{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T04:16:13Z","timestamp":1787026573199,"version":"build-2736575974"},"reference-count":66,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T00:00:00Z","timestamp":1776816000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Immunol."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Ulcerative colitis (UC) is a chronic, multifactorial inflammatory bowel disease. The involvement of small GTPase-related genes (SGRGs) in UC remains poorly defined. This study aims to identify SGRGs associated with UC and elucidate the molecular mechanisms by which they regulate the pathological progression of UC.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      Multiple transcriptomic datasets were integrated to identify candidate SGRGs in UC. Four machine learning algorithms were utilized for biomarker screening, followed by the construction of a diagnostic nomogram. The robustness of the identified biomarkers and the nomogram was rigorously evaluated through external validation in multiple independent cohorts. Furthermore, the associations between biomarkers and clinical severity, as well as their capacity for differential diagnosis (UC vs. Crohn\u2019s disease (CD)), were assessed. Functional enrichment, immune infiltration, and cross-dataset single-cell RNA sequencing (scRNA-seq) were employed for in-depth mechanistic investigation.\n                      <jats:italic>In vivo<\/jats:italic>\n                      experiments validated core gene expression.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Three biomarkers (\n                      <jats:italic>ARHGEF3<\/jats:italic>\n                      ,\n                      <jats:italic>S100A8<\/jats:italic>\n                      , and\n                      <jats:italic>RHOU<\/jats:italic>\n                      ) were successfully identified and validated across multiple independent cohorts. The nomogram constructed based on these biomarkers exhibited excellent diagnostic performance (AUC\u00a0=\u00a00.991 in the training set, 0.938 and 0.968 in the external validation cohort). Notably, the expression levels of these biomarkers significantly correlated with clinical severity and pathological mucosal states, demonstrating their capacity to reflect disease activity and monitor progression. Furthermore, among the identified biomarkers,\n                      <jats:italic>ARHGEF3<\/jats:italic>\n                      significantly differentiated UC from CD (p &amp;lt; 0.05), while all three markers exhibited discriminative potential with AUC values exceeding 0.6. Functional annotation illustrated that they were commonly enriched within pathways encompassing \u201cTight junction\u201d and \u201cLeukocyte transendothelial migration\u201d. Immune infiltration assessment demonstrated 27 differentially expressed immune cells (DEICs) among the UC and control groups. Cross-dataset scRNA-seq analysis further confirmed that macrophages were the key cells, exhibiting significant metabolic reprogramming and functional heterogeneity in UC.\n                      <jats:italic>In vivo<\/jats:italic>\n                      experiments confirmed the expression of above biomarkers in UC.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>\n                      This study systematically identifies\n                      <jats:italic>ARHGEF3<\/jats:italic>\n                      ,\n                      <jats:italic>S100A8<\/jats:italic>\n                      , and\n                      <jats:italic>RHOU<\/jats:italic>\n                      as novel UC diagnostic biomarkers, implicating them in disease progression via immune regulation and macrophage function. These findings provide a new basis for precise diagnosis and targeted therapy.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fimmu.2026.1782885","type":"journal-article","created":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T05:52:28Z","timestamp":1776837148000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Integrated transcriptome and single-cell RNA sequencing identifies small GTPase-associated biomarkers in ulcerative colitis"],"prefix":"10.3389","volume":"17","author":[{"given":"Meilin","family":"Chen","sequence":"first","affiliation":[{"name":"Department of Gastroenterology, Renmin Hospital of Wuhan University","place":["Wuhan, China"]},{"name":"Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University","place":["Wuhan, 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