ORIGINAL RESEARCH article
Front. Immunol.
Sec. Inflammation
Volume 16 - 2025 | doi: 10.3389/fimmu.2025.1599930
This article is part of the Research TopicRole of bioinformatics and AI in understanding inflammation and immune microenvironment dynamicsView all 9 articles
Integrated Multi-Omics and Machine Learning Reveals Immune-Metabolic Signatures in Osteoarthritis: From Bulk RNA-Seq to Single-Cell Resolution
Provisionally accepted
- 1Department of Orthopedic and Trauma Surgery, The Third Affiliated Hospital of Guangxi Medical University, Nanning, China
- 2Kunming Medical University, Kunming, Yunnan Province, China
- 3Youjiang Medical University for Nationalities, Baise, Guangx, China
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The aim of this study was to investigate the activation of immune-metabolic pathways in osteoarthritis (OA) and their role in disease progression.We employed differential expression analysis and Gene Set Enrichment Analysis Materials and Methods: Gene set enrichment analysis (GSEA) to identify activated immune-metabolism pathways in OA. Subsequently, Weighted gene co-expression network analysis (WGCNA) was used to identify gene modules associated with OA and immune-metabolism scores, followed by enrichment analysis to reveal the functional characteristics of these modules. To identify immune-metabolism related differentially expressed genes (DEGs), we utilized seven machine learning methods, including lasso regression, random forest, bagging, gradient boosting machines (GBM), Xgboost-xgbLinear, Xgboost-xgbtree, and decision trees, to construct predictive models and validate their reliability. Based on the expression profiles of hub immunemetabolism related DEGs, we stratified OA patients into two immune-metabolism related subgroups and deeply investigated the differences in immune profiles, drug responses, functions, and pathways between these subgroups. Additionally, we analyzed the expression and pseudotime trajectories of hub immune-metabolism related DEGs at the single-cell level. Through genome-wide association studies (GWAS), we explored the mechanisms of action of hub immune-metabolism related DEGs. Finally, real-time polymerase chain reaction (RT-PCR) was utilized to verify the expression of hub immune-metabolism related DEGs.Results：Immune-metabolism related pathways were significantly activated during the development of OA. Thirteen central immune metabolism-related genes (CX3CR1, ADIPOQ, IL17RA, APOD, EGFR, SPP1, PLA2G2A, CXCL14, RARB, ADM, CX3CL1, TNFSF10, and MPO) were identified. Predictive modeling by constructing these genes has good predictive power for identifying OA. These genes are mainly associated with endothelial cells. Single-cell analysis showed that they were all expressed in single cells and varied with cell differentiation. RT-PCR results suggested that they were all significantly expressed in OA.Conclusion：Our findings indicate that immune metabolism plays a key role in the development of OA and provide new perspectives for future therapeutic strategies
Keywords: osteoarthritis (OA), Immune-metabolism, weighted gene co-expression network analysis (WGCNA), machine learning, genome-wide association studies
Received: 25 Mar 2025; Accepted: 29 May 2025.
Copyright: © 2025 Wu, Zhao, Zhang, Zhao and He. 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) or licensor 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.
* Correspondence:
Yinteng Wu, Department of Orthopedic and Trauma Surgery, The Third Affiliated Hospital of Guangxi Medical University, Nanning, China
Shijian Zhao, Kunming Medical University, Kunming, 650500, Yunnan Province, China
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