Identification of BRD4 as a Shared Diagnostic Biomarker Between Bipolar Disorder and Multiple Sclerosis Using Machine Learning

Bipolar disorder (BD) is closely associated with multiple sclerosis (MS), but their shared molecular mechanisms and specific diagnostic biomarkers remain unclear. This study integrated microarray expression profiling and machine learning to identify shared core genes and underlying mechanisms between BD and MS. Gene expression datasets and peripheral-blood transcriptome datasets were obtained from the GEO database. After per-cohort quantile normalization, shared genes were identified by differential expression analysis and weighted gene co-expression network analysis. A protein–protein interaction network was constructed, and key genes were screened using the MCODE plugin. Three machine learning algorithms were combined to identify the final core gene. A total of 229 shared genes were initially obtained and further filtered to 55 key genes. BRD4 was identified as the final shared core gene. Discriminative performance (AUC, 95% CI) was: BD discovery GSE23848 (20 cases/15 controls) 0.713 (0.54–0.89); MS discovery GSE21942 (12/15) 0.998 (0.99–1.00); BD external GSE39653 (8/24) 0.775 (0.59–0.96); MS external GSE203241 (38/21) 0.828 (0.71–0.93). BRD4 shows moderate-to-good discriminative performance as a non-invasive peripheral biomarker and may aid further investigation of shared BD–MS mechanisms.