Hub genes, diagnostic model, and predicted drugs in systemic sclerosis by integrated bioinformatics analysis.

ABSTRACT: Background: Systemic sclerosis (scleroderma; SSc), a rare and heterogeneous connective tissue disease, remains unclear in terms of its underlying causative genes and effective therapeutic approaches. The purpose of the present study was to identify hub genes, diagnostic markers and explore potential small-molecule drugs of SSc. Methods: The cohorts of data used in this study were downloaded from the Gene Expression Complex (GEO) database. Integrated bioinformatic tools were utilized for exploration, including Weighted Gene Co-Expression Network Analysis (WGCNA), least absolute shrinkage and selection operator (LASSO) regression, gene set enrichment analysis (GSEA), Connectivity Map (CMap) analysis, molecular docking, and pharmacokinetic/toxicity properties exploration. Res

SUBMITTER: Yan YM 

PROVIDER: S-EPMC10369177 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature