{"title":"Integrating bioinformatics and machine learning to identify biomarkers of branched chain amino acid related genes in osteoarthritis.","authors":"Xiao-Zhi ZhaYang, Yan-Xiong Chen, Wen-Da Hua, Zheng-Lin Bai, Yun-Peng Jin, Xing-Wen Zhao, Quan-Fu Liu, Zeng-Dong Meng","doi":"10.1186/s12891-025-08779-6","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Branched-chain amino acids (BCAA) metabolism is significantly associated with osteoarthritis (OA), but the specific mechanism of BCAA related genes (BCAA-RGs) in OA is still unclear. Therefore, this research intended to identify potential biomarkers and mechanisms of action of BCAA-RGs in OA tissues.</p><p><strong>Methods: </strong>Differential genes were obtained from the Gene Expression Omnibus (GEO) database and intersections were taken with BCAA-RGs to identify candidate genes. The underlying mechanisms were revealed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Subsequently, by combining three machine learning algorithms to identify genes with highly correlated OA features. In addition, created diagnostic maps and subject Receiver operating characteristic curves (ROCs) to assess the ability of the signature genes to diagnose OA and to predict their possible roles in molecular regulatory network axes and molecular signaling pathways.</p><p><strong>Results: </strong>Eight candidate genes were acquired by intersecting 4,178 DEGs and 14 BCAA-RGs. Subsequently, five candidate biomarkers were obtained, namely SLC3A2, SLC7A5, SLC43A2, SLC43A1, and SLC7A7. Importantly, SLC3A2 and SLC7A5 were validated by validation set and qRT-PCR. Furthermore, the nomogram constructed by SLC3A2 and SLC7A5 exhibited excellent accuracy in predicting the incidence of OA. The enrichment results demonstrated that SLC3A2 and SLC7A5 were significantly enriched in ribosome, insulin signaling pathway, olfactory transduction, etc. Meanwhile, we also found XIST regulated SLC7A5 through hsa-miR-30e-5p, and regulated SLC3A2 through hsa-miR-7-5p.OIP5-AS1 regulated SLC7A5 and SLC3A2 through hsa-miR-7-5p. By the way, 150 drugs were identified, including Acetaminophen and Acrylamide, which exhibited simultaneous targeting of these two biomarkers.</p><p><strong>Conclusion: </strong>Based on bioinformatics, SLC3A2 and SLC7A5 were identified as biomarkers related to BCAA in OA, which may provide a new reference for the treatment and diagnosis of OA patients.</p>","PeriodicalId":9189,"journal":{"name":"BMC Musculoskeletal Disorders","volume":"26 1","pages":"517"},"PeriodicalIF":2.2000,"publicationDate":"2025-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"BMC Musculoskeletal Disorders","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1186/s12891-025-08779-6","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ORTHOPEDICS","Score":null,"Total":0}
引用次数: 0
Abstract
Background: Branched-chain amino acids (BCAA) metabolism is significantly associated with osteoarthritis (OA), but the specific mechanism of BCAA related genes (BCAA-RGs) in OA is still unclear. Therefore, this research intended to identify potential biomarkers and mechanisms of action of BCAA-RGs in OA tissues.
Methods: Differential genes were obtained from the Gene Expression Omnibus (GEO) database and intersections were taken with BCAA-RGs to identify candidate genes. The underlying mechanisms were revealed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Subsequently, by combining three machine learning algorithms to identify genes with highly correlated OA features. In addition, created diagnostic maps and subject Receiver operating characteristic curves (ROCs) to assess the ability of the signature genes to diagnose OA and to predict their possible roles in molecular regulatory network axes and molecular signaling pathways.
Results: Eight candidate genes were acquired by intersecting 4,178 DEGs and 14 BCAA-RGs. Subsequently, five candidate biomarkers were obtained, namely SLC3A2, SLC7A5, SLC43A2, SLC43A1, and SLC7A7. Importantly, SLC3A2 and SLC7A5 were validated by validation set and qRT-PCR. Furthermore, the nomogram constructed by SLC3A2 and SLC7A5 exhibited excellent accuracy in predicting the incidence of OA. The enrichment results demonstrated that SLC3A2 and SLC7A5 were significantly enriched in ribosome, insulin signaling pathway, olfactory transduction, etc. Meanwhile, we also found XIST regulated SLC7A5 through hsa-miR-30e-5p, and regulated SLC3A2 through hsa-miR-7-5p.OIP5-AS1 regulated SLC7A5 and SLC3A2 through hsa-miR-7-5p. By the way, 150 drugs were identified, including Acetaminophen and Acrylamide, which exhibited simultaneous targeting of these two biomarkers.
Conclusion: Based on bioinformatics, SLC3A2 and SLC7A5 were identified as biomarkers related to BCAA in OA, which may provide a new reference for the treatment and diagnosis of OA patients.
期刊介绍:
BMC Musculoskeletal Disorders is an open access, peer-reviewed journal that considers articles on all aspects of the prevention, diagnosis and management of musculoskeletal disorders, as well as related molecular genetics, pathophysiology, and epidemiology.
The scope of the Journal covers research into rheumatic diseases where the primary focus relates specifically to a component(s) of the musculoskeletal system.