Huang Lin, Zhou Zhihao, Deng Tang, Sun Yunhao, Wang Rui, Wu Ridong, Liu Yunyan, Ye Yanchen, Wang Kangjie, Yao Chen
Division of Vascular Surgery, the First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510800, China; National-Guangdong Joint Engineering Laboratory for Diagnosis and Treatment of Vascular Disease, First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, China.
Division of Vascular Surgery, the First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510800, China; National-Guangdong Joint Engineering Laboratory for Diagnosis and Treatment of Vascular Disease, First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, China.
Gene. 2024 Dec 30;931:148877. doi: 10.1016/j.gene.2024.148877. Epub 2024 Aug 22.
Abdominal aortic aneurysm (AAA) represents one of the most life-threatening cardiovascular diseases and is increasingly becoming a significant global public health concern. The aneurysms-osteoarthritis syndrome (AOS) has gained recognition, as patients with this syndrome often exhibit early-stage osteoarthritis (OA) and have a substantially increased risk of rupture, even with mild dilation of the aneurysm. The aim of this study was to discover potential biomarkers that can predict the occurrence of AAA rupture in patients with OA.
Two gene expression profile datasets (GSE98278, GSE51588) and two single-cell RNA-seq datasets (GSE164678, GSE152583) were obtained from the GEO database. Functional enrichment analysis, PPI network construction, and machine learning algorithms, including LASSO, Random Forest, and SVM-RFE, were utilized to identify hub genes. In addition, a nomogram and ROC curves were generated to predict the risk of rupture in patients with AAA. Moreover, we analyzed the immune cell infiltration in the AAA tissue microenvironment by CIBERSORT and validated key gene expression in different macrophage subtypes through single-cell analysis.
A total of 105 intersecting DEGs that showed consistent changes between rAAA and OA dataset were identified. From these DEGs, four hub genes (PAK1, FCGR1B, LOX and PDPN) were selected by machine learning. High predictive performance was observed for the nomogram based on these hub genes, with an AUC of 0.975 (95 % CI: 0.942-1.000). Abnormal immune cell infiltration was detected in rAAA and correlated significantly with the hub genes. Ruptured AAA cases exhibited higher nomoscore values and lower M2 macrophage infiltration compared to stable AAA. Validation in animal models (PPE+BAPN-induced rAAA) confirmed the significant role of these biomarkers in AAA pathology.
The present study successfully identified four potential hub genes (PAK1, FCGR1B, LOX and PDPN) and developed a robust predictive nomogram to assess the risk of AAA rupture. The findings also shed light on the connection between hub genes and immune cell components in the microenvironment of rAAA. These findings support future research on key genes in AAA patients with OA, providing insights for novel management strategies for AAA.
腹主动脉瘤（AAA）是最危及生命的心血管疾病之一，日益成为全球重大的公共卫生问题。动脉瘤 - 骨关节炎综合征（AOS）已得到认可，因为患有该综合征的患者常表现出早期骨关节炎（OA），并且即使动脉瘤轻度扩张，其破裂风险也会大幅增加。本研究的目的是发现能够预测OA患者AAA破裂发生的潜在生物标志物。
从基因表达综合数据库（GEO数据库）中获取了两个基因表达谱数据集（GSE98278、GSE51588）和两个单细胞RNA测序数据集（GSE164678、GSE152583）。利用功能富集分析、蛋白质 - 蛋白质相互作用（PPI）网络构建以及机器学习算法（包括套索回归、随机森林和支持向量机递归特征消除法）来识别核心基因。此外，生成了列线图和ROC曲线以预测AAA患者的破裂风险。此外，我们通过CIBERSORT分析了AAA组织微环境中的免疫细胞浸润情况，并通过单细胞分析验证了不同巨噬细胞亚型中的关键基因表达。
共鉴定出105个在破裂性AAA（rAAA）和OA数据集之间表现出一致变化的交叉差异表达基因（DEG）。从这些DEG中，通过机器学习选择了四个核心基因（PAK1、FCGR1B、LOX和PDPN）。基于这些核心基因的列线图显示出较高的预测性能，曲线下面积（AUC）为0.975（95％置信区间：0.942 - 1.000）。在rAAA中检测到异常的免疫细胞浸润，并且与核心基因显著相关。与稳定型AAA相比，破裂性AAA病例表现出更高的列线图评分值和更低的M2巨噬细胞浸润。在动物模型（苯肾上腺素+血管紧张素Ⅱ诱导的rAAA）中的验证证实了这些生物标志物在AAA病理学中的重要作用。
本研究成功鉴定出四个潜在的核心基因（PAK1、FCGR1B、LOX和PDPN），并开发了一个强大的预测列线图来评估AAA破裂风险。这些发现还揭示了rAAA微环境中核心基因与免疫细胞成分之间的联系。这些发现为OA的AAA患者关键基因的未来研究提供了支持，为AAA的新型管理策略提供了见解。
