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ABSTRACT: Background
Currently, no molecular classification is established for bladder cancer based on metabolic characteristics. Therefore, we conducted a comprehensive analysis of bladder cancer metabolism-related genes using multiple publicly available datasets and aimed to identify subtypes according to distinctive metabolic characteristics.Methods
RNA-sequencing data of The Cancer Genome Atlas were subjected to non-negative matrix fractionation to classify bladder cancer according to metabolism-related gene expression; Gene Expression Omnibus and ArrayExpress datasets were used as validation cohorts. The sensitivity of metabolic types to predicted immunotherapy and chemotherapy was assessed. Kaplan-Meier curves were plotted to assess patient survival. Differentially expressed ge
SUBMITTER: Tang C
PROVIDER: S-EPMC8117567 | biostudies-literature | 2021 May
REPOSITORIES: biostudies-literature