A 13-Gene Metabolic Prognostic Signature Is Associated With Clinical and Immune Features in Stomach Adenocarcinoma - Ye, Zaisheng; Zheng, Miao; Zeng, Yi; Wei, Shenghong; Huang, He; Wang, Yi; Liu, Qinying; Lin, Zhitao; Chen, Shu; Zheng, Qiuhong; Chen, Luchuan (Frontiers in Oncology, 2021)
The patients with advanced stomach adenocarcinoma (STAD) were commonly showed high mortality and poor prognosis. Increasing evidence has suggested that the basic changes in metabolism may promote the growth and aggressiveness of STAD, so identification of metabolic prognostic signature in STAD would be meaningful. Integrative analysis was performed with 407 samples from The Cancer Genome Atlas (TCGA) and 433 samples from Gene Expression Omnibus (GEO) to develop a metabolic prognostic signature associated with clinical and immune features in STAD using Cox regression analysis and Least absolute shrinkage and selection operator (LASSO). The different proportion of immune cells and differently expressed immune related genes (DEIRGs) between high and low-risk score groups based on metabolic prognostic signature were evaluate to describe the association of cancer metabolism and immune response in STAD. A total of 883 metabolism-related genes both in TCGA and GEO database were analyzed to obtain 184 differently expressed metabolism-related genes (DEMRGs) between tumor tissues and normal tissues. The thirteen- metabolism-related-gene-signature prognostic model (GSTA2, POLD3, GLA, GGT5, DCK, CKMT2, ASAH1, OPLAH, ME1, ACYP1, NNMT, POLR1A, and RDH12) that constructed from 16 survival related DEMRGs were significantly related to STAD overall survival and tumor microenvironment immune cells. The univariate and multiple Cox regression analysis, and nomogram proved metabolism-based prognostic risk score (MPRS) could be independent risk factor. More importantly, the results were mutually verified by TCGA and GEO data. The study provided a metabolism-related-gene-signature prognostic model and explored the association between metabolism and immune microenvironment for future research.