Identification and prediction of molecular factors associated with ischemic stroke: an integrative analysis of DEGs, TFs, and PPI networks

Ischemic stroke (IS) is a complex neurological disorder characterized by the sudden disruption of blood flow to the brain, leading to severe and often irreversible damage. Despite advances in stroke management, the underlying molecular mechanisms and key factors involved in the development and progression of IS remain elusive. In recent years, the integration of high-throughput data analysis techniques has emerged as a powerful approach to unraveling the molecular intricacies of complex diseases. In this study, we comprehensively analyzed gene expression, protein–protein interactions (PPI), and gene regulatory networks to identify IS-associated molecular factors. We utilized publicly available datasets and employed bioinformatics tools to analyze the data. Our analysis revealed many differentially expressed genes (DEGs) in IS, with a predominant down-regulation of genes. Gene ontology (GO) analysis highlighted the involvement of various biological processes, including transcriptional regulation, cell cycle, immune system processes, and cell differentiation. These findings underscore the complexity of stroke pathology, involving dysregulated gene expression and disrupted cellular processes. Constructing PPI networks enabled us to identify specific subnetworks associated with critical biological processes relevant to stroke, such as nucleosome assembly, protein translation, glycosylation, protein folding, and mRNA splicing. These subnetworks provide insights into the dysregulated molecular mechanisms contributing to stroke progression. Furthermore, we focused on identifying differentially expressed transcription factors (DE-TFs) within the gene regulatory network. Several up-regulated DE-TFs, including E2F1, MYB, GFI1B, and NUCKS1, were identified, suggesting their potential involvement in the dysregulation of gene expression in IS.