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基于血管生成和上皮间充质转化相关基因的心肌梗死诊断模型的鉴定和验证
Authors Li Z, Wang S, Yin X, Tao D, Wang X, Zhang J
Received 23 May 2024
Accepted for publication 3 July 2024
Published 23 July 2024 Volume 2024:17 Pages 3239—3255
DOI https://doi.org/10.2147/IJGM.S465411
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 3
Editor who approved publication: Prof. Dr. Yuriy Sirenko
Zhengmei Li,1 Shiai Wang,2 Xunli Yin,2 Dong Tao,2 Xuebing Wang,2 Junli Zhang3
1School of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Taian, Shandong, People’s Republic of China; 2Department of Cardiovascular Medicine, The Seventh People’s Hospital of Jinan, Jinan, Shandong, People’s Republic of China; 3Department of Emergency Medicine, The Second Affiliated Hospital of Shandong First Medical University, Taian, Shandong, People’s Republic of China
Correspondence: Junli Zhang, Email tyfyzjl@163.com
Background: Myocardial infarction (MI) is a chronic cardiovascular disease. This study aims to discern potentially angiogenesis- and epithelial mesenchymal transition (EMT)-related genes as biomarkers for MI diagnosis through bioinformatics.
Methods: All datasets and angiogenesis- and EMT-related genes were collected from the public database. The differentially expressed genes (DEGs) of MI and MI-related genes were acquired. DEGs, MI-related genes, and angiogenesis- and EMT-related genes were intersected to obtain hub genes. Functional enrichment, immune microenvironment, and transcription factors (TFs)-hub genes regulatory network analysis were performed. The diagnostic markers and models were developed and validated. Drug prediction and molecular docking were performed. Finally, diagnostic markers expressions were validated using RT-qPCR.
Results: A total of 224 angiogenesis- and EMT-related genes, 2,897 DEGs, 1,217 MI-related genes, and 9 hub genes were acquired. The immune infiltration levels of plasma cells, T cells CD4 memory activated, monocytes, macrophages M0, mast cells resting, and neutrophils were higher in patients with MI. LRPAP1, COLGALT1, QSOX1, THBD, VCAN, PLOD1, and PLAUR as the diagnostic markers were identified and used to construct diagnostic models, which can distinguish MI from controls well. Then, 9 drugs were screened, and the binding energies ranged from − 7.08 to − 5.21 kcal/mol. RT-qPCR results showed that the expression of LRPAP1, PLAUR, and PLOD1 was significantly increased in the MI group.
Conclusion: The 7 diagnostic markers may play potential roles in MI and could contribute to improved future diagnostics.
Keywords: myocardial infarction, angiogenesis, epithelial mesenchymal transition, diagnostic markers