A glycolysis-related lncRNAs signature for predicting prognosis and evaluating the tumor immune microenvironment of cutaneous melanoma

Glycolysis is a key metabolic process for tumor cells to obtain energy, and regulating this process has become a new approach for cancer therapy. However, the direct relationship between glycolysis-related long non-coding RNAs (lncRNAs) and cutaneous melanoma (CM) remains unclear. We obtained the transcriptomic data, corresponding clinical data, and somatic mutation data for CM from The Cancer Genome Atlas (TCGA) database. First, glycolysis-related lncRNAs were identified through correlation analysis. Subsequently, a prognostic model consisting of 11 glycolysis-related lncRNAs signatures (GRLS) was constructed using COX regression analysis and LASSO analysis, validating the model's independence, sensitivity, and accuracy. Based on risk scores, patients were divided into high-risk and low-risk groups. Differences between the high-risk and low-risk groups were studied in terms of overall survival (OS), functional enrichment, tumor immune microenvironment (TIME), somatic mutations, and drug sensitivity. In summary, we constructed a prognostic model based on glycolysis-related lncRNAs, which may predict the prognosis of CM patients and has the potential to improve treatment strategies to enhance patient outcomes.