Aims: Temporal lobe epilepsy (TLE) is the most common focal epilepsy syndrome in adults and frequently develops drug resistance. Studies have investigated the value of peripheral DNA methylation signature as molecular biomarker for diagnosis or prognosis. We aimed to explore methylation biomarkers for TLE diagnosis and pharmacoresistance prediction.
Methods: We initially conducted genome-wide DNA methylation profiling in TLE patients, and then selected candidate CpGs in training cohort and validated in another independent cohort by employing machine learning algorithms. Furthermore, nomogram comprising DNA methylation and clinicopathological data was generated to predict the drug response in the entire patient cohort. Lastly, bioinformatics analysis for CpG-associated genes was performed using Ingenuity Pathway Analysis.
Results: After screening and validation, eight CpGs were identified for diagnostic biomarker with an area under the curve (AUC) of 0.81 and six CpGs for drug-resistant prediction biomarker with an AUC of 0.79. The nomogram for drug-resistant prediction comprised methylation risk score, disease course, seizure frequency, and hippocampal sclerosis, with AUC as high as 0.96. Bioinformatics analysis indicated drug response-related CpGs corresponding genes closely related to DNA methylation.
Conclusions: This study demonstrates the ability to use peripheral DNA methylation signature as molecular biomarker for epilepsy diagnosis and drug-resistant prediction.
Keywords: DNA methylation; biomarker; machine learning; nomogram; temporal lobe epilepsy.
© 2020 The Authors. CNS Neuroscience & Therapeutics published by John Wiley & Sons Ltd.