Machine Learning Algorithm-Based Discovery of Potential Regulators of Immune-Related Dilated Cardiomyopathy.

IF 1.8 4区 医学 Q3 CARDIAC & CARDIOVASCULAR SYSTEMS
Yi-Ting Yang, Bao Zhen, Xue Cao, Hong-Yuan Xia, Ying-Zi Gong, Yan-Li Yang
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Abstract

Purpose: Dilated cardiomyopathy (DCM) is considered a severe non-ischemic myocardial disease, and there is currently no effective method for the early detection of DCM. Therefore, we aimed to use machine learning algorithms to discover more accurate factors to guide clinical drug development and precision medicine diagnosis.

Methods: Two datasets containing patients with DCM and healthy controls were downloaded from the Gene Expression Omnibus database. After data preprocessing, differentially expressed genes (DEGs) between the DCM patients and normal samples were identified using the limma package. In addition, to screen for DEGs closely associated with immune inflammation, we collected immune-related genes and defined overlapping genes as differential immune genes (Immune-DEGs). Protein-protein interaction (PPI) network construction and functional enrichment analysis were then functionally validated for the differential immune genes. Subsequently, we further screened the immune-DEGs using the least absolute shrinkage and selection operator (LASSO) technique and support vector machine algorithm (SVM), resulting in the screening of five potential modulators closely associated with DCM. Finally, the diagnostic efficacy of the modifiers was assessed using subject operating characteristic curves based on independent external data, and the intrinsic pathological mechanisms of different differential immune genes were explored by immune infiltration analysis.

Results: A consensus of 184 differential immune genes were identified, and the functional enrichment results of their PPI network modules suggested that inflammation, immune disorders, and viral infections play an essential role in the pathogenesis of DCM. Five signature genes were then further screened using LASSO and SVM algorithms: KLRC4, CCL4, IGHV3-33, ITGAL, and inducible T-cell kinase.

Conclusions: This study constructed a gene set of potential DCM regulators with five immune-related genes, which could provide a new strategy for the diagnosis and treatment of DCM.

基于机器学习算法的免疫相关扩张型心肌病潜在调节因子的发现。
目的:扩张型心肌病(DCM)被认为是一种严重的非缺血性心肌疾病,目前还没有有效的早期检测方法。因此,我们的目标是利用机器学习算法发现更准确的因素来指导临床药物开发和精准医学诊断。方法:从Gene Expression Omnibus数据库中下载DCM患者和健康对照两组数据集。数据预处理后,使用limma包鉴定DCM患者与正常样本之间的差异表达基因(deg)。此外,为了筛选与免疫炎症密切相关的deg,我们收集了免疫相关基因,并将重叠基因定义为差异免疫基因(immune- deg)。然后对差异免疫基因的蛋白-蛋白相互作用(PPI)网络构建和功能富集分析进行功能验证。随后,我们使用最小绝对收缩和选择算子(LASSO)技术和支持向量机算法(SVM)进一步筛选免疫deg,从而筛选出与DCM密切相关的五个潜在调节剂。最后,基于独立的外部数据,利用受试者工作特征曲线评估修饰剂的诊断效果,并通过免疫浸润分析探讨不同差异免疫基因的内在病理机制。结果:共鉴定出184个差异免疫基因,其PPI网络模块的功能富集结果表明,炎症、免疫紊乱和病毒感染在DCM的发病机制中起重要作用。然后使用LASSO和SVM算法进一步筛选五个特征基因:KLRC4, CCL4, IGHV3-33, ITGAL和诱导t细胞激酶。结论:本研究构建了包含5个免疫相关基因的DCM潜在调控基因集,可为DCM的诊断和治疗提供新的策略。
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来源期刊
Acta Cardiologica Sinica
Acta Cardiologica Sinica 医学-心血管系统
CiteScore
2.90
自引率
15.80%
发文量
144
审稿时长
>12 weeks
期刊介绍: Acta Cardiologica Sinica welcomes all the papers in the fields related to cardiovascular medicine including basic research, vascular biology, clinical pharmacology, clinical trial, critical care medicine, coronary artery disease, interventional cardiology, arrythmia and electrophysiology, atherosclerosis, hypertension, cardiomyopathy and heart failure, valvular and structure cardiac disease, pediatric cardiology, cardiovascular surgery, and so on. We received papers from more than 20 countries and areas of the world. Currently, 40% of the papers were submitted to Acta Cardiologica Sinica from Taiwan, 20% from China, and 20% from the other countries and areas in the world. The acceptance rate for publication was around 50% in general.
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