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Emerging evidence implicates autophagy as a key regulatory process in OA development; however, its molecular signatures, diagnostic value, and interaction with the synovial microenvironment remain incompletely defined. This study aimed to delineate autophagy\u2010associated molecular signatures in OA and characterize their biological relevance across bulk and single\u2010cell transcriptomic landscapes. An integrative analytical framework was applied to four synovial bulk transcriptome datasets (GSE55235, GSE12021, GSE55457, and GSE1919) and one single\u2010cell RNA\u2010sequencing dataset (GSE216651) obtained from the GEO database. Autophagy\u2010related genes were curated from publicly available repositories and intersected with differentially expressed genes identified following batch\u2010effect correction and data integration. Key autophagy\u2010associated genes associated with OA were prioritized through weighted gene co\u2010expression network analysis (WGCNA) combined with multiple machine learning algorithms. Diagnostic utility was assessed using receiver operating characteristic (ROC) analysis and nomogram modeling. Regulatory networks involving upstream microRNAs and transcription factors were inferred in silico. Immune landscape alterations were quantified using ssGSEA, while functional pathways were interrogated by single\u2010gene GSEA. Experimental validation included RT\u2010qPCR analysis in peripheral blood mononuclear cells (PBMCs), single\u2010cell resolution mapping of cellular heterogeneity, and ELISA\u2010based quantification of IL\u20106 with correlation to clinical parameters. Through integrative multi\u2010omics and machine learning analyses, eight autophagy\u2010associated hub genes (XIST, JUN, TNFAIP3, PPP1R15A, IL6, NAMPT, TRAF4, and MYC) were robustly identified as OA\u2010associated molecular features. All candidates demonstrated satisfactory diagnostic performance (AUC &gt;\u20090.7), which was consistently reproduced in independent validation cohorts and experimental assays. Immune deconvolution revealed widespread alterations in synovial immune cell composition, with significant correlations observed between hub gene expression and nine immune cell populations. Single\u2010cell transcriptomic profiling further uncovered distinct cell\u2010type\u2010specific expression patterns and highlighted intensified fibroblast\u2013macrophage communication networks within OA synovium, suggesting their central involvement in disease progression. By integrating bulk and single\u2010cell transcriptomic analyses with experimental validation, this study defines a set of autophagy\u2010associated molecular markers with diagnostic relevance in osteoarthritis. The identified hub genes not only enhance the understanding of OA\u2010related autophagy dysregulation but also represent potential targets for future mechanistic studies and translational intervention strategies. Importantly, these findings provide a robust molecular framework for early diagnostic screening and risk stratification, potentially facilitating more precise clinical management and personalized therapeutic interventions for OA patients.<\/jats:p>","DOI":"10.1111\/cbdd.70315","type":"journal-article","created":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T10:46:02Z","timestamp":1780483562000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Identification of Autophagy\u2010Associated Molecular Signatures in Osteoarthritis Revealed by Integrative Multi\u2010Omics and Experimental Validation"],"prefix":"10.1111","volume":"107","author":[{"given":"Xiaoqing","family":"Wang","sequence":"first","affiliation":[{"name":"North Sichuan Medical College  Nan Chong Sichuan China"},{"name":"Hyperuricemia and Gout Research Center Affiliated Hospital of North Sichuan Medical College  Nanchong China"},{"name":"Department of 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