摘要Objective:Circadian rhythm disruption(CRD)is a risk factor that correlates with poor prognosis across mul-tiple tumor types,including hepatocellular carcinoma(HCC).However,its mechanism remains unclear.This study aimed to define HCC subtypes based on CRD and explore their individual heterogeneity.Methods:To quantify CRD,the HCC CRD score(HCCcrds)was developed.Using machine learning algo-rithms,we identified CRD module genes and defined CRD-related HCC subtypes in The Cancer Genome Atlas liver HCC cohort(n=369),and the robustness of this method was validated.Furthermore,we used bioinformatics tools to investigate the cellular heterogeneity across these CRD subtypes.Results:We defined three distinct HCC subtypes that exhibit significant heterogeneity in prognosis.The CRD-related subtype with high HCCcrds was significantly correlated with worse prognosis,higher pathological grade,and advanced clinical stages,while the CRD-related subtype with low HCCcrds had bet-ter clinical outcomes.We also identified novel biomarkers for each subtype,such as nicotinamide n-methyltransferase and myristoylated alanine-rich protein kinase C substrate-like 1.Conclusion:We classify the HCC patients into three distinct groups based on circadian rhythm and identify their specific biomarkers.Within these groups greater HCCcrds was associated with worse prognosis.This approach has the potential to improve prediction of an individual's prognosis,guide precision treatments,and assist clinical decision making for HCC patients.