ORIGINAL RESEARCH article
Front. Immunol.
Sec. Inflammation
Volume 16 - 2025 | doi: 10.3389/fimmu.2025.1596912
This article is part of the Research TopicImmune Cell Dynamics in Chronic Inflammatory Diseases and Cancer ImmunopathologyView all articles
Analysis and validation of diagnostic biomarkers and immune cell infiltration characteristics in osteoarthritis by integrating bioinformatics and machine learning
Provisionally accepted
- 1Shanxi Bethune Hospital, Shanxi Medical University, Taiyuan, China
- 2Huazhong University of Science and Technology, Wuhan, Hubei Province, China
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Exosomes as important carriers of intercellular communication have frequently appeared in recent studies related to osteoarthritis (OA), while the specific mechanism of exosome action in osteoarthritis remains unclear. The aim of this study was to identify potential exosome-related biomarkers in osteoarthritis, to explore the role and mechanism of exosome-related genes in articular cartilage.The data on exosome related genes and normal and OA cartilage genes were obtained through online databases. The potential mechanisms of these genes were revealed by multiple gene enrichment analysis algorithms. Machine learning methods were utilized to identify exosome-related differential genes (ERDEGs) with highly correlated OA features (Hub OA-ERDEGs). In addition, we created a nomogram to assess the ability of Hub OA-ERDEGs to diagnose OA. Single-sample gene set enrichment analysis (ssGSEA) was used to observe the infiltration characteristics of immune cells in OA and their relationship with Hub OA-ERDEGs.The results of screening Hub OA-ERDEGs using machine learning algorithms show that: TOLLIP, ALB, HP, RHOBTB3, GSTM2, S100A8 and AKR1B1 were significantly up-regulated or down-regulated in OA samples and verified by qRT-PCR for validation. Using the ssGSEA algorithm, we discovered that 8 types of immune cell infiltration and 5 types of immune cell activation.
Keywords: Osteoarthritis, exosome, machine learning, Immune infiltration, biomarkers
Received: 20 Mar 2025; Accepted: 01 Aug 2025.
Copyright: © 2025 Li, Wei, Wu and Chen. 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: Chen Chen, Shanxi Bethune Hospital, Shanxi Medical University, Taiyuan, China
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