Predicting Prognostic Bidirectional Molecular Signatures Associating Myocardial Infarction and Lung Cancer: An In-Silico Perspective

Abstract

Myocardial Infarction (MI) and lung cancers substantially contributors to morbidity and mortality. They share common risk factors such as smoking, and hypertension. There is a pressing need to identify bidirectional molecular signatures linking MI and lung cancer relationship to create a unique triage to improve the clinical outcomes of the patient. In the current work, we extracted the common differentially expressed genes (DEGs) between MI and lung cancer and identified 2339 upregulated and 2973 downregulated genes in MI datasets; 952 upregulated and 653 downregulated genes in LUAD; 1466 upregulated and 2816 downregulated genes in LUSC. Among these, 27 genes (11 upregulated and 16 downregulated) were common across MI, LUAD, and LUSC. Functional enrichment analysis revealed shared biological processes, such as inflammatory response, and cell differentiation. KEGG pathway analysis highlighted common pathways such as B cell receptor signaling, TNF signaling. A protein-protein interaction study with STRING revealed a variety of interaction partners; miRNA-mRNA network analysis characterizes conserved binding sites for 6 genes while overall survival analysis for lung cancer patients shows a significant association with prognosis. In addition, machine learning models built using the Support Vector Machine and Random Forest algorithms demonstrated high AUROC values of 0.79 and 0.81 respectively on balanced dataset in the classifying MI patients from non-MI patients. Lastly, based on drug repurposing analysis, we proposed FDA-approved drugs such as Venetoclax, Lomitapide, Regorafenib that could potentially target these genes, indicating novel therapeutic options for the co-occurring conditions of MI and lung cancer. Our findings highlight the similarities in molecular makeup between lung cancer and MI, providing information for future investigations and therapeutic approaches.