Identification of osteoblastic autophagy-related genes for predicting diagnostic markers in osteoarthritis

• Published in: iScience Vol. 27; no. 6; p. 110130
• Main Authors: Cai, Rulong, Jiang, Qijun, Chen, Dongli, Feng, Qi, Liang, Xinzhi, Ouyang, Zhaoming, Liao, Weijian, Zhang, Rongkai, Fang, Hang
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 21.06.2024; Elsevier
• Subjects: Bioinformatics; Human genetics; Orthopedics; Bioinformatics; Orthopedics; Human genetics
• ISSN: 2589-0042; 2589-0042
• DOI: 10.1016/j.isci.2024.110130

The development of osteoarthritis (OA) involves subchondral bone lesions, but the role of osteoblastic autophagy-related genes (ARGs) in osteoarthritis is unclear. Through integrated analysis of single-cell dataset, Bulk RNA dataset, and 367 ARGs extracted from GeneCards, 40 ARGs were found. By employing multiple machine learning algorithms and PPI networks, three key genes (DDIT3, JUN, and VEGFA) were identified. Then the RF model constructed from these genes indicated great potential as a diagnostic tool. Furthermore, the model’s effectiveness in predicting OA has been confirmed through external validation datasets. Moreover, the expression of ARGs was examined in osteoblasts subject to excessive mechanical stress, human and mouse tissues. Finally, the role of ARGs in OA was confirmed through co-culturing explants and osteoblasts. Thus, osteoblastic ARGs could be crucial in OA development, providing potential diagnostic and treatment strategies. [Display omitted] •Investigated the relationship between osteoblastic ARGs and OA•Multiple machine learning approaches identified 3 key ARGs for OA diagnosis•Experimental validation of ARGs in vivo and vitro Orthopedics; Human genetics; Bioinformatics