{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T05:02:13Z","timestamp":1784869333504,"version":"3.55.0"},"reference-count":70,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T00:00:00Z","timestamp":1784851200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Endocrinol."],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Type 2 diabetes mellitus (T2DM) is among the most rapidly increasing metabolic disorders worldwide. Membrane proteins, integral components of biological membranes, are pivotal in insulin signal transduction and significantly contribute to the pathogenesis of T2DM. However, the systematic investigation of membrane proteins linked to T2DM remains insufficient.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>The T2DM dataset and membrane protein-related genes were sourced from the GEO database and the Uniprot website, respectively. Bioinformatics methodologies, including Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, and protein-protein interaction (PPI) network analysis, were employed to assess the differentially expressed membrane protein-related genes between the normal control group and the T2DM group. Subsequently, machine learning algorithms, including Gaussian Mixture Model (GMM), Random Forest (RF), and Support Vector Machine (SVM), were utilized to identify hub genes. Following this, a clinical diagnostic model was constructed, and the receiver operating characteristic (ROC) curve was plotted. Candidate genes were further examined in palmitic acid (PA)-induced NES2Y and HepG2 cell models and in high-fat diet\/streptozotocin-induced T2DM mice. Finally, Functional effects were assessed by gene knockdown, reverse transcription quantitative PCR (RT-qPCR), western blotting (WB), immunofluorescence (IF), co-immunoprecipitation, histological examination, and lipid staining.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>A total of 1,856 DEGs were identified between T2DM and control samples, including 599 upregulated genes and 1,257 downregulated genes, among which 42 were membrane proteinrelated T2DM DEGs. Machine learning algorithms were then applied to pinpoint two target genes: ICAM1 and EZR, the latter of which encodes Ezrin, a membrane\u2013cytoskeleton linker protein. The ROC curve analysis showed that the diagnostic model exhibited strong predictive capability, with an AUC value of 0.95. PA treatment increased lipid accumulation and EZR\/ICAM1 mRNA and Ezrin\/ICAM1 protein expression in the cell models. Ezrin co-immunoprecipitated with the insulin receptor. In PA-treated HepG2 cells, ICAM1 or EZR knockdown increased p85\u03b1 and AKT phosphorylation and GLUT4 protein expression. Ezrin and ICAM1 were also elevated in the livers of T2DM mice.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>ICAM1 and EZR may serve as potential diagnostic biomarkers and candidate therapeutic targets associated with T2DM. Furthermore, ICAM1 and EZR may be associated with insulin resistance by reducing glucose transport efficiency, potentially through modulation of the PI3K-AKT signaling pathway.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fendo.2026.1805548","type":"journal-article","created":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T04:24:49Z","timestamp":1784867089000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Machine learning and bioinformatics identify and validate membrane protein biomarkers for type 2 diabetes mellitus"],"prefix":"10.3389","volume":"17","author":[{"given":"Tong","family":"Liu","sequence":"first","affiliation":[{"name":"State Key Laboratory of Pathogenesis, Prevention and Treatment of Central Asian High Incidence Diseases, Clinical Medical Research Institute, The First Affiliated Hospital of Xinjiang Medical University","place":["Urumqi, Xinjiang, China"]},{"name":"Basic Medical 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