Cell Cycle-Related LncRNA-Based Prognostic Model for Hepatocellular Carcinoma: Integrating Immune Microenvironment and Treatment Response.

Abstract

Hepatocellular carcinoma (HCC) presents substantial genetic and phenotypic diversity, making it challenging to predict patient outcomes. There is a clear need for novel biomarkers to better identify high-risk individuals. Long non-coding RNAs (lncRNAs) are known to play key roles in cell cycle regulation and genomic stability, and their dysregulation has been closely linked to HCC progression. Developing a prognostic model based on cell cycle-related lncRNAs could open up new possibilities for immunotherapy in HCC patients. Transcriptomic data and clinical samples were obtained from the TCGA-HCC dataset. Cell cycle-related gene sets were sourced from existing studies, and coexpression analysis identified relevant lncRNAs (correlation coefficient >0.4, P<0.001). Univariate analysis identified prognostic lncRNAs, which were then used in a LASSO regression model to create a risk score. This model was validated via cross-validation. HCC samples were classified on the basis of their risk scores. Correlations between the risk score and tumor mutational burden (TMB), tumor immune infiltration, immune checkpoint gene expression, and immunotherapy response were evaluated via R packages and various methods (TIMER, CIBERSORT, CIBERSORT-ABS, QUANTISEQ, MCP-COUNTER, XCELL, and EPIC). Four cell cycle-related lncRNAs (AC009549.1, AC090018.2, PKD1P6-NPIPP1, and TMCC1-AS1) were significantly upregulated in HCC. These lncRNAs were used to create a risk score (risk score=0.492×AC009549.1+1.390×AC090018.2+1.622×PKD1P6-NPIPP1+0.858×TMCC1-AS1). This risk score had superior predictive value compared to traditional clinical factors (AUC=0.738). A nomogram was developed to illustrate the 1-year, 3-year, and 5-year overall survival (OS) rates for individual HCC patients. Significant differences in TMB, immune response, immune cell infiltration, immune checkpoint gene expression, and drug responsiveness were observed between the high-risk and low-risk groups. The risk score model we developed enhances the prognostication of HCC patients by identifying those at high risk for poor outcomes. This model could lead to new immunotherapy strategies for HCC patients.