ArticleBMC musculoskeletal disorders2024
Screening and validation of key genes associated with osteoarthritis.
Article in BMC musculoskeletal disorders, 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.
- A preliminary bioinformatic screen to identify SRI SMC2 PSIP1 TLE4 and MSX1 as potential diagnostic and prognostic markers of osteoarthritis.Scientific reports · 2025Article
- Integrating bioinformatics and machine learning to identify biomarkers of branched chain amino acid related genes in osteoarthritis.BMC musculoskeletal disorders · 2025Article
- Association analysis between nutritional factors within the genome and the risk of osteoarthritis.Frontiers in nutrition · 2025Article
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6 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundOsteoarthritis is recognized as a common geriatric condition characterized by irregular chronic pain. Its prevalence is steadily increasing, posing significant challenges to global public health, while some studies indicate a trend towards younger individuals being affected. This condition severely impacts patients' quality of life.
methodsUsing the Gene Expression Omnibus (GEO) database, we downloaded datasets GSE114007, GSE169077, and GSE206848. We utilized R software to screen and confirm differentially expressed genes (DEGs) related to the development of osteoarthritis. A cross-analysis of the three datasets was conducted, with the least overlapping dataset, GSE206848, selected as the validation set. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed on the DEGs from GSE114007 and GSE169077. Weighted Gene Co-Expression Network Analysis (WGCNA) was employed to identify modules closely associated with osteoarthritis, and genes from these intersecting modules were entered into the STRING database to construct Protein-Protein Interaction Networks. The top ten genes by connectivity were identified and validated using GSE206848. Key genes were identified and preliminarily validated using Quantitative Real-Time PCR (QPCR). Subsequent validation of related genes was carried out through Western Blot (WB) analysis.
resultsDifferentially expressed genes were identified from the GSE114007 and GSE169077 datasets and validated in the GSE206848 dataset, with ANGPTL4 selected as the key gene. QPCR results indicated a significant difference in ANGPTL4 expression levels between normal and osteoarthritic chondrocytes. Western Blot analysis confirmed a significant difference in ANGPTL4 protein expression between normal and osteoarthritic chondrocytes.
conclusionBased on the experimental findings, ANGPTL4 appears to be a potential key gene in osteoarthritis.
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