Interleukin 2 receptor subunit beta as a novel hub gene plays a potential role in the immune microenvironment of abdominal aortic aneurysms.

Abdominal aortic aneurysm (AAA) is potentially life threatening and characterized by immune-inflammatory cell infiltration and extracellular matrix degradation. Currently, pharmacotherapy mainly aims to control risk factors without reversion of the dilated aorta. This study analyzed the immune-inflammatory response and identified the immune-related hub genes of AAA. Gene Expression Omnibus datasets (GSE57691, GSE47472 and GSE7084) were downloaded. After identification of GSE57691 differentially expressed genes (DEGs), weighted gene co-expression network analysis of the DEGs was performed. Through enrichment analysis of each module and screening in Immunology Database and Analysis Portal, immune-related hub genes were identified via protein-protein interaction (PPI) network construction and lasso regression. CIBERSORT was utilized to analyze AAA immune infiltration. The correlations between the immune-related hub genes and infiltrating immune cells were investigated. Receiver operating characteristic (ROC) curve analysis was performed to determine immune-related hub gene cutoff values, which were validated in GSE47472 and GSE7084. In GSE57691, 1,018 DEGs were identified. Five modules were identified in the co-expression network. The blue and green modules were found to be related to immune-inflammatory responses, and 61 immune-related genes were identified. PPI and lasso regression analyses identified FOS, IL-6 and IL2RB as AAA immune-related hub genes. CIBERSORT analysis indicated significantly increased infiltration of naive B cells, memory activated CD4 T cells, follicular helper T cells, monocytes and M1 macrophages and significantly decreased infiltration of M2 macrophages in AAA compared with normal samples. IL2RB was more strongly associated with immune infiltration in AAA than were FOS and IL6. The IL2RB area under the ROC curve (AUC) value was > 0.9 in both the training and validation set, demonstrating its strong, stable diagnostic value in AAA. AAA and normal samples had different immune infiltration statuses. IL2RB was identified as an immune-related hub gene and a potential hub gene with significant diagnostic value in AAA.

Gao H ，Wang L ，Ren J ，Liu Y ，Liang S ，Zhang B ，Sun X ... -  《-》

Single-Cell Sequencing Analysis and Multiple Machine Learning Methods Identified G0S2 and HPSE as Novel Biomarkers for Abdominal Aortic Aneurysm.

Identifying biomarkers for abdominal aortic aneurysms (AAA) is key to understanding their pathogenesis, developing novel targeted therapeutics, and possibly improving patients outcomes and risk of rupture. Here, we identified AAA biomarkers from public databases using single-cell RNA-sequencing, weighted co-expression network (WGCNA), and differential expression analyses. Additionally, we used the multiple machine learning methods to identify biomarkers that differentiated large AAA from small AAA. Biomarkers were validated using GEO datasets. CIBERSORT was used to assess immune cell infiltration into AAA tissues and investigate the relationship between biomarkers and infiltrating immune cells. Therefore, 288 differentially expressed genes (DEGs) were screened for AAA and normal samples. The identified DEGs were mostly related to inflammatory responses, lipids, and atherosclerosis. For the large and small AAA samples, 17 DEGs, mostly related to necroptosis, were screened. As biomarkers for AAA, G0/G1 switch 2 (G0S2) (Area under the curve [AUC] = 0.861, 0.875, and 0.911, in GSE57691, GSE47472, and GSE7284, respectively) and for large AAA, heparinase (HPSE) (AUC = 0.669 and 0.754, in GSE57691 and GSE98278, respectively) were identified and further verified by qRT-PCR. Immune cell infiltration analysis revealed that the AAA process may be mediated by T follicular helper (Tfh) cells and the large AAA process may also be mediated by Tfh cells, M1, and M2 macrophages. Additionally, G0S2 expression was associated with neutrophils, activated and resting mast cells, M0 and M1 macrophages, regulatory T cells (Tregs), resting dendritic cells, and resting CD4 memory T cells. Moreover, HPSE expression was associated with M0 and M1 macrophages, activated and resting mast cells, Tregs, and resting CD4 memory T cells. Additional, G0S2 may be an effective diagnostic biomarker for AAA, whereas HPSE may be used to confer risk of rupture in large AAAs. Immune cells play a role in the onset and progression of AAA, which may improve its diagnosis and treatment.

Xiong T ，Lv XS ，Wu GJ ，Guo YX ，Liu C ，Hou FX ，Wang JK ，Fu YF ，Liu FQ ... -  《Frontiers in Immunology》

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

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.

Huang L ，Zhou Z ，Deng T ，Sun Y ，Wang R ，Wu R ，Liu Y ，Ye Y ，Wang K ，Yao C ... -  《-》

Construction of the circRNA-miRNA-mRNA Regulatory Network of an Abdominal Aortic Aneurysm to Explore Its Potential Pathogenesis.

Abdominal aortic aneurysm (AAA) is a progressive cardiovascular disease, which is a permanent and localized dilatation of the abdominal aorta with potentially fatal consequence of aortic rupture. Dysregulation of circRNAs is correlated with the development of various pathological events in cardiovascular diseases. However, the function of circRNAs in abdominal aortic aneurysm (AAA) is unknown and remains to be explored. This study is aimed at determining the regulatory mechanisms of circRNAs in AAAs. This study was aimed at exploring the underlying molecular mechanisms of abdominal aortic aneurysms based on the competing endogenous RNA (ceRNA) regulatory hypothesis of circRNA, miRNA, and mRNA. The expression profiles of circRNAs (GSE144431), miRNAs (GSE62179), and mRNAs (GSE7084, GSE57691, and GSE47472) in human tissue sample from the aneurysm group and normal group were obtained from the Gene Expression Omnibus database, respectively. The circRNA-miRNA-mRNA network was constructed by using Cytoscape 3.7.2 software; then, the protein-protein interaction (PPI) network was constructed by using the STRING database, and the hub genes were identified by using the cytoHubba plug-in. The circRNA-miRNA-hub gene regulatory subnetwork was formed to understand the regulatory axis of hub genes in AAAs. The present study identified 40 differentially expressed circRNAs (DECs) in the GSE144431, 90 differentially expressed miRNAs (DEmiRs) in the GSE62179, and 168 differentially expressed mRNAs (DEGs) with the same direction regulation (130 downregulated and 38 upregulated) in the GSE7084, GSE57691, and GSE47472 datasets identified regarding AAAs. The miRNA response elements (MREs) of three DECs were then predicted. Four overlapping miRNAs were obtained by intersecting the predicted miRNA and DEmiRs. Then, 17 overlapping mRNAs were obtained by intersecting the predicted target mRNAs of 4 miRNAs with 168 DEGs. Furthermore, the circRNA-miRNA-mRNA network was constructed through 3 circRNAs, 4 miRNAs, and 17 mRNAs, and three hub genes (SOD2, CCR7, and PGRMC1) were identified. Simultaneously, functional enrichment and pathway analysis were performed within genes in the circRNA-miRNA-mRNA network. Three of them (SOD2, CCR7, and PGRMC1) were suggested to be crucial based on functional enrichment, protein-protein interaction, and ceRNA network analysis. Furthermore, the expression of SOD2 and CCR7 may be regulated by hsa_circ_0011449/hsa_circ_0081968/hsa-let-7f-5p; the expression of PGRMC1 may be regulated by hsa_circ_0011449/hsa_circ_0081968-hsa-let-7f-5p/hsa-let-7e-5p. In conclusion, the ceRNA interaction axis we identified may be an important target for the treatment of abdominal aortic aneurysms. This study provided further understanding of the potential pathogenesis from the perspective of the circRNA-related competitive endogenous RNA network in AAAs.

Zhang H ，Bian C ，Tu S ，Yin F ，Guo P ，Zhang J ，Wu Y ，Yin Y ，Guo J ，Han Y ... -  《-》

Identification of key genes and pathways involved in abdominal aortic aneurysm initiation and progression.

Su Z ，Gu Y 《-》