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BackgroundOsteoarthritis (OA) of the knee is a degenerative disorder characterized by cartilage degradation, synovial inflammation, and structural remodeling. Synovial fluid (SF) biomarkers may improve diagnosis, staging, and patient stratification.MethodsFollowing PRISMA guidelines, a systematic search of PubMed and Scopus (2015-2025) was conducted. Human studies analyzing SF with proteomic techniques (LC-MS/MS, SWATH-MS, ELISA) were included. Extracted data were classified by OA stage, sample type, proteomic platform, and identified biomarkers. Functional enrichment was performed with ShinyGO, and protein-protein interaction (PPI) analysis with STRING (v12.0).ResultsSeven studies met the inclusion criteria. Reported biomarkers included Cathepsin G (CTSG), angiotensinogen (AGT), periostin (POSTN), haptoglobin (HP), complement components, and matrix-related proteins such as ADAMTS4 and LYVE-1, which are involved in extracellular matrix remodeling, inflammation, and joint tissue homeostasis. Functional annotation revealed enrichment in glycosaminoglycan binding, complement cascades, and redox pathways. PPI analysis identified central nodes including COL1A1, ACAN, COMP, and POSTN, together with matrix-degrading enzymes such as MMP1, MMP3, and MMP13, highlighting tightly connected extracellular matrix remodeling processes in OA.ConclusionThis review highlights reproducible SF biomarkers with diagnostic and prognostic potential in knee OA. Integrated proteomic and network analysis reinforces the multifactorial nature of OA and suggests key molecular targets. Translationally, a consistent biomarker signature could support early detection, more precise staging, and personalized management, enabling biomarker-guided diagnostics, targeted therapies, and the integration of precision medicine into OA care.
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Elvira Immacolata Parrotta
Giorgia Lucia Benedetto
Giovanni Cuda
Cartilage
University of Salerno
Magna Graecia University
Università Campus Bio-Medico
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Parrotta et al. (Wed,) studied this question.
www.synapsesocial.com/papers/6a080acea487c87a6a40ccd0 — DOI: https://doi.org/10.1177/19476035261446251