A Novel lncRNA–mRNA Integrated Prognostic Signature Predicts Survival and Immune Checkpoint Landscape in Hepatocellular Carcinoma

Background: Hepatocellular carcinoma (HCC) remains a leading cause of cancer-related mortality worldwide, largely due to its molecular heterogeneity and limited prognostic biomarkers. Long non-coding RNAs (lncRNAs) have emerged as important regulators in tumor biology, but their integrated prognostic value together with protein-coding genes in HCC remains incompletely understood. Methods: Transcriptomic and clinical data from the TCGA-LIHC cohort were used as the discovery dataset to identify prognostic genes associated with overall survival. A prognostic model integrating both lncRNAs and protein-coding genes was constructed using Cox regression analysis. Patients were stratified into high- and low-risk groups based on the calculated risk score. The predictive performance of the model was evaluated using Kaplan–Meier survival analysis, time-dependent receiver operating characteristic (ROC) curves, and concordance index (C-index) analysis. External validation was performed using the CPTAC cohort. A prognostic nomogram was constructed to estimate individualized survival probabilities, and calibration curves and decision curve analysis (DCA) were used to evaluate the clinical utility of the model. Associations between the prognostic signature and immune checkpoint genes were also explored. Functional enrichment analyses were conducted to investigate the biological processes associated with the signature genes. Results: A prognostic signature consisting of five genes (three lncRNAs and two protein-coding genes) was established. Patients in the high-risk group exhibited significantly worse overall survival compared with those in the low-risk group in both the discovery and validation cohorts. Time-dependent ROC analysis demonstrated favorable predictive performance for 1-, 3-, and 5-year survival. The prognostic model was successfully validated in the independent CPTAC cohort. The constructed nomogram showed good agreement between predicted and observed survival probabilities. Decision curve analysis indicated potential clinical utility of the model. In addition, the prognostic signature was associated with differential expression patterns of immune checkpoint genes. Functional enrichment analyses suggested that the signature genes were involved in metabolic and immune-related pathways. Conclusions: We developed and validated a novel five-gene prognostic signature integrating lncRNAs and protein-coding genes for hepatocellular carcinoma. The model demonstrated robust predictive performance and potential clinical applicability for survival prediction. These findings provide new insights into the molecular mechanisms of HCC and may contribute to improved risk stratification and personalized prognosis assessment.