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.
Integrating bioinformatics and machine learning to identify biomarkers of branched chain amino acid related genes in osteoarthritis.
整合生物信息学和机器学习技术,以识别骨关节炎中支链氨基酸相关基因的生物标志物
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作者:ZhaYang Xiao-Zhi, Chen Yan-Xiong, Hua Wen-Da, Bai Zheng-Lin, Jin Yun-Peng, Zhao Xing-Wen, Liu Quan-Fu, Meng Zeng-Dong
| 期刊: | BMC Musculoskeletal Disorders | 影响因子: | 2.400 |
| 时间: | 2025 | 起止号: | 2025 May 26; 26(1):517 |
| doi: | 10.1186/s12891-025-08779-6 | 研究方向: | 炎症/感染 |
| 疾病类型: | 关节炎 | ||
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