WO2023030422A1

WO2023030422A1 - Gene combination for human tumor grading and use thereof

Info

Publication number: WO2023030422A1
Application number: PCT/CN2022/116390
Authority: WO
Inventors: 熊耕砚; 周利群
Original assignee: 北京大学第一医院
Priority date: 2021-09-02
Filing date: 2022-09-01
Publication date: 2023-03-09
Also published as: CN113604571A

Abstract

The present application relates to the field of tumor grading detection, and in particular to a gene combination for human tumor grading and an application thereof. The gene combination used for human tumor grading comprises a gene set A, a gene fragment set B and a gene fragment set C.

Description

A gene combination for human tumor grading and its use

Cross References to Related Applications

This application claims the priority of the Chinese patent application with the application number 202111028348.8 and the title of the invention "A Gene Combination for Human Tumor Grading and Its Use" submitted to the China Patent Office on September 2, 2021, the entire content of which is passed Incorporated herein by reference.

technical field

This application relates to the field of tumor grade detection, in particular to a gene combination for human tumor grade and its application.

Background technique

Urothelial carcinoma is a common malignant tumor of the urinary system, mainly including bladder cancer, renal pelvis cancer, ureter cancer and urethral cancer. In recent years, the incidence of urothelial carcinoma has been increasing year by year, the most common of which is urothelial carcinoma of the bladder, which ranks fourth among all malignant tumors in men and ninth among all malignant tumors in women bit, and is rising at a rate of about 2.5% per year. The most common symptom of urothelial carcinoma is hematuria, and the main treatment method is comprehensive treatment based on surgery. Transurethral resection of bladder tumors can be used for bladder urothelial carcinoma with low malignancy and early stage, so as to preserve the bladder and improve the quality of life of patients; Urothelial carcinoma requires radical cystectomy + urinary diversion. Although the tumor is treated, the quality of life of the patient will be significantly reduced. Therefore, in the treatment of urothelial carcinoma, it is often necessary to grade the malignancy of the tumor tissue to assist doctors in the diagnosis of the disease, determine the treatment methods and plans, and evaluate the prognosis of patients such as tumor recurrence and survival. Currently, the WHO urothelial carcinoma grading system is the most widely used urothelial carcinoma malignancy grading system.

The WHO urothelial carcinoma grading system was proposed in 1973, and the malignancy and risk of the tumor are graded and evaluated mainly based on the nuclei of the tissue. According to the 1973 WHO grading standard, urothelial carcinoma is divided into three levels: G1 (well differentiated), G2 (moderately differentiated), and G3 (poorly differentiated). As the level increases, the malignancy of urothelial carcinoma becomes more severe. High, the risk of recurrence and metastasis after disease treatment also increases. In 2004, the WHO urothelial carcinoma grading system was updated, and urothelial carcinoma was divided into low-grade urothelial carcinoma and high-grade urothelial carcinoma. Among them, high-grade urothelial carcinoma has a higher degree of malignancy. The risk of recurrence and metastasis after disease treatment also increases. However, the WHO urothelial carcinoma grading system is a purely pathological image classification system, which has the following defects: 1. It needs to be judged according to the personal experience of pathologists, which has a certain degree of subjectivity, and there are huge differences between different pathologists; 2. The actual In operation, the 1973 version of G2 grade (moderately differentiated) pathology should be finally judged as high-grade or low-grade urothelial carcinoma in the 2004 version. There are significant differences between different pathologists. The above defects are likely to lead to inaccurate classification of the malignancy of the disease, errors in the judgment of disease progression and prognosis, and affect the diagnosis and treatment of the disease.

With the maturity and promotion of next-generation sequencing technology, the method of using genetic testing to diagnose diseases has attracted widespread attention. For example, through whole exome sequencing, the mutation and copy number variation of marker genes can be detected to diagnose tumors or Assess tumor progression. This method overcomes the shortcomings of subjectivity and difficulty in traditional tumor grading, and is of great significance for early diagnosis of tumors, selection of treatment methods, and judgment of prognosis. However, the existing gene combinations and methods based on next-generation sequencing technology for grading the malignancy of urothelial carcinoma lack external validation, the malignancy grading is unreliable, and cannot be applied clinically. Therefore, it is urgent to find a method based on specific gene detection. The new tumor malignancy and risk grading system grades the malignancy of urothelial carcinoma.

Contents of the invention

Therefore, the technical problem to be solved in this application is to provide a gene combination for grading urothelial carcinoma and its application, which can grade the malignancy of urothelial carcinoma and can be used for prognosis prediction of urothelial carcinoma patients, In order to achieve this goal, this application uses the whole exome sequencing technology to screen the data of patients with urothelial carcinoma in Peking University First Hospital who were specially screened and grouped, and finally obtain this gene combination for the detection of urothelial cancer. The grade of malignancy and prognosis prediction provide clinicians and patients with more accurate malignancy information and disease prediction information of urothelial carcinoma.

The application provides a gene combination for human tumor grading, the gene combination is composed of gene set A, gene fragment set B and/or gene fragment set C;

The gene set A includes: ACVR1B, ATP4B, AZGP1, BRAF, BRCA1, BRCA2, CRYZL1, DCTN1, E2F3, EGFR, ERBB2, ERCC2, ESPL1, FGFR3, FKBP6, GZMM, H3-5, IGLL5, IRF7, METTL24, At least one of MORN5, MTOR, NPY1R, PET100, PIK3CA, PPARG, PTPN11, PTX4, RGPD8, SERPINA12, STAG2 and ZNF141;

所述的基因片段集B包括：chr1:155289001-155911999、chr1:226259001-226736999、chr8:30515001-30892999、chr8:42065001-43147999、chr9:134612001-135906999、chr11:48509001-55112999、chr14:24768001-105145999 , at least one of chr16:1-73083999, chr18:1-319999 and chr18:15325001-19749999; the position of the gene segment in the gene segment set B is annotated with GRCh37 as the standard, in GRCh38 or the new version of the human reference genome that will appear in the future , its number may change, but the targeted fragment position and the genes available for detection will not change;

The gene fragment set C includes: at least one of chr3: 12030001-12639999, chr17: 2293001-2306999 and chr17: 37830001-37979999; the position of the gene fragment in the gene fragment set C is annotated with GRCh37 as the standard, and in GRCh38 or In the new version of the human reference genome that will appear in the future, its number may change, but the pointed objective fragment position and the genes that can be used for detection will not change;

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

Table 1 Gene Fragment Set B

Optionally, the detailed genes included in the gene fragment set C are as follows:

Table 2 Gene fragment set C

基因片段位置gene fragment position	可用于检测的基因Genes Available for Testing
chr3:12030001-12639999Chr3: 12030001-12639999	TIMP4,SYN2,PPARG,TSEN2,MKRN2中至少一个At least one of TIMP4, SYN2, PPARG, TSEN2, MKRN2
chr17:2293001-2306999chr17:2293001-2306999	MNTMNT
chr17:37830001-37979999chr17:37830001-37979999	GRB7,ERBB2,IKZF3中至少一个At least one of GRB7, ERBB2, IKZF3

Optionally, the gene set A includes: ACVR1B, ATP4B, AZGP1, BRAF, CRYZL1, DCTN1, FKBP6, GZMM, H3-5, IGLL5, IRF7, METTL24, MORN5, NPY1R, PET100, PTPN11, PTX4, RGPD8, SERPINA12 and at least one of ZNF141;

可选的，所述基因片段集B包括：chr1:155289001-155911999、chr1:226259001-226736999、chr8:30515001-30892999、chr8:42065001-43147999、chr9:134612001-135906999、chr14:24768001-105145999、chr16: At least one of 1-73083999, chr18:1-319999 and chr18:15325001-19749999;

Optionally, the gene fragment set C includes: chr17:2293001-2306999.

Use of the gene combination in preparing products for human tumor grade detection.

Optionally, the tumor is a tumor of the urinary system or pan-cancer;

Optionally, the tumor of the urinary system is a malignant tumor of the urinary system;

Optionally, the tumor of the urinary system is urothelial carcinoma;

Optionally, the pan-cancer is a cancer type in TCGA pan-cancer data.

Optionally, the tumor grade refers to the judgment of tumor malignancy and the prediction of tumor prognosis;

Optionally, the tumor grades are divided into high-risk group and low-risk group.

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

Optionally, the product is for detection of exons and related intron regions of genes in gene set A, gene fragment set B and gene fragment set C.

Optionally, the method for grading the tumor comprises the following steps:

Step S1: Evaluate the gene mutation of the genes contained in the gene set A in the cancer cell tissue, and evaluate the gene copy number variation of the gene fragment set B and the gene fragment set C in the cancer cell tissue;

Step S2: Based on the evaluation results of step S1, the degree of malignancy of the cancer is judged and the prognosis of the tumor is predicted.

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.

Optionally, in the step S1, by comparing the sequencing data of the tumor tissue and the normal tissue, it is used to evaluate the gene mutation of the genes contained in the gene set A, and simultaneously evaluate the gene fragment set B and the gene fragment set Gene copy number variation in C.

Optionally, in the step S2, if at least one gene in the gene set A has a gene mutation, or at least one segment in the gene segment set B has a gene copy number reduction, or at least one segment in the gene segment set C has a gene copy number increase, the tumor is graded as a low-risk group; on the contrary, there is no gene mutation or copy number variation in gene set A, and at the same time, there is no gene copy number decrease in any fragment in gene fragment set B, and there is no gene fragment number in gene fragment set C Gene copy number gain in any segment was graded as a high-risk group.

Optionally, the gene mutation status in gene set A can be used alone, without using the gene copy number status of gene fragment set B and gene fragment set C, to grade tumor malignancy and predict tumor prognosis.

Optionally, in the step S2, if there is a gene mutation in at least one gene in the gene set A, the tumor is graded as a low-risk group; otherwise, that is, no gene in the gene set A has a gene mutation, and the tumor is graded as a high risk group risk group.

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 tumor malignancy and predict tumor prognosis.

The present invention also provides a method for detecting human tumor grade using the above-mentioned gene combination.

Optionally, the tumor is a tumor of the urinary system or pan-cancer;

Optionally, the tumor of the urinary system is urothelial carcinoma;

Optionally, the pan-cancer is a cancer type in TCGA pan-cancer data.

Optionally, the tumor grade refers to the judgment of tumor malignancy and the prediction of tumor prognosis, which is used to guide clinical diagnosis and treatment;

Optionally, the method includes using primers, probes, reagents, kits, gene chips or detection systems for detecting the genotypes of the genes in the gene combination.

Optionally, the method is to detect exons and related intron regions of genes in gene set A, gene fragment set B and gene fragment set C.

Optionally, the method for grading the tumor comprises the following steps:

Optionally, in step S1, by comparing the sequencing data of the tumor tissue and normal tissue, it is used to evaluate the gene mutation of the genes contained in the gene set A, and simultaneously evaluate the gene fragment set B and the gene fragment set C gene copy number variation.

Optionally, in the step S2, if at least one gene in the gene set A has a gene mutation, or at least one segment in the gene segment set B has a gene copy number reduction, or at least one segment in the gene segment set C has a gene copy number increase, the tumor is graded as a low-risk group; on the contrary, there is no gene mutation in gene set A, and at the same time, there is no gene copy number reduction in any fragment in gene fragment set B, and there is no gene in any fragment in gene fragment set C. Copy number gain, the tumor was graded as high risk group.

Optionally, in the step S2, if there is a gene mutation in at least one gene in the gene set A, the tumor is graded as a low-risk group; otherwise, that is, no gene in the gene set A has a gene mutation, and the tumor is graded as a high risk group. risk group.

Optionally, select any gene fragments from the gene combination to form a new gene combination, and use the same tumor grading method to grade tumor malignancy and predict tumor prognosis, so as to guide clinical diagnosis and treatment.

The technical solution of the present application has the following advantages:

1. The detection gene combination in this application is obtained from the high-throughput sequencing data of actual urothelial cancer cases in Peking University First Hospital through specific paired cluster analysis, and the real data has higher The reliability and credibility of the system can accurately grade the degree of malignancy and predict the prognosis of urothelial carcinoma and pan-cancer.

2. The gene combination described in this application includes the diversity of the gene combination, from which a variety of gene combinations can be optimized for judging the malignancy of urothelial carcinoma and pan-cancer, and for different clinical situations.

3. Compared with whole exome sequencing, this application conducts targeted sequencing analysis for specific genes and DNA fragments, which can significantly improve the sequencing depth and accuracy at the same cost. In this case, the cost can be significantly saved, and the universality is wide.

4. Compared with the WHO urothelial carcinoma pathological grading system, this application is not affected by the subjective impression of pathologists at all, and has excellent objectivity and credibility.

Description of drawings

In order to more clearly illustrate the specific embodiments of the present application or the technical solutions in the prior art, the following will briefly introduce the accompanying drawings that need to be used in the description of the specific embodiments or prior art. Obviously, the accompanying drawings in the following description The drawings are some implementations of the present application, and those skilled in the art can obtain other drawings based on these drawings without creative work.

Fig. 1 is the Kaplan-Meier survival analysis graph with tumor-specific survival as the primary endpoint after the urothelial carcinoma malignancy grading standard is graded using the gene combination in Example 1 of the present application in Experimental Example 1 of the present application;

Fig. 2 is a Kaplan-Meier survival analysis chart with tumor progression-free survival as the primary endpoint after the urothelial carcinoma malignancy grading standard is graded using the gene combination in Example 1 of the present application in Experimental Example 1 of the present application;

Fig. 3 is a Kaplan-Meier survival analysis chart with tumor disease-free survival as the primary endpoint after the urothelial carcinoma malignancy grading standard is graded using the gene combination in Example 1 of the present application in Experimental Example 1 of the present application;

Fig. 4 is a Kaplan-Meier survival analysis chart with overall survival as the primary endpoint after the urothelial carcinoma malignancy grading standard is graded using the gene combination in Example 1 of the present application in Experimental Example 1 of the present application;

Fig. 5 is a graph of Kaplan-Meier survival analysis with tumor-specific survival as the primary end point after using the gene combination 1 of the present application to grade the malignancy of urothelial carcinoma in Experimental Example 2 of the present application.

Fig. 6 is a graph of Kaplan-Meier survival analysis with tumor disease-free survival as the primary endpoint after grading the malignancy of urothelial carcinoma using gene combination 1 of the present application in Experimental Example 2 of the present application.

Fig. 7 is a graph of Kaplan-Meier survival analysis with the overall survival as the primary endpoint after the urothelial carcinoma malignancy grading standard was graded using the gene combination 1 of the present application in Experimental Example 2 of the present application.

Fig. 8 is a graph of Kaplan-Meier survival analysis with tumor-specific survival as the primary end point after using the gene set A of the present application to grade the malignant degree of urothelial carcinoma in Experimental Example 3 of the present application.

Fig. 9 is a graph of Kaplan-Meier survival analysis with tumor progression-free survival as the primary endpoint after grading the malignancy of urothelial carcinoma using the gene set A of the present application in Experimental Example 3 of the present application.

Fig. 10 is a graph of Kaplan-Meier survival analysis with tumor disease-free survival as the primary endpoint after grading the malignancy of urothelial carcinoma using the gene set A of the present application in Experimental Example 3 of the present application.

Fig. 11 is a graph of Kaplan-Meier survival analysis with overall survival as the primary endpoint after grading the malignancy of urothelial carcinoma using the gene set A of the present application in Experimental Example 3 of the present application.

Fig. 12 is a Kaplan-Meier survival analysis graph with tumor disease-free survival as the primary endpoint after the pan-cancer malignancy grading standard is graded using the gene combination in Example 1 of the present application in Experimental Example 4 of the present application;

Fig. 13 is a Kaplan-Meier survival analysis chart with tumor progression-free survival as the primary endpoint after using the gene combination in Example 1 of the present application in Experimental Example 4 of the present application to perform pan-cancer malignancy grading standard grading;

Figure 14 is a Kaplan-Meier survival analysis chart with overall survival as the primary endpoint after the pan-cancer malignancy grading standard is graded using the gene combination in Example 1 of the present application in Experimental Example 4 of the present application;

Fig. 15 is a Kaplan-Meier survival analysis chart with tumor-specific survival as the primary endpoint after the pan-cancer malignancy grading standard is graded using the gene combination 1 of the present application in Experimental Example 5 of the present application;

Fig. 16 is a Kaplan-Meier survival analysis graph with tumor progression-free survival as the primary endpoint after the pan-cancer malignancy grading standard grading was performed using the gene combination 1 of the present application in Experimental Example 5 of the present application;

Fig. 17 is a Kaplan-Meier survival analysis graph with tumor disease-free survival as the primary endpoint after the pan-cancer malignancy grading standard is graded using the gene combination 1 of the present application in Experimental Example 5 of the present application;

Fig. 18 is a Kaplan-Meier survival analysis chart with overall survival as the primary endpoint after the pan-cancer malignancy grading standard is graded using the gene combination 1 of the present application in Experimental Example 5 of the present application;

Fig. 19 is a graph of Kaplan-Meier survival analysis with tumor-specific survival as the primary endpoint after using the gene set A of the present application to perform pan-cancer malignancy grading standard grading in Experimental Example 6 of the present application.

Fig. 20 is a graph of Kaplan-Meier survival analysis with tumor progression-free survival as the primary endpoint after using the gene set A of the present application to perform pan-cancer malignancy grading standard grading in Experimental Example 6 of the present application.

Fig. 21 is a graph of Kaplan-Meier survival analysis with tumor disease-free survival as the primary endpoint after using the gene set A of the present application to perform pan-cancer malignancy grading standard grading in Experimental Example 6 of the present application.

Fig. 22 is a graph of Kaplan-Meier survival analysis with overall survival as the primary endpoint after using the gene set A of the present application to perform pan-cancer malignancy grading standard grading in Experimental Example 6 of the present application.

Detailed ways

The following examples are provided in order to further understand the application better, and are not limited to the best implementation mode, and do not limit the content and protection scope of the application. Anyone under the inspiration of the application or the application Any product identical or similar to the present application obtained by combining features of other prior art falls within the protection scope of the present application.

If no specific experimental steps or conditions are indicated in the examples, it can be carried out according to the operation or conditions of the conventional experimental steps described in the literature in this field. The reagents or instruments used, whose manufacturers are not indicated, are all commercially available conventional reagent products.

Embodiment 1 is used for the gene combination (Panel) of human tumor classification

This application mainly uses Peking University First Hospital urothelial carcinoma exome sequencing high-throughput database for screening, and confirmed a gene combination (panel) for human tumor grading, the gene combination includes gene set A, gene fragment Set B and gene fragment set C;

Table 1 Gene Fragment Set B

Table 2 Gene Fragment Set C

Example 2 A method for grading the degree of malignancy and predicting prognosis of human urothelial carcinoma

This embodiment provides a method for detecting the grade of human tumors, including using the gene panel in Embodiment 1 to grade the malignancy of human urothelial carcinoma and predict the prognosis, and the specific steps are as follows:

(1) Take urothelial carcinoma tissue and healthy control tissue specimens, the urothelial carcinoma tissue specimens can be urothelial carcinoma cell lines, fresh urothelial carcinoma specimens, frozen urothelial carcinoma specimens or paraffin-embedded urine Urothelial carcinoma specimens; healthy control tissues can be tissues from known healthy people, or paracancerous tissues or peripheral blood from urothelial carcinoma patients themselves. In this embodiment, the paraffin-embedded urothelial carcinoma specimens were selected, and the healthy control tissue was normal paracancerous tissue, DNA was extracted by conventional methods, and a library was constructed by conventional methods, and finally the gene combination (panel ) to perform targeted high-throughput sequencing, compare the sequencing data of urothelial cancer tissue and healthy tissue, and obtain the gene mutation (Mutation) of each gene of gene set A in the gene combination of the urothelial cancer tissue, and the gene fragment Copy number variation (CNV) of each gene in set B and gene fragment set C.

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.

(2) Judgment based on the mutation and/or variation of each gene in the gene combination of the urothelial carcinoma tissue obtained in step (1):

If there is a gene mutation in at least one gene in gene set A, or there is a gene copy number decrease in at least one region in gene fragment set B, or there is a gene copy number increase in at least one region in gene fragment set C, the urothelial carcinoma Patients belong to the low-risk group, with lower tumor malignancy and better tumor prognosis; on the contrary, no genes in gene set A have gene mutations, and at the same time, no gene fragments in gene set B have gene copy number reduction, and at the same time, gene set B has In C, there is no gene copy number increase in any segment, and the urothelial carcinoma patients are in the high-risk group, with higher tumor malignancy and poor tumor prognosis.

This application allows only the detection of gene mutations without the need for detection of copy number variations, that is, the detection of gene mutations in gene set A alone, and the same criteria are used to grade the malignancy of urothelial carcinoma and predict tumor prognosis: if there is gene set A Gene mutation of at least one gene in gene set A, the urothelial cancer patients are in the low-risk group, the tumor malignancy is lower, and the tumor prognosis is better; on the contrary, there is no gene mutation in gene set A, and the urinary tract cancer patients are in the low-risk group. Patients with roadside carcinoma belong to the high-risk group, with higher tumor malignancy and poor prognosis.

Example 3

As an alternative implementation of Example 2, in this application, the genes in the gene combination (panel) in Example 1 are allowed to be selected and recombined to form a new gene combination. The judging criteria are, from the gene set For the genes selected in A, the gene mutation of at least one of them indicates that the urothelial cancer patients are in the low-risk group; for the gene fragments selected from gene fragment set B, the gene copy number of at least one region decrease, indicating that the patients with urothelial carcinoma are in the low-risk group; for the gene fragments selected from the gene fragment set C, at least one region of the gene copy number increases, indicating that the patients with urothelial carcinoma are in the low-risk group On the contrary, there is no gene mutation or copy number variation in the genes selected in gene set A, and at the same time, there is no copy number reduction in the fragments selected in gene fragment set B, and at the same time, there is no copy number reduction in the fragments selected in gene fragment set C No copy number gain occurred, and the urothelial carcinoma patient was a high-risk group.

Example 4 A method for grading and predicting the degree of malignancy of human pancancer (Pancancer)

This embodiment provides a method for human tumor grading detection, including, using the gene combination (panel) in Example 1 to perform human pancancer (Pancancer) malignancy grading and prognosis prediction, and the specific steps are as follows:

(1) Take pan-cancer (here pan-cancer is defined as all cancer types in TCGA pan-cancer data, including adrenal cancer, urothelial cancer, breast cancer, cervical cancer, bile duct cancer, colon cancer, lymphoma, esophageal cancer, gum Glioblastoma, head and neck squamous cell carcinoma, chromophobe renal cell carcinoma, clear cell renal 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, thymus cancer, endometrial cancer, uterine Sarcoma, uveal melanoma, the pan-cancer mentioned in this embodiment are all defined as this, 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; healthy control tissues can be tissues from known healthy people, or paracancerous tissues or peripheral blood of pan-cancer patients themselves. In this example, paraffin-embedded pan-cancer specimens were selected, and normal paracancerous tissues were used as healthy control tissues. DNA was extracted by conventional methods, and libraries were constructed by conventional methods. Finally, the gene combination (panel) in Example 1 was used to carry out Targeted high-throughput sequencing, compare the sequencing data of pan-cancer tissue and healthy tissue, and obtain the gene mutation (Mutation) of each gene in gene set A in the gene combination of the pan-cancer tissue, as well as gene fragment set B and gene fragment set Copy number variation (CNV) status of each fragment in C.

(2) Judgment based on the mutation and/or variation of each gene in the gene combination of the pan-cancer tissue obtained in step (1):

If there is a gene mutation of at least one gene in gene set A, or there is a gene copy number decrease in at least one region in gene fragment set B, or there is an increase in gene copy number in at least one region in gene fragment set C, the pan-cancer patient is The low-risk group has a better tumor prognosis; on the contrary, there is no gene mutation in gene set A, and there is no gene copy number reduction in any fragment in gene fragment set B, and there is no gene copy in any fragment in gene fragment set C The number of patients with pan-cancer increases, and the pan-cancer patients are a high-risk group with poor tumor prognosis.

This application allows the detection of gene mutations only, without the need to detect copy number variations, that is, the mutations of genes in gene set A are detected separately, and the same criteria are used to grade the degree of malignancy of pan-cancer and predict tumor prognosis: if there is at least If there is a gene mutation in one gene, the pan-cancer patients are in the low-risk group, the tumor malignancy is lower, and they have better tumor prognosis; on the contrary, if there is no gene mutation in gene set A, the pan-cancer patients are in the high-risk group. The risk group, with higher tumor malignancy, has poorer tumor prognosis.

Example 5

As an alternative implementation of Example 4, in this application, the genes in the gene combination (panel) in Example 1 are allowed to be selected and recombined to form a new gene combination. The judging criteria are: from the gene set For the genes selected in A, the gene mutation of at least one gene indicates that the pan-cancer patients are in the low-risk group; for the gene fragments selected from the gene fragment set B, the copy number of at least one region of the gene is reduced, It indicates that the pan-cancer patients belong to the low-risk group; if the gene fragments selected from the gene fragment set C have an increased gene copy number in at least one region, it indicates that the pan-cancer patients belong to the low-risk group. On the contrary, there is no gene mutation in the genes selected in gene set A, and at the same time, there is no copy number decrease in the fragments selected in gene fragment set B, and there is no copy number increase in the fragments selected in gene fragment set C, The pan-cancer patients are a high-risk group.

Experimental Example 1 Feasibility verification of the gene combination and detection method used for human tumor grading in evaluating the malignancy grading and prognosis prediction of human urothelial carcinoma

Urothelial carcinoma is the most common pathological type of urothelial tumors, accounting for more than 90% of all urothelial carcinomas. TCGA (PanCancer Atlas) Bladder Urothelial Cancer Database is a globally recognized bladder urothelial cancer database , the database was updated in 2020, and after merging the data of Memorial Sloan Kettering Cancer Center (MSKCC) bladder urothelial carcinoma, the MSK/TCGA2020 bladder urothelial carcinoma database was formed and released to the public. The MSK 2015 upper urinary tract urothelial cancer (renal pelvis cancer, ureteral cancer) database is a highly reliable upper urinary tract urothelial cancer database with complete follow-up data released to the public. After merging the above databases, it can be used to test the feasibility and reliability of the application for evaluating the grade of malignancy and predicting prognosis of urothelial carcinoma. For the convenience of description, the combined new database is collectively referred to as the MSK/TCGA database in this application.

The MSK/TCGA database has a total of 972 patients with urothelial carcinoma, of which 928 patients have complete gene mutation and copy number variation data, which are suitable for the application conditions of this application. 861 patients in this database have complete prognostic data of overall survival, which can be used for prognostic detection of overall survival; 704 patients in this database have complete prognostic data of tumor progression-free survival, which can be used for tumor progression-free survival Prognosis detection; 391 patients in this database have complete tumor-specific (Disease Specific) survival prognosis data, which can be used for tumor-specific survival prognosis detection; 401 patients in this database have complete tumor-specific disease-free (Disease Free) survival data , which can be used for prognostic detection of tumor disease-free survival.

Implementation according to the method described in Example 2, in this experimental example, select all the genes in the gene set A in Example 1, all the segments in the gene fragment set B and all the fragments in the gene fragment set C for implementation, and the gene fragment set B The genes actually used for detection in gene fragment set C are shown in Table 3. The above-mentioned 928 patients were classified into high-risk group and low-risk group, and the high-risk group accounted for 29.4%, and the low-risk group accounted for 70.6%. Through the Kaplan-Meier survival analysis, it can be seen that the high-risk group and the low-risk group The tumor-specific survival (Figure 1), tumor progression-free survival (Figure 2), tumor disease-free survival (Figure 3) and overall survival (Figure 4) in the low-risk group were all statistically different and in line with the grouping expectations of this application: The low-risk group had significantly better tumor-specific survival (Log-rank p value = 7.065e-6), tumor progression-free survival (Log-rank p value = 5.47e-7), tumor disease-free survival (Log-rank p-value=5.853e-5) and overall survival (Log-rank p-value=7.60e-13). Therefore, it is accurate and reliable to use the gene combination of the present application to grade the malignant degree and predict the prognosis of patients with urothelial carcinoma.

The genes actually detected for the gene fragment sets B and C in the experimental example 1 of Table 3

Feasibility verification of the optimal gene combination and detection method in Experimental Example 2 in assessing the malignant degree grading and prognosis prediction of human urothelial carcinoma

This application allows to select any gene fragments from the gene panel and combine them to form a new gene combination, and use the same criteria to grade the malignancy of urothelial carcinoma and predict the prognosis of the tumor. Here, the gene set A1 (Table 4) is selected from the gene set A of the gene combination (panel), the gene fragment set B1 (Table 5) is selected from the gene fragment set B, and the gene fragment is selected from the gene fragment set C Set C1 (Table 5) constitutes gene panel 1 (panel 1), which is used for grading the malignancy of urothelial carcinoma and predicting prognosis, and uses the MSK/TCGA database for feasibility analysis. Similarly, the judgment criteria are: if there is a gene mutation in at least one gene in gene set A1, or there is a gene copy number decrease in at least one region in gene fragment set B1, or there is an increase in gene copy number in at least one region in gene fragment set C1 , indicating that the urothelial carcinoma patients are in the low-risk group and have better tumor prognosis; on the contrary, no genes in the gene set A1 have gene mutations, and at the same time, there is no gene copy number reduction in any fragments in the gene fragment set B1, and at the same time If there is no gene copy number increase in any fragment in the gene fragment set C1, the patients with this type of urothelial carcinoma are in the high-risk group and have poor 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). Since there are fewer genes to be detected, it has a lower cost.

Table 4 Gene set A1

Table 5 Gene fragment sets B1 and C1

Carry out according to the method described in embodiment 2, gene group 1 (panel 1) carries out malignant degree classification to above-mentioned 926 patients, is successfully divided into high-risk group and low-risk group, wherein high-risk group accounts for 79.9%, low-risk group Accounting for 20.1%, through Kaplan-Meier survival analysis, it can be seen that there are statistical differences in tumor-specific survival (Figure 5), tumor-free survival (Figure 6) and overall survival (Figure 7) between the high-risk group and the low-risk group And meet the grouping expectations of this application: the low-risk group has significantly better tumor-specific survival (Log-rank p value = 7.319e-3), tumor disease-free survival (Log-rank p value = 2.361e-3) and Overall survival (Log-rank p-value=1.325e-3). Therefore, using other gene combinations selected from the gene combination (panel) in this application can still be used for grading the degree of malignancy and predicting the prognosis of patients with urothelial carcinoma.

Experimental Example 3 Feasibility verification of the optimal gene combination and detection method (only detection of gene mutations) in evaluating the grade of malignancy and prognosis of human urothelial carcinoma

This application allows the detection of gene mutations only, without the detection of copy number variations, and uses the same criteria to grade the malignancy of urothelial carcinoma and predict tumor prognosis. Here, the gene mutations of all genes in gene set A are used for grading the malignancy of urothelial carcinoma and predicting prognosis, and the MSK/TCGA database is used for feasibility analysis. Similarly, the judging criteria are: if there is a gene mutation in at least one gene in gene set A, it indicates that the urothelial carcinoma patients are in the low-risk group and have a better tumor prognosis; otherwise, there is no gene in gene set A If there is a gene mutation, the patients with this type of urothelial carcinoma belong to the high-risk group and have a poor tumor prognosis. It should be noted that in this experimental example, only gene mutations are detected, and copy number variations are not detected, so the cost is lower.

According to the method described in Example 2, the above-mentioned 926 patients were graded for malignancy using gene combination A, and were successfully divided into high-risk group and low-risk group, of which the high-risk group accounted for 33.6%, and the low-risk group accounted for 66.4%. %, through Kaplan-Meier survival analysis, we can see the tumor-specific survival (Figure 8), tumor progression-free survival (Figure 9), tumor disease-free survival (Figure 10) and overall survival (Figure 11) of high-risk group and low-risk group ) all have statistical differences and meet the grouping expectations of this application: the low-risk group has significantly better tumor-specific survival (Log-rank p value=1.431e-4), tumor progression-free survival (Log-rank p value=1.431e-4), tumor progression-free survival (Log-rank p value= 1.297e-6), tumor disease-free survival (Log-rank p value = 3.228e-4) and overall survival (Log-rank p value = 5.79e-11). Therefore, only the gene mutation status of gene set A in this application is detected, and the gene copy number variation is not detected, and the malignant degree and prognosis prediction of urothelial carcinoma can still be carried out. Gene mutation detection of other gene combinations can still grade the malignancy and predict the prognosis of patients with urothelial carcinoma.

Experimental Example 4 Feasibility verification of the gene combination and detection method used for human tumor grading in evaluating the malignant degree grading and prognosis prediction of human pancancer (Pancancer)

The TCGA (PanCancer Atlas) pan-cancer database is a globally recognized pan-cancer database, which can be used to test the feasibility and credibility of this application for assessing pan-cancer malignancy grading and prognosis prediction.

TCGA (PanCancer Atlas) pan-cancer data has a total of 10,953 patients (10,967 cases) pan-cancer data, which can be used to test the feasibility and reliability of this application for assessing pan-cancer malignancy grading and prognosis prediction.

According to the method described in Example 4, in this experimental example, all the genes in the gene set A, the gene fragment set B and all the fragments in the gene fragment set C are selected for implementation in this experimental example, the gene fragment set B and the gene fragment set The genes actually used for detection in C are the same as Table 3 in Experimental Example 1. The above-mentioned 10967 patients were graded for malignancy and successfully divided into high-risk group and low-risk group, of which the high-risk group accounted for 59.5%, and the low-risk group accounted for 40.5%. Through the Kaplan-Meier survival analysis, it can be seen that the high-risk group and the low-risk group accounted for 40.5%. The tumor disease-free survival (Figure 12), tumor progression-free survival (Figure 13) and overall survival (Figure 14) of the low-risk group were all statistically different and met the grouping expectations of this application: the low-risk group had significantly better tumor Disease-free survival (Log-rank p value=5.993e-6), tumor progression-free survival (Log-rank p value=8.726e-3) and overall survival (Log-rank p value=0.0428). Therefore, it is accurate and reliable to use the gene combination of the application to grade the malignant degree and predict the prognosis of pan-cancer patients.

Experimental Example 5: Feasibility verification of the optimal gene combination and detection method in assessing human pan-cancer malignancy grading and prognosis prediction

This application allows to select any gene fragments from the gene panel and combine them to form a new gene combination, and use the same judgment standard to grade the degree of malignancy of pan-cancer and predict the prognosis of tumors. Here, according to the method described in Example 5, select and use the gene combination 1 (panel 1) in Experimental Example 2 for pan-cancer malignancy grading and prognosis prediction, and use the TCGA (PanCancer Atlas) pan-cancer database for feasibility analyze. Similarly, the judgment criteria are: if there is a gene mutation in at least one gene in gene set A1, or there is a gene copy number decrease in at least one region in gene fragment set B1, or there is an increase in gene copy number in at least one region in gene fragment set C1 , indicating that the pan-cancer patients are a low-risk group with better tumor prognosis; on the contrary, no genes in the gene set A1 have gene mutations, and at the same time, no gene fragments in the gene fragment set B1 have gene copy number reduction, and at the same time, the gene fragments If there is no gene copy number increase in any fragment in set C1, then this type of pan-cancer patients is a high-risk group with poor 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). Since there are fewer genes to be detected, it has a lower cost.

Carry out according to the method described in embodiment 4 and embodiment 5, gene combination 1 (panel 1) carry out malignancy classification to 10967 routine patients in the above-mentioned pan-cancer database, successfully divide into high-risk group and low-risk group, wherein high-risk group It accounted for 81.5%, and the low-risk group accounted for 18.5%. Through Kaplan-Meier survival analysis, it can be seen that the high-risk group and low-risk group have tumor-specific survival (Figure 15), tumor progression-free survival (Figure 16), and tumor-free survival. Survival (Figure 17) and overall survival (Figure 18) were statistically different and in line with the grouping expectations of this application: the low-risk group had significantly better tumor-specific survival (Log-rank p value = 7.74e-8), Tumor progression-free survival (Log-rank p value=2.368e-3), tumor disease-free survival (Log-rank p value=5.455e-3) and overall survival (Log-rank p value=8.92e-9). Therefore, using other gene combinations selected from the gene panels (panels) in this application can still be used to grade the degree of malignancy and predict the prognosis of pan-cancer patients.

Experimental Example 6: Feasibility verification of the optimal gene combination and detection method (only detection of gene mutation) in assessing human pan-cancer malignancy grading and prognosis prediction

This application allows the detection of gene mutations only, without the detection of copy number variations, and uses the same criteria to grade the malignancy of urothelial carcinoma and predict tumor prognosis. Here, the gene mutations of all genes in gene set A in Example 1 are used for pan-cancer malignancy grading and prognosis prediction, and the TCGA pan-cancer database is used for feasibility analysis. Similarly, the criterion for judging is: if there is a gene mutation in at least one gene in gene set A, it indicates that the pan-cancer patients are in the low-risk group and have a better tumor prognosis; otherwise, no gene in gene set A has a gene mutation mutation, the pan-cancer patients belong to the high-risk group with poor tumor prognosis. It should be noted that in this experimental example, only gene mutations are detected, and copy number variations are not detected, so the cost is lower.

According to the methods described in Example 2, Example 4, and Example 5, the above-mentioned 10,967 patients were graded for malignancy using gene combination A, and were successfully divided into high-risk group and low-risk group, of which the high-risk group accounted for 64.1 %, the low-risk group accounted for 35.9%. Through the Kaplan-Meier survival analysis, it can be seen that the tumor-specific survival (Fig. 19), tumor progression-free survival (Fig. 20), tumor disease-free survival (Fig. 21) and overall survival (Fig. 22) were statistically different and met the grouping expectations of this application: the low-risk group had significantly better tumor-specific survival (Log-rank p value = 0.0320), tumor progression-free survival (Log -rank p-value=7.190e-3), tumor disease-free survival (Log-rank p-value=8.83e-8) and overall survival (Log-rank p-value=6.108e-3). Therefore, only the gene mutation status of gene set A in this application is detected, and when gene copy number variation is not detected, the malignancy degree and prognosis prediction of pan-cancer can still be performed. It can be expected that for other genes selected from gene set A Gene mutation detection by gene combination can still grade the malignancy and predict the prognosis of pan-cancer patients.

Apparently, the above-mentioned embodiments are only examples for clear description, rather than limiting the implementation. For those of ordinary skill in the art, other changes or changes in different forms can be made on the basis of the above description. It is not necessary and impossible to exhaustively list all the implementation manners here. And the obvious changes or changes derived therefrom are still within the scope of protection of the present invention.

Claims

A gene combination for grading human tumors, characterized in that the gene combination consists of gene set A, gene fragment set B and/or gene fragment set C;

The gene set A includes: ACVR1B, ATP4B, AZGP1, BRAF, BRCA1, BRCA2, CRYZL1, DCTN1, E2F3, EGFR, ERBB2, ERCC2, ESPL1, FGFR3, FKBP6, GZMM, H3-5, IGLL5, IRF7, METTL24, At least one of MORN5, MTOR, NPY1R, PET100, PIK3CA, PPARG, PTPN11, PTX4, RGPD8, SERPINA12, STAG2 and ZNF141;

所述的基因片段集B包括：chr1:155289001-155911999、chr1:226259001-226736999、chr8:30515001-30892999、chr8:42065001-43147999、chr9:134612001-135906999、chr11:48509001-55112999、chr14:24768001-105145999 , at least one of chr16:1-73083999, chr18:1-319999 and chr18:15325001-19749999;

The gene fragment set C includes: at least one of chr3:12030001-12639999, chr17:2293001-2306999 and chr17:37830001-37979999.
A gene combination for human tumor grading according to claim 1, wherein the detailed genes included in the gene fragment set B are as follows:

Table 1 Gene Fragment Set B
A gene combination for human tumor grading according to claim 1, wherein the detailed genes included in the gene fragment set C are as follows:

Table 2 Gene fragment set C

gene fragment location Genes Available for Testing Chr3: 12030001-12639999 At least one of TIMP4, SYN2, PPARG, TSEN2, MKRN2 chr17:2293001-2306999 MNT chr17:37830001-37979999 At least one of GRB7, ERBB2, IKZF3

Optionally, the gene set A includes: ACVR1B, ATP4B, AZGP1, BRAF, CRYZL1, DCTN1, FKBP6, GZMM, H3-5, IGLL5, IRF7, METTL24, MORN5, NPY1R, PET100, PTPN11, PTX4, RGPD8, SERPINA12 and at least one of ZNF141;

所述基因片段集B包括：chr1:155289001-155911999、chr1:226259001-226736999、chr8:30515001-30892999、chr8:42065001-43147999、chr9:134612001-135906999、chr14:24768001-105145999、chr16:1-73083999、 At least one of chr18:1-319999 and chr18:15325001-19749999;

The gene fragment set C includes: chr17:2293001-2306999.
Use of the gene combination described in any one of claims 1-3 in the preparation of products for human tumor grading detection.
The use according to claim 4, wherein the tumor is a tumor of the urinary system or pan-cancer;

Optionally, the tumor of the urinary system is a malignant tumor of the urinary system;

Optionally, the tumor of the urinary system is urothelial carcinoma;

Optionally, the pan-cancer is a cancer type in TCGA pan-cancer data.
The use according to claim 4 or 5, characterized in that the tumor grade refers to the judgment of tumor malignancy and the prediction of tumor prognosis, which is used to guide clinical diagnosis and treatment;

Optionally, the tumor grades are divided into high-risk group and low-risk group.
The use according to any one of claims 4-6, wherein the product includes primers, probes, reagents, test kits, gene chips or detection systems for detecting the genotype of genes in the gene combination .
The use according to claim 7, characterized in that the product is for detection of exons and related intron regions of genes in gene set A, gene fragment set B and gene fragment set C.
The use according to any one of claims 4-8, wherein the method for grading tumors comprises the following steps:

Step S1: Evaluate the gene mutation of the genes contained in the gene set A in the cancer cell tissue, and evaluate the gene copy number variation of the gene fragment set B and the gene fragment set C in the cancer cell tissue;

Step S2: Based on the evaluation results of step S1, the degree of malignancy of the cancer is judged and the prognosis of the tumor is predicted.
The use according to claim 9, wherein 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 mutation Copy number reduction.
The use according to claim 9 or 10, characterized in that in step S1, by comparing the sequencing data of the tumor tissue and normal tissue, it is used to evaluate the gene mutation of the genes contained in the gene set A, and at the same time evaluate The gene copy number variation of the gene fragment set B and the gene fragment set C.
The use according to claim 8 or 9 or 10 or 11, characterized in that in step S2, if at least one gene in the gene set A has a gene mutation, or at least one gene fragment in the gene set B has a gene copy number Decrease, or at least one fragment in gene fragment set C has gene copy number increase, and the tumor is graded as a low-risk group; on the contrary, no gene in gene set A has gene mutation, and at the same time, there is no gene in any fragment in gene fragment set B If the copy number is reduced, and there is no gene copy number increase in any fragment in the gene fragment set C, the tumor is graded as a high-risk group.
The use according to claim 8 or 9 or 10 or 11, characterized in that in step S2, if at least one gene in the gene set A has a gene mutation, the tumor is graded as a low-risk group; In set A, no genes are mutated, and the tumors are graded as high-risk group.
The use according to any one of claims 4-13, characterized in that any gene fragments are selected from the gene combination for combination to form a new gene combination, and the tumor malignancy is graded using the same tumor grading method And tumor prognosis prediction, so as to guide clinical diagnosis and treatment.
A method for detecting human tumor grades using the gene combination described in any one of claims 1-3.
The method according to claim 15, wherein the tumor is a tumor of the urinary system or pan-cancer;

Optionally, the tumor of the urinary system is a malignant tumor of the urinary system;

Optionally, the tumor of the urinary system is urothelial carcinoma;

Optionally, the pan-cancer is a cancer type in TCGA pan-cancer data.
The method according to claim 15 or 16, characterized in that the tumor grade refers to the judgment of tumor malignancy and the prediction of tumor prognosis, which is used to guide clinical diagnosis and treatment;

Optionally, the tumor grades are divided into high-risk group and low-risk group.
The method according to any one of claims 15-17, characterized in that the method comprises using primers, probes, reagents, kits, gene chips or detection systems for detecting the genotypes of genes in the gene combination.
The method according to claim 18, characterized in that, the method is to detect exons and related intron regions of genes in gene set A, gene fragment set B and gene fragment set C.
The method according to any one of claims 15-19, wherein the method for grading the tumor comprises the following steps:

Step S1: Evaluate the gene mutation of the genes contained in the gene set A in the cancer cell tissue, and evaluate the gene copy number variation of the gene fragment set B and the gene fragment set C in the cancer cell tissue;

Step S2: Based on the evaluation results of step S1, the degree of malignancy of the cancer is judged and the prognosis of the tumor is predicted.
The method according to claim 20, wherein 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 mutation Copy number reduction.
The method according to claim 20 or 21, characterized in that, in step S1, by comparing the sequencing data of the tumor tissue and normal tissue, it is used to evaluate the gene mutation of the genes contained in the gene set A, and at the same time evaluate The gene copy number variation of the gene fragment set B and the gene fragment set C.
The method according to claim 19 or 20 or 21 or 22, characterized in that in step S2, if at least one gene in the gene set A has a gene mutation, or at least one gene fragment in the gene set B has a gene copy number Decrease, or at least one fragment in gene fragment set C has gene copy number increase, and the tumor is graded as a low-risk group; on the contrary, no gene in gene set A has gene mutation, and at the same time, there is no gene in any fragment in gene fragment set B If the copy number is reduced, and there is no gene copy number increase in any fragment in the gene fragment set C, the tumor is graded as a high-risk group.
The method according to claim 19 or 20 or 21 or 22, characterized in that in step S2, if at least one gene in the gene set A has a gene mutation, the tumor is graded as a low-risk group; In set A, no genes are mutated, and the tumors are graded as high-risk group.
The method according to any one of claims 15-24, characterized in that any gene fragments are selected from the gene combination for combination to form a new gene combination, and the tumor malignancy is graded using the same tumor grading method And tumor prognosis prediction, so as to guide clinical diagnosis and treatment.