Identification of diagnostic biomarkers and potential therapeutic targets for biliary atresia via WGCNA and machine learning methods.

ABSTRACT: Biliary atresia (BA) is a severe and progressive biliary obstructive disease in infants that requires early diagnosis and new therapeutic targets. This study employed bioinformatics methods to identify diagnostic biomarkers and potential therapeutic targets for BA. Our analysis of mRNA expression from Gene Expression Omnibus datasets revealed 3,273 differentially expressed genes between patients with BA and those without BA (nBA). Weighted gene coexpression network analysis determined that the turquoise gene coexpression module, consisting of 298 genes, is predominantly associated with BA. The machine learning method then filtered out the top 2 important genes, CXCL8 and TMSB10, from the turquoise module. The area under receiver operating characteristic curves for TMSB10</

SUBMITTER: Xu L 

PROVIDER: S-EPMC11233743 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature