Background Hepatocellular carcinoma (HCC) is one of the most lethal malignancies worldwide. Dysregulation of circadian clock genes (CCGs) plays a vital role in the progression of various cancers, including HCC. However, comprehensive bioinformatic analyses evaluating the prognostic significance of CCGs in HCC remain limited. Objective This study aimed to develop a CCG-based prognostic signature and evaluate its predictive value relative to conventional TNM staging. Methods Differential expression analysis of CCGs between HCC and normal tissues was performed using The Cancer Genome Atlas (TCGA) and GSE76427 datasets. A protein-protein interaction network was constructed, and pathway enrichment analysis was conducted. A prognostic signature was developed using LASSO‑Cox regression, and its predictive performance was evaluated through survival analysis, time‑dependent ROC curves, and independent validation. Results Differential expression analysis identified 232 CCGs dysregulated in HCC. This study constructed a four-gene prognostic signature (DUSP13, HAAO, FLT3, IL1RN) to stratify patients into high- and low-risk groups. The high‑risk group exhibited significantly shorter overall survival ( P < 0.0001). Notably, multivariate Cox regression validated the risk score as an independent prognostic factor (HR = 12.86, P < 0.05), with predictive performance (1-, 3-, and 5-year AUCs: 0.700, 0.669, and 0.638) that complements conventional staging. A nomogram integrating the risk score with clinical variables showed improved calibration over TNM stage alone, although the improvement was only moderate. Conclusion The four-CCG signature provides independent prognostic information beyond TNM staging in HCC. However, these findings are derived entirely from in silico analysis and require experimental validation before clinical translation.
Kong et al. (Wed,) studied this question.