Construction and Validation of a Prognostic Model of Metabolism-Related Genes Driven by Somatic Mutation in Bladder Cancer.

Background: Metabolic reprogramming is an important player in the prognosis of cancer patients. However, metabolism-related genes (MRGs) that are essential to the prognosis of bladder cancer (BLCA) are nor yet fully understood. The purpose of this study is to use bioinformatics methods to establish prognostic models based on MRGs in BLCA to screen potential biomarkers.

Methods: Based on the transcriptomic data from BLCA patients in The Cancer Genome Atlas Program (TCGA) and Gene Expression Omnibus (GEO) databases, we identified the differentially expressed genes related to metabolism and analyzed the functional enrichment by edgeR package. A prognostic model was generated using univariate Cox regression analysis and validated using GEO dataset. The prognostic risk model was analyzed by the Kaplan-Meier curve. The single cell RNA sequencing (scRNA-seq) revealed the gene interaction networks and traced the development trajectories of distinct cell lineages. The levels of key metabolism-related biomarkers were verified by quantitative real-time polymerase chain reaction (qRT-PCR).

Results: We screened 201 differentially expressed metabolism-related genes (DEMRGs), which were significantly enriched in oxidative phosphorylation. The risk model was constructed by 5 biomarkers. qRT-PCR analysis verified that there is a significant higher expression of and in carcinoma tissue.

Conclusions: This study constructed a novel prognostic model based on a combination of clinical and molecular factors that related to metabolic reprogramming, which has the potential to improve the prediction of independent prognosis indicators and management of BLCA patients, leading to better treatment outcomes and survival rates.