Fig. 2

Prognostic value of the senescence-metabolism-related risk model (SeMRM) in two independent renal cell carcinoma (ccRCC) cohorts. (A) Kaplan‒Meier analysis of the overall survival (OS) curve of patients with low or high senescence-metabolism-related risk score (SeMRM) subgroups from two independent validation cohorts (International Cancer Genome Consortium (ICGC) KIRC and GSE29609). (B) Receiver operating characteristic (ROC) curve for predicting 1-year, 3-year and 5-year OS in the ICGC KIRC and GSE29609 cohorts. (C) ROC analysis showed that in the ICGC KIRC and GSE29609 cohorts, the predictive accuracy of SeMRM in OS was better than that of other clinical features. Univariate and multivariate Cox regression analysis of SeMRM and clinical features in the (D, E) ICGC KIRC and GSE29609 cohorts. (F, G) Immunohistochemistry (IHC) staining was used to detect the expression of key metabolism-related differentially expressed genes (mDEGs) (NME2, CD44, COL1A1, ENO2, ENO1, FGF1, and PTGER4) in ccRCC tissue arrays from 61 normal tissues and 153 tumor tissues. A representative image is shown. Statistical analysis of the IHC staining immunoreactivity score (IRS). *p < 0. 05; **p < 0. 01; ***p < 0. 001