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Identification of Crucial Genes and Pathways Associated with Atherosclerotic Plaque in Diabetic Patients

Authors Li YY, Zhang S, Wang H, Zhang SX, Xu T, Chen SW, Zhang Y, Chen Y

Received 16 October 2020

Accepted for publication 26 December 2020

Published 4 February 2021 Volume 2021:14 Pages 211—220

DOI https://doi.org/10.2147/PGPM.S281705

Checked for plagiarism Yes

Review by Single anonymous peer review

Peer reviewer comments 2

Editor who approved publication: Dr Martin Bluth


Yuan-Yuan Li,* Sheng Zhang,* Hua Wang, Shun-Xiao Zhang, Ting Xu, Shu-Wen Chen, Yan Zhang, Yue Chen

Department of Endocrinology, Baoshan Branch, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, 201999, People’s Republic of China

*These authors contributed equally to this work

Correspondence: Yan Zhang; Yue Chen
Department of Endocrinology, Baoshan Branch, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, No. 181 You-Yi Road, Shanghai, 201999, People’s Republic of China
Tel +86 13818856902; +86 13701994461
Email yan_2999@hotmail.com; 13701994461@163.com

Background: Patients with diabetes have more calcification in atherosclerotic plaque and a higher occurrence of secondary cardiovascular events than patients without diabetes. The objective of this study was to identify crucial genes involved in the development of diabetic atherosclerotic plaque using a bioinformatics approach.
Methods: Microarray dataset GSE118481 was downloaded from the Gene Expression Omnibus (GEO) database; the dataset included 6 patients with diabetic atherosclerotic plaque (DBT) and 6 nondiabetic patients with atherosclerotic plaque (Ctrl). Differentially expressed genes (DEG) between the DBT and Ctrl groups were identified and then subjected to functional enrichment analysis. Based on the enriched pathways of DEGs, diabetic atherosclerotic plaque-related pathways were screened using the comparative toxicogenomics database (CTD). We then constructed a protein–protein interaction (PPI) network and transcription factor (TF)–miRNA–mRNA network.
Results: A total of 243 DEGs were obtained in the DBT group compared with the Ctrl group, including 85 up-regulated and 158 down-regulated DEGs. Functional enrichment analysis showed that up-regulated DEGs were mainly enriched in isoprenoid metabolic process, DNA-binding TF activity, and response to virus. Additionally, DEGs participating in the toll-like receptor signaling pathway were closely related to diabetes, carotid stenosis, and insulin resistance. The TF–miRNA–mRNA network showed that toll-like receptor 4 (TLR4), BCL2-like 11 (BCL2L11), and glutamate-cysteine ligase catalytic subunit (GCLC) were hub genes. Furthermore, TLR4 was regulated by TF signal transducer and activator of transcription 6 (STAT6); BCL2L11 was targeted by hsa-miR-24-3p; and GCLC was regulated by nuclear factor, erythroid 2 like 2 (NFE2L2).
Conclusion: Identification of hub genes and pathways increased our understanding of the molecular mechanisms underlying the atherosclerotic plaque in patients with or without diabetes. These crucial genes (TLR4, BC2L11, and GCLC) might function as molecular biomarkers for diabetic atherosclerotic plaque.

Keywords: diabetes, atherosclerotic plaque, differentially expressed genes, bioinformatics analysis

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