A gene combination for prognosis evaluation of renal clear cell carcinoma and application thereof

Abstract

The present application relates to the field of cancer diagnosis, and particularly relates to a gene combination for prognosis evaluation of renal clear cell carcinoma and application of the gene combination in preparation of a kit for prognosis evaluation of renal clear cell carcinoma. The gene combination of the present application is derived from a molecular marker significantly related to cancer embolus of renal clear cell carcinoma. Discovery of the gene combination model provides a brand-new strategy for predicting recurrence risk and long-term survival condition of patients after surgery of renal clear cell carcinoma. The gene combination plays an important role in judging prognosis of patients with renal clear cell carcinoma, can evaluate risk level of tumor progression or death of patients after surgery of renal clear cell carcinoma, helps clinicians to implement individualized precision treatment strategy, improves survival rate of patients after surgery, and has important guiding significance for follow-up monitoring and sequential treatment management of patients after surgery of renal clear cell carcinoma.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of cancer diagnosis, in particular, a kidney clear cell carcinoma prognosis evaluation gene combination and application thereof. BACKGROUND

[0002] Clear cell renal cell carcinoma (ccRCC) is the most lethal cancer in urology. About 4-15% of ccRCC patients have primary tumors invading the venous system to form renal vein or inferior vena cava thrombosis. Venous tumor thrombosis (VTT) is a fatal complication of ccRCC. Patients with VTT who are not treated in time have poor prognosis, with a median survival of 5 months and a 1-year disease-specific survival rate of 29%. With the development of surgical operation, the survival of patients with VTT has been significantly improved, but the incidence of postoperative complications and perioperative mortality cannot be ignored. In addition, the clinical benefits of preoperative targeted therapy have been proven to be limited. Therefore, it is necessary to describe the occurrence and development of VTT and explore potential genomic features.

[0003] Previous studies have shown that ccRCC exhibits extensive functional and intratumoral genomic heterogeneity, which is determined by spatial location. Multi-region whole exome sequencing results show that VTT contains live tumor cells with mutation heterogeneity compared with primary tumors. However, the molecular characteristics and protein expression profile of VTT are still unclear. Therefore, it is urgent to understand the special biological characteristics of VTT through comprehensive analysis of transcriptome and proteome. SUMMARY

[0004] The purpose of the present application is to provide a kidney clear cell carcinoma prognosis evaluation gene combination and its application in preparing a kidney clear cell carcinoma prognosis evaluation kit.

[0005] In the previous study, the inventors performed transcriptome and proteome analysis on the cancer embolus, tumor and normal tissue of ccRCC patients to reveal the unique molecular characteristics of VTT. Subsequently, functional pathway enrichment analysis was performed to further understand the molecular mechanism of carcinogenesis, and the functions of all proteins and the importance of these interactions in biological processes, molecular functions and signal transduction were revealed through protein-protein interaction (PPI) network. The study screened 31 genes most commonly regulated. Further, through analysis of the TCGA database, the range of key genes related to metastasis was continuously narrowed down using statistical tools such as COX regression model, and it was found that the mRNA expression levels of DEPTOR, DPEP1, NAT8, PLOD2, SLC7A5 and SUSD2 were closely related to the poor prognosis of patients with renal clear cell carcinoma, and could be used to elucidate the potential prognostic and therapeutic targets of ccRCC.

[0006] In a first aspect of the present application, a prognostic evaluation gene combination of renal clear cell carcinoma is provided, which is composed of six genes including DEP domain-containing mTOR-interacting protein (DEPTOR), Dipeptidase 1 (DPEP1), N-acetyltransferase 8 (NAT8), Procollagen-lysine, 2-oxoglutarate 5-dioxygenase 2 (PLOD2), Large neutral amino acids transporter small subunit 1 (SLC7A5) and Sushi domain-containing protein 2 (SUSD2).

[0007] In a second aspect of the present application, a detection product of the prognostic evaluation gene combination of renal clear cell carcinoma is provided, which comprises reagents for detecting the expression level of the gene combination in a biological sample.

[0008] Further, the biological sample is a tumor specimen section surgically resected from a patient with renal clear cell carcinoma.

[0009] Further, the expression level is the mRNA expression level of the gene.

[0010] Further, the reagent comprises PCR primers with detection specificity for the above genes, and the primer sequences are shown in SEQ ID NO. 1-SEQ ID NO. 12 respectively.

[0011]

[0012] In a third aspect of the present application, a combination of prognostic evaluation genes for renal clear cell carcinoma is provided, and a detection product of the combination of prognostic evaluation genes for renal clear cell carcinoma is used in the establishment of a prognostic evaluation system for renal clear cell carcinoma.

[0013] In a fourth aspect of the present application, a combination of tumor prognosis-related genes is provided, and a detection product of the combination of prognostic evaluation genes for renal clear cell carcinoma is used in the preparation of a prognostic evaluation kit for renal clear cell carcinoma.

[0014] Further, the prognostic evaluation kit comprises a reagent combination for detecting the relative expression levels of DEPTOR, DPEP1, NAT8, PLOD2, SLC7A5 and SUSD2 in a biological sample.

[0015] In a fifth aspect of the present application, a prognostic evaluation kit for renal clear cell carcinoma is provided, and the kit comprises reagents for detecting the relative expression levels of DEPTOR, DPEP1, NAT8, PLOD2, SLC7A5 and SUSD2 in a biological sample.

[0016] Further, the kit is composed of a reverse transcription system, a primer system and an amplification system, and the primer system comprises PCR primers shown in SEQ ID NO. 1-SEQ ID NO. 12.

[0017] The present application also provides a method for prognostic evaluation of renal clear cell carcinoma using the above prognostic evaluation kit, which specifically comprises the following steps:

[0018] (a) using the reagents in the kit to perform reverse transcription and amplification on a tumor sample to obtain the mRNA expression level of each gene;

[0019] (b) the renal clear cell carcinoma prognosis risk score is calculated according to the following formula: risk score = 1.0025*PLOD2 expression level + 0.9993*NAT8 expression level + 0.9869*DPEP1 expression level + 0.9780*DEPTOR expression level + 1.0044*SLC7A5 expression level + 1.0258*SUSD2 expression level.

[0020] In a sixth aspect of the present application, a prognostic evaluation system for renal clear cell carcinoma is provided, and the system comprises:

[0021] A processor and a memory coupled to the processor, the memory storing instructions that, when executed by the processor, use the following steps:

[0022] The mRNA expression levels of the prognostic evaluation gene combination of renal clear cell carcinoma are input into the following calculation formula to obtain a risk score; the calculation formula is as follows:

[0023] The risk score = 1.0025*PLOD2 expression level + 0.9993*NAT8 expression level + 0.9869*DPEP1 expression level + 0.9780*DEPTOR expression level + 1.0044*SLC7A5 expression level + 1.0258*SUSD2 expression level. When the risk score is higher than or equal to 4.41, it is classified into a high-risk group, i.e., the patient has a poor prognosis; when the risk score is lower than 4.41, it is classified into a low-risk group, i.e., the patient has a better prognosis.

[0024] In a seventh aspect of the present application, the use of the prognostic evaluation system as described above in the preparation of a product for predicting the prognosis risk of renal clear cell carcinoma is provided.

[0025] Further, in the product for predicting the prognosis risk of renal clear cell carcinoma, the mRNA expression levels of the prognostic evaluation gene combination are input into a calculation formula to obtain a risk score.

[0026] In an eighth aspect of the present application, a computer readable storage medium having a computer program stored thereon is provided, the computer program being executed by a processor using the following steps:

[0027] The mRNA expression levels of the prognostic evaluation gene combination of renal clear cell carcinoma are input into the following calculation formula to obtain a risk score; the calculation formula is as follows:

[0028] The risk score = 1.0025*PLOD2 expression level + 0.9993*NAT8 expression level + 0.9869*DPEP1 expression level + 0.9780*DEPTOR expression level + 1.0044*SLC7A5 expression level + 1.0258*SUSD2 expression level.

[0029] In a ninth aspect of the present application, a product for predicting the prognosis of renal clear cell carcinoma is provided, comprising:

[0030] The expression level determination product of the prognostic evaluation gene combination of renal clear cell carcinoma;

[0031] The prognostic evaluation system of renal clear cell carcinoma, or the computer readable storage medium.

[0032] In a tenth aspect, the present application provides a method for constructing a transcriptomic and proteomic cancer embolus-related renal clear cell carcinoma prognosis model, comprising the following steps:

[0033] The differential genes and differential proteins are analyzed using the "limma" package in R statistical software, and are divided into three groups: the renal cancer group and the normal kidney tissue, the cancer embolus group and the normal kidney tissue, and the cancer embolus group and the renal cancer group. The differential genes and differential proteins are defined by |log2 FC|>2 and P<0.05. The survival package (version 3.3.1; https: / / github.com / therneau / survival), e1071 package (version 1.7.1; https: / / cran.r-project.org / web / packages / e1071) and caret package (version 6.0.76; https: / / cran.r-project.org / web / packages / caret) are used to further screen the optimal prognosis gene set of OS based on the SVM-RFE method, which is an iterative reverse selection algorithm that can recursively delete a feature gene with the smallest ranking score until the optimal feature gene set is retained. Then, an SVM classifier is constructed based on the expression level of the optimal prognosis gene set to predict the overall survival rate. In addition, the results of the SVM classification analysis are verified using the external TCGA-KIRC dataset. The survival package (version 3.3.1; https: / / github.com / therneau / survival) is used. Then, a risk score model of the prognosis mRNA marker is established according to the following formula: risk score = ∑βdifferential gene x Exp differential gene. βdifferential gene represents the estimated contribution coefficient of the independent prognosis mRNA in the multivariate Cox regression analysis, and Exp differential gene represents the level of the independent prognosis gene. Then, all patients are divided into a high-risk group or a low-risk group, with the median of the risk score as the critical value. The Kaplan-Meier curve shows the difference in survival rate between the two groups of patients. Then, in order to evaluate the predictive value of the model, the ROC curve of the 1-year, 3-year and 5-year survival rate is drawn over time to obtain the area under the curve (AUC) value.

[0034] The present application has the following advantages:

[0035] 1、The application identifies a batch of metastasis-related biomarkers by complex bioinformatics screening, comparing transcriptomic and proteomic sequencing data from renal cancer primary tissue, cancer embolus tissue and paracancerous tissue, and first discovers that the mRNA expression levels of DEPTOR, DPEP1, NAT8, PLOD2, SLC7A5 and SUSD2 are closely related to the poor prognosis of renal clear cell carcinoma patients. The genome is used as a molecular marker to detect the expression amount, which can be used to guide the prognosis of renal clear cell carcinoma patients. The six genes regulate different pathways in renal clear cell carcinoma, and their combination for prognosis recurrence risk prediction has higher reliability, and the six genes can show significant prognosis difference in TCGA and Changhai urological surgery cohort, and the expression amount in tumor tissue is significantly higher than that in paracancerous tissue.

[0036] 2、The application uses transcriptome and proteomic sequencing and the transcriptome data of cancer embolus-related renal clear cell carcinoma (KIRC) in the public database TCGA to screen out differential genes layer by layer to construct a prognosis model of cancer embolus-related KIRC, and verifies that it has good prediction ability for renal clear cell carcinoma patients. The prognosis model of the application can be used to guide the molecular typing of clinical renal clear cell carcinoma, individualized treatment strategy, and further develop products related to diagnosis and treatment and prognosis of renal clear cell carcinoma according to the core genes in the prognosis model.

[0037] 3、The gene combination of the application is derived from the molecular markers significantly related to renal clear cell carcinoma cancer embolus, and the discovery of the gene combination model provides a new strategy for predicting the recurrence risk of renal clear cell carcinoma and the long-term survival status of patients after surgery, plays an important role in judging the prognosis of renal clear cell carcinoma patients, can evaluate the risk level of tumor progression or death of patients after renal clear cell carcinoma surgery, helps clinicians to implement individualized precision treatment strategy, improves the survival rate of patients after surgery, and has important guiding significance for postoperative follow-up monitoring and sequential treatment management of renal clear cell carcinoma patients.

[0038] 4、In terms of technology, the detection of the six genes is essentially quantitative PCR detection of tissue samples, which has the characteristics of simple operation, high sensitivity, good specificity, high repeatability, etc., and has been increasingly applied in clinical examination technology. This technology has been proven to be a high-sensitivity and high-accuracy detection method in modern experimental diagnostics, and the test technology is very mature. And the application adopts the standard curve quantitative method in this technology, which can accurately quantify the specific nucleic acid molecules in various samples. BRIEF DESCRIPTION OF DRAWINGS

[0039] Figure 1: Transcriptomic data of ccRCC related to VTT. (A) Volcano plot of all differentially expressed genes (DEGs) in normal, primary tumor and cancer embolus tissue transcriptomic analysis. (B) Venn diagram to illustrate the overlap of DEGs in different comparison groups. (C), (D), (E), (F) Transcriptomic KEGG pathway annotation.

[0040] Figure 2 : Proteomic data of ccRCC related to VTT. (A) Volcano plot of all differentially expressed proteins (DEPs) in normal, primary tumor and cancer embolus tissue proteomic analysis. (B) Venn diagram to illustrate the overlap of DEPs in different comparison groups. (C), (D), (E), (F) Proteomic KEGG pathway annotation.

[0041] Figure 3 : Integrated transcriptomic and proteomic data of ccRCC related to VTT. (A, B, C) Scatter plots of DEPs and DEGs expression correlation in primary tumor vs normal tissue, cancer embolus vs normal tissue, cancer embolus vs tumor primary tissue. (D) Venn diagram to show the consistently and differentially expressed genes / proteins and (E) related KEGG pathway annotation in tumorigenesis. (F) Venn diagram to illustrate the consistently and differentially expressed genes / proteins and (G) related KEGG pathway annotation in cancer embolus invasion.

[0042] Figure 4 : (A) RAB25, (B) GGT5, (C) SHMT2, (D) CADM4, etc. abnormal genes expression regulation trend in normal tissue, tumor tissue and cancer embolus tissue, based on external datasets of inventors and SRA database (PRJNA596338). Each row represents a case sample. *p<0.05, **p<0.01, ***p<0.001.

[0043] Figure 5 : Establishment of prognosis classifier related to cancer embolus. (A and B) Study the regulation expression trend of 6 hub genes in normal tissue, tumor tissue and cancer embolus tissue, based on external datasets of inventors and SRA database (PRJNA596338). (C) Different expression of 6 hub genes in ccRCC patients with different OS events based on TCGA database. (D) Kaplan-Meier curve of OS of ccRCC patients in high-risk group and low-risk group by prognosis classifier in TCGA database. (E) Receiver operating characteristic curve of prognosis classifier to predict OS of ccRCC patients. *p<0.05, **p<0.01, ***p<0.001.

[0044] Figure 6: Clinical validation of the clinical application of the plug-related prognostic classifier. (A) Protein expression differences of the 6 hub genes between normal and tumor tissues based on the SRA database (PRJNA596338). (B) Immunohistochemical analysis of the 6 hub proteins in tumor tissues of ccRCC patients. (C) Kaplan-Meier curves of disease-free survival of 40 ccRCC patients with high or low risk score based on ihc staining. (D) Receiver operating characteristic curve of the ihc-based prognostic classifier to predict 3-year disease-free survival of ccRCC patients. *p < 0.05, **p < 0.01, ***p < 0.001.

[0045] Figure 7 : Overview of ccRCC characteristics related to VTT. (A) Anatomical structure of the thrombus patient. (B, C) Score plots of the three-dimensional partial least squares discriminant analysis (PLS-DA) of the transcriptomic and proteomic data between individual samples.

[0046] Figure 8 : GO analysis of the transcriptomic data. (A, B) Functional annotation enrichment of significantly up- or down-regulated genes in tumorigenesis. (C, D) Functional annotation enrichment of significantly up- or down-regulated genes in thrombus invasion.

[0047] Figure 9 : Transcriptomic data analysis of transcription factors. (A and B) Transcription factors significantly up- or down-regulated in tumorigenesis. (C and D) Transcription factors significantly up- or down-regulated in thrombus invasion. (E) Venn diagram to illustrate the transcription factors expressed consistently and differentially in tumorigenesis and thrombus invasion. (F) Regulatory expression trend of the 6 transcription factors in normal, tumor and thrombus tissues.

[0048] Figure 10 : GO analysis of the proteomic data. (A, B) Functional annotation enrichment of significantly up- or down-regulated proteins in tumorigenesis. (C and D) Functional annotation enrichment of significantly up- or down-regulated proteins in thrombus invasion.

[0049] Figure 11 : Protein-protein interaction network analysis of the proteomic data. (A, B) Dense connected networks constructed from up- or down-regulated proteins in tumorigenesis. (C and D) Dense connected networks constructed from up- or down-regulated proteins in thrombus invasion.

[0050] Figure 12 : Thrombus-related gene signatures and prognostic stratification. (A) Risk score distribution and gene expression profiles of prognostic stratification of patients with different survival times and different statuses. (B) Risk ratio forest plot of the predictive classifier genes.

[0051] Figure 13ROC curve analysis of TNM staging alone for 1-, 3-, and 5-year survival rates of kidney cancer patients.

[0052] Figure 14 ROC curve analysis of TNM staging and DEPTOR, DPEP1, NAT8, PLOD2, SLC7A5 and SUSD2 expression for 1-, 3-, and 5-year survival rates of kidney cancer patients. DETAILED DESCRIPTION

[0053] The specific embodiments of the present application provided in the following examples are described in detail.

[0054] In the following examples, the tumor tissue samples of the renal clear cell carcinoma patients were all from the Changhai Hospital Affiliated to the Navy Medical University, and were diagnosed as renal clear cell carcinoma by pathologists.

[0055] The present application aims to explore and investigate the prognostic gene expression profile based on multiple cohorts. Transcriptome and proteome analysis of cancer emboli, tumor and normal tissues of 5 ccRCC patients were performed, and differential expression genes (DEG) were screened and identified according to normalized annotation information. Protein-protein interaction network (PPI) of DEG was developed, and modules were analyzed using STRING and Cytoscape. Multivariate Cox regression showed that there were 31 important participating genes in ccRCC patients, and 6 key genes in ccRCC patients were determined as independent oncogenes.

[0056] The different comprehensive scores of central gene mRNA expression were statistically significant in predicting overall survival (OS; p<0.001) in TCGA and real cohorts. At the same time, ROC curve was constructed to verify the specificity and sensitivity of Cox regression penalty score to predict prognosis. The AUC index of integrated gene score: 1-year AUC=0.77; 3-year AUC=0.78; 5-year AUC=0.78.

[0057] The present application research includes three stages: first, transcriptome and proteome analysis of cancer emboli, tumor and normal tissues of 5 ccRCC patients were performed, and the most commonly co-regulated key genes were generated; in the second stage, the correlation between the expression of key genes and prognosis in TCGA was evaluated, and their prognostic value in the real world was verified; in the third stage, the expression of key genes was verified in other cohorts. The following further detailed description is made in combination with the accompanying drawings.

[0058] Example 1: Screening of six marker genes

[0059] 1. A total of 5 patients with ccRCC complicated by VTT were included. The average diameter of the primary renal tumor was 8.8±3.6cm, and the length of the tumor thrombus was 6.0±3.1cm. Among the included patients, the shortest length of the renal vein tumor thrombus was 2cm in 1 case, and the length of the inferior vena cava tumor thrombus ranged from 5cm to 10cm in 4 cases. Figure 1 A). RNA sequencing and mass spectrometry results showed a highly correlated gene and protein expression between renal tumors and thrombotic tissue. Figure 1 ).

[0060] 2. Elucidation of differentially expressed genes (DEGs) and differentially expressed proteins (DEPs)

[0061] A meta-analysis of RNA-seq results from matched primary tumor tissue, tumor emboli tissue, and normal tissue revealed differential expression of 1131, 1258, and 63 transcripts, respectively, between tumor tissue and normal tissue, tumor emboli tissue and normal tissue, and tumor emboli tissue and tumor tissue. Figure 2 A). 1131 DEGs expressed in primary tumor tissue compared to normal tissue were defined as tumorigenesis-related genes, of which 505 were upregulated and 626 were downregulated. Furthermore, 795 DEGs co-expressed in tumor tissue compared to normal tissue, and 63 DEGs co-expressed in tumor thrombus tissue compared to normal tissue, were defined as tumor thrombus invasion-related genes, of which 382 were upregulated and 474 were downregulated. Figure 2 B).

[0062] Proteomic analysis of matched primary tumor tissue, tumor emboli, and normal tissue revealed 579, 659, and 39 DEPs in the tumor tissue compared to normal tissue, tumor emboli compared to normal tissue, and tumor emboli compared to tumor tissue, respectively. Figure 3 A). Based on the same screening criteria as those used for tumorigenesis and invasion-related genes in the transcriptome analysis above, 597 tumorigenesis-related proteins and 508 invasion-related proteins were identified.

[0063] Among tumorigenesis-related proteins, KEGG analysis revealed that upregulated proteins, including SLC7A5 and PYCR1, are primarily associated with central carbon metabolism, arginine metabolism, and proline metabolism. Figure 3 C), the downregulated proteins are associated with focal adhesion and tight junctions. Figure 3 D). Among invasion-related proteins, those upregulated by PLOD2 and FABP5 are mainly associated with lysine degradation, the PPAR signaling pathway, and DNA replication. Figure 3 E), while the proteins downregulated by GGT5 are mainly related to glutathione metabolism (E). Figure 3 F).

[0064] 3. Protein-protein interaction (PPI) network and functional annotation

[0065] In this study, the search tool for retrieval of interacting genes (STRING; http: / / string-db.org) online database (version 10.0) was used to predict the PPI network of DEG and analyze the degree of interaction between proteins. PI score > 0.9. Cytoscape (version 3.5) is an open bioinformatics software platform that provides the possibility of molecular graphs that can be used to visualize interactive network data.

[0066] To understand the role of DEG in ccRCC, functional attributes including biological processes (BP), molecular functions (MF) and cellular components (CC) were annotated from gene ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) perspective. The online database of the visualization and integrated discovery database (DAVID; http: / / david.ncifcrf.gov; version 6.8) was used to explore the role of development-related signaling pathways in ccRCC. P < 0.05 was considered statistically significant, and GO enrichment analysis was performed using a bubble chart.

[0067] ClueGO is a Cytoscape plug-in that visualizes large clusters of non-redundant biological terms in functionally grouped networks. GO: Biological processes and KEGG pathways of selected hub genes were registered and visualized using ClueGO (version 2.5.3) and CluePedia (version 1.5.3) (a functional extension of ClueGO, a plug-in of Cytoscape). Heat maps show potential co-expression relationships between 24 key genes and possible prognostic values, identifying hub nodes in the network with a connectivity > 10. The network associated with 24 genes and their co-regulated neighboring genes was obtained from the online platform of cBioPortal (http: / / www.cbioportal.org).

[0068] The PPI network of DEG was predicted and constructed in Figure 11 Apparently, SLC1A5, SLC7A5, PYCR1, RAB25, DPEP1 and GGT5 are the central hubs of the network. SLC1A5, SLC7A5 and PYCR1 are hub-up proteins, while DPEP1 and GGT5 are hub-down proteins, with a high degree of connectivity between tumor occurrence and thrombus invasion processes.

[0069] 4. Statistical analysis of TCGA cohort

[0070] To further search for hub genes associated with metastasis and narrow the scope of the study, a LASSO Cox regression model was constructed to find independent prognostic factors. Phenotype and transcriptomic data of selected hub genes in 457 ccRCC patients from TCGA database were shown. Expression profiles were identified as binary variables (high vs. low), respectively, indicating the median expression of each key gene in the TCGA cohort. The primary endpoint of patients was overall survival (OS).

[0071] LASSO Cox regression analysis suggested that a total of 31 genes, including STEAP3, FABP5, FKBP10, PYCR1, PLOD2, SLC7A5, CDCP1, ICAM1, CD70, NAT8, SUSD2, HSD11B2, ANXA3, EFHD1, ERMP1, ATP1A1, CHL1, CLDN10, PODXL, EPCAM, CGNL1, CDH16, CXCL12, LTF, GSTM3, DPEP1, DEPTOR, GPX3, SFRP1, SLC44A4, and TOM1L2 were important factors.

[0072] The Kaplan-Meier method was used to estimate the follow-up time in separate curves with 95% confidence intervals (95% CI) and log-rank tests, and the cutoff values were obtained using X-tile software. All hypothesis tests were two-way tests, and the p values in all tests were less than 0.05. The Kaplan-Meier method showed that the increased expression of 6 key genes (including DEPTOR, DPEP1, NAT8, PLOD2, SLC7A5, and SUSD2) was significantly associated with poor prognosis.

[0073] Example 2: External validation

[0074] The protein expression differences between normal tissues and tumor tissues of these survival-related DEGs were confirmed by external data from the SRA database (PRJNA596338) Figure 7 A).

[0075] To further clarify the relationship between the expression of 6 key genes (including DEPTOR, DPEP1, NAT8, PLOD2, SLC7A5, and SUSD2) in renal cell carcinoma tissues and patient prognosis, the present application detected the expression of 6 key genes in multiple samples by immunohistochemical staining (method as follows), and analyzed the relationship between the expression of 6 key genes and patient prognosis.

[0076] The main experimental steps of immunohistochemical staining include:

[0077] 1. Sectioning: Routine sectioning of 3 micron thick paraffin sections mounted on adhesive white slides.

[0078] 2. Baking: Leica Baking machine, 75°C for 30 minutes.

[0079] 3. De-waxing and Hydrating: Routine xylene (3 times, 5-10 minutes each time) de-waxing and gradient ethanol hydrating; PBS wash for 5 minutes x 3 times after hydration.

[0080] 4. Antigen Retrieval: Antigen retrieval was performed using base retrieval (EDTA, Fuzhou Maynew MVS-0099, 50X) with 1X retrieval solution. Microwave heat retrieval method was used, with the microwave oven on high heat for 6 minutes to boil, then the sections were placed in the boiling retrieval solution, and the heat was turned to medium-low for 20 minutes.

[0081] 5. Room temperature natural cooling.

[0082] 6. Endogenous peroxidase elimination: 3% H2O2 peroxide endogenous peroxidase elimination for 15 minutes, then PBS wash for 5 minutes x 3 times.

[0083] 7. Secondary antibody homologous serum incubation: goat serum blocking was used, room temperature for 1 hour.

[0084] 8. Primary antibody incubation: The experimental primary antibody was incubated overnight (about 14 hours) at 4°C.

[0085] 9. Secondary antibody incubation: After primary antibody incubation, PBS wash for 5 minutes x 3 times, biotinylated goat anti-rabbit secondary antibody was incubated at room temperature for 30 minutes; after incubation, PBS wash for 5 minutes x 3 times.

[0086] 10. Streptavidin-HRP incubation: Streptavidin-HRP was incubated for 15 minutes (Fuzhou Maynew), then PBS wash for 5 minutes x 3 times.

[0087] 11. DAB color development: Fuzhou Maynew DAB color developing solution (DAB-2031), after color development, tap water was used to rinse to stop the reaction.

[0088] 12. Hematoxylin counterstaining: Hematoxylin was heated to 50°C, and stained for about 15 seconds, then tap water was used to rinse.

[0089] 13. Hydrochloric acid differentiation: 1% hydrochloric acid alcohol (1 ml of concentrated hydrochloric acid, 100 ml of anhydrous ethanol) was used for differentiation, then tap water was used to rinse.

[0090] 14. Routine dehydration, transparency, and mounting: gradient ethanol dehydration, xylene transparency, and neutral resin mounting.

[0091] Immunohistochemical analysis of the tissue chip of 40 patients verified the clinical application effect of the prognosis classifierFigure 6 B) At a median follow-up of 45 months (IQR 45-47), 7 (17.5%) patients developed recurrence or metastasis. Kaplan-Meier curves showed that the disease-free survival of the IHC high-risk score group was lower than that of the low-risk score group (HR=0.181, P=0.026) Figure 6 C) In addition, the AUC of the 3-year disease-free survival prognosis model based on IHC was 0.933 (95% CI, 0.807-0.988) Figure 6 D)

[0092] In summary, the present application uses systematic microarray data analysis of well-defined and complete ccRCC primary tumor and metastatic tumor tissues to reveal a unique gene expression profile of tumor invasiveness. A total of 70 differentially expressed genes were identified, and 6 prognostic central genes were screened from them.

[0093] The results of the present application combined with genome validation in real-world cohort are very significant, as shown in Figure 6 40 cases of kidney cancer patients from the Department of Urology of Changzheng Hospital, with a median follow-up time of more than 45 months, can well reflect the predictive performance of the model. Therefore, in this study, systematic analysis of ccRCC primary tissues and metastatic ccRCC tissues can screen and identify a promising biomarker expression profile of tumor invasiveness.

[0094] In summary, the present application determines DEG and central genes that may be involved in early recurrence and poor prognosis of ccRCC. The expression levels of DEPTOR, DPEP1, NAT8, PLOD2, SLC7A5 and SUSD2 have high prognostic value and may help us better understand the potential carcinogenesis or progression of ccRCC.

[0095] Example 3:

[0096] We combined the expression levels of the six genes DEPTOR, DPEP1, NAT8, PLOD2, SLC7A5 and SUSD2 with TNM staging and compared them with simple TNM staging, as shown in Figure 13 , 14 The AUC of simple TNM staging for predicting one-year survival rate was 0.88, three-year AUC was 0.81, and five-year AUC was 0.77. Obviously, for long-term survival rate, simple TNM staging cannot be well predicted. However, if the six genes are combined, the AUC of one-year survival rate, three-year survival rate and five-year survival rate reaches 0.88, 0.87 and 0.87 respectively, and the accuracy of predicting long-term survival period is significantly improved.

[0097] The preferred embodiments of the present application have been disclosed with the above particularity, but the application is not limited to the embodiments disclosed, and variations and modifications can be made by those skilled in the art without deviating from the spirit of the application, and such variations and modifications are intended to be included within the scope of the application as defined in the following claims.

Claims

1. A gene combination for prognostic assessment of clear cell renal cell carcinoma, characterized in that, The prognostic assessment gene combination consists of six genes: DEPTOR, DPEP1, NAT8, PLOD2, SLC7A5, and SUSD2.

2. A detection product for a gene combination for prognostic assessment of clear cell renal cell carcinoma, characterized in that, Includes reagents for detecting the expression level of the gene combination of claim 1 in a biological sample.

3. The application of a detection product of the prognostic assessment gene combination for clear cell renal cell carcinoma as described in claim 1 or the prognostic assessment gene combination for clear cell renal cell carcinoma as described in claim 2 in establishing a prognostic assessment system for clear cell renal cell carcinoma.

4. The use of a detection product of the prognostic assessment gene combination for clear cell renal cell carcinoma as described in claim 1 or the prognostic assessment gene combination for clear cell renal cell carcinoma as described in claim 2 in the preparation of a prognostic assessment kit for clear cell renal cell carcinoma.

5. A prognostic assessment kit for clear cell renal cell carcinoma, characterized in that, The kit contains reagents for detecting the relative expression levels of DEPTOR, DPEP1, NAT8, PLOD2, SLC7A5, and SUSD2 in biological samples.

6. A prognostic assessment system for clear cell renal cell carcinoma, characterized in that, The system includes: A processor and a memory, the memory being coupled to the processor, the memory storing instructions that, when executed by the processor, use the following steps: The mRNA expression levels of the prognostic assessment gene combination for clear cell renal cell carcinoma as described in claim 1 are input into the following calculation formula to obtain a risk score; the calculation formula is as follows: Risk score = 1.0025 × PLOD2 expression level + 0.9993 × NAT8 expression level + 0.9869 × DPEP1 expression level + 0.9780 × DEPTOR expression level + 1.0044 × SLC7A5 expression level + 1.0258 × SUSD2 expression level.

7. The use of the prognostic assessment system of claim 6 in the preparation of a product for predicting the prognostic risk of clear cell renal cell carcinoma.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it uses the following steps: The mRNA expression levels of the prognostic assessment gene combination for clear cell renal cell carcinoma as described in claim 1 are input into the following calculation formula to obtain a risk score; the calculation formula is as follows: Risk score = 1.0025 × PLOD2 expression level + 0.9993 × NAT8 expression level + 0.9869 × DPEP1 expression level + 0.9780 × DEPTOR expression level + 1.0044 × SLC7A5 expression level + 1.0258 × SUSD2 expression level.

9. A prognostic diagnostic product for clear cell renal cell carcinoma, characterized in that, include: The product for determining the expression level of the prognostic gene combination for clear cell renal cell carcinoma as described in claim 1, and; The prognostic assessment system for clear cell renal cell carcinoma as described in claim 6, or the computer-readable storage medium as described in claim 8.

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