Decoding epilepsy's molecular blueprint: Machine learning unravels transcriptomic subtypes and regulatory networks

OBJECTIVE: Drug-resistant epilepsy (DRE) affects approximately one-third of patients with epilepsy. The molecular heterogeneity underlying DRE remains poorly defined, largely due to limited access to resected brain tissue and substantial genetic diversity. Current classifications rely primarily on clinical symptoms and histopathological features rather than molecular mechanisms, constraining mechanistic insight and the development of targeted therapies. This study aimed to develop a transcriptome-based, machine learning-guided framework for molecular classification of DRE. METHODS: We performed comprehensive RNA sequencing on 153 surgically resected samples from 95 patients with DRE. Two transcriptomic subtypes were identified through unsupervised clustering. We also leveraged a weighted correlation network-based framework and systematic transcriptional signature comparison and developed a classification model using machine learning algorithms. RESULTS: Unsupervised clustering revealed two molecular subtypes that diverged from traditional pathological classifications, indicating an alternative transcriptomic basis for epilepsy pathogenesis. A classification model was constructed based on four key differentially regulated pathways: (1) neuroactive ligand-receptor interaction, (2) cAMP signaling, (3) γ-aminobutyric acid (GABA)ergic synapse, and (4) calcium signaling. Among the tested algorithms, the random forest model demonstrated superior performance, achieving 96% classification accuracy with an area under the curve (AUC) of .95. SIGNIFICANCE: These molecular subtypes and their pathways could serve as key molecular hallmarks of epilepsy, offering valuable insights for developing targeted therapies. Moreover, our findings introduce a novel framework for classifying epilepsy based on its molecular nature, potentially connecting the clinical symptoms with the underlying causes more effectively.