Elucidating a novel prognostic signature for bladder cancer by integrating hypoxia and lactate metabolism-related genes: comprehensive bioinformatics analyses and experimental evidence.

BACKGROUND: Bladder cancer (BLCA) is a highly heterogeneous malignancy with high morbidity and mortality. Massive lactate production and hypoxia are characteristics of the tumor microenvironment (TME). However, our understanding of the clinical value of hypoxia and lactate metabolism (HLM) in BLCA remains limited. METHODS: K-means clustering algorithm was used to classify molecular subtypes. The prognostic model was developed via univariate cox regression, random forest, and stepwise multivariate cox regression analyses. We subsequently systematically correlated the hypoxia and lactate metabolism-related risk score with the TME, BLCA consensus subtypes, and potential predictive value for drug therapy efficacy. Single-cell analyses demonstrated the expression of the modeling genes in various cell subtypes in the TME, and experimental validation was performed to examine the expression and function of GALK1 and TFRC. RESULTS: The TCGA cohort was classified into two subtypes. A 9-gene signature was established on the basis of genes associated with HLM, which predicted prognosis with exceptional efficacy. Patients with high risk scores had a poor prognosis, abundant infiltration of tumor-promoting immune cells and suppressed immune function. Furthermore, we anticipated that these patients were insensitive to immunotherapy and conventional chemotherapeutic agents. In addition, such patients were more inclined to the basal subtype. The modeling genes GALK1 and TFRC were highly expressed in BLCA and promoted tumor cell proliferation and migration. CONCLUSIONS: Our signature further illustrated heterogeneity of BLCA. This signature could predict prognosis, consensus subtypes and treatment efficacy. We believe that this signature can optimize individual treatment decisions.