Exploration of Acetylation-related Biomarkers in Osteoarthritis Through Bioinformatics Analysis
Shuchang Li, Jiefeng Yin, Jie Huang, Xifan Zheng, Jun Yao
Abstract
Osteoarthritis (OA) is characterized as a chronic degenerative disorder affecting the joints. A growing body of evidence indicates that acetylation may play a role in the disease’s pathogenesis. However, the underlying molecular mechanisms remain largely undefined. The objective of this study was to explore potential biomarkers linked to acetylation in OA through a comprehensive bioinformatics analysis. We utilized datasets GSE55235, GSE55457, and GSE12021 from the Gene Expression Omnibus (GEO) to identify differentially expressed genes (DEGs) by employing the limma package, followed by functional enrichment analyses.
Introduction
Osteoarthritis (OA) is a prevalent degenerative joint disease characterized by the progressive degradation of articular cartilage, synovial inflammation, and alterations in subchondral bone structure. The disease primarily affects older adults, contributing significantly to chronic pain, functional disability, and an increased burden on healthcare systems globally. Despite the high prevalence and substantial economic impact of OA, no effective disease-modifying treatment is currently available, with existing therapeutic options primarily focused on symptom management, such as pain relief and physical rehabilitation.
Materials and Methods:
In this study, the R limma package was utilized to identify DEGs within the processed microarray data, using the following filtering criteria: |log2 Fold Change (FC)| > 0.5 and p value < 0.05. Genes that satisfied these conditions were classified as DEGs. Following this, Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were conducted to further investigate the functional annotations and pathways correlated with the identified DEGs.
Discussion:
OA is a complex chronic disorder frequently characterized by synovitis and cartilage destruction. However, the relationship between OA and acetylation remains unclear. Therefore, studying the expression of acetylation-related genes (For detailed information, see S12 File) and their role in the pathogenesis of OA is crucial for elucidating the pathogenic mechanisms and discovering novel biomarkers. In this study, we identified 197 ACEDEGs through bioinformatics analysis and determined MYC and JUN as candidate hub genes with potential diagnostic value for OA.
Acknowledgments:
We thank GEO database, CIBERSORT database, KEGG database, GO database, GSEA database, and their contributors for the valuable public datasets used in this study.
Citation: Li S, Yin J, Huang J, Zheng X, Yao J (2026) Exploration of acetylation-related biomarkers in osteoarthritis through bioinformatics analysis. PLoS One 21(8): e0357231. https://doi.org/10.1371/journal.pone.0357231
Editor: Zeyneb Kurt, The University of Sheffield, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND
Received: February 8, 2026; Accepted: August 12, 2026; Published: August 28, 2026.
Copyright: © 2026 Li et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All relevant data are within the manuscript and its Supporting information files.
Funding: This research was financially supported by the Guangxi Natural Science Foundation (2023GXNSFAA026402).
Competing interests: The authors have declared that no competing interests exist.
