Integrated machine learning and single-cell analysis identify chromatin-remodeling gene signature for diagnosis and prognosis in nasopharyngeal carcinoma.

ABSTRACT: This study examines the function of chromatin-remodeling genes (CRGs) in nasopharyngeal carcinoma (NPC), with an emphasis on their potential as prognostic and diagnostic biomarkers. We examined gene expression information collected from multiple datasets (GSE12452, GSE53819, GSE61218, and GSE102349) using a multi-stage methodology; we also performed differential expression, weighted gene co-expression network analysis, and functional enrichment analyses to identify pathogenic CRGs. A prognostic signature of six key genes-CDC6, EZH2, PHF14, PRC1, RAD54B, and UHRF1-was developed through machine learning methods and further validated in independent datasets. The identified genes were used to build a diagnostic model, which performed well (AUC > 0.8) in both training and validation cohorts. Th

SUBMITTER: Chang Y 

PROVIDER: S-EPMC12628411 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature