Abstract
Blood-based biomarkers in epilepsy could constitute important research tools advancing neurobiological understanding and valuable clinical tools for better diagnosis and follow-up. An interesting question is whether biomarker patterns could contribute additional understanding compared to individual marker values. We analyzed OLINK proteomics data from a large epilepsy cohort in which we have previously found four differentially expressed proteins (CDH15, PAEP, LTBP3, PHOSPHO1). Using two machine-learning techniques, we identified ten consensus candidate protein biomarkers (CDH15, PAEP, LTBP3, PHOSPHO1, NEFL, SFRP1, TDGF1, DUSP3, WWP2 and DSG3) that contributed to the classification of patients as being seizure-free or not. Six out of the ten consensus proteins were identified as differentially expressed in our previous study (although NEFL and TDGF1 not significantly so after multiple testing correction). The remaining four consensus proteins were newly identified by machine learning and were chosen for detailed analysis. In comparison to the four significantly differentially expressed proteins (CDH15, PAEP, LTBP3, PHOSPHO1), the newly identified consensus proteins (SFRP1, DSG3, DUSP3, and WWP2) and in particular a combination of all eight proteins, outperformed individual proteins in identifying individuals with recent seizures, highlighting the potential of multi-protein profiles. These findings emphasize the need for integrative bioinformatic approaches in epilepsy research and underscore the role of neuroinflammation and immune pathways in epileptogenesis. Our results support the applicability of plasma protein profiling for developing future blood-based tests for epilepsy seizure prediction, diagnosis, and treatment. Further validations in independent cohorts are required to establish these candidate biomarkers in clinical practice.