Lipid metabolism subtypes reflects the prognosis and immune profiles of bladder cancer patients by NMF clustering and building related signature

Lipid metabolism is crucial in tumor formation and progression. However, the role of lipid metabolism genes (LMGs) in bladder cancer (BLCA) are unknown. The purpose of this study was to construct a LMGs-related subtypes that predicted the treatment and prognosis of BLCA patients.

The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases were used for this study. The gene set enrichment analysis (GSEA) was utilized to distinguish functional differences between high-risk (HR) and low-risk (LR) groups. Single-sample GSEA (ssGSEA) was employed to determine potential associations between prognostic outcomes and immune status.

First, BLCA patients were divided into two subtypes by non-negative matrix factorization (NMF) clustering, and there were substantial variations in survival status, immune cell infiltration and immune classification between the two subtypes. Next, a prognostic signature involving 8 LMGs was identified (AKR1B1, SCD, CYP27B1, UGCG, SGPL1, FASN, TNFAIP8L3, PLA2G2A). HR patients exhibited worse outcome than LR patients. Multivariate Cox regression analysis confirmed that LMGs-related signature was an independent prognostic indicator of BLCA patients’ survival. Compared with clinicopathological variables, LMGs-related signature showed higher prognostic predictive ability, with an area under curve of 0.720 at 5 years of follow-up. Through immunotherapy analysis, drug sensitivity analysis, TIDE score and immune cell infiltration characteristics, LMGs-related signature was confirmed to accurately predict the prognosis and treatment response of BLCA patients.

Our newly established prognostic signature, which involved eight LMGs, can give prognostic distinction for BLCA and may eventually lead to novel targets for treatment.