Identification of key circadian rhythm genes in skin aging based on bioinformatics and machine learning.

ABSTRACT: Skin aging is often accompanied by disruption of circadian rhythm and abnormal expression of circadian rhythm-related genes. In this study, we downloaded skin aging expression datasets from the GEO database and utilized bioinformatics and machine learning methods to explore circadian rhythm genes and pathways involved in skin aging, revealing the pathological and molecular mechanisms of skin aging. Results showed that 39 circadian rhythm-related genes (CRGs) were identified in skin aging, and these CRGs were enriched in signaling pathways such as glucagon signaling pathway, insulin resistance, thyroid hormone signaling pathway, and adipocytokine signaling pathway. Three key skin aging-related CRGs, SIRT1, ARNTL, and ATF4, were identified based on machine learning. Additionally, we found th

SUBMITTER: Xiao X 

PROVIDER: S-EPMC10637791 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature