Chhotaray Sangeeta, Jal Soumya
School of Paramedics and Allied Health Sciences, Centurion University of Technology and Management, Bhubaneswar, Odisha, India.
Curr Cardiol Rev. 2025;21(3):78-95. doi: 10.2174/011573403X340118241113025519.
Atherosclerosis is a chronic disease caused by the accumulation of lipids, inflammatory cells, and fibrous elements in arterial walls, leading to plaque formation and cardiovascular conditions like coronary artery disease, stroke, and peripheral arterial disease. Factors like hyperlipidemia, hypertension, smoking, and diabetes contribute to its development. Diagnosis relies on imaging and biomarkers, while management includes lifestyle modifications, pharmacotherapy, and surgical interventions. Computational biology is transforming biological knowledge into clinical practice by identifying biomarkers that can predict clinical outcomes. This involves omics data, predictive modeling, and data integration. Statistical analysis-based methods are also being developed to develop and integrate methods for screening, diagnosing, and prognosing atherosclerosis.
The present work aimed to uncover critical genes and pathways to enhance the understanding of the mechanism of atherosclerosis. GSE23746 was analyzed to find differentially expressed genes (DEGs) using 19 control samples and 76 atherosclerotic samples.
A total of 76 DEGs were identified. Analysed DEGs using Gene Ontology (GO) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) to generate enrichment datasets. A Protein- protein Interaction (PPI) network of DEGs was created utilizing the Search Tool for the Retrieval of Interacting Genes (STRING).
Ten hub genes, namely EGR1, PTGS2, TNF, NFKBIA, CXCL8, TNFAIP3, CCL3, IL1B, PTPRC, and CD83, were found to be significantly linked to atherosclerosis. Furthermore, the metabolic pathway analysis through KEGG and STRING provides potential targets for therapeutic interventions through HUB genes to diagnose the illness at an early stage, which aids in the reduction of cardiovascular risk. From risk factor profiling to the discovery of novel biomarkers, several components such as phospholipids, ANGPTL3, LCAT, and the proteinencoded OCT-1 gene, play a vital role in crucial processes. These compounds are potential therapeutic targets for early diagnosis of atherosclerotic lesions and future novel biomarkers.
动脉粥样硬化是一种慢性疾病，由脂质、炎症细胞和纤维成分在动脉壁堆积引起，导致斑块形成以及冠心病、中风和外周动脉疾病等心血管疾病。高脂血症、高血压、吸烟和糖尿病等因素会促使其发展。诊断依赖于影像学检查和生物标志物，而治疗包括生活方式改变、药物治疗和手术干预。计算生物学正通过识别可预测临床结果的生物标志物，将生物学知识转化为临床实践。这涉及组学数据、预测建模和数据整合。基于统计分析的方法也在不断发展，以开发和整合用于动脉粥样硬化筛查、诊断和预后的方法。
本研究旨在揭示关键基因和通路，以加深对动脉粥样硬化发病机制的理解。利用19个对照样本和76个动脉粥样硬化样本对GSE23746进行分析，以寻找差异表达基因（DEG）。
共鉴定出76个差异表达基因。使用基因本体论（GO）和京都基因与基因组百科全书（KEGG）对差异表达基因进行分析，以生成富集数据集。利用检索相互作用基因的搜索工具（STRING）创建差异表达基因的蛋白质-蛋白质相互作用（PPI）网络。
发现10个核心基因，即EGR1、PTGS2、TNF、NFKBIA、CXCL8、TNFAIP3、CCL3、IL1B、PTPRC和CD83，与动脉粥样硬化显著相关。此外，通过KEGG和STRING进行的代谢途径分析为通过核心基因进行治疗干预提供了潜在靶点，有助于早期诊断疾病，从而降低心血管风险。从风险因素分析到新型生物标志物的发现，磷脂、ANGPTL3、LCAT和蛋白质编码的OCT-1基因等几个成分在关键过程中发挥着重要作用。这些化合物是早期诊断动脉粥样硬化病变的潜在治疗靶点和未来的新型生物标志物。
