Construction and Evaluation of Prognostic Model With Metabolism-related genes in Renal clear cell cancer

Objective To explore the prognostic value of carbohydrate metabolism-related genes (CRGs) in clear cell renal cell carcinoma (ccRCC) and establish a prognostic model.Methods The ccRCC mRNA expression data were downloaded from TCGA database, and the CRGs set was obtained from MSigDB and KEGG database. ccRCC prognostic risk model was constructed by screening prognostic-related CRGs through Univariate COX regression and LASSO regression and calculated patient risk score (RS). Patients were divided into high- and low-risk groups according to the median RS, and the survival differences between the two groups were analyzed using the Kaplan-Meier survival curves and multiple bioinformatics tools were used to analyze the difference between two groups in tumor immune cell infiltration, somatic mutations, and immune response. RS and clinicopathological factors were included in Cox regression analysis to verify whether RS was an independent risk factor affecting patients' survival and constructed a nomogram, calibration curves and C-index were used to validate the accuracy of the prediction model. GO and KEGG enrichment analyses were performed to further explore the biological functions and signaling pathways associated with CRGs. qRT-PCR method was used to detect the expression of glucose metabolism-related genes in ccRCC samples.Results Eight glucose metabolism-related differential genes including CENPA, ADH5, ABCG2, RBCK1, IDUA, PANK1, CYP3A4 and HK3 were screened and involved in constructing a prognostic model of renal clear cell carcinoma. K-M analyse shows that patients in the low-risk group had a better prognosis (P<0.001), the RS was closely related to immune cell infiltration, somatic mutation and immunotherapy response. RS was an independent risk factor affecting the prognosis of ccRCC (P<0.001), and the nomogram constructed by combining RS with the corresponding clinical variables had significant prognostic predictive effects, with C-indexes of 0.85, 0.77, and 0.79 at 1, 3, and 5 years, respectively. In addition, GO and KEGG analysis revealed that differentially expressed CRGs were associated with biological processes such as tricarboxylic acid cycle, mitochondrial matrix, oxidoreductase activity, and metabolic regulation. The qRT-PCR experiment confirmed that the expression levels of glucose metabolism-related genes were significantly different between renal clear cell carcinoma and normal tissues.Conclusions A multigene prognostic model of carbohydrate metabolism can be used to predict the survival of patients with renal clear cell carcinoma.