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Identification and Evaluation of Key Biomarkers of Acute Myocardial Infarction by Machine Learning

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Intelligent Computing Theories and Application (ICIC 2022)

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Abstract

Acute myocardial infarction (AMI) is a severe disease that can occur in all age groups. About 8.5 million patients die of this disease every year. Although the diagnostic technology of AMI is relatively mature, there are still many limitations. We aim to use comprehensive bioinformatics and machine learning algorithms to study the potential molecular mechanism of acute myocardial infarction and seek new prevention and treatment strategies. Methods: The expression profiles of GSE66360 and GSE48060 were downloaded from the Gene Expression Omnibus database, microarray datasets were integrated, and differential genes were obtained to be further analyzed by bioinformatic technique. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, Disease Oncology (DO) analysis and Gene Set Enrichment Analysis (GSEA) were performed on the differential genes using R software, respectively. Then the Lasso algorithm was used to identify the AMI-related essential genes in the training set and validate them in the test set. Potential mechanistic analyses of the development of AMI included the following: the expression differences of crucial genes, differences in immune cell infiltration, immune cell correlation, and the correlation between critical genes and immune cells between normal and AMI samples. Results: Finally, five essential genes were screened, including CLEC4D, CSF3R, SLC11A1, CLEC12A, and TAGAP. The expression of critical genes differed between normal, and AMI samples and the genes can be used as a diagnostic factor in patients. Meanwhile, normal and AMI samples showed significant differences in immune infiltration, and the expression of critical genes was closely related to the abundance of immune cell infiltration. Conclusion: In this study, five essential genes were screened, and the underlying molecular mechanisms of AMI pathogenesis were analyzed, which may provide theoretical support for the diagnosis, prevention, prognosis evaluation and targeted immune therapy of AMI patients.

Z. Zhan and T. Zhao—Contributed to the work equally

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References

  1. Thygesen, K., Alpert, J.S., Jaffe, A.S.: Third universal definition of myocardial infarction. Eur. Heart J. 50, 2173–2195 (2012)

    Google Scholar 

  2. Libby, P.: Mechanisms of acute coronary syndromes and their implications for therapy. N. Engl. J. Med. 368, 2004–2013 (2013)

    Article  Google Scholar 

  3. White, H.D., Chew, D.P.: Acute myocardial infarction. Lancet. Cardiol. Clin. 2, 79–94 (1984)

    Article  Google Scholar 

  4. Yeh, R.W., Sidney, S., Chandra, M., Sorel, M., Selby, J.V., Go, A.S.: Population trends in the incidence and outcomes of acute myocardial infarction. New England J. Med. 362, 2155–2165 (2010)

    Google Scholar 

  5. Murray, C.J.L., et al.: Global, regional, and national disability-adjusted life years (DALYs) for 306 diseases and injuries and healthy life expectancy (HALE) for 188 countries, 1990–2013: Quantifying the epidemiological transition (2015)

    Google Scholar 

  6. Disability-adjusted life years (DALYs) for 291 diseases and injuries in 21 regions, 1990–2010: a systematic analysis for the global burden of disease study 2010. Lancet (London, England) 380, 2197 (2012)

    Google Scholar 

  7. French, J.K., Hellkamp, A.S., Armstrong, P.W., Eric, C.: Mechanical complications after percutaneous coronary intervention in ST-elevation myocardial infarction (from APEX-AMI). American J. Cardiol. 105 (2010)

    Google Scholar 

  8. Cai, W., Li, H., Zhang, Y., Han, G.: Identification of key biomarkers and immune infiltration in the synovial tissue of osteoarthritis by bioinformatics analysis. PeerJ 8, e8390 (2020)

    Article  Google Scholar 

  9. Zhang, X., Zhang, W., Jiang, Y., Liu, K., Song, F.: Identification of functional lncRNAs in gastric cancer by integrative analysis of GEO and TCGA data. J. Cellular Biochem. 120 (2019)

    Google Scholar 

  10. Suzuki, T., Kano, S., Suzuki, M., Yasukawa, S., Homma, A.: Enhanced angiogenesis in salivary duct carcinoma ex-pleomorphic adenoma. Front. Oncol. 10, 603717 (2021)

    Article  Google Scholar 

  11. Tibshirani, R.: Regression shrinkage and selection via the lasso. J. Royal Statist. Soc. Ser. B (Methodological) 58 (1996)

    Google Scholar 

  12. Barrett, T., et al.: NCBI GEO: archive for functional genomics data sets—update. Nucleic Acids Res. 39, 1005–1010 (2013)

    Article  Google Scholar 

  13. Meltzer, D.P.S.: GEOquery: a bridge between the gene expression omnibus (GEO) and BioConductor. Bioinformatics 23, 1846–1847 (2007)

    Article  Google Scholar 

  14. Ritchie, M.E., et al.: Limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015)

    Article  Google Scholar 

  15. Yu, G., Wang, L.G., Han, Y., He, Q.Y.: clusterProfiler: an r package for comparing biological themes among gene clusters. Omics-a J. Integ. Biol. 16, 284–287 (2012)

    Article  Google Scholar 

  16. Robin, X., et al.: pROC: an open-source package for r and s+ to analyze and compare ROC curves. BMC Bioinform. 12, 77 (2011)

    Article  Google Scholar 

  17. Deng, Y.J., Ren, E.H., Yuan, W.H., Zhang, G.Z., Xie, Q.Q.: GRB10 and E2F3 as diagnostic markers of osteoarthritis and their correlation with immune infiltration. Diagnostics 10, 171 (2020)

    Article  Google Scholar 

  18. Weintraub, W.S., et al.: Value of primordial and primary prevention for cardiovascular disease: a policy statement from the american heart association. Circulation 124, 967 (2011)

    Article  Google Scholar 

  19. Bruyninckx, R., Aertgeerts, B., Bruyninckx, P., Buntinx, F.: Signs and symptoms in diagnosing acute myocardial infarction and acute coronary syndrome: a diagnostic meta-analysis. Br. J. Gen. Pract. 58, 105–111 (2008)

    Article  Google Scholar 

  20. Asari, P., et al.: Acute myocardial infarction hospital admissions and deaths in England: A national follow-back and follow-forward record-linkage study. Lancet Public Health 2, e191 (2017)

    Google Scholar 

  21. Xu, J.Y., Xiong, Y.Y., Lu, X.T., Yang, Y.J.: Regulation of type 2 immunity in myocardial infarction. Other 10 (2019)

    Google Scholar 

  22. Weil, B.R., Neelamegham, S.: Selectins and immune cells in acute myocardial infarction and post-infarction ventricular remodeling: pathophysiology and novel treatments. Front. Immunol. 10 (2019)

    Google Scholar 

  23. Suresh, R., et al.: Transcriptome from circulating cells suggests dysregulated pathways associated with long-term recurrent events following first-time myocardial infarction. J. Mol. Cell. Cardiol. 74, 13–21 (2014)

    Article  Google Scholar 

  24. Sun, J., et al.: Deficiency of antigen-presenting cell invariant chain reduces atherosclerosis in mice. Circulation 122, 808–820 (2010)

    Article  Google Scholar 

  25. Tobin, S.W., Alibhai, F.J., Weisel, R.D., Li, R.K.: Considering cause and effect of immune cell aging on cardiac repair after myocardial infarction. Cells. 9 (1894)

    Google Scholar 

  26. Suykens, J., Vandewalle, J.: Least squares support vector machine classifiers. Neural Process. Lett. 9, 293–300 (1999)

    Article  Google Scholar 

  27. Singh, N., Gedda, M.R., Tiwari, N., Singh, S.P., Bajpai, S., Singh, R.K.: Solute carrier protein family 11 member 1 (Slc11a1) activation efficiently inhibits leishmania donovani survival in host macrophages. J. Parasitic Diseases 41 (2017)

    Google Scholar 

  28. Franco, M., et al.: Slc11a1 (Nramp1) alleles interact with acute inflammation loci to modulate wound-healing traits in mice. Mammalian Genome Off. J. Int. Mammalian Genome Soc. 18, 263 (2007)

    Google Scholar 

  29. Marcelo, D.F., et al.: Pristane-induced arthritis loci interact with the Slc11a1 gene to determine susceptibility in mice selected for high inflammation. PLoS ONE 9, e88302 (2014)

    Article  Google Scholar 

  30. Friedman, M.A., Choi, D., Planck, S.R., Rosenbaum, J.T., Sibley, C.H.: Gene expression pathways across multiple tissues in antineutrophil cytoplasmic antibody-associated vasculitis reveal core pathways of disease pathology. J. Rheumatol. 46 (2019)

    Google Scholar 

  31. Lechermeier, C.G., D’Orazio, A., Romanos, M., Lillesaar, C., Drepper, C.: Distribution of transcripts of the GFOD gene family members gfod1 and gfod2 in the zebrafish central nervous system. Gene Expr. Patterns 36, 119111 (2020)

    Article  Google Scholar 

  32. Franzke, A., et al.: G-CSF as immune regulator in t cells expressing the g-CSF receptor: Implications for transplantation and autoimmune diseases. Blood 102, 734–739 (2003)

    Article  Google Scholar 

  33. Morris, K.T., et al.: G-CSF and g-CSFR are highly expressed in human gastric and colon cancers and promote carcinoma cell proliferation and migration. Br. J. Cancer 110, 1211–1220 (2014)

    Article  Google Scholar 

  34. Maria, A., Attya, B., Peter, J., Yang, Z.: Identification and in silico analysis of functional SNPs of human TAGAP protein: a comprehensive study. PLoS ONE 13, e0188143 (2018)

    Article  Google Scholar 

  35. Graham, L.M., et al.: The c-type lectin receptor CLECSF8 (CLEC4D) is expressed by myeloid cells and triggers cellular activation through SYK kinase. J. Biol. Chem. 287, 25964–25974 (2012)

    Article  Google Scholar 

  36. Newby, A.C.: Pathogenesis of atherosclerosis. Principles; Practice of Geriatric Medicine (1949)

    Google Scholar 

  37. Carbone, F., Nencioni, A., Mach, F., Vuilleumier, N., Montecucco, F.: Pathophysiological role of neutrophils in acute myocardial infarction. Thrombosis and Haemostasis (2017)

    Google Scholar 

  38. Nahrendorf, M., Pittet, M.J., Swirski, F.K.: Basic science for clinicians monocytes: protagonists of infarct inflammation and repair after myocardial infarction (2019)

    Google Scholar 

  39. Nikolaos, G., Frangogiannis: regulation of the inflammatory response in cardiac repair. Circulation research (2012)

    Google Scholar 

  40. Carbone, F., Nencioni, A., Mach, F., Vuilleumier, N., Montecucco, F.: Pathophysiological role of neutrophils in acute myocardial infarction. Thromb. Haemost. 109, 501–514 (2013)

    Article  Google Scholar 

  41. Nahrendorf, M., Swirski, F.K.: Regulating repair: regulatory t cells in myocardial infarction. Circ. Res. 115, 7–9 (2014)

    Article  Google Scholar 

Download references

Acknowledgement

This study was supported by Provincial Science and Technology Grant of Shanxi Province (20210302124588), Science and technology innovation project of Shanxi province universities (2019L0683).

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Authors and Affiliations

  1. Changzhi Medical College, Changzhi, 046000, Shanxi, China

    Zhenrun Zhan, Tingting Zhao, Xiaodan Bi, Jinpeng Yang & Pengyong Han

  2. Heping Hospital Affiliated to Changzhi Medical College, Changzhi, 046000, Shanxi, China

    Zhenrun Zhan, Tingting Zhao, Xiaodan Bi & Jinpeng Yang

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  1. Zhenrun Zhan
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  3. Xiaodan Bi
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  4. Jinpeng Yang
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Correspondence to Pengyong Han .

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Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. University of Ulsan, Ulsan, Korea (Republic of)

    Kang-Hyun Jo

  3. Xi’an Polytechnic University, Xi’an, China

    Junfeng Jing

  4. The University of Wollongong, North Wollongong, NSW, Australia

    Prashan Premaratne

  5. Polytecnic of Bari, Bari, Italy

    Vitoantonio Bevilacqua

  6. Liverpool John Moores University, Liverpool, UK

    Abir Hussain

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Zhan, Z., Zhao, T., Bi, X., Yang, J., Han, P. (2022). Identification and Evaluation of Key Biomarkers of Acute Myocardial Infarction by Machine Learning. In: Huang, DS., Jo, KH., Jing, J., Premaratne, P., Bevilacqua, V., Hussain, A. (eds) Intelligent Computing Theories and Application. ICIC 2022. Lecture Notes in Computer Science, vol 13394. Springer, Cham. https://doi.org/10.1007/978-3-031-13829-4_9

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  • DOI: https://doi.org/10.1007/978-3-031-13829-4_9

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