WGCNA and machine learning analysis identifi ed SAMD9 and IFIT3 as primary Sjogren's Syndrome key genes.

ABSTRACT:

Background

Current treatments for primary Sjögren's Syndrome (pSS) are with limited effect, partially due to the heterogeneity and uncleared mechanism.

Methods

We got GSE40568 (Japan) and GSE40611 (USA), and analyzed them with WGCNA to find key Differentially expressed genes (DEGs) between pSS and healthy salivary glands (SG). Key pSS genes (KPGs) were further selected through 3 machine-learning methods. The expression of KPGs was validated via two other GEO datasets (GSE127952 and GSE154926). Infiltrated immune cells, ceRNA network, and potential compounds were explored.

Results

Our study identified 376 DEGs from the pSS patients, with 186 genes located in the "plum2" module, showing the strongest correlation with clinical characteristics. SAMD9 and IFIT3 emerged as

SUBMITTER: Liu S 

PROVIDER: S-EPMC11068537 | biostudies-literature | 2024 May

REPOSITORIES: biostudies-literature