Assessment tool based on fatty acid metabolic signatures for predicting the prognosis and treatment response in bladder cancer

Abstract

BackgroundFatty acid metabolism (FAM) is closely connected with tumorigenesis as well as disease progression and affects the efficacy of platinum-based drugs. Exploring biomarkers related to FAM in bladder cancer (BLCA) is essential to improve cancer prognosis. MethodsHigh-throughput sequencing data from The Cancer Genome Atlas (TCGA) were bioinformatically resolved to identify molecular subtypes of fatty acid metabolic profiles in BLCA using coherent clustering analysis. Based on fatty acid metabolic profile, a prognostic model was created using COX and LASSO COX models. CIBERSORT, Estimation of STromal and Immune cells in MAlignant Tumours using Expression (ESTIMATE), MCP-Count, and single sample gene set enrichment analysis (ssGSEA) were used to assess the differences in tumor microenvironment (TME) among different molecular subtypes, prognostic groups. Kaplan-Meier (K-M) survival curve was plotted to assess patients’ prognosis. Receiver operating characteristic curve (ROC) and the clinical prognostic value of prognostic models was evaluated by the Nomogram. ResultsThree molecular subtypes (FAMC1, FAMC2, FAMC3) of fatty acid metabolic patterns were determined. FAMC1 showed significant prognostic advantage with immunoreactivity. Five key prognostic FAMGs were identified and RiskScore was developed. We found that patients with low RiskScore showed significantly better immune microenvironment status, survival and response to immunotherapy. Similarly, both Nomogram and RiskScore demonstrated excellent prognostic value. ConclusionsIn conclusion, our study showed that the RiskScore was closely related to the clinical traits of BLCA patients. The RiskScore may provide essential clinical guidance for predicting prognosis and treatment response in bladder cancer.