A Potassium Channel-Related Signature for Prognosis and Immune Landscape Prediction of Hepatocellular Carcinoma

Potassium channel-related genes (PCRGs) play an important role in hepatocellular carcinoma (HCC) development, recurrence, and immunotherapy tolerance. We aimed to develop a new prognostic model associated with PCRGs that can be used for prognosis and immunotherapy prediction in HCC patients. The transcriptional profiles and clinical data related to HCC were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Differentially expressed PCRGs were identified using the "edgeR" package. Prognostic model associated with PCRGs were constructed using univariate analysis, least absolute shrinkage and selection operator (LASSO), and multivariate regression analysis. The prognostic value of the model was evaluated through Kaplan-Meier (K-M) survival analysis and receiver operating characteristic (ROC) curves. Additionally, the tumor immune microenvironment was assessed using single sample gene set enrichment analysis (ssGSEA) and the CIBERSORT algorithm. Finally, potential drugs targeting signature genes were predicted. We successfully constructed a prognostic signature based on PCRGs, and the prognostic results were superior in the low-risk group. The nomogram demonstrated satisfactory predictive performance in estimating overall survival (OS) in HCC patients. The results of immune cell infiltration and predictions of immunotherapy response revealed that the low-risk group exhibited a more favorable response to immunotherapy. In addition, signature gene expression was significantly correlated with antitumor drug sensitivity. In conclusion, the characteristics of PCRGs serve as valuable tools for accurately assessing the prognosis and tumor microenvironment of HCC patients. Additionally, PCRGs markers can facilitate precision therapy in HCC management.