Bioinformation Analysis of Key Genes and Pathways of Acute Myocardial Infarction to Predict Potential Medicine

  • Zien Xu
Keywords: Acute Myocardial Infarction; Bioinformatics Analysis; Gene Expression Omnibus


This study was aimed to retrieval differentially expressed genes and biological signaling pathways of Acute Myocardial Infarction (AMI) based on integrated bioinformatics analysis[1]. Using Gene Expression Omnibus (GEO) dataset was used to download mRNA expression profile GSE66360 containing 50 healthy cohorts and 49 patients experiencing Acute Myocardial Infarction to study the the potential mechanism and predict medicine target in Acute Myocardial Infarction (AMI). The consistently differentially expressed genes(DEGs) were identified,and functional annotation and pathway analysis of these genes were excavated with GO, KEGG and Reactome. The protein-protein interaction network(PPI) of DEGs was created with Cytoscape and STRING to screen the hub genes including TNF, IL1B, FN1, CD4, PLEK, JUN, TLR4, FOS, LRRK2, TLR2. On the ground of discovered information, the potential drugs were calculated. The functional genes enrich mainly in Cytokine-cytokine receptor interaction, osteoclast differentiation, neutrophil degranulation and signaling by interleukins and so on. This study digs the pathological process of AMI and may aid in learning the deeper molecular mechanism of AMI and predict potential drugs of this disease.


Zhao XY, et al. (2018).Identification of key genes and pathways associated with osteogenic differentiation of adipose stem cells.. Journal of cellular physiology(12).

GonzálezRamírez Javier, et al.(2022). Acute Myocardial Infarction and Periodontitis: Importance of Awareness and Prevention in Latin America. Applied Sciences(6).

Wightman DP., et al. (2021). A genome-wide association study with 1,126,563 individuals identifies new risk loci for Alzheimer’s disease. Nature Genetics(9).

Clough, E., & Barrett, T. (2016). The Gene Expression Omnibus Database. Methods in molecular biology (Clifton, N.J.), 1418, 93–110.

Liu, Z., et al. (2023). Identification of key genes and small molecule drugs in osteoarthritis by integrated bioinformatics analysis. Biochemistry and biophysics reports, 34, 101450.

Yang, L., et al. (2022). Identification of protein-protein interaction associated functions based on gene ontology and KEGG pathway. Frontiers in genetics, 13, 1011659.

Lim DW, Choi MS & Kim SM.(2023). Bioinformatics and Connectivity Map Analysis Suggest Viral Infection as a Critical Causative Factor of Hashimoto’s Thyroiditis. International Journal of Molecular Sciences(2).

Justin Lamb, et al. (2006). The Connectivity Map: Using Gene-Expression Signatures to Connect Small Molecules, Genes, and Disease. Science(5795).

Daniel C, et al. (2021). Fast cluster-based computation of exact betweenness centrality in large graphs. Journal of Big Data(1).

Scott JD, et al. (2012). Ferroptosis: An Iron-Dependent Form of Nonapoptotic Cell Death. Cell(5).

Park Tae-Jun, et al. (2019). Quantitative proteomic analyses reveal that GPX4 down regulation during myocardial infarction contributes to ferroptosis in cardiomyocytes. Cell death & disease(11).

Liu, H., et al. (2018). NR4A2 protects cardiomyocytes against myocardial infarction injury by promoting autophagy. Cell death discovery, 4, 27.

Shimamoto Akira, et al. (2006). Inhibition of Toll-like receptor 4 with eritoran attenuates myocardial ischemia-reperfusion injury. Circulation(1 Suppl).

Hammond MD, et al(2014). CCR2+ Ly6C(hi) inflammatory monocyte recruitment exacerbates acute disability following intracerebral hemorrhage. The Journal of neuroscience : the official journal of the Society for Neuroscience(11).

Original Research Article