Combining Machine Learning, Single-Cell Sequencing Data, and Mendelian Randomization Studies to Explore the Correlation Between Ischemic Stroke and Inflammatory Pathway Genes.

Background: Ischemic stroke (IS) is a severe neurological disorder, with inflammation playing a crucial role in its development. This study is aimed at investigating the gene expression profiles related to inflammation in IS patients and determining their association with disease progression. Methods: We analyzed two distinct gene expression datasets from public repositories to compare gene expression between IS patients and healthy controls. Key inflammatory pathway-related genes (IPRGs) associated with IS were identified through differential expression analysis and advanced machine learning techniques. Consensus clustering analysis was used to identify various inflammatory expression signatures in IS. Single-cell sequencing was performed to dissect inflammation-related signaling pathways. Additionally, Mendelian randomization studies were conducted to assess the causal relationship between tumor necrosis factor (TNF) and IS. Results: Four pivotal genes-HLA-DRA, IL1A, IL15, and TNF-were found to be upregulated in IS patients and significantly correlated with inflammatory levels. A diagnostic model was developed and validated using a nomogram. Single-cell sequencing analysis revealed variations in inflammatory pathway enrichment scores across different cell types, enhancing our understanding of immune cell infiltration patterns in IS patients and highlighting the critical roles of macrophages and monocytes in inflammation. Mendelian randomization studies suggested that TNF may have a negative regulatory effect on the risk of IS. Conclusion: This study provides insights into the gene expression profiles associated with inflammation in IS patients and identifies key IPRGs. These findings offer valuable information for understanding the pathogenesis of IS and emphasize the importance of inflammation in disease development. Our research also presents potential therapeutic targets and predictive tools for future stroke research and clinical practice.