Comprehensive analyses of A 12-metabolism-associated gene signature and its connection with tumor metastases in clear cell renal cell carcinoma - Diaoyi Tan; Daojia Miao; Chuanyi Zhao; Jian Shi; Qingyang Lv; Zhiyong Xiong; Hongmei Yang; Xiaoping Zhang (BMC Cancer, 2023)
Abstract Background The outcomes of patients with clear cell renal cell carcinoma (ccRCC) were dreadful due to lethal local recurrence and distant metastases. Accumulating evidence suggested that ccRCC was considered a metabolic disease and metabolism-associated genes (MAGs) exerted essential functions in tumor metastases. Thus, this study intends to seek whether the dysregulated metabolism promotes ccRCC metastases and explores underlying mechanisms. Method Weighted gene co-expression network analysis (WGCNA) was employed based on 2131 MAGs to select genes mostly associated with ccRCC metastases for subsequent univariate Cox regression. On this basis, least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox regression were employed to create a prognostic signature based on the cancer genome atlas kidney renal clear cell carcinoma (TCGA-KIRC) cohort. The prognostic signature was confirmed using E-MTAB-1980 and GSE22541 cohorts. Kaplan–Meier, receiver operating characteristic (ROC) curve, and univariate and multivariate Cox regression were applied to detect the predictability and independence of the signature in ccRCC patients. Functional enrichment analyses, immune cell infiltration examinations, and somatic variant investigations were employed to detect the biological roles of the signature. Result A 12-gene-metabolism-associated prognostic signature, termed the MAPS by our team, was constructed. According to the MAPS, patients were divided into low- and high-risk subgroups and high-risk patients displayed inferior outcomes. The MAPS was validated as an independent and reliable biomarker in ccRCC patients for forecasting the prognosis and progression of ccRCC patients. Functionally, the MAPS was closely associated with metabolism dysregulation, tumor metastases, and immune responses in which the high-risk tumors were in an immunosuppressive status. Besides, high-risk patients benefited more from immunotherapy and held a higher tumor mutation burden (TMB) than low-risk patients. Conclusion The 12-gene MAPS with prominent biological roles could independently and reliably forecast the outcomes of ccRCC patients, and provide clues to uncover the latent mechanism in which dysregulated metabolism controlled ccRCC metastases.