Kunming Medical University, Kunming, 650000, Yunnan, China.
Department of Vascular Surgery, Fuwai Yunnan Cardiovascular Hospital, Affiliated Cardiovascular Hospital of Kunming Medical University, Kunming, 650000, Yunnan, China.
Sci Rep. 2023 Sep 8;13(1):14794. doi: 10.1038/s41598-023-41017-4.
Aortic dissection (AD) is a life-threatening condition in which the inner layer of the aorta tears. It has been reported that metabolic syndrome (MS) has a close linkage with aortic dissection. However, the inter-relational mechanisms between them were still unclear. This article explored the hub gene signatures and potential molecular mechanisms in AD and MS. We obtained five bulk RNA-seq datasets of AD, one single cell RNA-seq (scRNA-seq) dataset of ascending thoracic aortic aneurysm (ATAA), and one bulk RNA-seq dataset of MS from the gene expression omnibus (GEO) database. Identification of differentially expressed genes (DEGs) and key modules via weighted gene co-expression network analysis (WGCNA), functional enrichment analysis, and machine learning algorithms (Random Forest and LASSO regression) were used to identify hub genes for diagnosing AD with MS. XGBoost further improved the diagnostic performance of the model. The receiver operating characteristic (ROC) and precision-recall (PR) curves were developed to assess the diagnostic value. Then, immune cell infiltration and metabolism-associated pathways analyses were created to investigate immune cell and metabolism-associated pathway dysregulation in AD and MS. Finally, the scRNA-seq dataset was performed to confirm the expression levels of identified hub genes. 406 common DEGs were identified between the merged AD and MS datasets. Functional enrichment analysis revealed these DEGs were enriched for applicable terms of metabolism, cellular processes, organismal systems, and human diseases. Besides, the positively related key modules of AD and MS were mainly enriched in transcription factor binding and inflammatory response. In contrast, the negatively related modules were significantly associated with adaptive immune response and regulation of nuclease activity. Through machine learning, nine genes with common diagnostic effects were found in AD and MS, including MAD2L2, IMP4, PRPF4, CHSY1, SLC20A1, SLC9A1, TIPRL, DPYD, and MAPKAPK2. In the training set, the AUC of the hub gene on RP and RR curves was 1. In the AD verification set, the AUC of the Hub gene on RP and RR curves were 0.946 and 0.955, respectively. In the MS set, the AUC of the Hub gene on RP and RR curves were 0.978 and 0.98, respectively. scRNA-seq analysis revealed that the SLC20A1 was found to be relevant in fatty acid metabolic pathways and expressed in endothelial cells. Our study revealed the common pathogenesis of AD and MS. These common pathways and hub genes might provide new ideas for further mechanism research.
主动脉夹层（AD）是一种危及生命的疾病，其特征是主动脉的内层撕裂。据报道，代谢综合征（MS）与主动脉夹层密切相关。然而，它们之间的相互关系机制尚不清楚。本文探讨了 AD 和 MS 中的枢纽基因特征和潜在的分子机制。我们从基因表达综合数据库（GEO）获得了五个 AD 的批量 RNA-seq 数据集，一个升主动脉瘤（ATAA）的单细胞 RNA-seq（scRNA-seq）数据集，以及一个 MS 的批量 RNA-seq 数据集。通过加权基因共表达网络分析（WGCNA）、功能富集分析和机器学习算法（随机森林和 LASSO 回归）识别差异表达基因（DEGs）和关键模块，以识别诊断 AD 合并 MS 的枢纽基因。XGBoost 进一步提高了模型的诊断性能。开发了接收器工作特征（ROC）和精度-召回（PR）曲线来评估诊断价值。然后，进行免疫细胞浸润和代谢相关途径分析，以研究 AD 和 MS 中免疫细胞和代谢相关途径的失调。最后，对 scRNA-seq 数据集进行了分析，以验证鉴定的枢纽基因的表达水平。在合并的 AD 和 MS 数据集之间鉴定出 406 个共同的 DEG。功能富集分析表明，这些 DEG 富集了代谢、细胞过程、机体系统和人类疾病的适用术语。此外，AD 和 MS 的正相关关键模块主要富集在转录因子结合和炎症反应中。相比之下，负相关模块与适应性免疫反应和核酸酶活性调节显著相关。通过机器学习，在 AD 和 MS 中发现了具有共同诊断效果的 9 个基因，包括 MAD2L2、IMP4、PRPF4、CHSY1、SLC20A1、SLC9A1、TIPRL、DPYD 和 MAPKAPK2。在训练集中，枢纽基因在 RP 和 RR 曲线上的 AUC 为 1。在 AD 验证集中，枢纽基因在 RP 和 RR 曲线上的 AUC 分别为 0.946 和 0.955。在 MS 集中，枢纽基因在 RP 和 RR 曲线上的 AUC 分别为 0.978 和 0.98。scRNA-seq 分析表明，SLC20A1 与脂肪酸代谢途径有关，并在内皮细胞中表达。我们的研究揭示了 AD 和 MS 的共同发病机制。这些共同的途径和枢纽基因可能为进一步的机制研究提供新的思路。
