Cross-sectional study uncovers altered peripheral neuroinflammatory microRNA signatures in pharmacoresistant temporal lobe epilepsy, highlighting potential biomarkers for treatment response.
Neuroinflammation is increasingly recognized as a critical contributor to epileptogenesis and pharmacoresistance in temporal lobe epilepsy (TLE). MicroRNAs (miRNAs) are key post-transcriptional regulators of immune signaling, neuronal excitability, and inflammatory responses; however, their contribution to treatment resistance remains incompletely understood. This study aimed to characterize peripheral neuroinflammatory miRNA signatures associated with pharmacoresistance in TLE and evaluate their discriminatory performance and correlation-based co-expression patterns. In this cross-sectional study, 80 patients with TLE were stratified into responders (n = 40) and non-responders (n = 40) according to International League Against Epilepsy criteria for treatment response. Expression levels of ten candidate miRNAs implicated in neuroinflammation, neuronal homeostasis, and apoptosis were quantified in peripheral blood mononuclear cells (PBMC) using RT-qPCR. Differential expression, receiver operating characteristic (ROC), and correlation-based network analyses were performed. Responders exhibited significantly higher expression of miR-146a, miR-132, miR-139, miR-128, and miR-29b, whereas non-responders demonstrated increased expression of miR-21, miR-155, miR-223, and miR-34a. Among individual miRNAs, miR-128 (AUC = 0.77) and miR-146a (AUC = 0.75) showed the strongest discriminatory performance. Pharmacoresistant TLE is associated with a distinct pattern of neuroinflammatory miRNA dysregulation and altered correlation network organization. Collectively, these findings support the concept that peripheral neuroimmune alterations are associated with pharmacoresistance and identify PBMC-derived neuroinflammatory miRNAs as promising candidate biomarker signatures supporting further validation in larger, independent cohorts with prospective study design.
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Deepak et al. (2026) studied this question.
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