Background Skin cutaneous malignant melanoma (SKCM) is among the most aggressive forms of skin cancer, notorious for its rapid progression and poor prognosis under late diagnosis. This study investigates the role of circadian rhythm-related genes (CRGs) in SKCM addressing a gap in understanding how CRGs affect tumor progression and patient outcomes.Methods An analysis of CRGs expression was conducted on SKCM samples derived from The Cancer Genome Atlas datasets(TCGA). Moreover, a correlation between various subtypes and their clinical features was identified. The study employed various bioinformatics methods, including differential expression analysis, consensus clustering, and survival analysis, to investigate the role of CRGs. The functional consequences of various CRG expression patterns were further investigated using immune infiltration analysis and gene set variation analysis (GSVA). A scoring system based on CRGs was developed to predict overall survival (OS) and treatment responses in SKCM patients. The predictive accuracy of this score system was then tested, and a nomogram was used to improve its clinical usefulness.Results Key findings from this study include significant genetic alterations in circadian rhythm-related genes (CRGs) in skin cutaneous melanoma (SKCM), such as mutations and CNVs. Two molecular subtypes with distinct clinical outcomes and immune profiles were identified. A prognostic model based on six CRGs (CMTM, TNPO1, CTBS, UTRN, HK2, and LIF) was developed and validated with TCGA and GEO datasets, showing high predictive accuracy for overall survival (OS). A high CRGs score correlated with poor OS, immune checkpoint expression, and reduced sensitivity to several chemotherapeutics, including AKT inhibitor VIII and Camptothecin.Conclusions This work provides valuable insights into the circadian regulation of SKCM and underscores the potential of CRGs as biomarkers for prognosis and targets for therapeutic interventions. The novel molecular subtypes and CRGs prognostic scoring model introduced in this study offer significant contributions to the understanding and management of SKCM.
