{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T14:11:40Z","timestamp":1790604700789,"version":"4.1.0"},"reference-count":108,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T00:00:00Z","timestamp":1790553600000},"content-version":"vor","delay-in-days":270,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["International Journal of Genomics"],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Osteoarthritis (OA) is becoming increasingly common, and RHO GTPase family members have been implicated in inflammatory processes associated with OA. We therefore examined the diagnostic utility of RHO GTPase\u2010related genes (RGPRGs) in OA.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>The OA expression datasets were collected from public repositories, whereas RGPRGs were assembled from published literature. In the training cohort, we first performed differential expression analysis to screen for differentially expressed genes (DEGs) between OA and control samples. RHO GTPase\u2010related DEGs were obtained by intersecting the DEG list with the RGPRG set, followed by feature selection with the Boruta and XGBoost algorithms. Candidate biomarkers were further evaluated by receiver operating characteristic (ROC) analysis and cross\u2010dataset expression validation. The selected biomarkers were used to establish and validate an artificial neural network (ANN) model, and their biological associations were examined using gene set enrichment analysis (GSEA) and immune infiltration analysis. In GSE152805, key cell populations were identified and subsequently characterized by pseudotime, cell\u2013cell communication, pathway activity, and metabolic analyses.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Analysis yielded 801 DEGs, 30 candidate genes, 14 candidate biomarkers, and 3 final biomarkers. The ANN achieved an area under the curve (AUC) of 0.940 in the training set and 0.833 in the validation set. Following Benjamini\u2013Hochberg adjustment, VAV1, STMN2, and RND1 were associated with 34, 7, and 31 significant KEGG pathways, respectively, and five pathways were common to all three (adjusted\n                      <jats:italic>p<\/jats:italic>\n                      &lt; 0.05 and |\n                      <jats:italic>N<\/jats:italic>\n                      <jats:italic>E<\/jats:italic>\n                      <jats:italic>S<\/jats:italic>\n                      | &gt; 1). STMN2 also showed a significant positive association with natural killer (NK) cells (correlation\u2009coefficient\u2009(cor) = 0.604,\n                      <jats:italic>p<\/jats:italic>\n                      &lt; 0.01). Fibroblasts were selected as the key cell population and displayed strong communication with endothelial cells. Along the fibroblast pseudotime trajectory, RND1 expression decreased initially, increased subsequently, and declined again at later stages. Single\u2010cell Reactome analysis further indicated functional differences among fibroblast subpopulations involving cell interactions, immune signaling, transcriptional regulation, and metabolic processes.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>VAV1, STMN2, and RND1 emerged as reliable diagnostic biomarkers for OA and may provide molecular candidates for early detection and therapeutic investigation. The findings also extend current insight into RHO GTPase\u2010related processes in OA and support further evaluation of biomarker\u2010based diagnostic strategies.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1155\/ijog\/7632269","type":"journal-article","created":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T13:50:34Z","timestamp":1790603434000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Revealing the Diagnostic Potential of Three RHO GTPase\u2010Related Genes in Osteoarthritis Based on Single\u2010Cell RNA Sequencing and GEO Dataset 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