COMPREHENSIVE BIOINFORMATICS METHOD TO EXPLORE IMMUNE-RELATED GENES IN THE PATHOGENESIS OF MYOCARDIAL INFARCTION

Comprehensive Bioinformatics Method to Explore Immune-Related Genes in the Pathogenesis of Myocardial Infarction

Comprehensive Bioinformatics Method to Explore Immune-Related Genes in the Pathogenesis of Myocardial Infarction

Blog Article

Abstract


Myocardial infarction (MI), commonly known as a heart attack, remains a leading cause of mortality worldwide. Emerging evidence suggests that immune system dysregulation plays a crucial role in MI pathogenesis. This study employs a comprehensive bioinformatics approach to identify immune-related genes associated with MI, providing new insights into potential biomarkers and therapeutic targets.



Introduction


MI occurs due to the obstruction of coronary arteries, leading to ischemic damage in myocardial tissue. Recent research highlights the involvement of inflammatory responses and immune-mediated mechanisms in the progression of MI. Identifying key immune-related genes can improve our understanding of disease pathology and aid in developing targeted therapies.



Methods


1. Data Collection and Preprocessing



  • Gene expression datasets related to MI were retrieved from public databases such as the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA).

  • Standardized data preprocessing included background correction, normalization, and removal of batch effects.


2. Identification of Differentially Expressed Genes (DEGs)



  • Differential gene expression analysis was conducted using the limma package in R.

  • A cutoff threshold of p < 0.05 and |log2 fold change| > 1 was applied to identify significantly altered genes.


3. Functional Enrichment Analysis



  • Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed to determine the biological roles of identified genes.

  • Gene Set Enrichment Analysis (GSEA) was used to uncover enriched immune-related pathways.


4. Immune Cell Infiltration Analysis



  • CIBERSORT and xCell algorithms were employed to estimate immune cell composition in MI-related datasets.

  • Correlations between differentially expressed genes and immune cell infiltration levels were analyzed.


5. Protein-Protein Interaction (PPI) Network Construction



  • The STRING database was used to construct a PPI network of significant immune-related genes.

  • Hub genes were identified using the Cytoscape software and the MCC algorithm from the cytoHubba plugin.


6. Validation of Key Genes



  • Independent validation of key immune-related genes was performed using external datasets and real-time PCR data from MI patient samples.

  • Machine learning models were applied to evaluate the predictive power of selected genes.


Results



  • Several immune-related genes, including IL6, TNF, CXCL8, CCL2, and TLR4, were significantly upregulated in MI patients.

  • Enrichment analysis revealed strong associations with inflammatory and immune response pathways such as NF-κB signaling, cytokine-cytokine receptor interaction, and T-cell receptor signaling.

  • Immune cell infiltration analysis showed increased levels of macrophages, neutrophils, and dendritic cells in MI patients, while T-regulatory cell populations were significantly reduced.

  • The PPI network identified TNF and IL6 as central hub genes, indicating their critical role in MI pathogenesis.


Discussion



  • The findings highlight the involvement of immune-related genes in myocardial injury and repair mechanisms.

  • Targeting pro-inflammatory cytokines such as IL6 and TNF may offer new therapeutic opportunities for MI treatment.

  • Future studies should focus on integrating multi-omics approaches and single-cell sequencing data to refine the understanding of immune-mediated MI mechanisms.


Conclusion


This study provides a bioinformatics-driven framework for identifying immune-related genes in MI pathogenesis. The identified genes and pathways could serve as potential biomarkers or therapeutic targets, paving the way for personalized medicine in cardiovascular diseases.



Keywords


Myocardial infarction, immune-related genes, bioinformatics, inflammation, cytokines, biomarker discovery, gene expression



https://cvia-journal.org/immune-related-genes/

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