OBJECTIVES: Rapid advances in transcriptomics have driven efforts to identify deregulated pathways in multiple sclerosis (MS) tissues, though many detected differentially expressed genes are likely false positives, with only a small fraction reflecting actual pathological events. Robust, integrative methods are essential for accurately understanding the molecular mechanisms underlying MS pathology. METHODS: We conducted a gene prioritization analysis of MS white matter pathology transcriptomic studies. Articles were sought in Scopus and PubMed up to July 31, 2024. Potentially eligible publications were those that provided either transcriptomics datasets (deposited in GEO) or lists of differentially expressed genes comparing MS white matter to control white matter. RESULTS: Applying a vote-count strategy to search for the intersection of genes reported in multiple independent studies with a consistent fold-change direction, followed by a Monte Carlo simulation, we identified 528 highly significant differentially expressed multi-study genes (p <
0.0001
10,000 simulations). Functional enrichment analysis revealed deregulation of the folate pathway in MS normal-appearing white matter, and tumor necrosis factor (TNF) -related and complement-related pathways in active and chronic active lesions, respectively. Network analysis identified 6 key signaling hubs: PTPRC, HLA-B, MYC, MMP2, COL11A2, MAG. The major nodes identified revealed mechanistic concordance with published in vivo MS models, supporting their value as potential therapeutic targets. INTERPRETATION: Our strategy provides a robust framework for integrating gene expression data, effectively identifying the intricate pathways altered in human diseased tissues. This method holds potential for translating findings into drug development strategies. ANN NEUROL 2025.