Analysis and Construction of a Molecular Diagnosis Model of Drug-Resistant Epilepsy Based on Bioinformatics

Abstract

Background: Epilepsy is a complex chronic disease of the nervous system which influences the health of approximately 70 million patients worldwide. In the past few decades, despite the development of novel antiepileptic drugs, around one-third of patients with epilepsy have developed drug-resistant epilepsy. We performed a bioinformatic analysis to explore the underlying diagnostic markers and mechanisms of drug-resistant epilepsy.Methods: Weighted correlation network analysis (WGCNA) was applied to genes in epilepsy samples downloaded from the Gene Expression Omnibus database to determine key modules. The least absolute shrinkage and selection operator (LASSO) regression and support vector machine-recursive feature elimination (SVM-RFE) algorithms were used to screen the genes resistant to carbamazepine, phenytoin, and valproate, and sensitivity of the three-class classification SVM model was verified through the receiver operator characteristic (ROC) curve. A protein–protein interaction (PPI) network was utilized to analyze the protein interaction relationship. Finally, ingenuity pathway analysis (IPA) was adopted to conduct disease and function pathway and network analysis.Results: Through WGCNA, 72 genes stood out from the key modules related to drug resistance and were identified as candidate resistance genes. Intersection analysis of the results of the LASSO and SVM-RFE algorithms selected 11, 4, and 5 drug-resistant genes for carbamazepine, phenytoin, and valproate, respectively. Subsequent union analysis obtained 17 hub resistance genes to construct a three-class classification SVM model. ROC showed that the model could accurately predict patient resistance. Expression of 17 hub resistance genes in healthy subjects and patients was significantly different. The PPI showed that there are six resistance genes (CD247, CTSW, IL2RB, MATK, NKG7, and PRF1) that may play a central role in the resistance of epilepsy patients. Finally, IPA revealed that resistance genes (PRKCH and S1PR5) were involved in “CREB signaling in Neurons.”Conclusion: We obtained a three-class SVM model that can accurately predict the drug resistance of patients with epilepsy, which provides a new theoretical basis for research and treatment in the field of drug-resistant epilepsy. Moreover, resistance genes PRKCH and S1PR5 may cooperate with other resistance genes to exhibit resistance effects by regulation of the cAMP-response element-binding protein (CREB) signaling pathway.

Comprehensive analysis identified and validated BRD4, CHD1, and KDM7A as potential biomarkers for osteoarthritis

“…To screen for biomarkers with the highest diagnostic value for disease, we use three machine learning methods (LASSO, SVM-RFE, and RF) to screen for potential biomarkers. LASSO is a regression analysis algorithm that uses regularization to improve prediction accuracy 11 . The SVM algorithm can generate hyperplanes with maximum margin in the feature space to distinguish between positive and negative instances 12 .…”

Development and validation of a nomogram for the early prediction of drug resistance in children with epilepsy

“…Most of the existing prediction models have focused on identifying independent risk factors for DRE (10). Scholars have developed several tools, including formulas, machine learning methods, prediction models of logistic regression, clinical prediction rules (CPR), deep learning methods, molecular diagnosis model, and integrative prediction algorithm based on combined clinical-EEG functional connectivity features and circulating microRNAs from plasma small extracellular vesicles, for the early prediction of the probability of DRE (11)(12)(13)(14)(15)(16)(17)(18). Nevertheless, all of these tools were neither simple enough nor convenient for practical application.…”