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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| Hauptverfasser: | , , , , , |
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| Format: | Artigo |
| Sprache: | Inglês |
| Veröffentlicht: |
Elsevier
2024-05-01
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| Schriftenreihe: | Heliyon |
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| Online-Zugang: | http://www.sciencedirect.com/science/article/pii/S2405844024056834 |
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