Department of Computer Science and Biomedical Informatics, University of Thessaly, 35131, Lamia, Greece.
Izmir Biomedicine and Genome Institute, Dokuz Eylül University Health Campus, 35340, Izmir, Turkey.
BMC Med Genomics. 2018 Nov 27;11(1):109. doi: 10.1186/s12920-018-0427-x.
Myocardial infarction (MI) is a multifactorial disease with complex pathogenesis, mainly the result of the interplay of genetic and environmental risk factors. The regulation of thrombosis, inflammation and cholesterol and lipid metabolism are the main factors that have been proposed thus far to be involved in the pathogenesis of MI. Traditional risk-estimation tools depend largely on conventional risk factors but there is a need for identification of novel biochemical and genetic markers. The aim of the study is to identify differentially expressed genes that are consistently associated with the incidence myocardial infarction (MI), which could be potentially incorporated into the traditional cardiovascular diseases risk factors models.
The biomedical literature and gene expression databases, PubMed and GEO, respectively, were searched following the PRISMA guidelines. The key inclusion criteria were gene expression data derived from case-control studies on MI patients from blood samples. Gene expression datasets regarding the effect of medicinal drugs on MI were excluded. The t-test was applied to gene expression data from case-control studies in MI patients.
A total of 162 articles and 174 gene expression datasets were retrieved. Of those a total of 4 gene expression datasets met the inclusion criteria, which contained data on 31,180 loci in 93 MI patients and 89 healthy individuals. Collectively, 626 differentially expressed genes were detected in MI patients as compared to non-affected individuals at an FDR q-value = 0.01. Of those, 88 genes/gene products were interconnected in an interaction network. Totally, 15 genes were identified as hubs of the network.
Functional enrichment analyses revealed that the DEGs and that they are mainly involved in inflammatory/wound healing, RNA processing/transport mechanisms and a yet not fully characterized pathway implicated in RNA transport and nuclear pore proteins. The overlap between the DEGs identified in this study and the genes identified through genetic-association studies is minimal. These data could be useful in future studies on the molecular mechanisms of MI as well as diagnostic and prognostic markers.
心肌梗死（MI）是一种多因素疾病，具有复杂的发病机制，主要是遗传和环境风险因素相互作用的结果。血栓形成、炎症和胆固醇及脂质代谢的调节是迄今为止被认为参与 MI 发病机制的主要因素。传统的风险评估工具主要依赖于常规风险因素，但需要确定新的生化和遗传标志物。本研究旨在鉴定与心肌梗死（MI）发病一致的差异表达基因，这些基因可能被纳入传统的心血管疾病风险因素模型。
根据 PRISMA 指南，分别检索生物医学文献和基因表达数据库 PubMed 和 GEO。主要纳入标准是从血液样本中 MI 患者的病例对照研究中获得的基因表达数据。排除关于药物对 MI 影响的基因表达数据集。对 MI 患者病例对照研究中的基因表达数据应用 t 检验。
共检索到 162 篇文章和 174 个基因表达数据集。其中，共有 4 个基因表达数据集符合纳入标准，其中包含 93 名 MI 患者和 89 名健康个体的 31180 个基因座的数据。与非患病个体相比，MI 患者中共有 626 个基因表达差异，FDR q 值 = 0.01。其中，88 个基因/基因产物相互连接在一个相互作用网络中。共有 15 个基因被鉴定为网络的枢纽。
功能富集分析表明，差异表达基因主要参与炎症/伤口愈合、RNA 加工/运输机制以及尚未完全表征的与 RNA 运输和核孔蛋白相关的途径。本研究中鉴定的 DEGs 与通过遗传关联研究鉴定的基因之间的重叠很小。这些数据可能对未来 MI 分子机制的研究以及诊断和预后标志物的研究有用。
