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

Aug 5, 2026

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Osteoarthritis (OA) and metabolic syndrome (MetS) share overlapping pathological processes, and immune dysregulation may influence disease onset and progression. This study integrates gene-expression data from public GEO resources and applies differential-expression analysis, weighted gene co-expression network analysis, enrichment assessment, immune infiltration profiling, and machine-learning feature screening to uncover immune-related diagnostic candidates. Key immune genes were validated through nomograms and receiver operating characteristic evaluation, supporting their diagnostic utility for identifying OA in patients with MetS.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages13

Reading Time33 min

AccessFree to download

Introduction

Methods

Results

Conclusion

TYPE Original Research PUBLISHED 17 April 2023 DOI 10.3389/fimmu.2023.1134412 OPEN ACCESS EDITED BY Michael V. Volin, Midwestern University, United States REVIEWED BY Ming Lu, Anhui Medical University, China Maria Andersson, Lund University, Sweden *CORRESPONDENCE Xiaofeng Zhang [zxfenghlj@163.com](zxfenghlj@163.com) †These authors have contributed equally to this work and share ﬁrst authorship SPECIALTY SECTION This article was submitted to Inﬂammation, a section of the journal Frontiers in Immunology RECEIVED 30 December 2022 ACCEPTED 27 March 2023 PUBLISHED 17 April 2023 CITATION Li J, Wang G, Xv X, Li Z, Shen Y, Zhang Cand Zhang X (2023) Identiﬁcation of immune-associated genes in diagnosing osteoarthritis with metabolic syndrome by integrated bioinformatics analysis and machine learning. Front. Immunol. 14:1134412 . doi: 10.3389/fimmu.2023.1134412 COPYRIGHT © 2023 Li, Wang, Xv, Li, Shen, Zhang and Zhang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. Identiﬁcation of immuneassociated genes in diagnosing osteoarthritis with metabolic syndrome by integrated bioinformatics analysis and machine learning Junchen Li 1†, Genghong Wang 1†, Xilin Xv 2,3, Zhigang Li 4, Yiwei Shen 1, Cheng Zhang 1 and Xiaofeng Zhang 3,5* 1The Graduate School, Heilongjiang University of Chinese Medicine, Harbin, China, 2The Third Afﬁliated Hospital of Heilongjiang University of Chinese Medicine, Harbin, China, 3Teaching and Research Section of Orthopedics and Traumatology, Heilongjiang University of Chinese Medicine, Harbin, China, 4The Second Department of Orthopedics and Traumatology, The Second Afﬁliated Hospital of Heilongjiang University of Chinese Medicine, Harbin, China, 5The Bone Injury Teaching Laboratory, Heilongjiang University of Chinese Medicine, Harbin, China Background: In 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. Methods: We 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. Results: After 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) . Conclusion: Eight 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. KEYWORDS differentially expressed genes, osteoarthritis, metabolic syndrome, machine learning, immune inﬁltration Frontiers in Immu

FAQs

What was the main objective of this study?

To identify immune-associated key candidate genes that could help diagnose osteoarthritis in patients with metabolic syndrome.

Which data and analytical methods were used to discover candidate immune genes?

The study mined three OA and one MetS datasets from the GEO database, then used limma, WGCNA, enrichment analysis, immune infiltration analysis, and machine-learning algorithms.

How were the identified genes evaluated for diagnostic performance?

Candidate genes were assessed using nomograms and receiver operating characteristic (ROC) curves, showing high diagnostic value across the reported AUC range.

Aug 5, 2026

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