CN113430266A

CN113430266A - Application of G6PC and genome thereof in preparation of renal clear cell carcinoma diagnosis or prognosis evaluation system

Info

Publication number: CN113430266A
Application number: CN202110711681.2A
Authority: CN
Inventors: 徐文浩; 张海梁; 叶定伟; 田熙; 瞿元元; 艾合太木江·安外尔; 刘王睿; 宿佳琦
Original assignee: Fudan University Shanghai Cancer Center
Current assignee: Fudan University Shanghai Cancer Center
Priority date: 2021-06-25
Filing date: 2021-06-25
Publication date: 2021-09-24

Abstract

The invention relates to the technical field of medical biological detection, and provides application of G6PC and a genome thereof in preparation of a renal clear cell carcinoma diagnosis or prognosis evaluation system, wherein the combined genome is the combination of ADCY10, AMY2A, CA8, CEL, CYP26A1, CYP3A4, G6PC, HAO1, HK3, HMGCS2, ITPKA, NOS1, P4HA3, PFKFB1, PSAT1, RDH8, RRM2, TYR, UGT1A7 and UROC 1; further provides a kit for diagnosing or prognostically evaluating clear cell renal cancer and a system for prognostically evaluating clear cell renal cancer. The invention identifies and verifies that G6PC can be used as a molecular marker for predicting prognosis and abnormal infiltration of immune cells by using glucose 6 phosphate kinase as an encoding protein, and verifies 380 cases of ccRCC specimens from a double-denier tumor cohort and transcriptome and proteomics from ICGC, TCGA, TCPA, CTPAC and RECA-EU with median follow-up time of more than 72 months for the first time, so that the G6PC is clear to be important for disease progression of renal clear cell carcinoma, and provides new evidence for development of novel targeted drugs.

Description

Application of G6PC and genome thereof in preparation of renal clear cell carcinoma diagnosis or prognosis evaluation system

Technical Field

The invention belongs to the technical field of medical biological detection, and relates to application of G6PC in diagnosis, prognosis evaluation and treatment of renal clear cell carcinoma, and also relates to application of 20 genes including G6PC in a metabolic prediction model.

Background

Renal cell carcinoma is one of the most common malignancies of the urogenital system, accounting for about 5% of all new adult male cases and 3% of female cases. According to statistics of the United states, 73,820 new cases and 14,770 dead cases of kidney cancer exist in the whole United states in 2019, about 6.68 ten thousand new cases of kidney cancer in China are detected every year, and the second place of the incidence rate of clear cell carcinoma of the kidney of the urinary system is in China. Clear cell renal carcinoma (ccRCC) is the most common pathological type of renal carcinoma with high malignancy, accounting for about 70-85% of all renal carcinoma patients, with metastasis occurring in the initial diagnosis of about 25-30% of ccRCC patients, and the 5-year survival rate of metastatic ccRCC is only 32%. Furthermore, even patients with initially therapeutically effective ccRCC experience disease progression over time, when a large proportion of patients will lack subsequent effective treatment.

In recent years, novel immunotherapies, represented by PD-1/PD-L1, CTLA4 inhibitors, have rapidly emerged in the field of ccRCC therapy, and have shown encouraging effects in patients with advanced refractory disease. In 2020, ASCO GU publishes the follow-up result of 5 years of CheckMate 025 research, and shows that the 5-year survival rate of the second line treatment of the monoclonal antibody reaches up to 26%, the survival benefit and advantage of immunotherapy are fully embodied, and a new chapter begins to appear in the treatment strategy of the ccRCC high-risk patients.

Immune checkpoint inhibitors in combination with TKI exert a variety of effects from inducing normalization of anti-tumor immunity, inhibiting development of advanced ccRCC, and modulating Tumor Microenvironment (TME), the success of which depends largely on the insight into the interaction of tumor cells and TME. With the progress of research, more evidences show that the curative effect of immunotherapy not only depends on the activation of tumor immune microenvironment, but also depends on the strength of individual antitumor immune response of traditional therapies such as targeted therapy and the like. Therefore, the method has important significance for exploring a potential mechanism of TME-driven tumorigenesis and development, improving the efficiency of various existing treatments and discovering a new accurate target point of ccRCC treatment.

In TME, tumor cells and immune cells reprogram their metabolic patterns to adapt to hypoxic, acidic, and low-nutrition microenvironments. For example, tumor cells exhibit enhanced aerobic glycolysis (Warburg effect) but reduced oxidative phosphorylation, which has a large impact on T cell-mediated antitumor immune responses and tumor infiltrating myeloid cell activity; macrophages tend to polarize to M2 type, showing up-regulated fatty acid synthesis and β -oxidation. The activation of tumor cell cancer promotion signals not only affects the malignant biological behavior of the tumor cells, but also promotes the generation and development of the tumor, and can deflect the functional phenotype of tumor infiltration immune cells by changing the metabolic secretion spectrum and TME of the tumor cells to induce the formation of tumor immune escape. Therefore, metabolic reprogramming of tumor cells and immune cells is important for understanding evil behaviors of the tumor cells, tumor immune response and the game process of tumor immune escape, and a new direction is provided for regulating and controlling tumor immunity.

The method for constructing the clinical prediction model based on the retrospective data is helpful for researching and realizing the prediction of multiple prognostic factors on clinical outcome, more likely changes the clinical practice of people and has strong clinical guidance value. In 2018, RCClnc4 has been shown to have precise prognostic significance in early stage ccRCC. The present study aims at first establishing and validating an effective prognostic metabolic prediction model that registers large-scale transcriptome metabolic genes for ccRCC patients. We hypothesize that Metabolic Prediction Model (MPMs) classifiers can facilitate risk management and treatment strategies for ccRCC patients and identify new targets in MPMs collaborative networks.

Disclosure of Invention

The invention is carried out to solve the technical problems, provides application of G6PC in diagnosis, prognosis evaluation and treatment of renal clear cell carcinoma, and also relates to application of 20 genes including G6PC in a metabolism prediction model for carrying out immunotherapy.

The inventors tried to establish metabolic features to improve post-operative risk stratification and identify new targets in ccRCC patient prediction models, identifying 58 Metabolic Differentially Expressed Genes (MDEG) in total, with significant prognostic value. Then, 20-mRNA signature model, Metabolic Prediction Model (MPM) was constructed in ccRCC patients from both TCGA and CTPAC cohorts using LASSO regression analysis. The risk score for MPM significantly predicts prognosis for ccRCC patients in TCGA (p <0.001, HR 3.131, AUC 0.768) and CTPAC cohort (p 0.046, HR 2.893, AUC 0.777). The 20-mRNA includes ADCY10, AMY2A, CA8, CEL, CYP26A1, CYP3A4, G6PC, HAO1, HK3, HMGCS2, ITPKA, NOS1, P4HA3, PFKFB1, PSAT1, RDH8, RRM2, TYR, UGT1A7, and UROC 1.

Further analysis, G6PC is a pivot gene in the PPI network of MPM, showing significant prognostic value in 718 ccRCC patients from multiple cohorts; next, the expression of glucose 6-phosphatase (G6Pase), a protein encoding G6PC, was detected in normal kidney tissue as higher than in ccRCC tissue. Through practical validation, low G6Pase expression was significantly associated with poor prognosis (p <0.0001, HR ═ 0.316) and aggressive progression (p <0.0001, HR ═ 0.414) in 322 ccRCC patients in the FUSCC cohort; meanwhile, the promoter methylation level of G6PC was significantly higher in ccRCC samples with aggressive progression status.

From this, it is clear that G6PC is significantly involved in abnormal immune infiltration of the ccRCC microenvironment, and is significantly negatively associated with checkpoint immune characteristics, dendritic cells, Th1 cells, and the like. MPM was modeled using large-scale ccRCC transcriptome data and G6PC was determined as a potential prognostic target for 1,040 ccRCC patients from multiple cohorts. These findings help manage risk assessment and provide valuable insight into the therapeutic strategy of ccRCC.

The technical scheme adopted by the invention is as follows:

in a first aspect of the invention, the application of a reagent for detecting the expression level of G6PC in the preparation of a transparent cell kidney cancer diagnosis or prognosis evaluation reagent or kit is provided.

Preferably, the reagent for detecting the expression level of G6PC is a reagent for detecting the mRNA expression level of the G6PC gene, the glucose 6-phosphatase, which is a G6Pase protein encoded by the G6PC gene, or the methylation level of the promoter of the G6PC gene.

Further preferably, the reagent for detecting the mRNA expression level of the G6PC gene comprises a PCR primer with detection specificity to G6PC, and the sequence of the primer is shown as SEQ ID NO. 13-14.

Kaplan-Meier survival analysis showed that low G6PC mRNA expression levels were significantly associated with poor prognosis (fig. 5I-J); a significant increase in G6Pase expression was found in normal tissues (fig. 6B), with low G6Pase expression significantly correlated with poor prognosis and aggressive progression (fig. 6C-D); promoter methylation levels of G6PC were significantly lower in the primary ccRCC samples than in the normal samples (fig. 7C), increased significantly with increasing individual cancer stage, and highest in the stage 4 samples (fig. 7D). Promoter methylation levels of G6PC were significantly higher in ccRCC samples with lymph node metastasis compared to pN0 patients (fig. 8F-G).

In a second aspect of the present invention, there is provided a clear cell renal cancer diagnosis or prognosis evaluation kit comprising a reagent for detecting the expression level of mRNA of the G6PC gene, a reagent for detecting the expression level of glucose 6-phosphatase, a G6Pase protein encoded by the G6PC gene, or/and a reagent for detecting the methylation level of a promoter of the G6PC gene.

In a third aspect of the invention, the application of the reagent for promoting or improving the expression level of G6PC in the preparation of the medicine for treating clear cell renal cancer is provided, and the clear cell renal cancer treatment is realized by targeting G6 PC.

Preferably, the agent that promotes or increases the expression level of G6PC is an agent that increases the expression level of mRNA from the G6PC gene or increases the expression level of glucose 6-phosphatase, a G6Pase protein encoded by the G6PC gene.

In a fourth aspect of the invention, the invention provides the use of a combined genome in the preparation of a renal clear cell carcinoma prognosis evaluation reagent or kit, wherein the combined genome is a combination of ADCY10, AMY2A, CA8, CEL, CYP26a1, CYP3a4, G6PC, HAO1, HK3, HMGCS2, ITPKA, NOS1, P4HA3, PFKFB1, PSAT1, RDH8, RRM2, TYR, UGT1a7 and UROC 1.

Preferably, the prognosis evaluation reagent is a reagent for detecting the expression level of each gene in the combined genome in the biological sample; the prognosis evaluation kit comprises a reagent for detecting the expression quantity of each gene in the combined genome in a biological sample.

Further, the reagent for detecting the expression level of each gene of the combined genome in the biological sample is selected from the group consisting of: the PCR primers have detection specificity on each gene, the sequences of the PCR primers are shown as SEQ ID No. 1-40, and the sequences are shown in Table 1:

TABLE 1 Combined genome PCR primer set

In a fifth aspect of the invention, a renal clear cell carcinoma prognosis evaluation system is provided, which comprises the prognosis evaluation kit and a subgroup classification model installed on a terminal carrier.

The immune subgroup classification model carries out sample score calculation according to the following formula based on each gene expression quantity, and determines the metabolic typing of the current sample according to the score, wherein the sample score is equal to

0.02108354×ADCY10+0.055415221×AMY2A+(-0.039834066)×CA8+0.030574711×CEL+0.073959631×CYP26A1+(-0.099395461)×CYP3A4+(-0.029400148)×G6PC+0.002392772×HAO1+0.08863801×HK3+(-0.00986571)×HMGCS2+0.073210921×ITPKA+(-0.045158291)×NOS1+0.041592301×P4HA3+0.108183411×PFKFB1+0.032215715×PSAT1+0.032054991×RDH8+0.018262536×RRM2+0.028898214×TYR+0.02533731×UGT1A7+0.142174067×UROC1。

In a sixth aspect of the present invention, there is provided a method for performing auxiliary prognosis evaluation of renal clear cell carcinoma using the above prognosis evaluation system, comprising the steps of:

A. extracting total RNA of a sample to be detected;

B. carrying out reverse transcription on the total RNA in the step A to obtain cDNA;

C. respectively carrying out quantitative detection on the expression quantity of each gene in the combined genome by adopting a real-time quantitative PCR technology, wherein primers used in the detection process are shown as SEQ ID NO 1-40;

D. and calculating a prognosis evaluation score according to the formula based on the expression quantity of each gene, determining the metabolic typing of the current sample according to the score, and formulating a treatment strategy based on the metabolic typing.

The invention has the following beneficial guarantee and effects:

the invention discovers 20 key metabolism-related molecular markers influencing the prognosis of the ccRCC by exploring a unique mode of metabolism-related genes participating in the generation and development of the ccRCC based on transcriptome data of more than 1000 ccRCC patients from two queues of TCGA and CTPAC for the first time.

According to the invention, a metabolic correlation clinical prediction model is successfully established for the first time through a strict algorithm such as a multilayer logic algorithm and a fitting regression forest tree, the prediction efficiency of the risk score is obviously higher than that of the traditional clinical pathological characteristics (AUC 0.768TCGA, ACU 0.777CTPAC), and the method is not seen internationally.

The invention identifies and verifies that G6PC can be used as a molecular marker for predicting prognosis and abnormal infiltration of immune cells by using glucose 6 phosphate kinase as an encoding protein, and verifies 380 cases of ccRCC specimens from a double-denier tumor cohort and transcriptome and proteomics from ICGC, TCGA, TCPA, CTPAC and RECA-EU with median follow-up time of more than 72 months for the first time, so that the G6PC is clear to be important for disease progression of renal clear cell carcinoma, and provides new evidence for development of novel targeted drugs.

In the aspect of technical implementation, detection of each gene in the combined genome is essentially quantitative detection of blood and other liquid genomes, and if a PCR (polymerase chain reaction) technology is adopted, the combined genome has the characteristics of simplicity and convenience in operation, sensitivity in detection, good specificity, high repeatability and the like, and is increasingly applied to clinical examination technology nowadays. The technology is proved to be a high-sensitivity and high-accuracy detection method in modern experimental diagnostics, the experimental technology is mature, and a standard curve quantitative method in the technology is adopted, so that the characteristic nucleic acid molecules in various samples can be accurately quantified.

Drawings

Fig. 1 illustrates identification of MDEG in TCGA and CPTAC queues: (A) identification of important MDEG using Limma package in 905 metabolic genes; (B) hierarchical partitions of important MDEG were obtained from TCGA cohort-based DNA microarrays, mRNA expression of these genes was performed in 534 ccRCC patients and 72 normal humans, high in red and low in green; (C) univariate Cox regression analysis of 58 significant MDEGs in the TCGA cohort (p <0.05) were performed in forest plots; (D) calculating a 20-mRNA signature model (MPM) for ccRCC patients using LASSO regression analysis, Kaplan-Meier survival analysis showed that the significant predictive value of the risk score was dependent on the MPM in the TCGA cohort; (E) Kaplan-Meier survival analysis showed that significant predictive value of risk scores was dependent on MPM in the CTPAC cohort, with high risk groups labeled with red and low risk groups labeled with blue.

Wherein, MDEGs, metabolism differential expression gene; MPMs, metabolic prediction models; ccRCC, clear cell renal carcinoma; TCGA, cancer genomic map; CTPAC, clinical proteomics tumor analysis consortium.

FIG. 2 shows that the survival risk assessment of MPM consists of metabolic 20-mRNA features in the TCGA and CTPAC cohorts. The distribution of survival time, survival status (a), risk score (B) and stratification (C) of the 20 features in tumor and normal samples is shown in the TCGA cohort. The distribution of survival time, survival status (D), risk score (E) and stratification (F) for 20 features in tumor and normal samples are shown in the CTPAC cohort.

Fig. 3 is a Cox regression analysis, ROC analysis, and nomogram of independent prognostic factors and MPM for ccRCC patients. (A-D) univariate and multivariate Cox regression analyses incorporating the clinicopathologic parameters and MPM are illustrated in TCGA and CTPAC cohorts using forest plots. The risk score for MPM significantly predicts prognosis for ccRCC patients in TCGA (p <0.001, HR ═ 3.131) and CTPAC cohort (p ═ 0.046, HR ═ 2.893). (E-F) ROC analysis showed that MPM has a strong predictive value in the TCGA (AUC 0.768) and CTPAC (AUC 0.777) cohorts. (G) Nomograms were constructed based on 5 independent prognostic factors, including ISUP stratification, pathology M staging, pathology T staging, AJCC staging and MPM risk score for ccRCC patients.

Figure 4 shows GSEA shows significantly altered KEGG pathways based on differential risk score of MPM in ccRCC patients with available transcriptomics data from TCGA and CTPAC cohorts. (A) The top 5 significantly altered the KEGG pathway for high-risk or low-risk ccRCC patients in the TCGA cohort. (B) The top 5 patients with high-risk or low-risk ccRCC in the CTPAC cohort significantly changed the significant KEGG pathway. (C) Tumor environmental purity was measured using the ESTIMATE algorithm and shown to be significantly correlated with MPM risk score of ccRCC patients from the TCGA cohort (r)²＝0.2373，p<0.0001). (D) Immune purity in ccRCC environment was significantly correlated with the risk score of MPM (r)²＝0.3007，p<0.0001)。

Figure 5 shows that G6PC is a pivot gene in the PPI network of MPM, showing significant prognostic value in 699 ccRCC patients from TCGA, CTPAC and RECA-EU cohorts. (A) PPI networks are constructed in 20 metabolic mRNA signatures in MPM. (B-C) G6PC mRNA expression and tumor environmental purity (r) in ccRCC²＝-0.1012,p<0.0001) and immunological purity (r)²＝-0.1205,p<0.0001) is in negative correlation. (D-F) published data based on TCGA, CPTAC and RECA-EU (ICGC)) The cohort shows differential mRNA expression of G6PC in ccRCC and adjacent normal tissues. (G-H) Kaplan-Meier survival analysis showed low G6PC mRNA expression levels with poor OS (p)<0.0001, HR ═ 0.35) and PFS (p)<0.0001, HR ═ 0.35) were significantly correlated. (I-J) low G6PCmRNA expression levels were significantly associated with poor prognosis in the CTPAC (p 0.0035, HR 0.218) and ICGA (p 0.0443, HR 0.446) cohorts.

Fig. 6 shows that G6Pase differential expression predicts the outcome of 322 ccRCC patients from the FUSCC cohort. (A) High G6Pase expression was detected in normal kidney tissue (especially in tubular cells but not glomerular cells), but not in ccRCC tissue from human protein maps. (B) G6Pase expression was found to be significantly elevated in normal tissues compared to ccRCC tissues from the FUSCC cohort. (C-D) low G6Pase expression was significantly associated with poor prognosis (p <0.0001, HR ═ 0.316) and aggressive progression (p <0.0001, HR ═ 0.414) in 322 ccRCC patients from the FUSCC cohort.

Figure 7 shows most co-expressed genes and promoter methylation levels of G6PC in ccRCC. (A-B) the first 50 genes co-expressed with G6PC were extracted and shown in a heat map of ccRCC. (C) Promoter methylation levels of G6PC in the primary ccRCC sample were significantly lower than in the normal sample (p < 0.0001). (D) The promoter methylation level of G6PC climbed significantly with increasing individual cancer stage and was highest in stage 4 samples. (E) The promoter methylation level of G6PC climbed significantly with increasing individual tumor grade and was highest in stage 4 samples. (F) Promoter methylation levels of G6PC were significantly higher in ccRCC samples with lymph node metastasis compared to pN0 patients (p < 0.05).

Figure 8 shows the potential role of G6PC in pan-cancer and ccRCC microenvironments. (A) As a major regulator of hepatic glucose production, high G6PC activity expression was found in liver and kidney tissues. Expression of G6PC was significantly elevated in normal tissues compared to renal cell carcinoma (KIRC, KIRP, KICH), while expression of G6PC was significantly reduced in normal tissues compared to hepatocellular carcinoma and cholangiocarcinoma; (B) loss of elevated arm levels of G6PC compared to normal samples leads to poor quality B cell, CD8+ cells, CD4+ cells, macrophages, neutrophils, dendritic cell infiltration (p < 0.05). (C) G6PC is significantly involved in abnormal immune infiltration of ccRCC cells and microenvironment, co-suppression and co-stimulatory activity with checkpoint immune features, dendritic cells, Th1 cells, MHC class I, cytolytic activity, inflammatory promotion, HLA, APC (cor. < -0.7). (D-G) GSEA showed that G6PC was significantly involved in several signaling pathways, including bile acid metabolism, fatty acid metabolism, epithelial mesenchymal transition, and E2F target in ccRCC.

Figure 9 shows that a total of 100 up-and down-regulated genes are associated with differential G6PC expression in ccRCC.

Detailed Description

The present invention will now be described in detail with reference to examples and drawings, but the practice of the invention is not limited thereto.

Materials and methods

1. Raw data collection and processing

The present study used publicly available mRNA expression and clinical data from the ccRCC cohort. Consent and ethical approval for the enrolled patients was available in the relevant original article publishing the data set. A total of 718 ccRCC patients from an online dataset comprising 534 ccRCC samples and 72 normal samples obtained from the cancer genomic map (TCGA) database (https:// portal. gdc. cancer. gov.); 93 ccRCC samples and 20 normal samples obtained from the Clinical Proteomic Tumor Analysis Consortium (CPTAC); 91 ccRCC samples from RECA-EU (available in the International Cancer Genome Consortium (ICGC)) were also included.

2. Identification of genes differentially expressed by metabolism

And (4) screening 41 metabolic pathways according to a KEGG pathway map. 911 metabolic genes were identified for significant Metabolic Differential Expression Genes (MDEG) using Limma R package (version 3.6.3) with FDR <0.05 and | logFC | > 0.5. The cross-metabolic genes between TCGA and CPTAC cohort were selected for further analysis.

3. Development of Metabolic Prediction Model (MPM) and survival analysis

Univariate Cox regression analysis was used to identify the prognostic impact of important MDEG and presented in the surview using survival R package. Then, LASSO regression analysis was performed using glmnet and survival R package to construct 20-mRNA signature model, Metabolic Prediction Model (MPM) in ccRCC patients from TCGA and CTPAC cohorts. Calculating a risk score for each ccRCC patient based on the MPM, thereby classifying ccRCC patients into a low risk group and a high risk group.

For survival analysis, TCGA and CTPAC cohorts were selected, with relevant long-term survival data for patients at surgical resection and pathologically diagnosed as ccRCC. Survival data is of two types: overall survival and progression-free survival. The log-rank test in a separate curve and the Kaplan-Meier method with 95% confidence interval (95% CI) were used to perform subsequent duration analysis. At the same time, survival risk assessment and hierarchical clustering of MPM was shown in patients from TCGA or CTPAC cohorts.

4. Cox regression analysis and receiver operating characteristic curve construction

All ccRCC patients with complete transcriptome information and associated clinicopathologic parameters from TCGA and CTPAC cohort were included for subsequent analysis. Independent prognostic value of metabolic clusters was assessed using univariate and multivariate Cox regression analysis using the surrep forest map with survival R package. Using the survival ROCR package, receiver operating characteristic curves (ROCs) were constructed for traditional clinicopathologic parameters and risk scores of MPMs in TCGA and CTPAC cohorts. The area under the curve (AUC) was used to assess the predictive value of these prognostic features. Furthermore, nomograms were developed based on all independent prognostic factors in the TCGA cohort.

5. Gene set enrichment analysis

Gene Set Enrichment Analysis (GSEA) was performed using 1000 displacement assays to find the most enriched signaling pathway and significant involvement in metabolic pathways involving the metabolic pathways of the molecular signature database v4.0(MSigDB, P <0.01, FDR < 0.25).

6. Assessment of tumor microenvironment purity

The total and immune scores of patients from the TCGA cohort were assessed using an estimation package (http:// r-form. rproject. org; repos. rford, dependencies. TRUE). Correlation between Tumor Microenvironment (TME) purity and risk score of MPM or G6PC expression was assessed using Pearson's r test.

7. Differential G6PC mRNA expression and survival analysis

A protein-protein interaction network of 20 features in MPM was constructed using an online database of search tools for retrieving interacting genes (STRING; http:// STRING-db. org, version 10.0). Differential expression G6PC levels between ccRCC and normal samples from TCGA, CPTAC and RECA-EU cohorts were assessed using Student t-test. Survival analysis of predicted prognostic ability of G6PC was performed on patients from the TCGA cohort using GEPIA (http:// GEPIA. cancer-pku. cn/detail. php # ##). The Kaplan-Meier method with 95% confidence intervals (95% CI) and log-rank test was used for survival analysis of CPTAC, RECA-EU and FUSCC.

8. Glucose 6-phosphatase (G6Pase) expression in ccRCC and Normal samples

G6Pase protein expression encoded by the G6PC gene was detected in ccRCC and normal samples from human protein profiles (https:// www.proteinatlas.org /) and Immunohistochemical (IHC) data, including the number of stains, intensity, location and patient data were obtained online. Formalin-fixed, paraffin-embedded ccRCC tissue and human kidney tissue were stained for anti-G6 Pase in a FUSCC cohort using ab243319(Abcam, usa) at 1/3000 dilution, and then evaluated independently by two experienced pathologists. As previously described, overall IHC scores from 0 to 12 were measured based on the product of staining intensity and degree scores. The low G6Pase expression set scores ranged from 0 to 2, and the high G6Pase expression set scores ranged from 3 to 12.

9. Immune cell infiltration of G6PC in ccRCC

TIMER (https:// geometry. shinyapps. io/TIMER /) assesses the various immune cell infiltrates and clinical significance of the system. In this study, a correlation between immune cell infiltration and variation in G6PC copy number and expression levels was performed.

10. Statistical analysis

All analyses were performed in R (version 3.6.0) and RStudio (version 1.2.1335) and GraphPad Prism 7. Unless otherwise stated, results are considered statistically significant when the p-value is less than 0.05. In all tests, both sides and p-values less than 0.05 were considered significant.

Second, result analysis

1. Identification of Metabolic Differential Expression Genes (MDEG) in TCGA and CPTAC cohorts

The expression of 911 metabolic genes was collected from 534 ccRCC and 72 normal samples in the TCGA cohort. Meanwhile, 905 of 911 metabolic genes were also found in 56 ccRCC patients and 47 normal persons in the CTPAC cohort.

These 905 metabolic genes were subsequently used for further analysis. 133 important MDEGs were identified among 905 metabolic genes and visualized in a volcano plot (fig. 1A); hierarchical partitioning of important MDEG was obtained from TCGA cohort-based DNA microarrays (fig. 1B); mRNA expression of these genes was performed in 534 ccRCC patients and 72 normal humans, with high red and low green. Meanwhile, univariate Cox regression analysis (fig. 1C) was performed on 58 significant MDEGs in TCGA cohort (p <0.05) in forest maps, and it is noted that LASSO regression analysis constructed a 20-mRNA signature model, a Metabolic Prediction Model (MPM), in ccRCC patients in TCGA or CTPAC cohort. Kaplan-Meier survival analysis showed that the significant predictive value of the risk score was dependent on MPM in TCGA (fig. 1D) or in the CTPAC cohort (fig. 1E), with the high risk group labeled in red and the low risk group labeled in blue.

2. Survival risk assessment of MPMs in TCGA or CTPAC cohorts

The survival risk assessment of MPM consists of metabolic 20-mRNA signatures, performed in TCGA or CTPAC cohorts. The distribution of survival time, status (fig. 2A), risk score (fig. 2B) and stratification (fig. 3C) of MPM in tumor and normal samples is shown in the TCGA cohort. Meanwhile, the distribution of survival time, status (fig. 2D), risk score (fig. 2E) and stratification (fig. 2F) of MPM in tumor and normal samples is shown in the CTPAC cohort.

3. Cox regression analysis, ROC analysis and nomograms of independent prognostic factors and MPM for ccRCC patients

Univariate and multivariate Cox regression analyses incorporating clinicopathologic parameters and MPM were illustrated in TCGA and CTPAC cohorts using forest plots (fig. 3A-D). The risk score for MPM significantly predicts prognosis for ccRCC patients in TCGA (p <0.001, HR ═ 3.131) and CTPAC cohort (p ═ 0.046, HR ═ 2.893). Furthermore, ROC analysis indicated that MPM has strong predictive value in TCGA (AUC 0.768) and CTPAC (AUC 0.777) cohorts (fig. 3E-F). Nomograms were constructed based on 5 independent prognostic factors for ccRCC patients, including ISUP stratification, pathology M staging, pathology T staging, AJCC staging and MPM risk score (fig. 3G).

4. KEGG pathway analysis by GSEA

GSEA showed significant changes in the KEGG pathway based on the differential risk score of MPM in ccRCC patients, with available transcriptomics data from TCGA and CTPAC cohorts. The first 5 significantly altered KEGG pathways in high-risk or low-risk ccRCC patients were performed in TCGA (fig. 4A) or CTPAC (fig. 4B) cohorts. Tumor environmental purity was measured using the ESTIMATE algorithm, which showed significant correlation with MPM risk score from ccRCC patients in TCGA cohort (r)²＝0.2373，p<0.0001) (fig. 4C). At the same time, immune purity in ccRCC environment was significantly correlated with the risk score of MPM (r)²＝0.3007，p<0.0001) (fig. 4D).

5. Hub genes in MPMs PPI networks

G6PC is a pivot gene in the PPI network of MPM, showing significant prognostic value in 699 ccRCC patients from TCGA, CTPAC and ICGC cohorts. PPI networks were constructed in 20 metabolic mRNA signatures in MPM (fig. 5A). G6PC mRNA expression and tumor environmental purity (r) in ccRCC²＝-0.1012，p<0.0001) and immunological purity (r)²＝-0.1205，p<0.0001) in negative correlation (FIGS. 5B-C). Differential mRNA expression of G6PC in ccRCC and neighboring normal tissues was shown based on TCGA, CPTAC, and ICGC cohorts (fig. 5D-F). In addition, Kaplan-Meie survival analysis showed low G6PC mRNA expression levels with poor OS (p) in the TCGA cohort<0.0001, HR ═ 0.35) and PFS (p)<0.0001, HR ═ 0.35) were significantly correlated (fig. 5G-H). In the CTPAC (p 0.0035, HR 0.218) and ICGA (p 0.0443, HR 0.446) cohorts, low G6PC mRNA expression levels were significantly correlated with poor prognosis (fig. 5I-J).

6. G6Pase differential expression can predict the outcome of FUSCC cohort

G6Pase is highly expressed in normal kidney tissue (especially in tubular cells but not glomerular cells), whereas no human protein profile was detected in ccRCC tissue (fig. 6A). Meanwhile, a significant increase in G6Pase expression was found in normal tissues compared to ccRCC tissues from the FUSCC cohort (fig. 6B). Furthermore, low G6Pase expression was significantly associated with poor prognosis (p <0.0001, HR ═ 0.316) and aggressive progression (p <0.0001, HR ═ 0.414) in 322 ccRCC patients from the FUSCC cohort (fig. 6C-D).

7. Most of the coexpressed genes and promoter methylation levels of G6PC in ccRCC

The first 50 genes co-expressed with G6PC were extracted and shown in the heat map of ccRCC (fig. 7A-B). Promoter methylation levels of G6PC were significantly lower in the primary ccRCC sample than the normal sample (fig. 7C, p < 0.0001). The promoter methylation level of G6PC climbed significantly with increasing individual cancer stage and was highest in the stage 4 sample (fig. 7D). Promoter methylation levels of G6PC rose significantly with increasing individual tumor grade and were highest in the grade 4 sample (fig. 7E). Promoter methylation levels of G6PC were significantly higher in ccRCC samples with lymph node metastasis compared to pN0 patients (fig. 7F, p < 0.05).

8. Potential role of G6PC in ccRCC immune microenvironment

As a major regulator of glucose production, G6PC is highly expressed in liver and kidney tissues. G6PC expression was significantly higher in normal tissues compared to renal cell carcinoma (KIRC, KIRP, KICH), while G6PC expression was significantly reduced in normal samples compared to hepatocellular carcinoma and cholangiocarcinoma (fig. 8A). At the same time, copy number changes in G6PC were significantly correlated with the level of environmental immune cell infiltration (fig. 8B). Loss of elevated arm levels of G6PC compared to normal samples resulted in poor quality B cells, CD8⁺Cell, CD4⁺Infiltration of cells, macrophages, neutrophils, dendritic cells (p)<0.05)。

Furthermore, G6PC was significantly involved in abnormal immune infiltration of ccRCC cells and microenvironment, co-suppression and co-stimulatory activity with checkpoint immune features, dendritic cells, Th1 cells, MHC class I, cytolytic activity, inflammatory promotion, HLA, APC (cor. < -0.7, fig. 8C). In addition, GSEA suggested that G6PC was significantly involved in several signaling pathways, including bile acid metabolism, fatty acid metabolism, epithelial-mesenchymal transition, and the E2F target in ccRCC (fig. 8D-G).

A total of 100 up-and down-regulated genes associated with differential G6PC expression were then visualized in ccRCC (fig. 9). Spearman correlation and estimated statistical significance between G6PC expression and related genes and immune cell markers was shown in KIRC patients using TIMER.

Third, discussion of results

The control of energy metabolism in the human body is a complex and prudent process, and metabolic disorders may lead to the occurrence and development of various diseases, for example, abnormal lipid metabolism may reduce growth rate and impair fertility, while glucose metabolism disorder may lead to diabetes and hypertension. Importantly, the relationship between metabolic reprogramming and tumorigenesis has received increasing attention in recent years. Gotinas et al state that activation of lipid metabolism promotes tumor cell survival and tumor progression in pancreatic cancer, and studies have found that abnormal glucose metabolism plays a key role in tumorigenesis. On the one hand, metabolic changes promote the proliferation of tumors and on the other hand also help us to better understand the characteristics of cancer. For example, the activation of oncogenic pathways such as PI3K/AKT/mTOR is related to the change of bioenergy pathways such as glycolysis, fatty acid and glutamine metabolism, and provides a new target point for tumor treatment.

ccRCC is one of the most common types of renal cell carcinoma in the world and is associated with poor prognosis due to its high metastasis and recurrence rate. Metabolic reprogramming in ccRCC is most often associated with VHL mutations, which occur in about 90% of cases. In VHL mutant diseases, activation of the metabolic pathway mediated by HIF leads to activation of the pathway as opposed to the effects of hypoxia in an normoxic environment. Previous studies have found that ccRCC produces energy mainly through the accumulation of lactic acid, known as the Warburg effect or aerobic glycolysis. HIF-1 α, as a significant driving force behind the Warburg effect in ccRCC, increases GLUT-1 expression, thereby promoting intracellular glucose uptake. Interestingly, the increase in GLUT-1 expression in ccRCC was coupled with infiltrated CD8⁺Reduction in T cell numbers was associated, suggesting that glucose metabolism may be by another mechanism in renal cell carcinomaSuppressing the immune system. Therefore, the relationship between metabolic reprogramming and ccRCC is worth further exploration.

G6PC (Glucose-6-Phosphatase Catalytic Subunit) is a protein-encoding gene and is closely associated with glycogen storage disease and hypoglycemia. The invention discovers that the expression of G6PC in the ccRCC is far lower than that in normal tissues in a plurality of queues such as TCGA, CPTAC, ICGC and FUSCC. Survival analysis indicated that the expression level of G6PC was positively correlated with the prognosis of the patient, indicating that G6PC may have tumor suppressor properties in ccRCC.

In summary, the present invention first provides the opportunity to fully elucidate the prognostic MDEG pattern, establish a new prognostic model MPM using large-scale ccRCC transcriptome data, and identify G6PC as a potential prognostic target for 1040 ccRCC patients from multiple cohorts. These findings help manage risk assessment and provide valuable insight into the therapeutic strategy of ccRCC.

While the preferred embodiments of the present invention have been described in detail, it will be understood by those skilled in the art that the invention is not limited thereto, and that various changes and modifications may be made without departing from the spirit of the invention, and the scope of the appended claims is to be accorded the full scope of the invention.

Claims

1. The application of the reagent for detecting the expression quantity of G6PC in preparing a transparent cell kidney cancer diagnosis or prognosis evaluation reagent or kit.

2. Use according to claim 1, characterized in that:

wherein the reagent for detecting the expression level of G6PC is a reagent for detecting the mRNA expression level of the G6PC gene, the expression level of glucose 6-phosphatase of the G6Pase protein coded by the G6PC gene or the methylation level of a promoter of the G6PC gene.

3. The use of claim 2, wherein the reagent for detecting the mRNA expression level of the G6PC gene comprises a PCR primer with detection specificity to G6PC, and the sequence of the primer is shown as SEQ ID NO. 13-14.

4. A kit for diagnosing or prognostically evaluating clear cell renal cancer, which comprises a reagent for detecting the expression level of mRNA of the G6PC gene, a reagent for detecting the expression level of glucose 6-phosphatase, a G6Pase protein encoded by the G6PC gene, or/and a reagent for detecting the methylation level of a promoter of the G6PC gene.

5. Application of a reagent for promoting or improving the expression level of G6PC in preparing a medicine for treating clear cell renal carcinoma.

6. Use according to claim 5, characterized in that:

wherein the agent for promoting or increasing the expression level of G6PC is an agent for increasing the expression level of mRNA of the G6PC gene or increasing the expression level of glucose 6-phosphatase of the G6Pase protein encoded by the G6PC gene.

7. The application of the combined genome in preparing the renal clear cell carcinoma prognosis evaluation reagent or kit is characterized in that the combined genome is the combination of ADCY10, AMY2A, CA8, CEL, CYP26A1, CYP3A4, G6PC, HAO1, HK3, HMGCS2, ITPKA, NOS1, P4HA3, PFKFB1, PSAT1, RDH8, RRM2, TYR, UGT1A7 and UROC 1.

8. Use according to claim 7, characterized in that:

wherein the prognosis evaluation reagent is a reagent for detecting the expression level of each gene in the combined genome in a biological sample,

the prognosis evaluation kit comprises a reagent for detecting the expression quantity of each gene in the combined genome in a biological sample.

9. Use according to claim 8, characterized in that:

the reagent for detecting the expression quantity of each gene of the combined genome in the biological sample is selected from PCR primers with detection specificity to each gene, and the sequence of the PCR primers is shown as SEQ ID NO. 1-40.

10. A renal clear cell carcinoma prognosis evaluation system comprising the prognosis evaluation kit of claim 8 or 9 and a subpopulation classification model mounted on a terminal carrier,