---
title: "Acetylation-related biomarkers in osteoarthritis: bioinformatics identification of JUN and MYC"
id: "plos-one-5-exploration-of-acetylation-related-biomarkers-in-osteoarthritis-through"
canonical_url: "https://medichelpline.com/clinical-feed/plos-one-5-exploration-of-acetylation-related-biomarkers-in-osteoarthritis-through"
content_type: "clinical_feed_article"
specialty: "Rheumatology"
source_name: "PLOS ONE (Medicine)"
source_url: "https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357231"
published_at: "2026-08-28T14:00:00.000Z"
evidence_level: "Journal Feed"
license: "CC-BY-NC-4.0 / Informational Use"
---
# Acetylation-related biomarkers in osteoarthritis: bioinformatics identification of JUN and MYC
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/plos-one-5-exploration-of-acetylation-related-biomarkers-in-osteoarthritis-through
- **Specialty:** [Rheumatology](https://medichelpline.com/clinical-feed/rheumatology.md)
- **Primary Source:** PLOS ONE (Medicine)
- **Source URL:** [Original Journal Publication](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357231)
- **Published At:** 2026-08-28T14:00:00.000Z
- **Evidence Rating:** Journal Feed
## Executive GIST (TL;DR)
- Osteoarthritis (OA) is a chronic degenerative joint disease with limited disease-modifying treatments; identification of biomarkers could improve diagnosis and therapy. - The study integrated synovial membrane microarray datasets GSE55235, GSE55457, and GSE12021 as a training cohort (29 OA, 29 controls) and used GSE82107 as a validation cohort (10 OA, 7 controls). - A list of **4,405 acetylation-related genes** was extracted from GeneCards using a relevance score cutoff > 2. - Differentially expressed genes (DEGs) were identified with |log2FC| > 0.5 and p < 0.05 using the limma package; functional enrichment (GO/KEGG) was performed on DEGs. - Weighted gene co-expression network analysis (**WGCNA**) was conducted with a soft threshold of 14, yielding five merged modules; the red module was selected as the core OA-associated module. - Intersection of core module genes, acetylation-related genes, and DEGs produced acetylation-related DEGs (ACEDEGs); a protein–protein interaction (PPI) network was built via STRING and analyzed in Cytoscape, with top genes prioritized by the MCC algorithm. - Three machine learning approaches (LASSO, Random Forest, XGBoost) were applied to refine hub gene selection. - A diagnostic prediction model (nomogram) was constructed and evaluated: training AUC = **0.983**, validation AUC = **0.743**. - Immune cell infiltration analyses using CIBERSORT revealed significant alterations in immune cell composition between OA and controls. - Experimental validation by qRT-PCR and Western blot confirmed down-regulation of **JUN** and **MYC** in OA synovial samples. - The study proposes **JUN** and **MYC** as novel acetylation-related biomarkers for OA and suggests potential roles in OA pathophysiology and diagnostics.
## Clinical Analysis & Structured Key Points
[ Skip to main content ](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357231#main-content) Advertisement * [plos.org](https://plos.org/) * [Create account](https://community.plos.org/registration/new) * [Sign in](https://journals.plos.org/user/secure/login?page=%2Fplosone%2Farticle%3Fid%3D10.1371%2Fjournal.pone.0357231) * * About * Browse * Publish * [](https://journals.plos.org/plosone/ "PLOS One") * Search [advanced search](https://journals.plos.org/plosone/search) * [Browse Topics](https://journals.plos.org/plosone/subjectAreaBrowse) Browse Subject Areas ? Click through the PLOS taxonomy to find articles in your field. For more information about PLOS Subject Areas, click [here](https://github.com/PLOS/plos-thesaurus/blob/master/README.md "Link opens in new window"). [](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357231) [](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357231) * 0 [Save](https://journals.plos.org/plosone/article/metrics?id=10.1371/journal.pone.0357231#savedHeader) [Total Mendeley and Citeulike bookmarks.](https://journals.plos.org/plosone/article/metrics?id=10.1371/journal.pone.0357231#savedHeader) * 0 [Citation](https://journals.plos.org/plosone/article/metrics?id=10.1371/journal.pone.0357231#citedHeader) [Paper's citation count computed by Dimensions.](https://journals.plos.org/plosone/article/metrics?id=10.1371/journal.pone.0357231#citedHeader) * 20 [View](https://journals.plos.org/plosone/article/metrics?id=10.1371/journal.pone.0357231#viewedHeader) [PLOS views and downloads.](https://journals.plos.org/plosone/article/metrics?id=10.1371/journal.pone.0357231#viewedHeader) * 0 [Share](https://journals.plos.org/plosone/article/metrics?id=10.1371/journal.pone.0357231#discussedHeader) [Sum of Facebook, Twitter, Reddit and Wikipedia activity.](https://journals.plos.org/plosone/article/metrics?id=10.1371/journal.pone.0357231#discussedHeader) Open Access Peer-reviewed Research Article # Exploration of acetylation-related biomarkers in osteoarthritis through bioinformatics analysis * Shuchang Li , Contributed equally to this work with: Shuchang Li, Jiefeng Yin Roles Data curation, Formal analysis, Investigation, Methodology, Software, Writing – original draft Affiliation Bone and Joint Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China ⨯ * Jiefeng Yin , Contributed equally to this work with: Shuchang Li, Jiefeng Yin Roles Data curation, Methodology, Validation, Visualization, Writing – original draft Affiliation Bone and Joint Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China ⨯ * Jie Huang, Roles Supervision, Validation, Visualization, Writing – original draft Affiliation Department of Surgery II, Wuxiang Hospital, The Third Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China ⨯ * Xifan Zheng, Roles Formal analysis, Resources, Visualization Affiliation Bone and Joint Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China ⨯ * Jun Yao Roles Conceptualization, Funding acquisition, Project administration, Writing – review & editing * E-mail: yaojun800524@126.com Affiliation Bone and Joint Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China [ ![ORCID logo](https://journals.plos.org/resource/img/orcid_16x16.png) https://orcid.org/0000-0003-2085-1665 ](https://orcid.org/0000-0003-2085-1665 "ORCID Registry") ⨯ # Exploration of acetylation-related biomarkers in osteoarthritis through bioinformatics analysis * Shuchang Li, * Jiefeng Yin, * Jie Huang, * Xifan Zheng, * Jun Yao ![PLOS](https://journals.plos.org/resource/img/logo-plos-full-color.svg) x * Published: August 28, 2026 * * [Article](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357231) * [Authors](https://journals.plos.org/plosone/article/authors?id=10.1371/journal.pone.0357231) * [Metrics](https://journals.plos.org/plosone/article/metrics?id=10.1371/journal.pone.0357231) * [Comments](https://journals.plos.org/plosone/article/comments?id=10.1371/journal.pone.0357231) * [Media Coverage](http://plos.altmetric.com/details/doi/10.1371/journal.pone.0357231) * [Peer Review](https://journals.plos.org/plosone/article/peerReview?id=10.1371/journal.pone.0357231) * [Abstract](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357231#abstract0) * [Introduction](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357231#sec001) * [Materials and methods](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357231#sec002) * [Results](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357231#sec015) * [Discussion](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357231#sec024) * [Conclusion](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357231#sec025) * [Supporting information](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357231#sec026) * [Acknowledgments](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357231#ack) * [References](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357231#references) * [Reader Comments](https://journals.plos.org/plosone/article/comments?id=10.1371/journal.pone.0357231) * [Figures](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357231) ## 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. By implementing weighted gene co‑expression network analysis (WGCNA), we identified key modules and subsequently recognized acetylation-related differentially expressed genes (ACEDEGs). A protein-protein interaction (PPI) network was constructed for these ACEDEGs, and machine learning algorithms were applied to discover potential biomarkers. We established and validated a diagnostic prediction model demonstrating significant diagnostic efficacy (AUC: 0.983 for training and 0.743 for validation). Furthermore, analyses indicated notable alterations in immune cell infiltration through CIBERSORT. Additionally, qRT-PCR and Western blotting corroborated the down-regulation of biomarkers JUN and MYC in OA. In summary, JUN and MYC were identified as novel acetylation-related biomarkers in OA. These findings offer valuable insights into the disease’s pathophysiology and suggest new pathways for diagnostic and therapeutic strategies. ## Figures ![Fig 7](https://journals.plos.org/plosone/article/figure/image?size=inline&id=10.1371/journal.pone.0357231.g007) ![Fig 8](https://journals.plos.org/plosone/article/figure/image?size=inline&id=10.1371/journal.pone.0357231.g008) ![Fig 1](https://journals.plos.org/plosone/article/figure/image?size=inline&id=10.1371/journal.pone.0357231.g001) ![Fig 2](https://journals.plos.org/plosone/article/figure/image?size=inline&id=10.1371/journal.pone.0357231.g002) ![Fig 3](https://journals.plos.org/plosone/article/figure/image?size=inline&id=10.1371/journal.pone.0357231.g003) ![Fig 4](https://journals.plos.org/plosone/article/figure/image?size=inline&id=10.1371/journal.pone.0357231.g004) ![Fig 5](https://journals.plos.org/plosone/article/figure/image?size=inline&id=10.1371/journal.pone.0357231.g005) ![Fig 6](https://journals.plos.org/plosone/article/figure/image?size=inline&id=10.1371/journal.pone.0357231.g006) ![Fig 7](https://journals.plos.org/plosone/article/figure/image?size=inline&id=10.1371/journal.pone.0357231.g007) ![Fig 8](https://journals.plos.org/plosone/article/figure/image?size=inline&id=10.1371/journal.pone.0357231.g008) ![Fig 1](https://journals.plos.org/plosone/article/figure/image?size=inline&id=10.1371/journal.pone.0357231.g001) ![Fig 2](https://journals.plos.org/plosone/article/figure/image?size=inline&id=10.1371/journal.pone.0357231.g002) ![Fig 3](https://journals.plos.org/plosone/article/figure/image?size=inline&id=10.1371/journal.pone.0357231.g003) **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](http://creativecommons.org/licenses/by/4.0/), 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](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357231#sec026) 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. ## 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 [[1](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357231#pone.0357231.ref001)]. The disease primarily affects older adults, contributing significantly to chronic pain, functional disability, and an increased burden on healthcare systems globally [[2](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357231#pone.0357231.ref002),[3](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357231#pone.0357231.ref003)]. 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 [[4](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357231#pone.0357231.ref004),[5](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357231#pone.0357231.ref005)]. This gap in effective management highlights the pressing need for innovative approaches to enhance OA diagnosis and treatment, particularly in identifying reliable biomarkers that can facilitate early intervention and disease monitoring [[6](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357231#pone.0357231.ref006)]. Recent advancements in molecular biology and genomics have highlighted the critical role of various cellular processes in the pathogenesis of OA [[7](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357231#pone.0357231.ref007),[8](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357231#pone.0357231.ref008)]. Among these, post-translational modifications, such as acetylation, have emerged as significant contributors to the regulation of gene expression and cellular function. Dysregulation of acetylation has been related to several pathological processes associated with OA, including inflammation and cartilage degradation [[9](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357231#pone.0357231.ref009)]. However, the specific mechanisms by which acetylation influences OA remain poorly understood, indicating a knowledge gap that warrants further exploration [[10](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357231#pone.0357231.ref010),[11](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357231#pone.0357231.ref011)]. Previous studies have suggested that the identification of acetylation-related biomarkers may yield valuable insights into OA pathology and potential therapeutic targets [[12](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357231#pone.0357231.ref012),[13](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357231#pone.0357231.ref013)]. To address this knowledge gap, our study aimed to explore the role of acetylation-related genes in OA pathogenesis. Utilizing advanced computational techniques, we aimed identify key acetylation-related differentially expressed genes (DEGs) that may serve as robust biomarkers for OA. This approach not only enhances our understanding of the molecular foundations of OA but also enables the identification of potential therapeutic targets for clinical application [[14](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357231#pone.0357231.ref014),[15](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357231#pone.0357231.ref015)]. The integration of bioinformatics tools, such as weighted gene co-expression network analysis (WGCNA) and protein–protein interaction (PPI) networks, combined with experimental validation methods like quantitative reverse transcription polymerase chain reaction (qRT-PCR) and Western blotting (WB), provides a comprehensive framework for our investigation [[16](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357231#pone.0357231.ref016),[17](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357231#pone.0357231.ref017)]. Our primary objective is to identify acetylation-related biomarkers and evaluate their diagnostic utility and interactions within the immune microenvironment of OA. By elucidating the roles of these genes in OA progression and their potential as biomarkers, this research could facilitate the development of more effective diagnostic and therapeutic strategies [[18](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357231#pone.0357231.ref018)]. Additionally, we aimed to construct a predictive nomogram based on our findings to facilitate risk stratification and early diagnosis of OA, thereby improving patient outcomes and informing clinical decision-making [[19](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357231#pone.0357231.ref019)–[21](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357231#pone.0357231.ref021)]. In summary, this study seeks to address the deficiencies in OA research by investigating the role of acetylation-related genes and their potential as biomarkers for disease diagnosis and treatment. Through a combination of computational and experimental methodologies, we aim to provide new insights into the molecular mechanisms of OA, ultimately contributing to the development of highly effective therapeutic interventions and improving the quality of life for affected patients [[22](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357231#pone.0357231.ref022)]. The anticipated identification of reliable biomarkers may foster the establishment of targeted therapies that address the underlying pathophysiological processes of OA, thereby significantly advancing OA research [[23](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357231#pone.0357231.ref023),[24](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357231#pone.0357231.ref024)]. ## Materials and methods ### Data acquisition and preprocessing The GSE55235, GSE55457, and GSE12021 datasets containing data on the synovial membrane were obtained from the Gene Expression Omnibus (GEO) database ( ). Probe names were converted to gene names using Perl and with the help of platform annotation files. Background correction and normalization of each dataset were performed using the R package limma, and the three synovial datasets from the same platform were integrated using the R package sva to remove batch effects [[25](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357231#pone.0357231.ref025)]. For the subsequent analyses, the integrated dataset was utilized as the training cohort,which contained 29 OA groups and 29 control groups. The GSE82107 dataset was designated as the validation cohort, which included 10 OA groups and 7 control groups. The genes associated with Acetylation were obtained from the GeneCards database ( , (GeneCards Version 5.25 Updated: Jul 16, 2025)). A total of 4,405 acetylation-related genes were obtained by applying a relevance score threshold of greater than 2 as a criterion for selection. ### Identification of DEGs and enrichment analysis 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 ). We then set the network to have a minimum required interaction score greater than 0.4 based on the STRING online database,. Subsequently, The PPI network was imported into Cytoscape (version 3.9.1), and the top 13 genes were screened for subsequent analysis based on the MCC algorithm of the cytoHubba plugin. ### Screening for hub genes via machine learning algorithms Three machine learning algorithms, namely, Least Absolute Shrinkage and Selection Operator (LASSO), Random Forests (RF), and eXtreme Gradient Boosting (XGBoost) were used to screen OA hub genes. The “glmnet” package was used for LASSO regression analysis, tenfold cross-validation was adopted, and the minimum lambda value was identified as the optimal solution. We performed RF using the randomForest package and XGBoost using the XGBoost package. ### Nomogram construction and receiver operating characteristic (ROC) evaluation Univariate a
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