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Combinatorial Chemistry & High Throughput Screening

Editor-in-Chief

ISSN (Print): 1386-2073
ISSN (Online): 1875-5402

Research Article

Identification of Hypoxia-related Genes in Acute Myocardial Infarction using Bioinformatics Analysis

Author(s): Huasong Xia, Yi Chen, Qiang Chen and Yanqing Wu*

Volume 26, Issue 4, 2023

Published on: 17 August, 2022

Page: [728 - 742] Pages: 15

DOI: 10.2174/1386207325666220517110651

Price: $65

Abstract

Background: Acute myocardial infarction (AMI) remains one of the most fatal diseases worldwide. Persistent ischemia and hypoxia are implicated as significant mechanisms in the development of AMI. However, no hypoxia-related gene targets of AMI have been identified to date. This study aimed to identify potential genes and drugs for AMI using bioinformatics analysis.

Materials and Methods: Two datasets both related to AMI (GSE76387 and GSE161427) were downloaded from the Gene Expression Omnibus to identify differentially expressed genes (DEGs) between AMI and sham mice. Gene ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed. A protein-protein interaction (PPI) network was constructed to identify hub genes using Cytoscape. Candidate genes were identified by the intersection of hub genes and hypoxia-related genes. Western blotting was used to validate the candidate genes in the AMI mouse model. Furthermore, the Drug-Gene Interaction Database was used to predict potential therapeutic drugs targeting all hub genes.

Results: Fifty-three upregulated and 16 downregulated genes closely related to AMI were identified. The DEGs were primarily enriched in protein, heparin, and integrin binding. KEGG analysis suggested that focal adhesion, PI3K-Akt signaling pathway, and extracellular matrix-receptor interaction are crucial pathways for AMI. The PPI network analysis identified 14 hub genes, two of which were hypoxia-related. Several agents were found to have therapeutic potential for AMI.

Conclusion: This study suggests that connective tissue growth factors and the collagen family members may be candidate targets in treating AMI. Agents targeting these candidates may be potential treatments.

Keywords: Acute myocardial infarction, microarray expression profile, differentially expressed genes, hypoxia-related genes, protein-protein interaction network, AMI.

[1]
Alpert, J.S.; Thygesen, K.A.; White, H.D.; Jaffe, A.S. Diagnostic and therapeutic implications of type 2 myocardial infarction: Review and commentary. Am. J. Med., 2014, 127(2), 105-108.
[http://dx.doi.org/10.1016/j.amjmed.2013.09.031] [PMID: 24462011]
[2]
DeFilippis, A.P.; Chapman, A.R.; Mills, N.L.; de Lemos, J.A.; Arbab-Zadeh, A.; Newby, L.K.; Morrow, D.A. Assessment and treatment of patients with type 2 myocardial infarction and acute nonischemic myocardial injury. Circulation, 2019, 140(20), 1661-1678.
[http://dx.doi.org/10.1161/CIRCULATIONAHA.119.040631] [PMID: 31416350]
[3]
Virani, S.S.; Alonso, A.; Benjamin, E.J.; Bittencourt, M.S.; Callaway, C.W.; Carson, A.P.; Chamberlain, A.M.; Chang, A.R.; Cheng, S.; Delling, F.N.; Djousse, L.; Elkind, M.S.V.; Ferguson, J.F.; Fornage, M.; Khan, S.S.; Kissela, B.M.; Knutson, K.L.; Kwan, T.W.; Lackland, D.T.; Lewis, T.T.; Lichtman, J.H.; Longenecker, C.T.; Loop, M.S.; Lutsey, P.L.; Martin, S.S.; Matsushita, K.; Moran, A.E.; Mussolino, M.E.; Perak, A.M.; Rosamond, W.D.; Roth, G.A.; Sampson, U.K.A.; Satou, G.M.; Schroeder, E.B.; Shah, S.H.; Shay, C.M.; Spartano, N.L.; Stokes, A.; Tirschwell, D.L.; VanWagner, L.B.; Tsao, C.W. American heart association council on epidemiology and prevention statistics committee and stroke statistics subcommittee. heart disease and stroke statistics-2020 update: A report from the American heart association. Circulation, 2020, 141(9), e139-e596.
[http://dx.doi.org/10.1161/CIR.0000000000000757] [PMID: 31992061]
[4]
Song, J.; Murugiah, K.; Hu, S.; Gao, Y.; Li, X.; Krumholz, H.M.; Zheng, X.; China, P.C.G. Incidence, predictors, and prognostic impact of recurrent acute myocardial infarction in China. Heart, 2020, 107(4), 313-318.
[http://dx.doi.org/10.1136/heartjnl-2020-317165] [PMID: 32938773]
[5]
Piacentini, L.; Karliner, J.S. Altered gene expression during hypoxia and reoxygenation of the heart. Pharmacol. Ther., 1999, 83(1), 21-37.
[http://dx.doi.org/10.1016/S0163-7258(99)00010-8] [PMID: 10501593]
[6]
Liu, M.; Galli, G.; Wang, Y.; Fan, Q.; Wang, Z.; Wang, X.; Xiao, W. Novel therapeutic targets for hypoxia-related cardiovascular diseases: The role of HIF-1. Front. Physiol., 2020, 11, 774.
[http://dx.doi.org/10.3389/fphys.2020.00774] [PMID: 32760290]
[7]
Loor, G.; Schumacker, P.T. Role of hypoxia-inducible factor in cell survival during myocardial ischemia-reperfusion. Cell Death Differ., 2008, 15(4), 686-690.
[http://dx.doi.org/10.1038/cdd.2008.13] [PMID: 18259200]
[8]
Mujalli, A.; Banaganapalli, B.; Alrayes, N.M.; Shaik, N.A.; Elango, R.; Al-Aama, J.Y. Myocardial infarction biomarker discovery with integrated gene expression, pathways and biological networks analysis. Genomics, 2020, 112(6), 5072-5085.
[http://dx.doi.org/10.1016/j.ygeno.2020.09.004] [PMID: 32920122]
[9]
Chen, D.Q.; Kong, X.S.; Shen, X.B.; Huang, M.Z.; Zheng, J.P.; Sun, J.; Xu, S.H. Identification of differentially expressed genes and signaling pathways in acute myocardial infarction based on integrated bioinformatics analysis. Cardiovasc. Ther., 2019, 2019, 8490707.
[http://dx.doi.org/10.1155/2019/8490707] [PMID: 31772617]
[10]
Li, Y.; He, X.N.; Li, C.; Gong, L.; Liu, M. Identification of candidate genes and micrornas for acute myocardial infarction by weighted gene coexpression network analysis. BioMed Res. Int., 2019, 2019, 5742608.
[http://dx.doi.org/10.1155/2019/5742608] [PMID: 30886860]
[11]
Wu, X.; Sun, L.; Wang, Z. Identification of lncRNA competitively regulated subpathways in myocardial infarction. Exp. Ther. Med., 2019, 17(4), 3041-3046.
[http://dx.doi.org/10.3892/etm.2019.7320] [PMID: 30936975]
[12]
Wang, S.; Cao, N. Uncovering potential differentially expressed miRNAs and targeted mRNAs in myocardial infarction based on integrating analysis. Mol. Med. Rep., 2020, 22(5), 4383-4395.
[http://dx.doi.org/10.3892/mmr.2020.11517] [PMID: 33000230]
[13]
Matboli, M.; Shafei, A.E.; Agwa, S.H.A.; Elzahy, S.S.; Anwar, A.K.; Mansour, A.R.; Gaber, A.I.; Said, A.E.A.; Lwis, P.; Hamdy, M. Identification of novel molecular network expression in acute myocardial infarction. Curr. Genomics, 2019, 20(5), 340-348.
[http://dx.doi.org/10.2174/1389202920666190820142043] [PMID: 32476991]
[14]
Zhong, Z.; Wu, H.; Zhong, W.; Zhang, Q.; Yu, Z. Expression profiling and bioinformatics analysis of circulating microRNAs in patients with acute myocardial infarction. J. Clin. Lab. Anal., 2020, 34(3), e23099.
[http://dx.doi.org/10.1002/jcla.23099] [PMID: 31721304]
[15]
Zhong, Z.; Hou, J.; Zhang, Q.; Zhong, W.; Li, B.; Li, C.; Liu, Z.; Yang, M.; Zhao, P. Circulating microRNA expression profiling and bioinformatics analysis of dysregulated microRNAs of patients with coronary artery disease. Medicine (Baltimore), 2018, 97(27), e11428.
[http://dx.doi.org/10.1097/MD.0000000000011428] [PMID: 29979444]
[16]
Wu, K.; Zhao, Q.; Li, Z.; Li, N.; Xiao, Q.; Li, X.; Zhao, Q. Bioinformatic screening for key miRNAs and genes associated with myocardial infarction. FEBS Open Bio, 2018, 8(6), 897-913.
[http://dx.doi.org/10.1002/2211-5463.12423] [PMID: 29928570]
[17]
Zhang, G.; Li, J.; Sun, H.; Yang, G. Screening for the biomarkers associated with myocardial infarction by bioinformatics analysis. J. Comput. Biol., 2020, 27(5), 779-785.
[http://dx.doi.org/10.1089/cmb.2019.0180] [PMID: 31502863]
[18]
Gao, Y.; Qi, G.X.; Guo, L.; Sun, Y.X. Bioinformatics analyses of differentially expressed genes associated with acute myocardial infarction. Cardiovasc. ther., 2016, 34(2), 67-75.
[http://dx.doi.org/10.1111/1755-5922.12171] [PMID: 26725916]
[19]
Zhang, X.; Lv, X.; Li, X.; Wang, Y.; Lin, H.Y.; Zhang, J.; Peng, C. Dysregulated circulating socs3 and haptoglobin expression associated with stable coronary artery disease and acute coronary syndrome: An integrated study based on bioinformatics analysis and case-control validation. Anatol. J. Cardiol., 2020, 24(3), 160-174.
[http://dx.doi.org/10.14744/AnatolJCardiol.2020.56346] [PMID: 32870172]
[20]
Xiao, S.J.; Zhou, Y.F.; Wu, Q.; Ma, W.R.; Chen, M.L.; Pan, D.F. Uncovering the differentially expressed genes and pathways involved in the progression of stable coronary artery disease to acute myocardial infarction using bioinformatics analysis. Eur. Rev. Med. Pharmacol. Sci., 2021, 25(1), 301-312.
[http://dx.doi.org/10.26355/eurrev_202101_24396] [PMID: 33506919]
[21]
Jia, F.; Chen, L.; Fang, L.; Chen, W. IRAK-M deletion aggravates acute inflammatory response and mitochondrial respiratory dysfunction following myocardial infarction: A bioinformatics analysis. J. Proteomics, 2022, 257, 104512.
[http://dx.doi.org/10.1016/j.jprot.2022.104512] [PMID: 35139396]
[22]
Yao, Y.; Zhao, J.; Zhou, X.; Hu, J.; Wang, Y. Potential role of a three-gene signature in predicting diagnosis in patients with myocardial infarction. Bioengineered, 2021, 12(1), 2734-2749.
[http://dx.doi.org/10.1080/21655979.2021.1938498] [PMID: 34130601]
[23]
Thackeray, J.T.; Hupe, H.C.; Wang, Y.; Bankstahl, J.P.; Berding, G.; Ross, T.L.; Bauersachs, J.; Wollert, K.C.; Bengel, F.M. Myocardial inflammation predicts remodeling and neuroinflammation after myocardial infarction. J. Am. Coll. Cardiol., 2018, 71(3), 263-275.
[http://dx.doi.org/10.1016/j.jacc.2017.11.024] [PMID: 29348018]
[24]
Liu, Y.; Lai, S.; Liang, L.; Zhang, D. Study on the interaction mechanism between C-reactive protein and platelets in the development of acute myocardial infarction. Ann. Transl. Med., 2021, 9(12), 1012.
[http://dx.doi.org/10.21037/atm-21-2733] [PMID: 34277812]
[25]
Shi, X.; Cao, Y.; Zhang, X.; Gu, C.; Liang, F.; Xue, J.; Ni, H.W.; Wang, Z.; Li, Y.; Wang, X.; Cai, Z.; Hocher, B.; Shen, L.H.; He, B. Comprehensive analysis of n6-methyladenosine rna methylation regulators expression identify distinct molecular subtypes of myocardial infarction. Front. Cell Dev. Biol., 2021, 9, 756483.
[http://dx.doi.org/10.3389/fcell.2021.756483] [PMID: 34778266]
[26]
Yu, Y.W.; Xue, Y.J.; Qian, L.L.; Chen, Z.; Que, J.Q.; Huang, K.Y.; Liu, S.; Weng, Y.B.; Rong, F.N.; Ji, K.T.; Zeng, J.N. Screening and identification of potential hub genes in myocardial infarction through bioinformatics analysis. Clin. Interv. Aging, 2020, 15, 2233-2243.
[http://dx.doi.org/10.2147/CIA.S281290] [PMID: 33293800]
[27]
Zhang, F.; Fu, X.; Kataoka, M.; Liu, N.; Wang, Y.; Gao, F.; Liang, T.; Dong, X.; Pei, J.; Hu, X.; Zhu, W.; Yu, H.; Cowan, D.B.; Hu, X.; Huang, Z.P.; Wang, J.; Wang, D.Z.; Chen, J. Long noncoding RNA Cfast regulates cardiac fibrosis. Mol. Ther. Nucleic Acids, 2020, 23, 377-392.
[http://dx.doi.org/10.1016/j.omtn.2020.11.013] [PMID: 33473324]
[28]
Qu, X.; Song, X.; Yuan, W.; Shu, Y.; Wang, Y.; Zhao, X.; Gao, M.; Lu, R.; Luo, S.; Zhao, W.; Zhang, Y.; Sun, L.; Lu, Y. Expression signature of lncRNAs and their potential roles in cardiac fibrosis of post-infarct mice. Biosci. Rep., 2016, 36(3), e00337.
[http://dx.doi.org/10.1042/BSR20150278] [PMID: 27129287]
[29]
Barrett, T.; Wilhite, S.E.; Ledoux, P.; Evangelista, C.; Kim, I.F.; Tomashevsky, M.; Marshall, K.A.; Phillippy, K.H.; Sherman, P.M.; Holko, M.; Yefanov, A.; Lee, H.; Zhang, N.; Robertson, C.L.; Serova, N.; Davis, S.; Soboleva, A. NCBI GEO: Archive for functional genomics data sets--update. Nucleic Acids Res., 2013, 41(Database issue), D991-D995.
[http://dx.doi.org/10.1093/nar/gks1193] [PMID: 23193258]
[30]
Huang, W.; Sherman, B.T.; Lempicki, R.A. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat. Protoc., 2009, 4(1), 44-57.
[http://dx.doi.org/10.1038/nprot.2008.211] [PMID: 19131956]
[31]
Szklarczyk, D.; Morris, J.H.; Cook, H.; Kuhn, M.; Wyder, S.; Simonovic, M.; Santos, A.; Doncheva, N.T.; Roth, A.; Bork, P.; Jensen, L.J.; von Mering, C. The STRING database in 2017: Quality-controlled protein-protein association networks, made broadly accessible. Nucleic Acids Res., 2017, 45(D1), D362-D368.
[http://dx.doi.org/10.1093/nar/gkw937] [PMID: 27924014]
[32]
Bandettini, W.P.; Kellman, P.; Mancini, C.; Booker, O.J.; Vasu, S.; Leung, S.W.; Wilson, J.R.; Shanbhag, S.M.; Chen, M.Y.; Arai, A.E. MultiContrast Delayed Enhancement (MCODE) improves detection of subendocardial myocardial infarction by late gadolinium enhancement cardiovascular magnetic resonance: A clinical validation study. J. Cardiovasc. Magn. Reson., 2012, 14(1), 83.
[http://dx.doi.org/10.1186/1532-429X-14-83] [PMID: 23199362]
[33]
Subramanian, A.; Tamayo, P.; Mootha, V.K.; Mukherjee, S.; Ebert, B.L.; Gillette, M.A.; Paulovich, A.; Pomeroy, S.L.; Golub, T.R.; Lander, E.S.; Mesirov, J.P. Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci. USA, 2005, 102(43), 15545-15550.
[http://dx.doi.org/10.1073/pnas.0506580102] [PMID: 16199517]
[34]
Huang, K.Y.; Wang, J.N.; Zhou, Y.Y.; Wu, S.Z.; Tao, L.Y.; Peng, Y.P.; Que, J.Q.; Xue, Y.J.; Ji, K.T. Antithrombin iii alleviates myocardial ischemia/reperfusion injury by inhibiting excessive autophagy in a phosphoinositide 3-kinase/akt-dependent manner. Front. Pharmacol., 2019, 10, 516.
[http://dx.doi.org/10.3389/fphar.2019.00516] [PMID: 31133861]
[35]
Griffith, M.; Griffith, O.L.; Coffman, A.C.; Weible, J.V.; McMichael, J.F.; Spies, N.C.; Koval, J.; Das, I.; Callaway, M.B.; Eldred, J.M.; Miller, C.A.; Subramanian, J.; Govindan, R.; Kumar, R.D.; Bose, R.; Ding, L.; Walker, J.R.; Larson, D.E.; Dooling, D.J.; Smith, S.M.; Ley, T.J.; Mardis, E.R.; Wilson, R.K. DGIdb: Mining the druggable genome. Nat. Methods, 2013, 10(12), 1209-1210.
[http://dx.doi.org/10.1038/nmeth.2689] [PMID: 24122041]
[36]
Vogel, B.; Claessen, B.E.; Arnold, S.V.; Chan, D.; Cohen, D.J.; Giannitsis, E.; Gibson, C.M.; Goto, S.; Katus, H.A.; Kerneis, M.; Kimura, T.; Kunadian, V.; Pinto, D.S.; Shiomi, H.; Spertus, J.A.; Steg, P.G.; Mehran, R. ST-segment elevation myocardial infarction. Nat. Rev. Dis. Primers, 2019, 5(1), 39.
[http://dx.doi.org/10.1038/s41572-019-0090-3] [PMID: 31171787]
[37]
Tan, N.S.; Goodman, S.G.; Cantor, W.J.; Russo, J.J.; Borgundvaag, B.; Fitchett, D.; Džavík, V.; Tan, M.K.; Elbarouni, B.; Lavi, S.; Bagai, A.; Heffernan, M.; Ko, D.T.; Yan, A.T. Efficacy of early invasive management after fibrinolysis for st-segment elevation myocardial infarction in relation to initial troponin status. Can. J. Cardiol., 2016, 32(10), 1221.e11-1221.e18.
[http://dx.doi.org/10.1016/j.cjca.2016.01.010] [PMID: 26975225]
[38]
Frangogiannis, N.G. The extracellular matrix in myocardial injury, repair, and remodeling. J. Clin. Invest., 2017, 127(5), 1600-1612.
[http://dx.doi.org/10.1172/JCI87491] [PMID: 28459429]
[39]
Wang, X.; Lu, L.; Tan, Y.; Jiang, L.; Zhao, M.; Gao, E.; Yu, S.; Liu, J. GPR 30 reduces myocardial infarct area and fibrosis in female ovariectomized mice by activating the PI3K/AKT pathway. Life Sci., 2019, 226, 22-32.
[http://dx.doi.org/10.1016/j.lfs.2019.03.049] [PMID: 30905784]
[40]
Wang, L.; Tian, X.; Cao, Y.; Ma, X.; Shang, L.; Li, H.; Zhang, X.; Deng, F.; Li, S.; Guo, T.; Yang, P. Cardiac shock wave therapy improves ventricular function by relieving fibrosis through pi3k/akt signaling pathway: evidence from a rat model of post-infarction heart failure. Front. Cardiovasc. Med., 2021, 8, 693875.
[http://dx.doi.org/10.3389/fcvm.2021.693875] [PMID: 34222384]
[41]
Jia, D.; Hou, L.; Lv, Y.; Xi, L.; Tian, Z. Postinfarction exercise training alleviates cardiac dysfunction and adverse remodeling via mitochondrial biogenesis and SIRT1/PGC-1α/PI3K/Akt signaling. J. Cell. Physiol., 2019, 234(12), 23705-23718.
[http://dx.doi.org/10.1002/jcp.28939] [PMID: 31187505]
[42]
Ruan, Y.; Jin, Q.; Zeng, J.; Ren, F.; Xie, Z.; Ji, K.; Wu, L.; Wu, J.; Li, L. Grape seed proanthocyanidin extract ameliorates cardiac remodelling after myocardial infarction through pi3k/akt pathway in mice. Front. Pharmacol., 2020, 11, 585984.
[http://dx.doi.org/10.3389/fphar.2020.585984] [PMID: 33343353]
[43]
Ricard-Blum, S. The collagen family. Cold Spring Harb. Perspect. Biol., 2011, 3(1), a004978.
[http://dx.doi.org/10.1101/cshperspect.a004978] [PMID: 21421911]
[44]
Wenstrup, R.J.; Florer, J.B.; Davidson, J.M.; Phillips, C.L.; Pfeiffer, B.J.; Menezes, D.W.; Chervoneva, I.; Birk, D.E. Murine model of the Ehlers-Danlos syndrome. col5a1 haploinsufficiency disrupts collagen fibril assembly at multiple stages. J. Biol. Chem., 2006, 281(18), 12888-12895.
[http://dx.doi.org/10.1074/jbc.M511528200] [PMID: 16492673]
[45]
Malfait, F.; Coucke, P.; Symoens, S.; Loeys, B.; Nuytinck, L.; De Paepe, A. The molecular basis of classic Ehlers-Danlos syndrome: A comprehensive study of biochemical and molecular findings in 48 unrelated patients. Hum. Mutat., 2005, 25(1), 28-37.
[http://dx.doi.org/10.1002/humu.20107] [PMID: 15580559]
[46]
DeNigris, J.; Yao, Q.; Birk, E.K.; Birk, D.E. Altered dermal fibroblast behavior in a collagen V haploinsufficient murine model of classic Ehlers-Danlos syndrome. Connect. Tissue Res., 2016, 57(1), 1-9.
[http://dx.doi.org/10.3109/03008207.2015.1081901] [PMID: 26713685]
[47]
Sun, M.; Connizzo, B.K.; Adams, S.M.; Freedman, B.R.; Wenstrup, R.J.; Soslowsky, L.J.; Birk, D.E. Targeted deletion of collagen V in tendons and ligaments results in a classic Ehlers-Danlos syndrome joint phenotype. Am. J. Pathol., 2015, 185(5), 1436-1447.
[http://dx.doi.org/10.1016/j.ajpath.2015.01.031] [PMID: 25797646]
[48]
Liu, W.; Wei, H.; Gao, Z.; Chen, G.; Liu, Y.; Gao, X.; Bai, G.; He, S.; Liu, T.; Xu, W.; Yang, X.; Jiao, J.; Xiao, J. COL5A1 may contribute the metastasis of lung adenocarcinoma. Gene, 2018, 665, 57-66.
[http://dx.doi.org/10.1016/j.gene.2018.04.066] [PMID: 29702185]
[49]
Dart, M.L.; Jankowska-Gan, E.; Huang, G.; Roenneburg, D.A.; Keller, M.R.; Torrealba, J.R.; Rhoads, A.; Kim, B.; Bobadilla, J.L.; Haynes, L.D.; Wilkes, D.S.; Burlingham, W.J.; Greenspan, D.S. Interleukin-17-dependent autoimmunity to collagen type V in atherosclerosis. Circ. Res., 2010, 107(9), 1106-1116.
[http://dx.doi.org/10.1161/CIRCRESAHA.110.221069] [PMID: 20814021]
[50]
Yokota, T.; McCourt, J.; Ma, F.; Ren, S.; Li, S.; Kim, T.H.; Kurmangaliyev, Y.Z.; Nasiri, R.; Ahadian, S.; Nguyen, T.; Tan, X.H.M.; Zhou, Y.; Wu, R.; Rodriguez, A.; Cohn, W.; Wang, Y.; Whitelegge, J.; Ryazantsev, S.; Khademhosseini, A.; Teitell, M.A.; Chiou, P.Y.; Birk, D.E.; Rowat, A.C.; Crosbie, R.H.; Pellegrini, M.; Seldin, M.; Lusis, A.J.; Deb, A.; Type, V. Type V collagen in scar tissue regulates the size of scar after heart injury. Cell, 2020, 182(3), 545-562.e23.
[http://dx.doi.org/10.1016/j.cell.2020.06.030] [PMID: 32621799]
[51]
Lindsey, M.L.; Iyer, R.P.; Zamilpa, R.; Yabluchanskiy, A.; DeLeon-Pennell, K.Y.; Hall, M.E.; Kaplan, A.; Zouein, F.A.; Bratton, D.; Flynn, E.R.; Cannon, P.L.; Tian, Y.; Jin, Y.F.; Lange, R.A.; Tokmina-Roszyk, D.; Fields, G.B.; de Castro Brás, L.E. A novel collagen matricryptin reduces left ventricular dilation post-myocardial infarction by promoting scar formation and angiogenesis. J. Am. Coll. Cardiol., 2015, 66(12), 1364-1374.
[http://dx.doi.org/10.1016/j.jacc.2015.07.035] [PMID: 26383724]
[52]
Zhang, L.X.; Zhang, S.H.; Wang, C.Q.; Bing, Q.; Zhao, Z.; Wang, J.; Zhang, L. Role and mechanism of microRNA-548c-3p/c-Myb in myocardial infarction fibrosis in rats. Eur. Rev. Med. Pharmacol. Sci., 2019, 23(11), 4908-4916.
[http://dx.doi.org/10.26355/eurrev_201906_18081] [PMID: 31210326]
[53]
Wang, Y.; Jin, B.J.; Chen, Q.; Yan, B.J.; Liu, Z.L. MicroRNA-29b upregulation improves myocardial fibrosis and cardiac function in myocardial infarction rats through targeting SH2B3. Eur. Rev. Med. Pharmacol. Sci., 2019, 23(22), 10115-10122.
[http://dx.doi.org/10.26355/eurrev_201911_19581] [PMID: 31799683]
[54]
Yuan, X.; Pan, J.; Wen, L.; Gong, B.; Li, J.; Gao, H.; Tan, W.; Liang, S.; Zhang, H.; Wang, X. MiR-144-3p enhances cardiac fibrosis after myocardial infarction by targeting PTEN. Front. Cell Dev. Biol., 2019, 7, 249.
[http://dx.doi.org/10.3389/fcell.2019.00249] [PMID: 31737623]
[55]
Yuan, X.; Pan, J.; Wen, L.; Gong, B.; Li, J.; Gao, H.; Tan, W.; Liang, S.; Zhang, H.; Wang, X. MiR-590-3p regulates proliferation, migration and collagen synthesis of cardiac fibroblast by targeting ZEB1. J. Cell. Mol. Med., 2020, 24(1), 227-237.
[http://dx.doi.org/10.1111/jcmm.14704] [PMID: 31675172]
[56]
Small, E.M.; Thatcher, J.E.; Sutherland, L.B.; Kinoshita, H.; Gerard, R.D.; Richardson, J.A.; Dimaio, J.M.; Sadek, H.; Kuwahara, K.; Olson, E.N. Myocardin-related transcription factor-a controls myofibroblast activation and fibrosis in response to myocardial infarction. Circ. Res., 2010, 107(2), 294-304.
[http://dx.doi.org/10.1161/CIRCRESAHA.110.223172] [PMID: 20558820]
[57]
Cheng, M.; An, S.; Li, J. Identifying key genes associated with acute myocardial infarction. Medicine (Baltimore), 2017, 96(42), e7741.
[http://dx.doi.org/10.1097/MD.0000000000007741] [PMID: 29049183]
[58]
Azuaje, F.; Zhang, L.; Jeanty, C.; Puhl, S.L.; Rodius, S.; Wagner, D.R. Analysis of a gene co-expression network establishes robust association between Col5a2 and ischemic heart disease. BMC Med. Genomics, 2013, 6(1), 13.
[http://dx.doi.org/10.1186/1755-8794-6-13] [PMID: 23574622]
[59]
Yamada, Y.; Kato, K.; Oguri, M.; Horibe, H.; Fujimaki, T.; Yasukochi, Y.; Takeuchi, I.; Sakuma, J. Identification of 13 novel susceptibility loci for early-onset myocardial infarction, hypertension, or chronic kidney disease. Int. J. Mol. Med., 2018, 42(5), 2415-2436.
[http://dx.doi.org/10.3892/ijmm.2018.3852] [PMID: 30226566]
[60]
Dean, R.G.; Balding, L.C.; Candido, R.; Burns, W.C.; Cao, Z.; Twigg, S.M.; Burrell, L.M. Connective tissue growth factor and cardiac fibrosis after myocardial infarction. J. Histochem. Cytochem., 2005, 53(10), 1245-1256.
[http://dx.doi.org/10.1369/jhc.4A6560.2005] [PMID: 15956033]
[61]
Gravning, J.; Ørn, S.; Kaasbøll, O.J.; Martinov, V.N.; Manhenke, C.; Dickstein, K.; Edvardsen, T.; Attramadal, H.; Ahmed, M.S. Myocardial connective tissue growth factor (CCN2/CTGF) attenuates left ventricular remodeling after myocardial infarction. PLoS One, 2012, 7(12), e52120.
[http://dx.doi.org/10.1371/journal.pone.0052120] [PMID: 23284892]
[62]
Hunt, K.J.; Jaffa, M.A.; Garrett, S.M.; Luttrell, D.K.; Lipson, K.E.; Lopes-Virella, M.F.; Luttrell, L.M.; Jaffa, A.A.; Investigators, V. Plasma connective tissue growth factor (ctgf/ccn2) levels predict myocardial infarction in the veterans affairs diabetes trial (vadt) cohort. Diabetes Care, 2018, 41(4), 840-846.
[http://dx.doi.org/10.2337/dc17-2083] [PMID: 29382658]
[63]
Gerritsen, K.G.; Falke, L.L.; van Vuuren, S.H.; Leeuwis, J.W.; Broekhuizen, R.; Nguyen, T.Q.; de Borst, G.J.; Nathoe, H.M.; Verhaar, M.C.; Kok, R.J.; Goldschmeding, R.; Visseren, F.L.; Group, S.S. Plasma CTGF is independently related to an increased risk of cardiovascular events and mortality in patients with atherosclerotic disease: The SMART study. Growth Factors, 2016, 34(3-4), 149-158.
[http://dx.doi.org/10.1080/08977194.2016.1210142] [PMID: 27686612]
[64]
Boekholdt, S.M.; Trip, M.D.; Peters, R.J.; Engelen, M.; Boer, J.M.; Feskens, E.J.; Zwinderman, A.H.; Kastelein, J.J.; Reitsma, P.H. Thrombospondin-2 polymorphism is associated with a reduced risk of premature myocardial infarction. Arterioscler. Thromb. Vasc. Biol., 2002, 22(12), e24-e27.
[http://dx.doi.org/10.1161/01.ATV.0000046235.22451.66] [PMID: 12482844]
[65]
Ambroziak, M.; Kuryłowicz, A.; Budaj, A. Increased coagulation factor XIII activity but not genetic variants of coagulation factors is associated with myocardial infarction in young patients. J. Thromb. Thrombolysis, 2019, 48(3), 519-527.
[http://dx.doi.org/10.1007/s11239-019-01856-3] [PMID: 30972713]
[66]
Wei, K.; Serpooshan, V.; Hurtado, C.; Diez-Cuñado, M.; Zhao, M.; Maruyama, S.; Zhu, W.; Fajardo, G.; Noseda, M.; Nakamura, K.; Tian, X.; Liu, Q.; Wang, A.; Matsuura, Y.; Bushway, P.; Cai, W.; Savchenko, A.; Mahmoudi, M.; Schneider, M.D.; van den Hoff, M.J.; Butte, M.J.; Yang, P.C.; Walsh, K.; Zhou, B.; Bernstein, D.; Mercola, M.; Ruiz-Lozano, P. Epicardial FSTL1 reconstitution regenerates the adult mammalian heart. Nature, 2015, 525(7570), 479-485.
[http://dx.doi.org/10.1038/nature15372] [PMID: 26375005]
[67]
Oshima, Y.; Ouchi, N.; Sato, K.; Izumiya, Y.; Pimentel, D.R.; Walsh, K. Follistatin-like 1 is an Akt-regulated cardioprotective factor that is secreted by the heart. Circulation, 2008, 117(24), 3099-3108.
[http://dx.doi.org/10.1161/CIRCULATIONAHA.108.767673] [PMID: 18519848]
[68]
Uematsu, M.; Nakamura, K.; Nakamura, T.; Watanabe, Y.; Yoshizaki, T.; Deyama, J.; Kobayashi, T.; Fujioka, D.; Saito, Y.; Kawabata, K.; Obata, J.E.; Kugiyama, K. persistent myocardial production of follistatin-like 1 is associated with left ventricular adverse remodeling in patients with myocardial infarction: Myocardial production of fstl1 in ami patients. J. Card. Fail., 2020, 26(8), 733-738.
[http://dx.doi.org/10.1016/j.cardfail.2020.05.015] [PMID: 32470377]
[69]
González-Santamaría, J.; Villalba, M.; Busnadiego, O.; López-Olañeta, M.M.; Sandoval, P.; Snabel, J.; López-Cabrera, M.; Erler, J.T.; Hanemaaijer, R.; Lara-Pezzi, E.; Rodríguez-Pascual, F. Matrix cross-linking lysyl oxidases are induced in response to myocardial infarction and promote cardiac dysfunction. Cardiovasc. Res., 2016, 109(1), 67-78.
[http://dx.doi.org/10.1093/cvr/cvv214] [PMID: 26260798]
[70]
Emini Veseli, B.; Perrotta, P.; De Meyer, G.R.A.; Roth, L.; Van der Donckt, C.; Martinet, W.; De Meyer, G.R.Y. Animal models of atherosclerosis. Eur. J. Pharmacol., 2017, 816, 3-13.
[http://dx.doi.org/10.1016/j.ejphar.2017.05.010] [PMID: 28483459]
[71]
Turley, T.N.; O’Byrne, M.M.; Kosel, M.L.; de Andrade, M.; Gulati, R.; Hayes, S.N.; Tweet, M.S.; Olson, T.M. Identification of susceptibility loci for spontaneous coronary artery dissection. JAMA Cardiol., 2020, 5(8), 929-938.
[http://dx.doi.org/10.1001/jamacardio.2020.0872] [PMID: 32374345]
[72]
Hui, P.; Bai, Y.; Su, X.; Quan, N.; Qiao, B.; Zheng, Y.; Shi, J.; Du, X.; Lu, J. The value of plasma fibrillin-1 level in patients with spontaneous coronary artery dissection. Int. J. Cardiol., 2020, 302, 150-156.
[http://dx.doi.org/10.1016/j.ijcard.2019.12.015] [PMID: 31884007]
[73]
Richeldi, L.; Fernández Pérez, E.R.; Costabel, U.; Albera, C.; Lederer, D.J.; Flaherty, K.R.; Ettinger, N.; Perez, R.; Scholand, M.B.; Goldin, J.; Peony Yu, K.H.; Neff, T.; Porter, S.; Zhong, M.; Gorina, E.; Kouchakji, E.; Raghu, G. Pamrevlumab, an anti-connective tissue growth factor therapy, for idiopathic pulmonary fibrosis (PRAISE): A phase 2, randomised, double-blind, placebo-controlled trial. Lancet Respir. Med., 2020, 8(1), 25-33.
[http://dx.doi.org/10.1016/S2213-2600(19)30262-0] [PMID: 31575509]
[74]
Barbe, M.F.; Hilliard, B.A.; Amin, M.; Harris, M.Y.; Hobson, L.J.; Cruz, G.E.; Popoff, S.N. Blocking CTGF/CCN2 reduces established skeletal muscle fibrosis in a rat model of overuse injury. FASEB J., 2020, 34(5), 6554-6569.
[http://dx.doi.org/10.1096/fj.202000240RR] [PMID: 32227398]

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