ArticleAging2024
Identification of aging-related genes in diagnosing osteoarthritis via integrating bioinformatics analysis and machine learning.
Article in Aging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 7 citations in OpenAlex.
- Article
- Article
- Review
- Identification of Key Glycolysis-Related Genes in Osteoarthritis and Their Correlation with Immune Infiltration Using Bioinformatics Analysis and Machine Learning.Open access rheumatology : research and reviews · 2025Article
- Research and analysis of differential gene expression in CD34 hematopoietic stem cells in myelodysplastic syndromes.PloS one · 2025Article
- A transcriptomic atlas at bulk and single-cell levels identifies novel transcriptional and splicing regulators of ECM homeostasis in osteoarthritis.Frontiers in genetics · 2025Article
- Machine learning potential predictor of idiopathic pulmonary fibrosis.Frontiers in genetics · 2024Article
Corrections and comments
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundOsteoarthritis (OA) is one of the main causes of pain and disability in the world, it may be caused by many factors. Aging plays a significant role in the onset and progression of OA. However, the mechanisms underlying it remain unknown. Our research aimed to uncover the role of aging-related genes in the progression of OA.
methodsIn Human OA datasets and aging-related genes were obtained from the GEO database and the HAGR website, respectively. Bioinformatics methods including Gene Ontology (GO), Kyoto Encyclopedia of Genes Genomes (KEGG) pathway enrichment, and Protein-protein interaction (PPI) network analysis were used to analyze differentially expressed aging-related genes (DEARGs) in the normal control group and the OA group. And then weighted gene coexpression network analysis (WGCNA), the least absolute shrinkage and selection operator (LASSO) regression, and the Random Forest (RF) machine learning algorithms were used to find the hub genes.
resultsFour overlapping hub genes:
conclusionFinally, these four-hub aging-related genes may help us understand the underlying mechanism of aging in osteoarthritis and could be used as possible diagnostic and therapeutic targets.
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Registered trials
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