Identification of immune-associated genes in diagnosing osteoarthritis with metabolic syndrome by integrated bioinformatics analysis and machine learning

Frontiers in Immunology

Immunology

Xiaofeng Zhang¹  Genghong Wang²  Cheng Zhang²  Yiwei Shen²  Junchen Li²  Zhigang Li³  Xilin Xv⁴ 

[1] Teaching and Research Section of Orthopedics and Traumatology, Heilongjiang University of Chinese Medicine, Harbin, China;The Bone Injury Teaching Laboratory, Heilongjiang University of Chinese Medicine, Harbin, China;The Graduate School, Heilongjiang University of Chinese Medicine, Harbin, China;The Second Department of Orthopedics and Traumatology, The Second Affiliated Hospital of Heilongjiang University of Chinese Medicine, Harbin, China;The Third Affiliated Hospital of Heilongjiang University of Chinese Medicine, Harbin, China;Teaching and Research Section of Orthopedics and Traumatology, Heilongjiang University of Chinese Medicine, Harbin, China;

关键词: differentially expressed genes;    osteoarthritis;    metabolic syndrome;    machine learning;    immune infiltration;   

DOI  :  10.3389/fimmu.2023.1134412

 received in 2022-12-30, accepted in 2023-03-27,  发布年份 2023

来源: Frontiers

【 摘 要 】

BackgroundIn the pathogenesis of osteoarthritis (OA) and metabolic syndrome (MetS), the immune system plays a particularly important role. The purpose of this study was to find key diagnostic candidate genes in OA patients who also had metabolic syndrome.MethodsWe searched the Gene Expression Omnibus (GEO) database for three OA and one MetS dataset. Limma, weighted gene co-expression network analysis (WGCNA), and machine learning algorithms were used to identify and analyze the immune genes associated with OA and MetS. They were evaluated using nomograms and receiver operating characteristic (ROC) curves, and finally, immune cells dysregulated in OA were investigated using immune infiltration analysis.ResultsAfter Limma analysis, the integrated OA dataset yielded 2263 DEGs, and the MetS dataset yielded the most relevant module containing 691 genes after WGCNA, with a total of 82 intersections between the two. The immune-related genes were mostly enriched in the enrichment analysis, and the immune infiltration analysis revealed an imbalance in multiple immune cells. Further machine learning screening yielded eight core genes that were evaluated by nomogram and diagnostic value and found to have a high diagnostic value (area under the curve from 0.82 to 0.96).ConclusionEight immune-related core genes were identified (FZD7, IRAK3, KDELR3, PHC2, RHOB, RNF170, SOX13, and ZKSCAN4), and a nomogram for the diagnosis of OA and MetS was established. This research could lead to the identification of potential peripheral blood diagnostic candidate genes for MetS patients who also suffer from OA.

【 授权许可】

Unknown   
Copyright © 2023 Li, Wang, Xv, Li, Shen, Zhang and Zhang