Identification and validation of diagnostic biomarkers of coronary artery disease progression in type 1 diabetes via integrated computational and bioinformatics strategies

Yufei Zhou; Chunjiang Liu; Zhongzheng Zhang; Jian Chen; Di Zhao; Linnan Li; Mingyue Tong; Gang Zhang

doi:10.1016/j.compbiomed.2023.106940

Identification and validation of diagnostic biomarkers of coronary artery disease progression in type 1 diabetes via integrated computational and bioinformatics strategies

Comput Biol Med. 2023 Jun:159:106940. doi: 10.1016/j.compbiomed.2023.106940. Epub 2023 Apr 15.

Authors

Yufei Zhou¹, Chunjiang Liu², Zhongzheng Zhang³, Jian Chen³, Di Zhao¹, Linnan Li¹, Mingyue Tong⁴, Gang Zhang⁵

Affiliations

¹ Shanghai Medical College, Fudan University, Shanghai, 200032, China.
² Department of General Surgery, Division of Vascular Surgery, Shaoxing People's Hospital, Shaoxing, 312000, China.
³ Department of Rehabilitation, The First Affiliated Hospital of Anhui Medical University, Anhui Public Health Clinical Center, Hefei, Anhui, 230000, China.
⁴ Department of Rehabilitation, The First Affiliated Hospital of Anhui Medical University, Anhui Public Health Clinical Center, Hefei, Anhui, 230000, China. Electronic address: 1070159032@qq.com.
⁵ Department of Rehabilitation, The First Affiliated Hospital of Anhui Medical University, Anhui Public Health Clinical Center, Hefei, Anhui, 230000, China. Electronic address: zhanggang@stu.ahmu.edu.cn.

PMID: 37075605
DOI: 10.1016/j.compbiomed.2023.106940

Abstract

Objective: Our study aimed to identify early peripheral blood diagnostic biomarkers and elucidate the immune mechanisms of coronary artery disease (CAD) progression in patients with type 1 diabetes mellitus (T1DM).

Methods: Three transcriptome datasets were retrieved from the Gene Expression Omnibus (GEO) database. Gene modules associated with T1DM were selected with weighted gene co-expression network analysis. Differentially expressed genes (DEGs) between CAD and acute myocardial infarction (AMI) peripheral blood tissues were identified using limma. Candidate biomarkers were selected with functional enrichment analysis, node gene selection from a constructed protein-protein interaction (PPI) network, and 3 machine learning algorithms. Candidate expression was compared, and the receiver operating characteristic curve (ROC) and nomogram were constructed. Immune cell infiltration was assessed with the CIBERSORT algorithm.

Results: A total of 1283 genes comprising 2 modules were detected as the most associated with T1DM. In addition, 451 DEGs related to CAD progression were identified. Among them, 182 were common to both diseases and mainly enriched in immune and inflammatory response regulation. The PPI network yielded 30 top node genes, and 6 were selected using the 3 machine learning algorithms. Upon validation, 4 genes (TLR2, CLEC4D, IL1R2, and NLRC4) were recognized as diagnostic biomarkers with the area under the curve (AUC) > 0.7. All 4 genes were positively correlated with neutrophils in patients with AMI.

Conclusion: We identified 4 peripheral blood biomarkers and provided a nomogram for early diagnosing CAD progression to AMI in patients with T1DM. The biomarkers were positively associated with neutrophils, indicating potential therapeutic targets.

Keywords: Acute myocardial infarction; Bioinformatics; Coronary artery disease; Diagnostic biomarker; Immune response; Machine learning; Type 1 diabetes mellitus.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Arteries
Biomarkers
Computational Biology
Coronary Artery Disease* / diagnosis
Coronary Artery Disease* / genetics
Diabetes Mellitus, Type 1* / diagnosis
Diabetes Mellitus, Type 1* / genetics
Humans
Myocardial Infarction* / diagnosis
Myocardial Infarction* / genetics

Substances

Biomarkers