Identification of novel genetic biomarkers and treatment targets for arteriosclerosis-related abdominal aortic aneurysm using bioinformatic tools

Fang Niu; Zongwei Liu; Peidong Liu; Hongrui Pan; Jiaxue Bi; Peng Li; Guangze Luo; Yonghui Chen; Xiaoxing Zhang; Xiangchen Dai; Fang Niu; Zongwei Liu; Peidong Liu; Hongrui Pan; Jiaxue Bi; Peng Li; Guangze Luo; Yonghui Chen; Xiaoxing Zhang; Xiangchen Dai

doi:10.3934/mbe.2021478

Mathematical Biosciences and Engineering

2021, Volume 18, Issue 6: 9761-9774. doi: 10.3934/mbe.2021478

Previous Article Next Article

Research article

Identification of novel genetic biomarkers and treatment targets for arteriosclerosis-related abdominal aortic aneurysm using bioinformatic tools

1.
Department of General Surgery, General Hospital of Tianjin Medical University, Tianjin Medical University, Tianjin, China
2.
Department of Neurosurgery, First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
† The authors contributed equally to this work

Received: 13 September 2021 Accepted: 25 October 2021 Published: 05 November 2021

A large number of epidemiological studies have confirmed that arteriosclerosis (AS) is a risk factor for abdominal aortic aneurysm (AAA). However, the relationship between AS and AAA remains controversial. The objective of this work is to better understand the association between the two diseases by identifying the co-differentially expressed genes under both pathological conditions, so as to identify potential genetic biomarkers and treatment targets for atherosclerosis-related aneurysms. Differentially-expressed genes (DEGs) shared by both AS and AAA patients were identified by bioinformatics analyses of Gene Expression Omnibus (GEO) datasets GSE100927 and GSE7084. These DEGs were then subjected to bioinformatic analyses of protein-protein interaction (PPI), Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Finally, the identified hub genes were further validated by qRT-PCR in AS (n = 4), AAA (n = 4), and healthy (n = 4) individuals. Differential expression analysis revealed a total of 169 and 37 genes that had increased and decreased expression levels, respectively, in both AS and AAA patients compared with healthy controls. The construction of a PPI network and key modules resulted in the identification of five hub genes (SPI1, TYROBP, TLR2, FCER1G, and MMP9) as candidate diagnostic biomarkers and treatment targets for patients with AS-related AAA. AS and AAA are indeed correlated; SPI1, TYROBP, TLR2, FCER1G and MMP9 genes are potential new genetic biomarkers for AS-related AAA.
- gene expression analysis,
- bioinformatics,
- biomarkers,
- therapeutic targets,
- arteriosclerosis-related abdominal aortic aneurysm
Citation: Fang Niu, Zongwei Liu, Peidong Liu, Hongrui Pan, Jiaxue Bi, Peng Li, Guangze Luo, Yonghui Chen, Xiaoxing Zhang, Xiangchen Dai. Identification of novel genetic biomarkers and treatment targets for arteriosclerosis-related abdominal aortic aneurysm using bioinformatic tools[J]. Mathematical Biosciences and Engineering, 2021, 18(6): 9761-9774. doi: 10.3934/mbe.2021478

Related Papers:

Abstract

A large number of epidemiological studies have confirmed that arteriosclerosis (AS) is a risk factor for abdominal aortic aneurysm (AAA). However, the relationship between AS and AAA remains controversial. The objective of this work is to better understand the association between the two diseases by identifying the co-differentially expressed genes under both pathological conditions, so as to identify potential genetic biomarkers and treatment targets for atherosclerosis-related aneurysms. Differentially-expressed genes (DEGs) shared by both AS and AAA patients were identified by bioinformatics analyses of Gene Expression Omnibus (GEO) datasets GSE100927 and GSE7084. These DEGs were then subjected to bioinformatic analyses of protein-protein interaction (PPI), Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Finally, the identified hub genes were further validated by qRT-PCR in AS (n = 4), AAA (n = 4), and healthy (n = 4) individuals. Differential expression analysis revealed a total of 169 and 37 genes that had increased and decreased expression levels, respectively, in both AS and AAA patients compared with healthy controls. The construction of a PPI network and key modules resulted in the identification of five hub genes (SPI1, TYROBP, TLR2, FCER1G, and MMP9) as candidate diagnostic biomarkers and treatment targets for patients with AS-related AAA. AS and AAA are indeed correlated; SPI1, TYROBP, TLR2, FCER1G and MMP9 genes are potential new genetic biomarkers for AS-related AAA.

References

[1]	D. Sidloff, P. Stather, N. Dattani, M. Bown, J. Thompson, R. Sayers, et al., Aneurysm global epidemiology study: public health measures can further reduce abdominal aortic aneurysm mortality, Circulation, 129 (2014), 747–753. doi: 10.1161/CIRCULATIONAHA.113.005457
[2]	B. J. Toghill, A. Saratzis, S. C. Harrison, A. R. Verissimo, E. B. Mallon, M. J. Bown, The potential role of DNA methylation in the pathogenesis of abdominal aortic aneurysm, Atherosclerosis, 241 (2015), 121–129. doi: 10.1016/j.atherosclerosis.2015.05.001
[3]	K. C. Kent, Clinical practice. Abdominal aortic aneurysms, N. Eng. J. Med., 371 (2014), 2101–2108. doi: 10.1056/NEJMcp1401430
[4]	C. A. Meijer, T. Stijnen, M. N. Wasser, J. F. Hamming, J. H. V. Bockel, J. H. Lindeman, et al., Doxycycline for stabilization of abdominal aortic aneurysms: a randomized trial, Ann. Intern. Med., 159 (2013), 815–823. doi: 10.7326/0003-4819-159-12-201312170-00007
[5]	A. Saratzis, M. J. Bown, The genetic basis for aortic aneurysmal disease, Heart, 100 (2014), 916–922. doi: 10.1136/heartjnl-2013-305130
[6]	E. O. Matthews, S. E. Rowbotham, J. V. Moxon, R. E. Jones, M. Vega de Ceniga, J. Golledge, Meta-analysis of the association between peripheral artery disease and growth of abdominal aortic aneurysms, Br. J. Surg., 104 (2017), 1765–1774. doi: 10.1002/bjs.10675
[7]	H. Takagi, T. Umemoto, ALICE (All-Literature Investigation of Cardiovascular Evidence) Group, Association of peripheral artery disease with abdominal aortic aneurysm growth, J. Vasc. Surg., 64 (2016), 506–513. doi: 10.1016/j.jvs.2016.01.059
[8]	H. Takagi, T. Umemoto, ALICE (All-Literature Investigation of Cardiovascular Evidence) Group, Coronary artery disease and abdominal aortic aneurysm growth, Vasc. Med., 21 (2016), 199–208. doi: 10.1177/1358863X15624026
[9]	Z. Chen, M. McGee, Q. Liu, R. H. Scheuermann, A distribution free summarization method for Affymetrix GeneChip arrays, Bioinformatics, 23 (2007), 321–327. doi: 10.1093/bioinformatics/btl609
[10]	G. Yu, L. G. Wang, Y. Han, Q. Y. He, clusterProfiler: an R package for comparing biological themes among gene clusters, Omics, 16 (2012), 284–287. doi: 10.1089/omi.2011.0118
[11]	G. Su, J. H. Morris, B. Demchak, G. D. Bader, Biological network exploration with Cytoscape 3, Curr. Protoc. Bioinf., 47 (2014), 1–24.
[12]	I. O. Peshkova, G. Schaefer, E. K. Koltsova, Atherosclerosis and aortic aneurysm-is inflammation a common denominator, FEBS J., 283 (2016), 1636–1652. doi: 10.1111/febs.13634
[13]	R. W. Mahley, Central nervous system lipoproteins: ApoE and regulation of cholesterol metabolism, Arterioscler. Thromb. Vasc. Biol., 36 (2016), 1305–1315. doi: 10.1161/ATVBAHA.116.307023
[14]	J. Wittwer, J. Marti-Jaun, M. Hersberger, Functional polymorphism in ALOX15 results in increased allele-specific transcription in macrophages through binding of the transcription factor SPI1, Hum. Mutat., 27 (2006), 78–87. doi: 10.1002/humu.20273
[15]	Y. Pan, C. Yu, J. Huang, Y. Rong, J. Chen, M. Chen, Bioinformatics analysis of vascular RNA-seq data revealed hub genes and pathways in a novel Tibetan minipig atherosclerosis model induced by a high fat/cholesterol diet, Lipids Health Dis., 19 (2020), 54. doi: 10.1186/s12944-020-01222-w
[16]	M. Kobayashi, H. Konishi, T. Takai, H. Kiyama, A DAP12-dependent signal promotes pro-inflammatory polarization in microglia following nerve injury and exacerbates degeneration of injured neurons, Glia, 63 (2015), 1073–1082. doi: 10.1002/glia.22802
[17]	H. M. Wang, J. H. Gao, J. L. Lu, Pravastatin improves atherosclerosis in mice with hyperlipidemia by inhibiting TREM-1/DAP12, Eur. Rev. Med. Pharmacol. Sci., 22 (2018), 4995–5003.
[18]	I. Hinterseher, C. M. Schworer, J. H. Lillvis, E. Stahl, R. Erdman, Z. Gatalica, et al., Immunohistochemical analysis of the natural killer cell cytotoxicity pathway in human abdominal aortic aneurysms, Int. J. Mol. Sci., 16 (2015), 11196–11212. doi: 10.3390/ijms160511196
[19]	Y. Liu, H. Yin, M. Zhao, Q. Lu, TLR2 and TLR4 in autoimmune diseases: a comprehensive review, Clin. Rev. Allergy Immunol., 47 (2014), 136–147. doi: 10.1007/s12016-013-8402-y
[20]	A. Jabłońska, C. Neumayer, M. Bolliger, C. Burghuber, M. Klinger, S. Demyanets, et al., Insight into the expression of toll-like receptors 2 and 4 in patients with abdominal aortic aneurysm, Mol. Biol. Rep., 47 (2020), 2685–2692. doi: 10.1007/s11033-020-05366-x
[21]	G. L. Lee, C. C. Yeh, J. Y. Wu, H. C. Lin, Y. F. Wang, Y. Y. Kuo, et al., TLR2 promotes vascular smooth muscle cell chondrogenic differentiation and consequent calcification via the concerted actions of osteoprotegerin suppression and IL-6-mediated RANKL induction, Arterioscler. Thromb. Vasc. Biol., 39 (2019), 432–445. doi: 10.1161/ATVBAHA.118.311874
[22]	P. Srikakulapu, D. Hu, C. Yin, S. K. Mohanta, S. V. Bontha, L. Peng, et al., Artery tertiary lymphoid organs control multilayered territorialized atherosclerosis B-cell responses in aged ApoE-/-mice, Arterioscler. Thromb. Vasc. Biol., 36 (2016), 1174–1185. doi: 10.1161/ATVBAHA.115.306983
[23]	Z. Li, J. Chyr, Z. Jia, L. Wang, X. Hu, X. Wu, et al., Identification of hub genes associated with hypertension and their interaction with miRNA based on weighted gene coexpression network analysis (WGCNA) analysis, Med. Sci. Monit., 26 (2020), e923514.
[24]	J. P. van Geffen, S. Brouns, J. Batista, H. McKinney, C. Kempster, M. Nagy, et al., High-throughput elucidation of thrombus formation reveals sources of platelet function variability, Haematologica, 104 (2019), 1256–1267. doi: 10.3324/haematol.2018.198853
[25]	J. Sokol, M. Skerenova, J. Ivankova, T. Simurda, J. Stasko, Association of genetic variability in selected genes in patients with deep vein thrombosis and platelet hyperaggregability, Clin. Appl. Thromb. Hemost., 24 (2018), 1027–1032. doi: 10.1177/1076029618779136
[26]	S. Zhou, S. Liu, X. Liu, W. Zhuang, Bioinformatics gene analysis of potential biomarkers and therapeutic targets for unstable atherosclerotic plaque-related stroke, J. Mol. Neurosci., (2020).
[27]	J. L. Johnson, Metalloproteinases in atherosclerosis, Eur. J. Pharmacol., 816 (2017), 93–106. doi: 10.1016/j.ejphar.2017.09.007
[28]	R. Hurks, A. Vink, I. E. Hoefer, J. P. de Vries, A. H. Schoneveld, M. L. Schermerhorn, et al., Atherosclerotic risk factors and atherosclerotic postoperative events are associated with low inflammation in abdominal aortic aneurysms, Atherosclerosis, 235 (2014), 632–641. doi: 10.1016/j.atherosclerosis.2014.05.928
[29]	J. A. Khan, M. N. Abdul Rahman, F. A. Mazari, Y. Shahin, G. Smith, L. Madden, et al., Intraluminal thrombus has a selective influence on matrix metalloproteinases and their inhibitors (tissue inhibitors of matrix metalloproteinases) in the wall of abdominal aortic aneurysms, Ann. Vasc. Surg., 26 (2012), 322–329. doi: 10.1016/j.avsg.2011.08.015

Reader Comments

Your name:*

Email:*
© 2021 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)