{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T19:12:45Z","timestamp":1776280365372,"version":"3.50.1"},"reference-count":38,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,5,9]],"date-time":"2025-05-09T00:00:00Z","timestamp":1746748800000},"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. Neurol."],"abstract":"<jats:sec><jats:title>Background<\/jats:title><jats:p>Anti-N-methyl-D-aspartate receptor (NMDAR) encephalitis is a critical neurological disorder mediated by autoimmune mechanisms, Previous literature suggests that immune inflammatory responses may be involved in the progression of anti NMDAR encephalitis, but its molecular regulatory mechanisms still remain uncertain. We aimed to identify transcriptome-wide landscape of mRNAs and explore the potential pathogenesis for anti-NMDAR encephalitis.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Peripheral blood mononuclear cells were obtained from six patients with anti-NMDAR encephalitis and six controls for RNA extraction and library creation. The Illumina HiSeq platform was used to do transcriptome sequencing. We utilized R software to identify differentially expressed genes (DEGs) and performed a functional enrichment analysis. Furthermore, random forest (RF) and support vector machine-recursive feature elimination (SVM-RFE) were employed to screen for and identify anti-NMDAR encephalitis diagnostic signatures. To verify the findings, we employed quantitative real-time polymerase chain reaction. Receiver operating characteristic curves were utilized to assess the diagnostic values. We evaluated the inflammatory state of anti-NMDAR encephalitis using cell-type identification by computing the relative subsets of RNA transcripts (CIBERSORT) and investigated the relationship between diagnostic biomarkers and immune cell subsets.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>899 DEGs were identified (568 upregulated and 331 downregulated), of which 78 were immune-related genes. The DEGs were found to be considerably enriched in immunological inflammation-related pathways, according to the functional enrichment analysis. Insulin-like factor 3 [area under the curve (AUC) = 0.917] and tumor protein translationally controlled regulator 1 (AUC = 0.944) were considered potential diagnostic indicator candidates of anti-NMDAR encephalitis, with statistically significant variations in expression. An immune cell analysis of immune cell proportions suggests that monocytes, CD8<jats:sup>+<\/jats:sup> T cells, and T regulatory cells may all be involved in the development of anti-NMDAR encephalitis.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>Transcriptome analysis reveals significant activation of peripheral immune-inflammatory pathways in anti-NMDAR encephalitis. INSL3 and TPT1 may serve as potential auxiliary diagnostic biomarkers, while monocyte, CD8+ T cell, and Treg infiltration likely synergistically drive disease progression.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fneur.2025.1568274","type":"journal-article","created":{"date-parts":[[2025,5,9]],"date-time":"2025-05-09T04:10:45Z","timestamp":1746763845000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Immune inflammatory regulation in Anti-NMDAR encephalitis: insights from transcriptome 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