Bioinformatics Identification of Conserved microRNAs and miRNA-targeted Biomarkers for Cardiovascular Disease

Authors

  • Ahsanul Kyum Siam Department of Statistics, Mawlana Bhashani Science and Technology University, Tangail, Bangladesh Author
  • Akram Hossain Department of Statistics, Mawlana Bhashani Science and Technology University, Tangail, Bangladesh Author
  • Tajrin Jahan Raisa Department of Statistics, Mawlana Bhashani Science and Technology University, Tangail, Bangladesh Author
  • Abul Hossain Department of Statistics, Mawlana Bhashani Science and Technology University, Tangail, Bangladesh Author
  • Arju Hossain Biochemistry and Biotechnology, Khwaja Yunus Ali University, Sirajganj, Bangladesh Author
  • Aminur Islam Department of Statistics, Mawlana Bhashani Science and Technology University, Tangail, Bangladesh Author
  • Mohammed Tanvir Department of Statistics, Mawlana Bhashani Science and Technology University, Tangail, Bangladesh Author

DOI:

https://doi.org/10.64229/xy16q389

Keywords:

Cardiovascular disease, microRNA, Hub genes, Bioinformatics, Protein-protein interaction, Biomarkers

Abstract

Background: Cardiovascular diseases (CVD) are a leading cause of global mortality. MicroRNAs (miRNAs) regulate gene networks involved in endothelial dysfunction, vascular remodeling, and atherosclerosis as demonstrated in previous cardiovascular studies, yet conserved miRNAs and their target genes in CVD remain incompletely defined. Methodology: Two Gene Expression Omnibus (GEO) datasets, GSE118578 and GSE89188, were analyzed using GEO2R with quantile normalization to identify differentially expressed miRNAs (DEmiRNAs). Overlapping miRNAs were determined using Venny 2.0.2. Target genes were predicted via miRTarBase, TargetScan, and miRDB. Protein-protein interaction (PPI) networks were constructed using STRING, and hub genes were identified with Cytoscape and CytoHubba employing eleven topological algorithms. Results: Seven conserved DEmiRNAs: hsa-miR-1225-5p, hsa-miR-483-5p, hsa-miR-296-5p, hsa-miR-188-5p, hsa-miR-630, hsa-miR-557, and hsa-miR-1246 were consistently dysregulated. A total of 352 high-confidence target genes were initially predicted, of which 270 were retained for downstream PPI and enrichment analysis. PPI network analysis revealed five hub genes: NOTCH1, RHOA, BCL2, GSK3B, and PTEN, which are involved in key cardiovascular pathways including FoxO, mTOR, and Wnt signaling. Conclusion: This study systematically identifies conserved miRNAs and hub genes that regulate oxidative stress, inflammation, and cardiovascular dysfunction, offering potential biomarkers for early diagnosis and promising targets for therapeutic intervention.

References

[1]Roth GA, Mensah GA, Johnson CO, Addolorato G, Ammirati E, Baddour LM, et al. Global burden of cardiovascular diseases and risk factors, 1990-2019: Update from the GBD 2019 study. Journal of the American College of Cardiology, 2020, 76(25), 2982-3021. DOI: 10.1016/j.jacc.2020.11.010

[2]Shi HT, Huang ZH, Xu TZ, Sun AJ, Ge JB. New diagnostic and therapeutic strategies for myocardial infarction via nanomaterials. eBioMedicine, 2022, 78, 103968. DOI: 10.1016/j.ebiom.2022.103968

[3]Tsao CW, Aday AW, Almarzooq ZI, Alonso A, Beaton AZ, Bittencourt MS, et al. Heart disease and stroke statistics-2022 update: A report from the American Heart Association. Circulation, 2022, 145(8), e153-e639. DOI: 10.1161/CIR.0000000000001052

[4]World Health Organization. Cardiovascular diseases (CVDs). 2021. Available from: https://www.who.int/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds) [accessed on 11 August 2025]

[5]Zhou X, Sun X, Zhao H, Xie F, Li B, Zhang J. Biomarker identification and risk assessment of cardiovascular disease based on untargeted metabolomics and machine learning. Scientific Reports, 2024, 14(1), 25755. DOI: 10.1038/s41598-024-77352-3

[6]van Kimmenade RR, Januzzi JL Jr. Emerging biomarkers in heart failure. Clinical Chemistry, 2012, 58(1), 127-138. DOI: 10.1373/clinchem.2011.165720

[7]Haider A, Bengs S, Luu J, Osto E, Siller-Matula JM, Muka T, Gebhard C. Sex and gender in cardiovascular medicine: Presentation and outcomes of acute coronary syndrome. European Heart Journal, 2020, 41(13), 1328-1336. DOI: 10.1093/eurheartj/ehz898

[8]Thupakula S, Nimmala SSR, Ravula H, Chekuri S, Padiya R. Emerging biomarkers for the detection of cardiovascular diseases. Egyptian Heart Journal, 2022, 74(1), 77. DOI: 10.1186/s43044-022-00317-2

[9]Doran S, Arif M, Lam S, Bayraktar A, Turkez H, Uhlen M, et al. Multi-omics approaches for revealing the complexity of cardiovascular disease. Briefings in Bioinformatics, 2021, 22(5), bbab061. DOI: 10.1093/bib/bbab061

[10]Fangma Y, Liu M, Liao J, Chen Z, Zheng Y. Dissecting the brain with spatially resolved multi-omics. Journal of Pharmaceutical Analysis, 2023, 13(7), 694-710. DOI: 10.1016/j.jpha.2023.04.003

[11]Lind L, Zanetti D, Ingelsson M, Gustafsson S, Ärnlöv J, Assimes TL. Large-scale plasma protein profiling of incident myocardial infarction, ischemic stroke, and heart failure. Journal of the American Heart Association, 2021, 10(23), e023330. DOI: 10.1161/JAHA.121.023330

[12]Zhao X, Gu J, Li M, Xi J, Sun W, Song G, Liu G. Pathway analysis of body mass index genome-wide association study highlights risk pathways in cardiovascular disease. Scientific Reports, 2015, 5, 13025. DOI: 10.1038/srep13025

[13]Jung M, Dodsworth M, Thum T. Inflammatory cells and their non-coding RNAs as targets for treating myocardial infarction. Basic Research in Cardiology, 2018, 114(1), 4. DOI: 10.1007/s00395-018-0712-z

[14]Wang Y, Chen J, Cowan DB, Wang DZ. Non-coding RNAs in cardiac regeneration: Mechanism of action and therapeutic potential. Seminars in Cell & Developmental Biology, 2021, 118, 150-162. DOI: 10.1016/j.semcdb.2021.07.007

[15]Goumans MJ, Ten Dijke P. TGF-β signaling in control of cardiovascular function. Cold Spring Harbor Perspectives in Biology, 2018, 10(2), a022210. DOI: 10.1101/cshperspect.a022210

[16]Xu GR, Zhang C, Yang HX, Sun JH, Zhang Y, Yao TT, et al. Modified citrus pectin ameliorates myocardial fibrosis and inflammation via suppressing galectin-3 and TLR4/MyD88/NF-κB signaling pathway. Biomedicine & Pharmacotherapy, 2020, 126, 110071. DOI: 10.1016/j.biopha.2020.110071

[17]Barrett T, Wilhite SE, Ledoux P, Evangelista C, Kim IF, Tomashevsky M, et al. NCBI GEO: Archive for functional genomics data sets-update. Nucleic Acids Research, 2013, 41(Database issue), D991-D995. DOI: 10.1093/nar/gks1193

[18]Zhao X, Dou J, Cao J, Wang Y, Gao Q, Zeng Q, et al. Uncovering the potential differentially expressed miRNAs as diagnostic biomarkers for hepatocellular carcinoma based on machine learning in The Cancer Genome Atlas database. Oncology Reports, 2020, 43(6), 1771-1784. DOI: 10.3892/or.2020.7551

[19]Oliveros JC. VENNY. An interactive tool for comparing lists with Venn Diagrams. 2007-2015. Available from: https://bioinfogp.cnb.csic.es/tools/venny/ [accessed on 20 May 2025].

[20]Hsu SD, Lin FM, Wu WY, Liang C, Huang WC, Chan WL, et al. miRTarBase: A database curates experimentally validated microRNA-target interactions. Nucleic Acids Research, 2011, 39(Database issue), D163-D169. DOI: 10.1093/nar/gkq1107

[21]Agarwal V, Bell GW, Nam JW, Bartel DP. Predicting effective microRNA target sites in mammalian mRNAs. eLife, 2015, 4, e05005. DOI: 10.7554/eLife.05005

[22]Chen Y, Wang X. miRDB: An online database for prediction of functional microRNA targets. Nucleic Acids Research, 2020, 48(D1), D127-D131. DOI: 10.1093/nar/gkz757

[23]Szklarczyk D, Gable AL, Lyon D, Junge A, Wyder S, Huerta-Cepas J, et al. STRING v11: Protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Research, 2019, 47(D1), D607-D613. DOI: 10.1093/nar/gky1131

[24]Chin CH, Chen SH, Wu HH, Ho CW, Ko MT, Lin CY. CytoHubba: Identifying hub objects and sub-networks from complex interactome. BMC Systems Biology, 2014, 8 Suppl 4(Suppl 4), S11. DOI: 10.1186/1752-0509-8-S4-S11

[25]Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, et al. Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Research, 2003, 13(11), 2498-2504. DOI: 10.1101/gr.1239303

[26]Tang D, Chen M, Huang X, Zhang G, Zeng L, Zhang G, et al. SRplot: A free online platform for data visualization and graphing. PLoS One, 2023, 18(11), e0294236. DOI: 10.1371/journal.pone.0294236

[27]Huang DW, Sherman BT, Tan Q, Kir J, Liu D, Bryant D, et al. DAVID Bioinformatics Resources: Expanded annotation database and novel algorithms to better extract biology from large gene lists. Nucleic Acids Research, 2007, 35(suppl_2), W169-W175. DOI: 10.1093/nar/gkm415

[28]Jin L, Zuo XY, Su WY, Zhao XL, Yuan MQ, Han LZ, et al. Pathway-based analysis tools for complex diseases: A review. Genomics, Proteomics & Bioinformatics, 2014, 12(5), 210-220. DOI: 10.1016/j.gpb.2014.10.002

[29]De Rosa S, Curcio A, Indolfi C. Emerging role of microRNAs in cardiovascular diseases. Circulation Journal, 2014, 78(3), 567-575. DOI: 10.1253/circj.cj-14-0086

[30]Zhang L, Yang Y. Identification and validation of circulating microRNAs as biomarkers for heart failure with preserved ejection fraction. European Heart Journal, 2024, 45(Supplement_1), ehae666.769, DOI: 10.1093/eurheartj/ehae666.769

[31]MSamadishadlou M, Rahbarghazi R, Piryaei Z, Esmaeili M, Avcı ÇB, Bani F, et al. Unlocking the potential of microRNAs: Machine learning identifies key biomarkers for myocardial infarction diagnosis. Cardiovascular Diabetology, 2023, 22(1), 247. DOI: 10.1186/s12933-023-01957-7

[32]Liu T, Zhang G, Wang Y, Rao M, Zhang Y, Guo A, et al. Identification of circular RNA-microRNA-messenger RNA regulatory network in atrial fibrillation by integrated analysis. BioMed Research InternationalVolume, 2020, 2020, 8037273. DOI: 10.1155/2020/8037273

[33]Shi J, Liu H, Wang H, Kong X. MicroRNA expression signature in degenerative aortic stenosis. BioMed Research International, 2016, 2016, 4682172. DOI: 10.1155/2016/4682172

[34]Qiao Z, Li J, Kou H, Chen X, Bao D, Shang G, et al. Hsa-miR-557 inhibits osteosarcoma growth through targeting KRAS. Frontiers in Genetics, 2022, 12, 789823. DOI: 10.3389/fgene.2021.789823

[35]Cazorla-Rivero S, Mura-Escorche G, Gonzalvo-Hernández F, Mayato D, Córdoba-Lanús E, Casanova C. Circulating miR-1246 in the progression of chronic obstructive pulmonary disease (COPD) in patients from the BODE cohort. International Journal of Chronic Obstructive Pulmonary Disease, 2020, 15, 2727-2737. DOI: 10.2147/COPD.S271864

[36]Moulton KS, Li M, Strand K, Burgett S, McClatchey P, Tucker R, et al. PTEN deficiency promotes pathological vascular remodeling of human coronary arteries. JCI Insight, 2018, 3(4), e97228. DOI: 10.1172/jci.insight.97228

[37]Kerstjens-Frederikse WS, van de Laar IM, Vos YJ, Verhagen JM, Berger RM, Lichtenbelt KD, et al. Cardiovascular malformations caused by NOTCH1 mutations do not keep left: Data on 428 probands with left-sided CHD and their families. Genetics Medicine, 2016, 18(9), 914-923. DOI: 10.1038/gim.2015.193

[38]Gao W, Guo N, Zhao S, Chen Z, Zhang W, Yan F, et al. FBXW7 promotes pathological cardiac hypertrophy by targeting EZH2-SIX1 signaling. Experimental Cell Research, 2020, 393(1), 112059. DOI: 10.1016/j.yexcr.2020.112059

[39]Dokumacioglu E, Duzcan I, Iskender H, Sahin A. RhoA/ROCK-1 signaling pathway and oxidative stress in coronary artery disease patients. Brazilian Journal of Cardiovascular Surgery, 2022, 37(2), 212-218. DOI: 10.21470/1678-9741-2020-0525

[40]Soh JEC, Shimizu A, Molla MR, Zankov DP, Nguyen LKC, Khan MR, et al. RhoA rescues cardiac senescence by regulating Parkin-mediated mitophagy. Journal of Biological Chemistry, 2023, 299(3), 102993. DOI: 10.1016/j.jbc.2023.102993

[41]Liu W, Ru L, Su C, Qi S, Qi X. Serum levels of inflammatory cytokines and expression of BCL2 and BAX mRNA in peripheral blood mononuclear cells and in patients with chronic heart failure. Medical Science Monitor, 2019, 25, 2633-2639. DOI: 10.12659/MSM.912457

[42]Lin J, Yang L, Huang J, Liu Y, Lei X, Chen R, et al. Insulin-like growth factor 1 and risk of cardiovascular disease: Results from the UK biobank cohort study. The Journal of Clinical Endocrinology & Metabolism, 2023, 108(9), e850-e860. DOI: 10.1210/clinem/dgad105

[43]Deng RM, Zhou J. The role of PI3K/AKT signaling pathway in myocardial ischemia-reperfusion injury. International Immunopharmacology, 2023, 123, 110714. DOI: 10.1016/j.intimp.2023.110714

[44]Walkowski B, Kleibert M, Majka M, Wojciechowska M. Insight into the role of the PI3K/Akt pathway in ischemic injury and post-infarct left ventricular remodeling in normal and diabetic heart. Cells, 2022, 11(9), 1553. DOI: 10.3390/cells11091553

[45]AlShatnawi MN, Shawashreh RA, Sunoqrot MA, Yaghi AR. A systematic review of epidermal growth factor receptor tyrosine kinase inhibitor-induced heart failure and its management. The Egyptian Journal of Internal Medicine, 2022, 34(1), 85. DOI: doi.org/10.1186/s43162-022-00176-y

[46]Wang CY, Zoungas S, Voskoboynik M, Mar V. Cardiovascular disease and malignant melanoma. Melanoma Research, 2022, 32(3), 135-141. DOI: 10.1097/CMR.0000000000000817

[47]Kostetskii I, Li J, Xiong Y, Zhou R, Ferrari VA, Patel VV, et al. Induced deletion of the N-cadherin gene in the heart leads to dissolution of the intercalated disc structure. Circulation Research, 2005, 96(3), 346-354. DOI: 0.1161/01.RES.0000156274.72390.2c

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Published

2026-02-26

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Vol. 1 No. 1 (2026)

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Copyright (c) 2026 Ahsanul Kyum Siam, Akram Hossain, Tajrin Jahan Raisa, Abul Hossain, Arju Hossain, Aminur Islam, Mohammed Tanvir (Author)

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How to Cite

Siam, A. K., Hossain, A., Raisa, T. J., Hossain, A., Hossain, A., Islam, A. ., & Tanvir, M. (2026). Bioinformatics Identification of Conserved microRNAs and miRNA-targeted Biomarkers for Cardiovascular Disease. Cardiovascular Disease Prevention and Control, 1(1), 1-10. https://doi.org/10.64229/xy16q389