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pmid:38707449
WGCNA and machine learning analysis identifi ed SAMD9 and IFIT3 as primary Sjögren's Syndrome key genes.
Shu Liu · Hongzhen Chen · Lin Tang · Mian Liu · Jinfeng Chen · Dandan Wang
Heliyon · 2024 doi:10.1016/j.heliyon.2024.e29652
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Experiment
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Data
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Analysis
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Execution
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Complete file
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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.\nResults: 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 KPGs with excellent diagnostic potential. SAMD9 demonstrated close association with immune cell infiltration. We constructed a lncRNA-miRNA-mRNA network comprising 2 KPGs, 12 miRNAs, 124 lncRNAs, and potential therapeutic targets.\nConclusion: In the investigation of pSS public datasets, our study revealed two potential critical mediators in the pathological process of pSS salivary glands, namely SAMD9 and IFIT3. 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