Construction of ferroptosis-related prediction model for pathogenesis, diagnosis and treatment of ruptured abdominal aortic aneurysm.

Abdominal aortic aneurysm (AAA) is a dangerous cardiovascular disease, which often brings great psychological burden and economic pressure to patients. If AAA rupture occurs, it is a serious threat to patients' lives. Therefore, it is of clinical value to actively explore the pathogenesis of ruptured AAA and prevent its occurrence. Ferroptosis is a new type of cell death dependent on lipid peroxidation, which plays an important role in many cardiovascular diseases. In this study, we used online data and analysis of ferroptosis-related genes to uncover the formation of ruptured AAA and potential therapeutic targets. We obtained ferroptosis-related differentially expressed genes (Fe-DEGs) from GSE98278 dataset and 259 known ferroptosis-related genes from FerrDb website. Enrichment analysis of differentially expressed genes (DEGs) was performed by gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG). Receiver Operating characteristic (ROC) curve was employed to evaluate the diagnostic abilities of Fe-DEGs. Transcription factors and miRNAs of Fe-DEGs were identified through PASTAA and miRDB, miRWalk, TargetScan respectively. Single-sample gene set enrichment analysis (ssGSEA) was used to observe immune infiltration between the stable group and the rupture group. DGIdb database was performed to find potential targeted drugs of DEGs. GO and KEGG enrichment analysis found that DEGs mainly enriched in "cellular divalent inorganic cation homeostasis," "cellular zinc ion homeostasis," "divalent inorganic cation homeostasis," "Mineral absorption," "Cytokine - cytokine receptor interaction," "Coronavirus disease - COVID-19." Two up-regulated Fe-DEGs MT1G and DDIT4 were found to further analysis. Both single and combined applications of MT1G and DDIT4 showed good diagnostic efficacy (AUC = 0.8254, 0.8548, 0.8577, respectively). Transcription factors STAT1 and PU1 of MT1G and ARNT and MAX of DDIT4 were identified. Meanwhile, has_miR-548p-MT1G pairs, has_miR-53-3p/has_miR-181b-5p/ has_miR-664a-3p-DDIT4 pairs were found. B cells, NK cells, Th2 cells were high expression in the rupture group compared with the stable group, while DCs, Th1 cells were low expression in the rupture group. Targeted drugs against immunity, GEMCITABINE and INDOMETHACIN were discovered. We preliminarily explored the clinical significance of Fe-DEGs MT1G and DDIT4 in the diagnosis of ruptured AAA, and proposed possible upstream regulatory transcription factors and miRNAs. In addition, we also analyzed the immune infiltration of stable and rupture groups, and found possible targeted drugs for immunotherapy.

Wang A ，Zhou L 《-》

Construction of the circRNA-miRNA-mRNA axis based on ferroptosis-related gene AKR1C1 to explore the potential pathogenesis of abdominal aortic aneurysm.

Abdominal aortic aneurysm (AAA) is a cardiovascular disease that seriously threatens human health and brings huge economic burden. At present, its pathogenesis remains unclear and its treatment is limited to surgical treatment. With the deepening and analysis of studies on the mechanism of ferroptosis, a new idea has been provided for the clinical management of AAA patients, including diagnosis, treatment and prevention. Therefore, this paper aims to construct a competitive endogenous RNA (ceRNA) regulatory axis based on ferroptosis to preliminarily explore the pathogenesis and potential therapeutic targets of AAA. We obtained upregulated and downregulated ferroptosis-related DEGs (FRGs) from GSE144431 dataset and 60 known ferroptosis-related genes. Pearson correlation analysis was used to find aldoketone reductase 1C (AKR1C1) in AAA samples. Enrichment analysis of these genes was performed via Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Correlation test between immune cells and AKR1C1 was investigated through single-sample gene set enrichment analysis (ssGSEA). The AKR1C1-miRNA pairs were predicted by the TargetScan database and miRWalk database. Circular RNA (CircRNA)-miRNA pairs were selected by the CircInteractome database. Overlapping miRNA between circRNA-miRNA and AKR1C1-miRNA pairs was visualized by Venn diagram. Finally, the circRNA-miRNA-mRNA axis was constructed by searching for upstream circRNA and downstream mRNA of overlapping miRNA. Only one downregulated AKR1C1 gene was found in GSE144431 and 60 ferroptosis-related genes. Functional Enrichment and Pathway Analysis of AKR1C1-related genes were further explored, and it was observed that they were mainly enriched in "response to oxidative stress," "glutathione biosynthetic process" and "nonribosomal peptide biosynthetic process," "Ferroptosis," "Glutathione metabolism" and "Chemical carcinogenesis-reactive oxygen species." They were also found to be significantly associated with most immune cells, including Activated Dendritic cells, CD56dim Natural killer cells, Gamma Delta T cells, Immature B cells, Plasmacytoid dendritic cell, Type 2 T helper cell, Activated CD4 T cell and Type 1 T helper cell. Has_circ_0005073-miRNA-543 and AKR1C1-miRNA-543 were identified by Online Database analysis. Therefore, we have established the has_circ_0005073/miRNA-543/AKR1C1 axis in AAA. We found AKR1C1 was differentially expressed between normal and AAA groups. Based on AKR1C1, we constructed the has_circ_0005073/miRNA-543/AKR1C1 axis to analyze AAA.

Huang X ，Deng H 《-》

Key ferroptosis-related genes in abdominal aortic aneurysm formation and rupture as determined by combining bioinformatics techniques.

Abdominal aortic aneurysm (AAA) is a cardiovascular disease with high mortality and pathogenesis closely related to various cell death types, e.g., autophagy, apoptosis and pyroptosis. However, the association between AAA and ferroptosis is unknown. GSE57691 and GSE98278 dataset were obtained from the Gene Expression Omnibus database, and a ferroptosis-related gene (FRG) set was downloaded from the FerrDb database. These data were normalized, and ferroptosis-related differentially expressed genes (FDEGs, AAA vs. normal samples) were identified using the limma package in R. FRGs expression was analyzed by Gene Set Expression Analysis (GSEA), and FDEGs were analyzed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes (KEGG) pathway enrichment analyses using the clusterProfiler package in R and ClueGO in Cytoscape. Protein-protein interaction networks were assembled using Cytoscape, and crucial FDEGs were identified using CytoHubba. Critical FDEG transcription factors (TFs) were predicted with iRegulon. FDEGs were verified in GSE98278 set, and key FDEGs in AAA (compared with normal samples) and ruptured AAA (RAAA; compared with AAA samples) were identified. Ferroptosis-related immune cell infiltration and correlations with key genes were analyzed by CIBERSORT. Key FEDGs were reverified in Ang II-induced AAA models of ApoE-/- and CD57B/6J mice by immunofluorescence assay. In AAA and normal samples, 40 FDEGs were identified, and the expression of suppressive FRGs was significantly downregulated with GSEA. For FDEGs, the GO terms were response to oxidative stress and cellular response to external stimulus, and the KEGG pathways were the TNF and NOD-like receptor signaling pathways. IL6, ALB, CAV1, PTGS2, NOX4, PRDX6, GPX4, HSPA5, HSPB1, and NCF2 were the most enriched genes in the crucial gene cluster. CEBPG, NFAT5, SOX10, GTF2IRD1, STAT1, and RELA were potential TFs affecting these crucial genes. Ferroptosis-related immune cells involved in AAA formation were CD8+ T, naive CD4+ T, and regulatory T cells (Tregs); M0 and M2 macrophages; and eosinophils. Tregs were also involved in RAAA. GPX4, SLC2A1, and PEBP1 expression was downregulated in both the RAAA and AAA samples. GPX4 and PEBP1 were more important in AAA because they influenced ferroptosis-related immune cell infiltration, and SLC2A1 was more important in RAAA. This is the first study to show that ferroptosis is crucial to AAA/RAAA formation. The TNF and NOD-like signaling pathways and ferroptosis-related immune cell infiltration play key roles in AAA/RAAA. GPX4 is a key ferroptosis-related gene in AAA. Ferroptosis and related genes might be promising targets in the treatment of AAA/RAAA.

Ren J ，Lv Y ，Wu L ，Chen S ，Lei C ，Yang D ，Li F ，Liu C ，Zheng Y ... -  《Frontiers in Cardiovascular Medicine》

Identification of key microRNAs and genes associated with abdominal aortic aneurysm based on the gene expression profile.

What is the central question of this study? The aim was to identify abdominal aortic aneurysm (AAA)-associated microRNAs and their target genes in AAA using microarray analysis. What is the main finding and its importance? The main finding was that miR-145 and miR-30c-2* were found to be downregulated microRNAs in AAA, which could exert suppressive effects on AAA progression, and that miR-145 might target RAC2, whereas miR-30c-2* might target PIK3CD, IL1B and RAC2. The findings obtained from the study provide an enhanced understanding of microRNA as a therapeutic target to limit AAA. The aim of the study was to identify abdominal aortic aneurysm (AAA)-associated microRNAs (miRNAs) and genes potentially contributing to AAA. Differential analysis was performed to screen out differentially expressed genes (DEGs) and miRNAs in expression datasets of AAA-related miRNAs [GSE51226 (mouse)] and genes [GSE51227 (mouse) and GSE7084 (human)]. Then, gene ontology (GO) enrichment analysis of DEGs was compared with aneurysm-related GO to screen out DEGs related to the disease. The target genes of differential miRNAs were predicted and used to construct a miRNA-DEG regulatory network, followed by Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of target genes. Moreover, the protein-protein interaction network of target genes of miRNAs in the core position (hub-miRNA) with AAA-related genes was constructed to screen out hub genes. Finally, the target relationship between hub-miRNAs and their target genes was verified. There were 20 upregulated miRNAs and 20 downregulated miRNAs in AAA screened from the GSE51226 dataset (mouse). In addition, there were 1154 upregulated genes and 821 downregulated genes in the GSE51227 dataset (mouse), of which 246 DEGs were enriched in aneurysm-related GO entries in AAA. miR-145 and miR-30c-2* were the key miRNAs of AAA, both of which were downregulated in AAA and influenced pathways so as to affect AAA by regulating their respective target genes. The disease-related gene ACTA2 was downregulated, whereas DEGs including PIK3CD, IL1B, RAC2 and SELL were upregulated in AAA. Finally, it was proved that miR-145 targeted RAC2 and SELL, whereas miR-30c-2* targeted PIK3CD, IL1B and RAC2. Taken together, miR-145 and miR-30c-2*, downregulated in AAA, could potentially affect AAA, and miR-145 might target RAC2, whereas miR-30c-2* might target PIK3CD, IL1B and RAC2.

Yang P ，Cai Z ，Wu K ，Hu Y ，Liu L ，Liao M ... -  《-》

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 ... -  《-》