先天性心脏病(CHD)代表了一系列受遗传和环境因素影响的先天性心脏缺陷。这项研究通过分析21,034个心脏成纤维细胞的基因表达谱来推进这一领域,73,296个心肌细胞,和35,673个内皮细胞,利用单细胞水平分析和机器学习技术。六种冠心病:扩张型心肌病(DCM),供体心脏(用作健康对照),肥厚型心肌病(HCM),心力衰竭伴左心发育不良综合征(HF_HLHS),新生儿左心发育不良综合征(Neo_HLHS),和法洛四联症(TOF),对每种心脏细胞类型进行了调查。每个细胞样品由29,266个基因特征表示。这些特征首先通过六种特征排序算法进行分析,导致几个功能列表。然后,这些列表被输入到增量特征选择中,包含两种分类算法,提取必要的基因特征和分类规则,构建高效的分类器。鉴定的必需基因可以是不同心脏细胞类型中的潜在CHD标志物。例如,LASSO确定了CHD亚型中各种心脏细胞类型特有的关键基因。对于扩张型和肥厚型心肌病,发现FOXO3在心脏成纤维细胞中上调。在心肌细胞中,不同的基因如TMTC1、ART3、ARHGAP24、SHROOM3和XIST与扩张型心肌病相关,左心新发育不良综合征,肥厚型心肌病,HF-左心发育不良综合征,和法洛四联症,分别。内皮细胞分析进一步显示COL25A1,NFIB,和KLF7作为扩张型心肌病的重要基因,肥厚型心肌病,和法洛四联症.LightGBM,Catboost,MCFS,射频,和XGBoost进一步描绘了特定CHD亚型的关键基因,证明机器学习在识别CHD特异性基因中的功效。此外,这项研究开发了定量规则来表示与CHD相关的基因表达模式.这项研究强调了机器学习在解开CHD分子复杂性方面的潜力,并为未来基于机制的研究奠定了基础。
Congenital heart disease (CHD) represents a spectrum of inborn heart defects influenced by genetic and environmental factors. This study advances the field by analyzing gene expression profiles in 21,034 cardiac fibroblasts, 73,296 cardiomyocytes, and 35,673 endothelial cells, utilizing single-cell level analysis and machine learning techniques. Six CHD conditions: dilated cardiomyopathy (DCM), donor hearts (used as healthy controls), hypertrophic cardiomyopathy (HCM), heart failure with hypoplastic left heart syndrome (HF_HLHS), Neonatal Hypoplastic Left Heart Syndrome (Neo_HLHS), and Tetralogy of Fallot (TOF), were investigated for each cardiac cell type. Each cell sample was represented by 29,266 gene features. These features were first analyzed by six feature-ranking algorithms, resulting in several feature lists. Then, these lists were fed into incremental feature selection, containing two classification algorithms, to extract essential gene features and classification rules and build efficient classifiers. The identified essential genes can be potential CHD markers in different cardiac cell types. For instance, the LASSO identified key genes specific to various heart cell types in CHD subtypes. FOXO3 was found to be up-regulated in cardiac fibroblasts for both Dilated and hypertrophic cardiomyopathy. In cardiomyocytes, distinct genes such as TMTC1, ART3, ARHGAP24, SHROOM3, and XIST were linked to dilated cardiomyopathy, Neo-Hypoplastic Left Heart Syndrome, hypertrophic cardiomyopathy, HF-Hypoplastic Left Heart Syndrome, and Tetralogy of Fallot, respectively. Endothelial cell analysis further revealed COL25A1, NFIB, and KLF7 as significant genes for dilated cardiomyopathy, hypertrophic cardiomyopathy, and Tetralogy of Fallot. LightGBM, Catboost, MCFS, RF, and XGBoost further delineated key genes for specific CHD subtypes, demonstrating the efficacy of machine learning in identifying CHD-specific genes. Additionally, this study developed quantitative rules for representing the gene expression patterns related to CHDs. This research underscores the potential of machine learning in unraveling the molecular complexities of CHD and establishes a foundation for future mechanism-based studies.