A nomoscore of four genes for predicting the rupture risk in abdominal aortic aneurysm patients with osteoarthritis

• Published in: Gene Vol. 931; p. 148877
• Main Authors: Huang, Lin, Zhou, Zhihao, Deng, Tang, Sun, Yunhao, Wang, Rui, Wu, Ridong, Liu, Yunyan, Ye, Yanchen, Wang, Kangjie, Yao, Chen
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 30.12.2024
• Subjects: Abdominal aortic aneurysm; Biomarker; Immune infiltration; Machine learning; Rupture; TFs; UMAP; AAA; Biomarker; VSMCs; DCA; AOS; UMIs; Abdominal aortic aneurysm; Rupture; Immune infiltration; GO; ECM; CVD; GEO; OA; DEGs; CVF; SVM-RFE; Machine learning; EVG; KEGG; rAAA; HE
• ISSN: 0378-1119; 1879-0038; 1879-0038
• DOI: 10.1016/j.gene.2024.148877

•Four genes (PAK1, FCGR1B, LOX and PDPN) were identified as potential predictors for AAA rupture risk.•Ruptured AAA shows reduced M2 macrophage infiltration compared to stable AAA.•PAK1 and FCGR1B are associated with M2 macrophages in rAAA.•Osteoarthritis may contribute to the occurrence of AAA rupture. 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.