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

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...

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Auteurs principaux: Shu Liu, Hongzhen Chen, Lin Tang, Mian Liu, Jinfeng Chen, Dandan Wang
Format: Artigo
Langue:Inglês
Publié: Elsevier 2024-05-01
Collection:Heliyon
Sujets:
Primary Sjögren's syndrome
SAMD9
IFIT3
Machine-learning
WGCNA
Accès en ligne:http://www.sciencedirect.com/science/article/pii/S2405844024056834
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