤如下：
(1)取泛癌(此处泛癌定义为TCGA泛癌数据中所有癌种，包括肾上腺癌，尿路上皮癌，乳腺癌，宫颈癌，胆管癌，结肠癌，淋巴瘤，食管癌，胶质母细胞瘤，头颈鳞状细胞癌，肾嫌色细胞癌，肾透明细胞癌，肾乳头状细胞癌，白血病，脑胶质瘤，肝细胞肝癌，肺腺癌，肺鳞癌，间皮瘤，卵巢浆液性囊腺癌，胰腺癌，嗜铬细胞瘤与副神经节瘤，前列腺癌，直肠癌，肉瘤，皮肤黑色素瘤，胃癌，睾丸癌，甲状腺癌，胸腺癌，子宫内膜癌，子宫肉瘤，葡萄膜黑色素瘤，本实施例中所提泛癌均定义为此，不再重复叙述)组织和健康对照组织标本，所述泛癌组织标本可以是泛癌细胞系、新鲜泛癌标本、冰冻泛癌标本或石蜡包埋泛癌标本；健康对照组织可以是已知公认健康人的组织，也可以是泛癌患者本人的癌旁组织。在本实施例中选择石蜡包埋的泛癌标本，健康对照组织使用的是癌旁正常组织，通过常规方法提取DNA，通过常规方法构建文库，最终使用实施例1中的基因组合(panel)进行靶向高通量测序，比较泛癌组织与健康组织的测序数据，得到所述泛癌组织的基因组合中基因集A各个基因的基因突变(Mutation)和拷贝数变异(CNV)情况，以及基因片段集B中各个基因的拷贝数变异(CNV)情况。
所述的基因突变包括碱基置换突变、缺失突变、插入突变和融合突变，所述基因拷贝数变异包括基因拷贝数增加和基因拷贝数减少。
(2)基于步骤(1)中获得的泛癌组织的基因组合中各基因的突变和/或变异情况进行判断：
如果存在基因集A中至少一个基因的基因突变或拷贝数变异，或存在基因片段集B中至少一个区域基因拷贝数增加，所述的泛癌患者为高风险组，具有更差的肿瘤预后；反之，基因集A中没有基因出现基因突变或拷贝数变异，同时基因片段集B中没有任何片段出现基因拷贝数增加，所述的泛癌患者为低风险组，具有较好的肿瘤预后。
实施例5
作为实施例4的可替换的实施方式，在本发明中，允许对实施例1中的基因组合(panel)中的基因进行挑选并重新组合，形成新的基因组合，评判标准为，从基因集A中挑选出来的基因，则其中至少一个基因的基因突变或拷贝数变异，表明所述的泛癌患者为高风险组；从基因片段集B中挑选出来的基因片段，则其中至少一个区域基因拷贝数增加，表明所述的泛癌患者为高风险组；反之，基因集A中挑选出的基因中没有出现基因突变或拷贝数变异，同时基因片段集B中挑选出来的片段中没有出现拷贝数增加，所述泛癌患者为低风险组。
实验例1 用于人肿瘤分级的基因组合和检测方法在评估人肾透明细胞癌恶性程度分级和预后预测的可行性验证
透明细胞癌是肾癌最常见的病理类型，占到全部肾癌的70％以上，TCGA(PanCancer Atlas)肾透明细胞癌数据库是全球公认的肾癌数据库，可用其检验本发明用于评估肾癌恶性程度分级和预后预测的可行性和可信度。
TCGA(PanCancer Atlas)肾透明细胞癌数据共有512例患者资料，其中354名患者具有完整的基因突变和拷贝数变异数据，适用于本发明的应用条件。
按照实施例2所述的方法实施，本实验例中选取实施例1中基因集A的全部基因和基因片段集B中的所有片段进行实施，基因片段集B中实际用于检测的基因如表2。将上述354例患者进行恶性程度分级，顺利分为高风险组和低风险组，其中高风险组占比46.6％，低风险组占比53.4％，通过Kaplan-Meier生存分析，可见高风险组和低风险组的肿瘤特异性生存(图1)、肿瘤无进展生存(图2)和总生存(图3)均具有统计学差异并符合本发明的分组预期：低风险组具有明显更好的肿瘤特异性生存(Log-rank p值＝7.800e-4)、肿瘤无进展生存(Log-rank p值＝3.060e-4)和总生存(Log-rank p值＝4.523e-3)。故使用本发明的基因组合对肾癌患者进行恶性程度分级和预后预测准确可靠。
表2实验例1中针对基因片段集B进行实际检测的基因
基因片段位置 实验例中用于检测的基因
chr2:179479501-179610249 TTN
chr2:207989501-208000249 KLF7
chr2:219719501-219840249 WNT6
chr2:3679501-3700249 COLEC11
chr3:126249501-126270249 CHST13
chr3:129319501-129330249 PLXND1
chr3:138659501-138770249 FOXL2
chr3:183999501-184020249 PSMD2
chr4:1189501-1230249 CTBP1
chr4:8579501-8590249 GPR78
chr4:9319501-9330249 USP17L5
chr5:150899501-150940249 FAT2
chr6:147819501-147840249 SAMD5
chr6:157089501-157110249 ARID1B
chr6:164889501-164900249 C6orf118
chr6:20399501-20410249 E2F3
chr6:26519501-26530249 HCG11
chr6:71659501-71670249 B3GAT2
chr6:73329501-73340249 KCNQ5
chr7:100539501-100560249 ACHE
chr8:1939501-1960249 KBTBD11
chr8:21999501-22070249 BMP1
chr8:29189501-29200249 DUSP4
chr9:91789501-91800249 SHC3
chr10:99419501-99440249 PI4K2A
chr11:17739501-17760249 MYOD1
chr11:63329501-63350249 PLAAT2
chr12:169501-250249 IQSEC3
chr12:54329501-54350249 HOXC13
chr12:63179501-63550249 AVPR1A
chr12:7269501-7310249 CLSTN3
chr13:114519501-114530249 GAS6
chr15:73649501-73670249 HCN4
chr15:74209501-74220249 LOXL1
chr15:78409501-78430249 CIB2
chr15:83859501-83880249 HDGFL3
chr18:8809501-8820249 MTCL1
chr19:24059501-24070249 ZNF726
chr19:4229501-4250249 EBI3
chr19:46879501-46900249 PPP5C
chr20:22559501-22570249 FOXA2
chr20:62189501-62200249 HELZ2
chr21:45949501-46110249 TSPEAR
chr22:19499501-19760249 SEPTIN5
chr22:36649501-38700249 MYH9
chr22:46309501-47080249 WNT7B
实验例2 优选的基因组合和检测方法在评估人肾透明细胞癌恶性程度分级和预后预测的可行性验证
本发明允许从基因组合(panel)中挑选任意基因片段进行组合，形成新的基因组合，使用相同的判断标准对肾癌恶性程度进行分级和肿瘤预后预测。此处从基因组合(panel)的基因集A中挑选出基因集A1，从基因片段集B中挑选出基因片段集B1，组成基因组合1(panel 1)，用于肾癌恶性程度分级和预后预测，并使用TCGA(PanCancer Atlas)肾透明细胞癌数据库进行可行性分析。同理的，判断标准为：如果存在基因集A1中至少一个基因的基因突变或拷贝数变异，或存在基因片段集B1中至少一个区域基因拷贝数增加，表明所述的肾癌患者为高风险组，具有更差的肿瘤预后；反之，基因集A1中没有基因出现基因突变或拷贝数变异，同时基因片段集B1中没有任何片段出现基因拷贝数增加，则该类肾癌患者为低风险组，具有较好的肿瘤预后。需要特殊说明的是，在本实验例中，基因组合1(panel 1)是在基因组合(panel)的基础上优选而来，具有更高的准确性(特异度更高)，相对于基因组合1(panel 1)，基因组合(panel)适用范围更广(灵敏性更高)。
表3基因集A1
Figure PCTCN2022078709-appb-000005
表4基因片段集B1
基因片段位置 实验例中用于检测的基因
chr2:179479501-179610249 TTN
chr2:207989501-208000249 KLF7
chr2:219719501-219840249 WNT6
chr3:126249501-126270249 CHST13
chr3:129319501-129330249 PLXND1
chr3:138659501-138770249 FOXL2
chr3:183999501-184020249 PSMD2
chr5:150899501-150940249 FAT2
chr7:100539501-100560249 ACHE
chr13:114519501-114530249 GAS6
按照实施例2所述的方法实施，基因组合1(panel 1)将上述354例患者进行恶性程度分级，顺利分为高风险组和低风险组，其中高风险组占比14.4％，低风险组占比85.6％，通过Kaplan-Meier生存分析，可见高风险组和低风险组的肿瘤特异性生存(图4)、肿瘤无进展生存(图5)和总生存(图6)均具有统计学差异并符合本发明的分组预期：低风险组具有明显更好的肿瘤特异性生存(Log-rank p值＝3.458e-4)、肿瘤无进展生存(Log-rank p值＝2.559e-4)和总生存(Log-rank p值＝1.703e-3)。故使用本发明从基因组合(panel)中挑选出的其他基因组合仍然可以对肾癌患者进行恶性程度分级和预后预测。
实验例3 用于人肿瘤分级的基因组合和检测方法在评估人泛癌(Pancancer)恶性程度分级和预后预测的可行性验证
TCGA(PanCancer Atlas)泛癌数据库是全球公认的泛癌数据库，可用其检验本发明用于评估泛癌恶性程度分级和预后预测的可行性和可信度。
TCGA(PanCancer Atlas)泛癌数据共有10967例泛癌资料，其中9896例具有完整的基因突变和拷贝数变异数据，适用于本发明的应用条件。
按照实施例4所述的方法实施，本实验例中选取实施例1中基因集A的全部基因和基因片段集B中的所有片段进行实施，基因片段集B中实际用于检测的基因同实验例1中表2。将上述9896例患者进行恶性程度分级，顺利分为高风险组和低风险组，其中高风险组占比77.0％，低风险组占比23.0％，通过Kaplan-Meier生存分析，可见高风险组和低风险组的肿瘤特异性生存(图7)、肿瘤无进展生存(图8)、肿瘤无病生存(图9)和总生存(图10)均具有统计学差异并符合本发明的分组预期：低风险组具有明显更好的肿瘤特异性生存(Log-rank p值<1.000e-10)、肿瘤无进展生存(Log-rank p值<1.000e-10)、肿瘤无病生存(Log-rank p值<1.000e-10)和总生存(Log-rank p值<1.000e-10)。故使用本发明的基因组合对泛癌患者进行恶性程度分级和预后预测准确可靠。
实验例4 优选的基因组合和检测方法在评估人泛癌恶性程度分级和预后预测的可行性验证
本发明允许从基因组合(panel)中挑选任意基因片段进行组合，形成新的基因组合，使用相同的判断标准对泛癌恶性程度进行分级和肿瘤预后预测。此处按实施例5所述方法，挑选并使用实验例2中的基因组合1(panel 1)，用于泛癌恶性程度分级和预后预测，并使用TCGA(PanCancer Atlas)泛癌数据库进行可行性分析。同理的，判断标准为：如果存在基因集A1中至少一个基因的基因突变或拷贝数变异，或存在基因片段集B1中至少一个区域基因拷贝数增加，表明所述的泛癌患者为高风险组，具有更差的肿瘤预后；反之，基因集A1中没有基因出现基因突变或拷贝数变异，同时基因片段集B1中没有任何片段出现基因拷贝数增加，则该类泛癌患者为低风险组，具有较好的肿瘤预后。需要特殊说明的是，在本实验例中，基因组合1(panel 1)是在基因组合(panel)的基础上优选而来，由于检测位点更少，故具有明显更低的实施成本。
按照实施例4所述的方法实施，基因组合1(panel 1)将上述泛癌数据库中9896例患者进行恶性 程度分级，顺利分为高风险组和低风险组，其中高风险组占比26.8％，低风险组占比73.2％，通过Kaplan-Meier生存分析，可见高风险组和低风险组的肿瘤特异性生存(图11)、肿瘤无进展生存(图12)和总生存(图13)均具有统计学差异并符合本发明的分组预期：低风险组具有明显更好的肿瘤特异性生存(Log-rank p值＝1.536e-4)、肿瘤无进展生存(Log-rank p值＝2.353e-3)和总生存(Log-rank p值＝5.302e-5)。故使用本发明从基因组合(panel)中挑选出的其他基因组合仍然可以对泛癌患者进行恶性程度分级和预后预测。
显然，上述实施例仅仅是为清楚地说明所作的举例，而并非对实施方式的限定。对于所属领域的普通技术人员来说，在上述说明的基础上还可以做出其它不同形式的变化或变动。这里无需也无法对所有的实施方式予以穷举。而由此所引伸出的显而易见的变化或变动仍处于本发明创造的保护范围之中。

Claims (12)

  1. 一种用于人肿瘤分级的基因组合，其特征在于，所述的基因组合由基因集A和基因片段集B组成；
    所述的基因集A包括：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和ZRSR2中至少一个；
    所述的基因片段集B包括：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、chr20:62189501-62200249、chr21:45949501-46110249、chr22:19499501-19760249、chr22:36649501-38700249和chr22:46309501-47080249至少一个；所述基因片段集B中基因片段位置以GRCh37为标准进行注释。
  2. 根据权利要求1所述的一种用于人肿瘤分级的基因组合，其特征在于，所述的基因片段集B中包括的详细基因如下表：
    表1基因片段集B
    Figure PCTCN2022078709-appb-100001
    Figure PCTCN2022078709-appb-100002
    Figure PCTCN2022078709-appb-100003
    可选的，所述的基因集A包括：ASAH1、CCDC136、FAM71E2、IFITM3、KRT9、PRB2、PROSER3、TCEAL5、U2AF2、USP35、WDR44和ZNF700至少一个；
    所述的基因片段集B包括：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和chr13:114519501-114530249至少一个。
  3. 权利要求1或2所述的基因组合在制备用于人肿瘤分级检测的产品中的用途。
  4. 根据权利要求3所述的用途，其特征在于，所述的肿瘤为泌尿系统肿瘤或泛癌；
    可选的，所述泌尿系统肿瘤为泌尿系统恶性肿瘤；
    可选的，所述泌尿系统肿瘤为肾癌；
    可选的，所述泛癌为TCGA泛癌数据中的癌种。
  5. 根据权利要求3或4所述的用途，其特征在于，所述的肿瘤分级是指肿瘤恶性程度判断和肿瘤预后的预测，用于指导临床诊疗；
    可选的，所述肿瘤分级分为高风险组和低风险组。
  6. 根据权利要求3-5任一项所述的用途，其特征在于，所述产品包括用于检测所述基因组合中基因的基因类型的引物、探针、试剂、试剂盒、基因芯片或检测系统。
  7. 根据权利要求6所述的应用，其特征在于，所述产品为针对基因集A和基因片段集B中基因的外显子和相关内含子区域进行检测。
  8. 根据权利要求5或6或7所述的应用，其特征在于，所述肿瘤分级的方法包括如下步骤：
    步骤S1:评估所述癌细胞组织中的基因集A中所包含基因的基因突变和基因拷贝数变异，评估癌细胞组织中的基因片段集B的基因拷贝数变异；
    步骤S2：基于步骤S1的评估结果，判断癌症恶性程度并进行肿瘤预后预测。
  9. 根据权利要求8所述的应用，其特征在于，所述的基因突变包括碱基置换突变、缺失突变、插入突变和/或融合突变，所述基因拷贝数变异包括基因拷贝数增加和/或基因拷贝数减少。
  10. 根据权利要求8或9所述的应用，其特征在于，在步骤S1中，通过比较所述肿瘤组织与正常组织的测序数据，用于评估所述基因集A中包含基因的基因突变和拷贝数变异，同时评估所述基因片段集B的基因拷贝数变异。
  11. 根据权利要求8或9或10所述的应用，其特征在于，所述步骤S2中，如果基因集A中至少一个基因出现基因突变或拷贝数变异，或基因片段集B中至少一个片段出现基因拷贝数增加，所述肿瘤分级为高风险组；反之，即基因集A中没有基因出现基因突变或拷贝数变异，同时基因片段集B中没有任何片段出现基因拷贝数增加，所述肿瘤分级为低风险组。
  12. 根据权利要求1-11任一项所述的应用，其特征在于，从所述基因组合中选择任意基因片段进行组合，形成新的基因组合，使用相同的肿瘤分级的方法对肿瘤恶性程度进行分级和肿瘤预后预测，从而指导临床诊疗。
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