Identifying novel circadian rhythm biomarkers for diagnosis and prognosis of melanoma by an integrated bioinformatics and machine learning approach.

ABSTRACT: Melanoma is a highly malignant skin tumor with poor prognosis. Circadian rhythm is closely related to melanoma pathogenesis. This study aimed to identify key circadian rhythm genes (CRGs) in melanoma and explore their potential as diagnostic and prognostic biomarkers. Microarray data of melanoma tissues and normal skins were obtained. Differentially expressed genes were identified and weighted gene co-expression network analysis (WGCNA) was performed to screen hub genes associated with melanoma. By overlapping hub genes with known CRGs, 125 melanoma-related CRGs were identified. Functional enrichment analysis revealed these CRGs were mainly involved in circadian rhythm and other cancer-related pathways. Three machine learning algorithms including LASSO regression, support vector machine-re

SUBMITTER: Xu Y 

PROVIDER: S-EPMC11386929 | biostudies-literature | 2024 Jun

REPOSITORIES: biostudies-literature