Integrating single-cell RNA sequencing, WGCNA, and machine learning to identify key biomarkers in hepatocellular carcinoma.

ABSTRACT: The microarray and single-cell RNA-sequencing (scRNA-seq) datasets of hepatocellular carcinoma (HCC) were downloaded from the Gene Expression Omnibus (GEO) database. Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were used to identify HCC-related biomarkers. Based on an analysis of scRNA-seq data, several marker genes expressed on tumor cells have been identified. Three machine-learning algorithms were used to identify shared diagnostic genes. Furthermore, logistic regression analysis was conducted to re-evaluate and identify essential biomarkers, which were then employed to develop a diagnostic prediction model. Additionally, AutoDockTools were used for molecular docking to investigate the association between the most sensitive drug and the core

SUBMITTER: Wang G 

PROVIDER: S-EPMC11962163 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature