ArticleFrontiers in immunology2023
Identification of aging-related biomarkers and immune infiltration characteristics in osteoarthritis based on bioinformatics analysis and machine learning.
Article in Frontiers in immunology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 75 papers.
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75 citing papers in PubMed, 72 citations in OpenAlex.
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- Identification of Autophagy-related Genes of the Osteoarthritis Using Machine Learning Algorithm.Biochemical genetics · 2026Article
- Cross-Sectional and Longitudinal Association Between Biological Aging Acceleration and the Risk of Osteoarthritis: A Cohort Study From UK Biobank.International journal of rheumatic diseases · 2026Article
- Genetic insights into lung squamous cell carcinoma: howTranslational cancer research · 2026Article
- Programmed cell death-related gene S100A9 promotes macrophage M1 polarization and chondrocyte apoptosis in rheumatoid arthritis.Biology direct · 2026Article
- Single-cell sequencing and machine learning-based prediction of spliceosome-associated factor 2 may represent potential targets for osteoarthritis.Osteoarthritis and cartilage open · 2026Article
- Cardiovascular health and osteoarthritis risk: a prospective cohort study with proteomic insights from UK Biobank.RMD open · 2026Article
- Identification and validation of MYC as an autophagy-related gene for diagnosis and immune infiltration in sepsis-induced myocardial dysfunction.Journal of cardiothoracic surgery · 2026Article
- TMEM92 drives EMT-associated invasiveness, cisplatin resistance, and immune suppression in head and neck squamous cell carcinoma.Scientific reports · 2026Article
- Interleukin-1 Receptor-Associated Kinase 3 Attenuates Chondrocyte Senescence and Osteoarthritis via Inhibition of the TLR7/9-NF-κB Axis.Journal of molecular histology · 2026Article
- Integrative analysis of single-cell and bulk RNA sequencing data using machine learning identifies PTGDS as a key subsynovial fibroblast gene in osteoarthritis.Journal of molecular histology · 2026Article
- Lidocaine inhibits hepatocellular carcinoma cell proliferation, migration, and invasion through the downregulation of SLC6A3.Scientific reports · 2026Article
- Review
- IMMT downregulation promotes osteoarthritis development by inducing mitochondrial dysfunction.Journal of orthopaedic surgery and research · 2026Article
- Article
- Genome Mining for Hub Genes Related to Endoplasmic Reticulum Stress in Pancreatitis: A Perspective from In Silico Characterization.Molecular biotechnology · 2026Article
- Identification of Hub Neutrophil Coexpressed Genes in Acute PancreatitisEndocrine, metabolic & immune disorders drug targets · 2026Article
- NF-κB signaling in osteoarthritis: integrating mechanical stress, innate immunity, and cartilage degeneration.Frontiers in immunology · 2026Review
- Integrated Bioinformatic and Experimental Identification of DHCR24 and NRG1 as Key Cellular Aging Genes in Diabetic Cardiomyopathy.Diabetes, metabolic syndrome and obesity : targets and therapy · 2026Article
- An exploratory analysis of disulfidptosis-related gene signatures in minimal change disease identifies metabolic and immune associations.Frontiers in cell and developmental biology · 2026Article
15 more citing papers are in PubMed but not listed here.
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Osteoarthritis (OA) is a degenerative disease closely related to aging. Nevertheless, the role and mechanisms of aging in osteoarthritis remain unclear. This study aims to identify potential aging-related biomarkers in OA and to explore the role and mechanisms of aging-related genes and the immune microenvironment in OA synovial tissue. Methods: Normal and OA synovial gene expression profile microarrays were obtained from the Gene Expression Omnibus (GEO) database and aging-related genes (ARGs) from the Human Aging Genomic Resources database (HAGR). Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Disease Ontology (DO), and Gene set variation analysis (GSVA) enrichment analysis were used to uncover the underlying mechanisms. To identify Hub ARDEGs with highly correlated OA features (Hub OA-ARDEGs), Weighted Gene Co-expression Network Analysis (WGCNA) and machine learning methods were used. Furthermore, we created diagnostic nomograms and receiver operating characteristic curves (ROC) to assess Hub OA-ARDEGs' ability to diagnose OA and predict which miRNAs and TFs they might act on. The Single sample gene set enrichment analysis (ssGSEA) algorithm was applied to look at the immune infiltration characteristics of OA and their relationship with Hub OA-ARDEGs. Results: We discovered 87 ARDEGs in normal and OA synovium samples. According to functional enrichment, ARDEGs are primarily associated with inflammatory regulation, cellular stress response, cell cycle regulation, and transcriptional regulation. Hub OA-ARDEGs with excellent OA diagnostic ability were identified as MCL1, SIK1, JUND, NFKBIA, and JUN. Wilcox test showed that Hub OA-ARDEGs were all significantly downregulated in OA and were validated in the validation set and by qRT-PCR. Using the ssGSEA algorithm, we discovered that 15 types of immune cell infiltration and six types of immune cell activation were significantly increased in OA synovial samples and well correlated with Hub OA-ARDEGs. Conclusion: Synovial aging may promote the progression of OA by inducing immune inflammation. MCL1, SIK1, JUND, NFKBIA, and JUN can be used as novel diagnostic biomolecular markers and potential therapeutic targets for OA.
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