Skip to main content
Key Specialties
Cardiology Diabetology Endocrinology Gastroenterology Geriatrics and Gerontology Gynecology and Obstetrics Hematology Infectious Disease Internal Medicine Nephrology Neurology Oncology Pediatrics Respiratory Medicine Rheumatology Surgery
Congress Coverage
2024 ACC Congress 2023 ESMO Congress 2023 EASD Congress 2023 ERS Congress 2023 ESC Congress 2023 EHA Congress 2023 ASCO Annual Meeting
Clinical Collections
Advances in Alzheimer’s

At a glance: The ONWARDS insulin icodec trials

A round-up of the ONWARDS phase 3 clinical trials evaluating the novel weekly insulin icodec in people with diabetes.
ADVANCED SEARCH
Log in
Top
BMC Cardiovascular Disorders
Published in: BMC Cardiovascular Disorders 1/2021

01-12-2021 | Intracranial Aneurysm | Research article

Bioinformatics analysis of common key genes and pathways of intracranial, abdominal, and thoracic aneurysms

Published in: BMC Cardiovascular Disorders | Issue 1/2021

Login to get access

Abstract

Background

Aneurysm is a severe and fatal disease. This study aims to comprehensively identify the highly conservative co-expression modules and hub genes in the abdominal aortic aneurysm (AAA), thoracic aortic aneurysm (TAA) and intracranial aneurysm (ICA) and facilitate the discovery of pathogenesis for aneurysm.

Methods

GSE57691, GSE122897, and GSE5180 microarray datasets were downloaded from the Gene Expression Omnibus database. We selected highly conservative modules using weighted gene co‑expression network analysis before performing the Gene Ontology, Kyoto Encyclopedia of Genes and Genomes pathway and Reactome enrichment analysis. The protein–protein interaction (PPI) network and the miRNA-hub genes network were constructed. Furtherly, we validated the preservation of hub genes in three other datasets.

Results

Two modules with 193 genes and 159 genes were identified as well preserved in AAA, TAA, and ICA. The enrichment analysis identified that these genes were involved in several biological processes such as positive regulation of cytosolic calcium ion concentration, hemostasis, and regulation of secretion by cells. Ten highly connected PPI networks were constructed, and 55 hub genes were identified. In the miRNA-hub genes network, CCR7 was the most connected gene, followed by TNF and CXCR4. The most connected miRNAs were hsa-mir-26b-5p and hsa-mir-335-5p. The hub gene module was proved to be preserved in all three datasets.

Conclusions

Our study highlighted and validated two highly conservative co-expression modules and miRNA-hub genes network in three kinds of aneurysms, which may promote understanding of the aneurysm and provide potential therapeutic targets and biomarkers of aneurysm.
Appendix
Available only for authorised users
Literature
1.
Calero A, Illig KA. Overview of aortic aneurysm management in the endovascular era. Semin Vasc Surg. 2016;29(1–2):3–17.PubMedCrossRef
2.
Guo MH, Appoo JJ, Saczkowski R, Smith HN, Ouzounian M, Gregory AJ, et al. Association of mortality and acute aortic events with ascending aortic aneurysm: a systematic review and meta-analysis. JAMA Netw Open. 2018;1(4):e181281.PubMedPubMedCentralCrossRef
3.
Sakalihasan N, Limet R, Defawe OD. Abdominal aortic aneurysm. Lancet (London, England). 2005;365(9470):1577–89.CrossRef
4.
Kuzmik GA, Sang AX, Elefteriades JA. Natural history of thoracic aortic aneurysms. J Vasc Surg. 2012;56(2):565–71.PubMedCrossRef
5.
Brown RD Jr, Broderick JP. Unruptured intracranial aneurysms: epidemiology, natural history, management options, and familial screening. Lancet Neurol. 2014;13(4):393–404.PubMedCrossRef
6.
Rouchaud A, Brandt MD, Rydberg AM, Kadirvel R, Flemming K, Kallmes DF, et al. Prevalence of Intracranial Aneurysms in Patients with Aortic Aneurysms. AJNR Am J Neuroradiol. 2016;37(9):1664–8.PubMedCrossRefPubMedCentral
7.
Kuzmik GA, Feldman M, Tranquilli M, Rizzo JA, Johnson M, Elefteriades JA. Concurrent intracranial and thoracic aortic aneurysms. Am J Cardiol. 2010;105(3):417–20.PubMedCrossRef
8.
Larsson E, Vishnevskaya L, Kalin B, Granath F, Swedenborg J, Hultgren R. High frequency of thoracic aneurysms in patients with abdominal aortic aneurysms. Ann Surg. 2011;253(1):180–4.PubMedCrossRef
9.
DeFreitas MR, Quint LE, Watcharotone K, Nan B, Ranella MJ, Hider JR, et al. Evaluation for abdominal aortic aneurysms is justified in patients with thoracic aortic aneurysms. Int J Cardiovasc Imaging. 2016;32(4):647–53.PubMedCrossRef
10.
Norrgård O, Angqvist KA, Fodstad H, Forssell A, Lindberg M. Co-existence of abdominal aortic aneurysms and intracranial aneurysms. Acta Neurochir. 1987;87(1–2):34–9.PubMedCrossRef
11.
Kim DH, Van Ginhoven G, Milewicz DM. Familial aggregation of both aortic and cerebral aneurysms: evidence for a common genetic basis in a subset of families. Neurosurgery. 2005;56(4):655–61; discussion -61.
12.
Ruigrok YM, Elias R, Wijmenga C, Rinkel GJ. A comparison of genetic chromosomal loci for intracranial, thoracic aortic, and abdominal aortic aneurysms in search of common genetic risk factors. Cardiovasc Pathol. 2008;17(1):40–7.PubMedCrossRef
13.
van’t Hof FN, Ruigrok YM, Lee CH, Ripke S, Anderson G, de Andrade M, et al. Shared genetic risk factors of intracranial, abdominal, and thoracic aneurysms. J Am Heart Assoc. 2016;5(7):e002603.
14.
Venkatesh P, Phillippi J, Chukkapalli S, Rivera-Kweh M, Velsko I, Gleason T, et al. Aneurysm-specific miR-221 and miR-146a participates in human thoracic and abdominal aortic aneurysms. Int J Mol Sci. 2017;18(4):875.PubMedCentralCrossRef
15.
Li T, Jiang B, Li X, Sun HY, Li XT, Jing JJ, et al. Serum matrix metalloproteinase-9 is a valuable biomarker for identification of abdominal and thoracic aortic aneurysm: a case-control study. BMC Cardiovasc Disord. 2018;18(1):202.PubMedPubMedCentralCrossRef
16.
Wang XL, Liu O, Qin YW, Zhang HJ, Lv Y. Association of the polymorphisms of MMP-9 and TIMP-3 genes with thoracic aortic dissection in Chinese Han population. Acta Pharmacol Sin. 2014;35(3):351–5.PubMedPubMedCentralCrossRef
17.
Kleinloog R, Verweij BH, van der Vlies P, Deelen P, Swertz MA, de Muynck L, et al. RNA sequencing analysis of intracranial aneurysm walls reveals involvement of lysosomes and immunoglobulins in rupture. Stroke. 2016;47(5):1286–93.PubMedCrossRef
18.
Majumdar R, Miller DV, Ballman KV, Unnikrishnan G, McKellar SH, Sarkar G, et al. Elevated expressions of osteopontin and tenascin C in ascending aortic aneurysms are associated with trileaflet aortic valves as compared with bicuspid aortic valves. Cardiovasc Pathol. 2007;16(3):144–50.PubMedCrossRef
19.
Biros E, Gäbel G, Moran CS, Schreurs C, Lindeman JH, Walker PJ, et al. Differential gene expression in human abdominal aortic aneurysm and aortic occlusive disease. Oncotarget. 2015;6(15):12984–96.PubMedPubMedCentralCrossRef
20.
Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015;43(7):e47.PubMedPubMedCentralCrossRef
21.
McCarthy DJ, Chen Y, Smyth GK. Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation. Nucleic Acids Res. 2012;40(10):4288–97.PubMedPubMedCentralCrossRef
22.
Davis S, Meltzer PS. GEOquery: a bridge between the Gene Expression Omnibus (GEO) and BioConductor. Bioinformatics (Oxford, England). 2007;23(14):1846–7.CrossRef
23.
Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinform. 2008;9:559.CrossRef
24.
Langfelder P, Horvath S. Fast R functions for robust correlations and hierarchical clustering. J Stat Softw. 2012;46(11).
25.
Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, et al. Gene ontology: tool for the unification of biology. Gene Ontol Consort Nat Genet. 2000;25(1):25–9.CrossRef
26.
27.
Zhou Y, Zhou B, Pache L, Chang M, Khodabakhshi AH, Tanaseichuk O, et al. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat Commun. 2019;10(1):1523.PubMedPubMedCentralCrossRef
28.
Krzywinski M, Schein J, Birol I, Connors J, Gascoyne R, Horsman D, et al. Circos: an information aesthetic for comparative genomics. Genome Res. 2009;19(9):1639–45.PubMedPubMedCentralCrossRef
29.
Stark C, Breitkreutz BJ, Reguly T, Boucher L, Breitkreutz A, Tyers M. BioGRID: a general repository for interaction datasets. Nucleic Acids Res. 2006;34(Database issue):D535–9.PubMedCrossRef
30.
Li T, Wernersson R, Hansen RB, Horn H, Mercer J, Slodkowicz G, et al. A scored human protein-protein interaction network to catalyze genomic interpretation. Nat Methods. 2017;14(1):61–4.PubMedCrossRef
31.
Türei D, Korcsmáros T, Saez-Rodriguez J. OmniPath: guidelines and gateway for literature-curated signaling pathway resources. Nat Methods. 2016;13(12):966–7.PubMedCrossRef
32.
Bader GD, Hogue CW. An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinform. 2003;4:2.CrossRef
33.
Fan Y, Siklenka K, Arora SK, Ribeiro P, Kimmins S, Xia J. miRNet—dissecting miRNA-target interactions and functional associations through network-based visual analysis. Nucleic Acids Res. 2016;44(W1):W135–41.PubMedPubMedCentralCrossRef
34.
Huang HY, Lin YC, Li J, Huang KY, Shrestha S, Hong HC, et al. miRTarBase 2020: updates to the experimentally validated microRNA-target interaction database. Nucleic Acids Res. 2020;48(D1):D148–54.PubMed
35.
Karagkouni D, Paraskevopoulou MD, Chatzopoulos S, Vlachos IS, Tastsoglou S, Kanellos I, et al. DIANA-TarBase v8: a decade-long collection of experimentally supported miRNA-gene interactions. Nucleic Acids Res. 2018;46(D1):D239–45.PubMedCrossRef
36.
Xiao F, Zuo Z, Cai G, Kang S, Gao X, Li T. miRecords: an integrated resource for microRNA-target interactions. Nucleic Acids Res. 2009;37(Database issue):D105–10.PubMedCrossRef
37.
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 Res. 2003;13(11):2498–504.PubMedPubMedCentralCrossRef
38.
Szklarczyk D, Franceschini A, Wyder S, Forslund K, Heller D, Huerta-Cepas J, et al. STRING v10: protein-protein interaction networks, integrated over the tree of life. Nucleic Acids Res. 2015;43(Database issue):D447–52.PubMedCrossRef
39.
40.
Ray S, Hossain SMM, Khatun L, Mukhopadhyay AJBB. A comprehensive analysis on preservation patterns of gene co-expression networks during Alzheimer’s disease progression. BMC Bioinform. 2017;18(1):579.CrossRef
41.
Li B, Zhang Y, Yu Y, Wang P, Wang Y, Wang Z, et al. Quantitative assessment of gene expression network module-validation methods. Sci Rep. 2015;5(1):1–14.
42.
Kapp AV, Tibshirani RJB. Are clusters found in one dataset present in another dataset? Biostatistics. 2007;8(1):9–31.CrossRefPubMed
43.
44.
Stafford N, Wilson C, Oceandy D, Neyses L, Cartwright EJ. The plasma membrane calcium ATPases and their role as major new players in human disease. Physiol Rev. 2017;97(3):1089–125.PubMedCrossRef
45.
Khan K, Campanero MR, Cotton J, Redondo JM, Armesilla AL. BS53 The role of plasma membrane calcium atpase 4 (PMCA4) in vascular remodelling during abdominal aortic aneurysm formation. Heart. 2019;105:A175.
46.
Bick AG, Wakimoto H, Kamer KJ, Sancak Y, Goldberger O, Axelsson A, et al. Cardiovascular homeostasis dependence on MICU2, a regulatory subunit of the mitochondrial calcium uniporter. Proc Natl Acad Sci USA. 2017;114(43):E9096–104.PubMedCrossRefPubMedCentral
47.
Bertolini MS, Chiurillo MA, Lander N, Vercesi AE, Docampo R. MICU1 and MICU2 play an essential role in mitochondrial Ca(2+) uptake, growth, and infectivity of the human pathogen Trypanosoma cruzi. mBio. 2019;10(3).
48.
49.
Banach-Orłowska M, Wyszyńska R, Pyrzyńska B, Maksymowicz M, Gołąb J, Miączyńska M. Cholesterol restricts lymphotoxin β receptor-triggered NF-κB signaling. Cell Commun Signal: CCS. 2019;17(1):171.PubMedCrossRefPubMedCentral
50.
Hu D, Mohanta SK, Yin C, Peng L, Ma Z, Srikakulapu P, et al. Artery tertiary lymphoid organs control aorta immunity and protect against atherosclerosis via vascular smooth muscle cell lymphotoxin β receptors. Immunity. 2015;42(6):1100–15.PubMedPubMedCentralCrossRef
51.
Korostynski M, Morga R, Piechota M, Hoinkis D, Golda S, Dziedzic T, et al. Inflammatory responses induced by the rupture of intracranial aneurysms are modulated by miRNAs. Mol Neurobiol. 2020;57(2):988–96.PubMedCrossRef
52.
53.
Wanhainen A, Mani K, Vorkapic E, De Basso R, Björck M, Länne T, et al. Screening of circulating microRNA biomarkers for prevalence of abdominal aortic aneurysm and aneurysm growth. Atherosclerosis. 2017;256:82–8.PubMedCrossRef
54.
Lu B, Liu L, Wang J, Chen Y, Li Z, Gopinath SCB, et al. Detection of microRNA-335-5p on an interdigitated electrode surface for determination of the severity of abdominal aortic aneurysms. Nanoscale Res Lett. 2020;15(1):105.PubMedPubMedCentralCrossRef
Metadata
Title
Bioinformatics analysis of common key genes and pathways of intracranial, abdominal, and thoracic aneurysms
Publication date
01-12-2021
Published in
BMC Cardiovascular Disorders / Issue 1/2021
Electronic ISSN: 1471-2261
DOI
https://doi.org/10.1186/s12872-020-01838-x

Other articles of this Issue 1/2021

BMC Cardiovascular Disorders 1/2021 Go to the issue

Case report

Primitive neuroectodermal tumor of the pericardium: a case report and literature review

Research article

Health-related quality of life in patients with recurrent pericarditis: results from a phase 2 study of rilonacept

Research article

Prognostic value of the modified model for end-stage liver disease (MELD) score including albumin in acute heart failure

Research article

Increased ratio of sST2/LVMI predicted cardiovascular mortality and heart failure rehospitalization in heart failure with reduced ejection fraction patients: a prospective cohort study

Study protocol

Rationale and design of the application value of Beijing Vascular Health Stratification (BVHS): predictive value of combined assessment of vascular structure and function for cardiovascular events in general Chinese population

Research article

Regional contributions to left ventricular stroke volume determined by cardiac magnetic resonance imaging in cardiac resynchronization therapy

ACC 2024 Congress Coverage

Heart Failure Video

Year in Review: Heart failure and cardiomyopathies

Watch now

Watch this official video from ACC.24. Dr. Biykem Bozkurt discuss last year's major advances in heart failure and cardiomyopathies.

Semaglutide benefits people with type 2 diabetes and obesity-related heart failure

16-04-2024 Type 2 Diabetes News

Incentivised to exercise

11-04-2024 Lifestyle Modifications News

New intervention for STEMI-related cardiogenic shock

10-04-2024 Myocardial Infarction News

» View more highlights on the ACC 2024 congress hub page

Image Credits