AccScience Publishing / EJMO / Online First / DOI: 10.36922/ejmo.8086
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ORIGINAL RESEARCH ARTICLE

Unveiling the molecular complexities of cardiovascular diseases: Insights into signaling proteins and therapeutic targets

Emad Fadhal1,2*
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1 Department of Mathematics and Statistics, College of Science, King Faisal University, Ahsa, Saudi Arabia
2 Department of Mathematics and Applied Mathematics, University of the Western Cape, Bellville, South Africa
EJMO, 8086 https://doi.org/10.36922/ejmo.8086
Submitted: 20 December 2024 | Revised: 9 January 2025 | Accepted: 15 January 2025 | Published: 28 January 2025
© 2025 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution -Noncommercial 4.0 International License (CC-by the license) ( https://creativecommons.org/licenses/by-nc/4.0/ )
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Abstract

Cardiovascular disease (CVD) encompasses a range of conditions affecting the cardiovascular system, including complications such as heart attacks and hypertension. As a leading global health concern and the primary contributor to mortality worldwide, understanding the mechanisms underlying CVD is critical due to its profound impact on individuals and society. This study explores the intricate landscape of CVD, focusing on key signaling proteins associated with heart attacks and hypertension. The findings highlight nine proteins (transforming growth factor beta 1, apolipoprotein E, nitric oxide synthase 3, plasminogen activator-tissue, interleukin 6, tumor necrosis factor, glycogen synthase kinase 3 beta, matrix metalloproteinase 9, and angiotensinogen) that are commonly implicated in pathways linked to these conditions, playing essential roles in physiological processes and contributing to the pathogenesis of CVD. In addition, the study emphasizes the significance of oncogenes and tumor suppressor genes in cardiovascular signaling pathways, identifying androgen receptor (AR), guanine nucleotide-binding protein, alpha stimulating (GNAS), and tumor protein p53 as critical contributors. Dysregulation of AR is linked to hypertension, atherosclerosis, and heart failure, underscoring its potential as a therapeutic target. Similarly, GNAS mutations disrupt signal transduction, impair heart function, and contribute to arteriosclerosis and hypertension. By examining the interplay between signaling proteins, oncogenes, and suppressor genes, this study offers valuable insights into the molecular mechanisms driving CVD. The identified targets provide promising opportunities for future studies aimed at developing targeted therapies for CVD.

Keywords
Cardiovascular disease
Signaling proteins
Heart attacks
Hypertension
Oncogenes and tumor suppressor genes
Therapeutic targets
Funding
None.
Conflict of interest
The author declares no conflict of interest.
References
  1. Kaptoge S, Pennells L, De Bacque D, et al. World Health Organization cardiovascular disease risk charts: Revised models to estimate risk in 21 global regions. Lancet Glob Health. 2019;7(10):e1332-e1345. doi: 10.1016/S2214-109X(19)30318-3

 

  1. Mendis S, Norrving B. Organizational update: World Health Organization. Stroke. 2014;45(2):e22-e23. doi: 10.1161/STROKEAHA.113.003377

 

  1. Iadecola C, Yaffe K, Biller J, et al. Impact of hypertension on cognitive function: A scientific statement from the American Heart Association. Hypertension. 2016;68(6):e67-e94. doi: 10.1161/HYP.0000000000000053

 

  1. Musselman DL, Evans DL, Nemeroff CB. The relationship of depression to cardiovascular disease: Epidemiology, biology, and treatment. Arch Gen Psychiatry. 1998;55(7):580-592. doi: 10.1001/archpsyc.55.7.580

 

  1. Hare DL, Toukhsati SR, Johansson P, Jaarsma T. Depression and cardiovascular disease: A clinical review. Eur Heart J. 2014;35:1365-1372. doi: 10.1093/eurheartj/eht462

 

  1. Thygesen K, Alpert JS, Jaffe AS, et al. Fourth universal definition of myocardial infarction (2018). J Am Coll Cardiol. 2018;72:2231-2264. doi: 10.1016/j.jacc.2018.08.1038

 

  1. Libby P, Buring JE, Badimon L, et al. Atherosclerosis. Nat Rev Dis Primers. 2219;5(1):56. doi: 10.1038/s41572-019-0106-z

 

  1. Roth GA, Johnson C, Abajobir A, et al. Global, regional, and national burden of cardiovascular diseases for 10 causes, 1990 to 2015. J Am Coll Cardiol. 2017;70(1):1-25. doi: 10.1016/j.jacc.2017.04.052

 

  1. Williams B, Mancia G, Spiering W, et al. 2018 ESC/ESH Guidelines for the management of arterial hypertension. Eur Heart J. 2018;39(33):3021-3104. doi: 10.33963/KP.2019.00001

 

  1. GBD 2017 Disease and Injury Incidence and Prevalence Collaborators. Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990-2017: A systematic analysis for the Global Burden of Disease Study 2017. Lancet. 2018;392(10159):1789-1858. doi: 10.1016/S0140-6736(18)32279-7

 

  1. Gomez-Cabrero D, Abugessaisa I, Maier D, et al. Data integration in the era of omics: Current and future challenges. BMC Syst Biol. 2014;8:I1. doi: 10.1186/1752-0509-8-S2-I1

 

  1. Ghiassian SD, Menche J, Barabási AL. A DIseAse MOdule detection (DIAMOnD) algorithm derived from a systematic analysis of connectivity patterns of disease proteins in the human interactome. PLoS Comput Biol. 2015;11(4):e1004120. doi: 10.1371/journal.pcbi.1004120

 

  1. Planas-Iglesias J, Guney E, García-García J, et al. Extending signaling pathways with protein-interaction networks. Application to apoptosis. OMICS. 20152;16(5):245-256. doi: 10.1089/omi.2011.0130

 

  1. Ni D, Lu S, Zhang J. Emerging roles of allosteric modulators in the regulation of protein‐protein interactions (PPIs): A new paradigm for PPI drug discovery. Med Res Rev. 2019;39(6):2314-2342. doi: 10.1002/med.21585

 

  1. Shao T, Wu A, Chen J, et al. Identification of module biomarkers from the dysregulated ceRNA-ceRNA interaction network in lung adenocarcinoma. Mol Biosyst. 2015;11(11):3048-3058. doi: 10.1039/C5MB00364D

 

  1. Wang D, Wang J, Lu M, Song F, Cui Q. Inferring the human microRNA functional similarity and functional network based on microRNA-associated diseases. Bioinformatics. 2010;26(13):1644-1650. doi: 10.1093/bioinformatics/btq241

 

  1. Vidal M, Cusick ME, Barabási AL. Interactome networks and human disease. Cell. 2011;144(6):986-998. doi: 10.1016/j.cell.2011.02.016

 

  1. Fadhal E, Gamieldien J, Mwambene EC. Protein interaction networks as metric spaces: A novel perspective on distribution of hubs. BMC Syst Biol. 2014;8:6. doi: 10.1186/1752-0509-8-6

 

  1. Fadhal E, Mwambene EC, Gamieldien J. Modelling human protein interaction networks as metric spaces has potential in disease research and drug target discovery. BMC Syst Biol. 2014;8:68. doi: 10.1186/1752-0509-8-68

 

  1. Fadhal E. Unraveling the significance of signal transduction pathways: Key players in cancer development and progression. J Cancer Ther Res. 2023;3(1):1-9. doi: 10.52793/JCTR.2023.3(1)-28

 

  1. Alqattan HR, Fadhal E. Exploring the significance of zone 1 in human protein interaction networks: Identifying potential therapeutic targets for cancer treatment. Onkol Radioter. 2023;17(8):249-253.

 

  1. Fadhal E, Gamieldien J, Mwambene EC. Self-similarity of human protein interaction networks: A novel strategy of distinguishing proteins. Sci Rep. 2015;5(1):7628. doi: 10.1038/srep07628

 

  1. Fadhal E. A Comprehensive analysis of the PI3K/AKT pathway: Unveiling key proteins and therapeutic targets for cancer treatment. Cancer Inform. 2023;22. doi: 10.1177/11769351231194273

 

  1. Vogelstein B, Kinzler KW. Cancer genome landscapes. Science. 2013;339(6127):1546-1558. doi: 10.1126/science.1235122

 

  1. Wu G, Feng X, Stein L. A human functional protein interaction network and its application to cancer data analysis. Genome Biol. 2010;11:R53. doi: 10.1186/gb-2010-11-5-r53

 

  1. Bult CJ, Eppig JT, Blake JA, Kadin JA, Richardson JE. The mouse genome database: Genotypes, phenotypes, and models of human disease. Nucleic Acids Res. 2012;41(D1):D885-D891. doi: 10.1093/nar/gks1115

 

  1. Wang Y, Miao X, Liu Y, et al. Dysregulation of histone acetyltransferases and deacetylases in cardiovascular diseases. Oxid Med Cell Longev. 2014;2014:641979. doi: 10.1155/2014/641979

 

  1. Kong P, Cui ZY, Huang XF, Zhang DD, Guo RJ, Han M. Inflammation and atherosclerosis: Signaling pathways and therapeutic intervention. Signal Transduct Target Ther. 2022;7(1):131. doi: 10.1038/s41392-022-00955-7

 

  1. Pang M, Zhang G, Shang C, et al. Advances in the study of KRAS in brain arteriovenous malformation. Cerebrovasc Dis. 2024;53(6):767-775. doi: 10.1159/000535139

 

  1. Zekavat SM, Viana-Huete V, Matesanz N, et al. TP53- mediated clonal hematopoiesis confers increased risk for incident atherosclerotic disease. Nat Cardiovasc Res. 2023;2(2):144-158. doi: 10.1038/s44161-022-00206-6

 

  1. Mercer J, Bennett M. The role of p53 in atherosclerosis. Cell Cycle. 2006;5(17):1907-1909. doi: 10.4161/cc.5.17.3166

 

  1. Liu PY, Death AK, Handelsman DJ. Androgens and cardiovascular disease. Endocr Rev. 2003;24(3):313-340. doi: 10.1210/er.2003-0005

 

  1. Shelkowitz E, Chan CM, Jones T, Nakano SJ, Meeks NJL. A novel GNAS variant presents with disorders of GNAS inactivation and cardiomyopathy. Am J Med Genet A. 2022;188(7):2147-2152. doi: 10.1002/ajmg.a.62726

 

  1. Matsusaka H, Ide T, Matsushima S, et al. Targeted deletion of p53 prevents cardiac rupture after myocardial infarction in mice. Cardiovasc Res. 2006;70(3):457-465. doi: 10.1016/j.cardiores.2006.02.001

 

  1. Hazarika D, Zimmermann R, Poria S. Misa: Modality-invariant and-specific Representations for Multimodal Sentiment Analysis. In: Proceedings of the 28th ACM International Conference on Multimedia; 2020.

 

  1. Lu Y, Boer JM, Barsova RM, et al. TGFB1 genetic polymorphisms and coronary heart disease risk: A meta-analysis. BMC Med Genet. 2012;13:39. doi: 10.1186/1471-2350-13-39

 

  1. Yeung AWK, Aggarwal BB, Orhan IE, et al. Resveratrol, a popular dietary supplement for human and animal health: Quantitative research literature analysis-a review. Anim Sci Pap Rep. 2019;37(2):103-118.

 

  1. Corder EH, Saunders AM, Strittmatter WJ, et al. Gene dose of apolipoprotein E type 4 allele and the risk of Alzheimer’s disease in late onset families. Science. 1993; 261(5123):921-923. doi: 10.1126/science.8346443

 

  1. Martins IJ, Hone E, Foster JK, et al. Apolipoprotein E, cholesterol metabolism, diabetes, and the convergence of risk factors for Alzheimer’s disease and cardiovascular disease. Mol Psychiatry. 2006;11(8):721-736. doi: 10.1038/sj.mp.4001854

 

  1. Fattakhov N, Smirnova L, Atochin D, et al. Haplotype analysis of endothelial nitric oxide synthase (NOS3) genetic variants and metabolic syndrome in healthy subjects and schizophrenia patients. Int J Obes (Lond). 2018;42(12):2036-2046. doi: 10.1038/s41366-018-0124-z

 

  1. Baleydier F, Bernard F, Ansari M. The possibilities of immunotherapy for children with primary immunodeficiencies associated with cancers. Biomolecules. 2020;10(8):1112. doi: 10.3390/biom10081112

 

  1. Cortellaro M, Boschetti C, Cofrancesco E, et al. The PLAT study: Hemostatic function in relation to atherothrombotic ischemic events in vascular disease patients. Principal results. PLAT Study Group. Progetto Lombardo Atero-Trombosi (PLAT) Study Group. Arterioscler Thromb. 1992;12(9): 1063-1070. doi: 10.1161/01.atv.12.9.1063

 

  1. Gabay C, Towne JE. Regulation and function of interleukin-36 cytokines in homeostasis and pathological conditions. J Leucoc Biol. 2015;97(4):645-652. doi: 10.1189/jlb.3RI1014-495R

 

  1. Tanaka T, Narazaki M, Kishimoto T. IL-6 in inflammation, immunity, and disease. Cold Spring Harb Perspect Biol. 2014;6(10):a016295. doi: 10.1101/cshperspect.a016295

 

  1. Tsukamoto M, Suzuki K, Seta N, Takeuchi T. Increased circulating CD14brightCD16+ intermediate monocytes are regulated by TNF-α and IL-6 axis in accordance with disease activity in patients with rheumatoid arthritis. Clin Exp Rheumatol. 2018;36(4):540-544.

 

  1. Li J, Ma S, Chen J, et al. GSK-3β contributes to parkinsonian dopaminergic neuron death: Evidence from conditional knockout mice and tideglusib. Front Mol Neurosci. 2020;13:81. doi: 10.3389/fnmol.2020.00081

 

  1. Cabral-Pacheco GA, Garza-Veloz I, Castruita-De la Rosa C, et al. The roles of matrix metalloproteinases and their inhibitors in human diseases. Int J Mol Sci. 2020;21(24):9739.doi: 10.3390/ijms21249739

 

  1. Mondal S, Adhikari N, Banerjee S, Amin SA, Jha T. Matrix metalloproteinase-9 (MMP-9) and its inhibitors in cancer: A minireview. Eur J Med Chem. 2020;194:112260. doi: 10.1016/j.ejmech.2020.112260

 

  1. Jeunemaitre X, Soubrier F, Kotelevtsev YV, et al. Molecular basis of human hypertension: Role of angiotensinogen. Cell. 1992;71(1):169-180. doi: 10.1016/0092-8674(92)90275-h

 

  1. Van Rijn JN, Hutter F. Hyperparameter Importance Across Datasets. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM; 2018.

 

  1. Boese AC, Kim SC, Yin KJ, Lee JP, Hamblin MH. Sex differences in vascular physiology and pathophysiology: Estrogen and androgen signaling in health and disease. Am J Physiol Heart Circ Physiol. 2017;313(3):H524-H545. doi: 10.1152/ajpheart.00217.2016

 

  1. Huang CK, Lee SO, Chang E, Pang H, Chang C. Androgen Receptor (AR) in cardiovascular diseases. J Endocrinol. 2016;229(1):R1-R16. doi: 10.1530/JOE-15-0518

 

  1. Laurent MR, De Schepper J, Trouet D, et al. Consensus recommendations for the diagnosis and management of X-linked hypophosphatemia in Belgium. Front Endocrinol (Lausanne). 2021;12:641543. doi: 10.3389/fendo.2021.641543

 

  1. Regard JB, Malhotra D, Gvozdenovic-Jeremic J, et al. Activation of Hedgehog signaling by loss of GNAS causes heterotopic ossification. Nat Med. 2013;19(11): 1505-1512. doi: 10.1038/nm.3314

 

  1. Seidel E, Scholl UI. Genetic mechanisms of human hypertension and their implications for blood pressure physiology. Physiol Genomics. 2017;49(11):630-652. doi: 10.1152/physiolgenomics.00032.2017

 

  1. Matthews C, Gorenne I, Scott S, et al. Vascular smooth muscle cells undergo telomere-based senescence in human atherosclerosis: Effects of telomerase and oxidative stress. Circ Res. 2006;99(2):156-164. doi: 10.1161/01.RES.0000233315.38086.bc

 

  1. Liu J, Zhang C, Feng Z. Tumor suppressor p53 and its gain-of-function mutants in cancer. Acta Biochim Biophys Sin (Shanghai). 2014;46(3):170-179. doi: 10.1093/abbs/gmt144

 

  1. López-Otín C, Blasco MA, Partridge L, Serrano M, Kroemer G. The hallmarks of aging. Cell. 2013;153(6):1194-1217. doi: 10.1016/j.cell.2013.05.039

 

  1. Demer LL, Tintut Y. Vascular calcification: Pathobiology of a multifaceted disease. Circulation. 2008;117(22):2938-2948. doi: 10.1161/CIRCULATIONAHA.107.743161

 

  1. Cai J, Pardali E, Sánchez-Duffhues G, Ten Dijke P. BMP signaling in vascular diseases. FEBS Lett. 2012;586(14): 1993-2002. doi: 10.1016/j.febslet.2012.04.030
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Eurasian Journal of Medicine and Oncology, Electronic ISSN: 2587-196X Print ISSN: 2587-2400, Published by AccScience Publishing
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