Integrated Transcriptomic and Machine Learning Analysis Reveals Immune-Related Regulatory Networks in Anti-NMDAR Encephalitis.

ABSTRACT: Anti-N-methyl-D-aspartate receptor (anti-NMDAR) encephalitis is an immune-mediated neurological disorder driven by dysregulated neuroimmune interactions, yet the molecular architecture linking tumor-associated immune activation, peripheral immunity, and neuronal dysfunction remains insufficiently understood. In this study, we established an integrative computational framework that combines multi-tissue transcriptomic profiling, weighted gene co-expression network analysis, immune deconvolution, and machine learning-based feature prioritization to systematically characterize the regulatory landscape of the disease. Joint analysis of three independent GEO datasets spanning ovarian teratoma tissue and peripheral blood transcriptomes identified 2001 consistently dysregulated mRNAs, defining a

SUBMITTER: Fang K 

PROVIDER: S-EPMC12842026 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature