{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,26]],"date-time":"2026-10-26T20:48:59Z","timestamp":1793047739438,"version":"4.5.0"},"reference-count":92,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T00:00:00Z","timestamp":1755820800000},"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. Immunol."],"abstract":"<jats:sec><jats:title>Objective<\/jats:title><jats:p>Diabetes mellitus combined with nonalcoholic fatty liver disease is a prevalent and intricate metabolic disorder that presents a significant global health challenge, imposing economic and emotional burdens on society and families. An in-depth understanding of the disease pathogenesis is crucial for enhancing diagnostic and therapeutic efficacy. Therefore, the study aims to identify and validate autophagy-related diagnostic biomarkers associated with T2DM-associated MAFLD, investigate regulatory mechanisms in disease progression, and explore cellular diversity within the same tissue using single-cell sequencing data.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>This study utilized four datasets retrieved from the Gene Expression Omnibus (GEO) database: GSE15653, GSE89632, GSE24807 and GSE23343. The analysis involved variance analysis, WGCNA analysis, PPI network construction, machine learning application, examination of autophagy-related gene sets, and diagnostic ROC analysis to identify and validate autophagy-related biomarkers in T2DM combined with MAFLD within an independent external dataset. Functional enrichment analysis, immune infiltration analysis, and validation of gene significance in T2DM combined with MAFLD progression were conducted using animal experiments to understand the biological functions and immunomodulatory roles of key biomarkers. Cellular diversity within liver tissues was characterized at the single-cell level, exploring interrelationships, differentiation, and developmental trajectories among cell populations through cellular communication and pseudo-temporal analyses.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The study identified four key biomarkers (IRAK3, TNFRSF1A, CX3CR1, JUNB). Real-time fluorescence quantitative PCR analysis in animal experiments demonstrated significantly higher mRNA expression levels of IRAK3, TNFRSF1A, CX3CR1, and JUNB in T2DM and MAFLD rat liver tissues compared to the control group. Quantitative immunohistochemical analysis revealed notably elevated protein expression levels of IRAK3, TNFRSF1A, CX3CR1, and JUNB in liver tissues of rats with T2DM and MAFLD when contrasted with the control group (P &amp;lt; 0.05). Enrichment analysis indicated associations of T2DM combined with MAFLD pathogenesis with pathways such as the NF-kappa B signaling pathway, MAPK signaling pathway, Fluid shear stress and atherosclerosis, Insulin resistance, and Cytokine-cytokine receptor interaction. Correlative analysis uncovered connections between immune infiltration and the identified genes. Single-cell transcriptomic analysis highlighted the differentiation of CX3CR1, JUNB, and TFRC in various single-cell-annotated populations. The pseudo-temporal analysis of epithelial cells identified enriched genes at crucial nodes related to \u201cLeukocyte transendothelial migration\u201d, \u201cLipid and atherosclerosis\u201d, and \u201cType II diabetes mellitus\u201d signaling pathways. Additionally, four cellular communication signaling pathways (TNF, CXCL, VEGF, and MIF) potentially significant in T2DM combined with MAFLD progression were identified through cell communication analysis.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>This study unveiled potential associations and key biomarkers (IRAK3, TNFRSF1A, CX3CR1, JUNB) concerning T2DM combined with MAFLD and relevant pathways, offering novel insights for the investigation of these two conditions.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fimmu.2025.1587225","type":"journal-article","created":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T05:26:49Z","timestamp":1755840409000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["Exploring the biological functions and immune regulatory roles of IRAK3, TNFRSF1A, CX3CR1, and JUNB in T2DM combined with MAFLD: integrated bioinformatics and single-cell analysis"],"prefix":"10.3389","volume":"16","author":[{"given":"Qin","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoqi","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kaidierdan","family":"Wushoulaji","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinyang","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Li","family":"Wan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ye","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xueli","family":"Gong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2025,8,22]]},"reference":[{"key":"B1","first-page":"CD005266","article-title":"Screening for type 2 diabetes mellitus","volume":"5","author":"Peer","year":"2020","journal-title":"Cochrane Database Syst Rev"},{"key":"B2","article-title":"Non-alcoholic fatty liver disease and diabetes mellitus","volume":"1307","author":"Khneizer","year":"2021","journal-title":"Adv Exp Med Biol"},{"key":"B3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/bs.acc.2022.06.001","article-title":"Long non-coding RNA in Non-alcoholic fatty liver disease","volume":"110","author":"Rohilla","year":"2022","journal-title":"Adv Clin Chem"},{"key":"B4","doi-asserted-by":"publisher","first-page":"6996","DOI":"10.3390\/ijms23136996","article-title":"Fibrogenic pathways in metabolic dysfunction associated fatty liver disease (MAFLD)","volume":"23","author":"Subramanian","year":"2022","journal-title":"Int J Mol Sci"},{"key":"B5","article-title":"Non-alcoholic fatty liver disease, an overview","volume":"18","year":"2019","journal-title":"Integr Med (Encinitas)"},{"key":"B6","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btm254","article-title":"GEOquery: a bridge between the gene expression omnibus (GEO) and bioConductor","volume":"23","author":"Davis","year":"2007","journal-title":"Bioinformatics"},{"key":"B7","doi-asserted-by":"publisher","DOI":"10.1002\/mus.20785","article-title":"Diabetes mellitus and the peripheral nervous system: manifestations and mechanisms","volume":"36","author":"Zochodne","year":"2007","journal-title":"Muscle Nerve"},{"key":"B8","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/gkv007","article-title":"limma powers differential expression analyses for RNA-sequencing and microarray studies","volume":"43","author":"Ritchie","year":"2015","journal-title":"Nucleic Acids Res"},{"key":"B9","first-page":"397","volume-title":"Limma: linear models for microarray data. 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