Identification of potential biomarkers and drug of ischemic stroke in patients with COVID-19 through machine learning

Abstract

The relationship between COVID-19 and ischemic stroke (IS) has attracted significant attention, yet the precise mechanism at the gene level remains unclear. This study aims to reveal potential biomarkers and drugs for COVID-19-related IS through bioinformatics methods. We collected two gene expression profiling datasets, GSE16561 and GSE213313, and selected GSE179879 and GSE196822 as validation sets for analysis. Through analysis, we identified 77 differentially expressed genes (DEGs) shared between COVID-19 and IS. Further gene enrichment analysis revealed that these genes are primarily involved in immune regulation. By constructing a protein-protein interaction network, we screened out nine hub genes, including FCGR3A, KLRB1, IL2RB, CD2, IL7R, CCR7, CD3D, GZMK, and ITK. In LASSO regression analysis, we evaluated the ROC curve's area under the curve (AUC) scores of key genes to assess their diagnostic accuracy. Subsequently, we performed random forest (RF), Support Vector Machine Recursive Feature Elimination (SVM-RFE), and neural network construction on hub genes to ensure accurate diagnosis of IS. Finally, by intersecting the results of three algorithms (LASSO regression, random forest, and SVM), CD3D and ITK were identified as the ultimate key genes. Based on this, we predicted potential targeted drug Blinatumomab. These research findings provide clues for a deeper understanding of the biological mechanisms of COVID-19-related IS and offer new insights for exploring novel treatment approaches.

Keywords

• Machine learning
• COVID-19
• Ischemic stroke
• Biomarkers
• Gene expression
• Bioinformatics