ArticleInternational journal of general medicine2024
Screening of Biomarkers Associated with Osteoarthritis Aging Genes and Immune Correlation Studies.
Article in International journal of general medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed, 12 citations in OpenAlex.
- miR-584-5p alleviates osteoarthritis by targeting HIF1A to regulate chondrocyte function and extracellular matrix metabolism.Journal of orthopaedic surgery and research · 2026Article
- Identification of aging-related signature genes in temporomandibular joint degeneration based on bioinformatics.BMC oral health · 2026Article
- Identification of key biomarkers related to fibrocartilage chondrocytes for osteoarthritis based on bulk, single-cell transcriptomic data.Frontiers in immunology · 2024Article
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: Osteoarthritis (OA) is a joint disease with a long and slow course, which is one of the major causes of disability in middle and old-aged people. This study was dedicated to excavating the cellular senescence-associated biomarkers of OA. Methods: The Gene Expression Omnibus (GEO) database was searched and five datasets pertaining to OA were obtained. After removing the batch effect, the GSE55235, GSE55457, GSE82107, and GSE12021 datasets were integrated together for screening of the candidate genes by differential analysis and weighted gene co-expression network analysis (WGCNA). Next, those genes were further filtered by machine learning algorithms to obtain cellular senescence-associated biomarkers of OA. Subsequently, enrichment analyses based on those biomarkers were conducted, and we profiled the infiltration levels of 22 types immune cells with the ERSORT algorithm. A lncRNA-miRNA-mRNA regulatory and drug-gene network were constructed. Finally, we validated the senescence-associated biomarkers at both in vivo and in vitro levels. Results: Five genes (BCL6, MCL1, SLC16A7, PIM1, and EPHA3) were authenticated as cellular senescence-associated biomarkers in OA. ROC curves demonstrated the reliable capacity of the five genes as a whole to discriminate OA samples from normal samples. The nomogram diagnostic model based on 5 genes proved to be a reliable predictor of OA. Single-gene GSEA results pointed to the involvement of the five biomarkers in immune-related pathways and oxidative phosphorylation in the development of OA. Immune infiltration analysis manifested that the five genes were significantly correlated with differential immune cells. Subsequently, a lncRNA-miRNA-mRNA network and gene-drug network containing were generated based on five cellular senescence-associated biomarkers in OA. Conclusion: A foundation for understanding the pathophysiology of OA and new insights into OA diagnosis and treatment were provided by the identification of five genes, namely BCL6, MCL1, SLC16A7, PIM1, and EPHA3, as biomarkers associated with cellular senescence in OA.
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