{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T23:50:21Z","timestamp":1790034621554,"version":"4.0.1"},"reference-count":65,"publisher":"PeerJ","license":[{"start":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T00:00:00Z","timestamp":1769731200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0"}],"funder":[{"name":"Chongqing Hospital of Traditional Chinese Medicine","award":["2023KY-ZSMY"],"award-info":[{"award-number":["2023KY-ZSMY"]}]},{"name":"Chongqing Jiangjin District Science and Technology Program","award":["ZYY2024002"],"award-info":[{"award-number":["ZYY2024002"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>The understanding of the pathogenesis of osteoarthritis (OA) is often fragmented, with studies focusing on individual tissues. A holistic view integrating multi-tissue molecular changes with systemic metabolic shifts is urgently needed. Glutamine metabolism, a central bioenergetic and biosynthetic hub, represents a critical but largely unexplored nexus in this disease network. This study leverages a multi-omics, multi-tissue approach to deconstruct the role of glutamine metabolism in OA and identify a robust, blood-based signature for potential diagnostic use.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      We conducted a comprehensive bioinformatic investigation by integrating multiple GEO transcriptomic datasets from cartilage, synovium, subchondral bone, and peripheral blood. A machine learning pipeline, incorporating weighted gene co-expression network analysis (WGCNA) and least absolute shrinkage and selection operator (LASSO) regression, was employed to identify a signature of glutamine metabolism-related genes (GMRGs). The signature\u2019s clinical relevance was then validated in an independent cohort of 62 subjects (31 OA patients\n                      <jats:italic>vs<\/jats:italic>\n                      . 31 healthy controls) using RT-qPCR on peripheral blood samples and plasma metabolomics. Furthermore, we computationally explored its potential regulatory mechanisms and predicted candidate therapeutic compounds.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Our multi-layered analysis identified a core three-gene signature (\n                      <jats:italic>F13A1<\/jats:italic>\n                      ,\n                      <jats:italic>IRS2<\/jats:italic>\n                      ,\n                      <jats:italic>RELA<\/jats:italic>\n                      ). Functional analysis linked this signature to pathways essential for OA pathogenesis, including mechanical stress, metabolic regulation, and inflammatory responses. Clinical validation in an independent cohort confirmed significant downregulation of all three genes in OA peripheral blood (\n                      <jats:italic>P<\/jats:italic>\n                      &lt; 0.001) and revealed distinct regulatory patterns, including disease-specific activation of\n                      <jats:italic>RELA<\/jats:italic>\n                      and a metabolic regulatory reversal of\n                      <jats:italic>IRS2<\/jats:italic>\n                      , as well as negative correlations with disease severity and alterations in circulating glutamine-related metabolites. The resulting diagnostic model showed strong discriminatory performance across both training and validation datasets. Plasma creatine emerged as an independent predictor of disease severity. Finally, exploratory analyses suggested potential epigenetic regulation and identified several candidate drugs capable of modulating the signature.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>This study identifies a blood-based, multi-omics-derived gene signature that links localized joint pathology with systemic metabolic dysfunction in osteoarthritis. The signature offers a robust non-invasive diagnostic marker and reveals new opportunities for patient stratification and therapeutic development.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.7717\/peerj.20701","type":"journal-article","created":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T08:47:35Z","timestamp":1769762855000},"page":"e20701","source":"Crossref","is-referenced-by-count":4,"title":["Identification and validation of a glutamine metabolism-related gene signature as a diagnostic and therapeutic target in osteoarthritis through integrated multi-omics analysis"],"prefix":"10.7717","volume":"14","author":[{"given":"Yang","family":"Lu","sequence":"first","affiliation":[{"name":"College of Traditional Chinese Medicine, Chongqing Medical University, Chongqing, China"},{"name":"Chongqing Jiangjin District Hospital of Traditional Chinese Medicine, Chongqing, 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