Identification of key genes and pathways in abdominal aortic aneurysm by integrated bioinformatics analysis

Comprehensive bioinformatics analysis revealed potential key genes and pathways underlying abdominal aortic aneurysm

Abdominal aortic aneurysm (AAA) is a permanent, asymptomatic segmental dilatation of the abdominal aorta, with a high mortality risk upon rupture. Identification of potential key genes and pathways may help to develop curative drugs for AAA. We conducted RNA-seq on abdominal aortic tissues from both AAA patients and normal individuals as a control group. Integrated bioinformatic analysis was subsequently performed to comprehensively reveal potential key genes and pathways. A total of 1148 differential expressed genes (DEGs) (631 up-regulated and 517 down-regulated) were identified in our study. Gene Ontology (GO) analysis revealed enrichment in terms related to extracellular matrix organization, while KEGG analysis indicated enrichment in hematopoietic cell lineage and ECM-receptor interaction. Protein-protein interaction (PPI) network analysis revealed several candidate key genes, and differential expression of 6 key genes (CXCL8, CCL2, PTGS2, SELL, CCR7, and CXCL1) was validated by Gene Expression Omnibus (GEO) datasets. Receiver operating characteristic curve (ROC) analysis demonstrated these genes' high discriminatory ability between AAA and normal tissues. Immunohistochemistry indicated that several key genes were highly expressed in AAA tissues. Single-cell RNA sequencing revealed differential distribution patterns of these identified key genes among various cell types. 26 potential drugs linked to our key genes were found through DGIdb. Overall, our study provides a comprehensive evaluation of potential key genes and pathways in AAA, which could pave the way for the development of curative pharmacological therapies.

Single-cell RNA sequencing provides novel insights to pathologic pathways in abdominal aortic aneurysm

There is gaining popularity in the use of single-cell technology and analysis in studying the pathogenesis of abdominal aortic aneurysm (AAA). As there are no current pharmacologic therapies for impeding aneurysm growth or preventing AAA rupture, identifying key pathways involved in AAA formation is critical for the development of future therapies. Single-cell RNA sequencing (scRNA-seq) technology provides an unbiased and global view of transcriptomic characteristics within each of the major cell types in aneurysmal tissues. In this brief review, we examine the current literature utilizing scRNA-seq for the analysis of AAA and discuss trends and future utility of this technology.

Identification of Novel Biomarkers for Abdominal Aortic Aneurysm Promoted by Obstructive Sleep Apnea

BACKGROUND

We sought to find new biomarkers for abdominal aortic aneurysms (AAA) caused by chronic intermittent hypoxia (CIH).

METHODS

The AAA mice model was created using Ang II. The mice were divided into normoxic and CIH groups. The structure of AAA was observed using abdominal ultrasonography, Elastica van Gieson (EVG), and hematoxylin and eosin (HE) staining. The expression of ɑ-SMA was investigated using immunohistochemistry. The novel biomarkers were screened using bioinformatics analysis. Quantitative real-time polymerase chain reaction (qRT-PCR) was employed to verify the expression of novel genes in both normal oxygen and CIH.

RESULTS

CIH appears to cause greater aortic dilation, higher AAA incidence, lower survival rate, thicker vessel wall, and more brittle elastic lamellae when compared to controls. The immunohistochemistry results showed that the expression of ɑ-SMA in the CIH group was reduced significantly. Four novel genes, including Homer2, Robo2, Ehf, and Asic1, were found to be differentially expressed between normal oxygen and CIH using qRT-PCR, indicating the same trend as bioinformatics analysis.

CONCLUSIONS

We discovered that CIH could hasten the occurrence and progression of AAA. Four genes (Homer2, Robo2, Ehf, and Asic1) may be novel biomarkers for AAA, which could aid in the search for new therapies for patients with AAA caused by CIH.

Astaxanthin Induces Transcriptomic Responses Associated with Lifespan Extension in Caenorhabditis elegans

Astaxanthin is a marine xanthophyll carotenoid which effectively prevents intracellular oxidative stress and has beneficial effects against various human diseases. It has been shown that astaxanthin protects Caenorhabditis elegans (C. elegans) from oxidative damages and extends the lifespan of C. elegans possibly by modulating genes involved in insulin/insulin-like growth factor (IGF) signaling (IIS) and the oxidoreductase system, although the exact mechanisms remain elusive. In this study, RNA sequencing analyses were employed to identify the differentially expressed genes in C. elegans in response to astaxanthin treatment. A total of 190 mRNAs and 6 microRNAs (miRNAs) were significantly changed by astaxanthin treatment in C. elegans. Gene ontology (GO) term and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses indicated that the mRNAs and miRNAs significantly altered by astaxanthin mainly function in innate immunity, lipid metabolism and stress responses, a significant portion of which are related to lifespan regulation in C. elegans. The study revealed novel mRNA and miRNA targets of astaxanthin, providing new insights for understanding the anti-aging mechanisms and the biological function of astaxanthin.

Establishment of a Combined Diagnostic Model of Abdominal Aortic Aneurysm with Random Forest and Artificial Neural Network

Objectives. Abdominal aortic aneurysm (AAA), a disease with high mortality, is limited by the current diagnostic methods in the early screening. This study aimed to screen novel and significant biomarkers and construct a diagnostic model for AAA by using a novel machine learning method, i.e., an ensemble of the random forest (RF) algorithm and artificial neural network (ANN). Methods and Results. Through a search of the Gene Expression Omnibus (GEO) database, two large-sample gene expression datasets (GSE57691 and GSE47472) were downloaded and preprocessed. Differentially expressed genes (DEGs) in GSE57691 were identified by R software, followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment using the Database for Annotation, Visualization, and Integrated Discovery (DAVID). Essential metabolic pathways related to positive regulation of cell death and NAD binding were found. Then, RF was used to identify key genes from the DEGs, and an AAA diagnostic model was established by ANN. A transcription factor (TF) regulatory network of key genes related to angiogenesis and endothelial migration was constructed. Finally, a validation dataset was used to validate the model and the area under the receiver operating characteristic curve (AUC) value was high. Conclusion. Potential AAA-associated gene biomarkers were identified by RF, and a novel early diagnostic model of AAA was established by ANN. The AUC indicated that the diagnostic model had a highly satisfactory diagnostic performance. In conclusion, this study will provide a promising theoretical basis for further clinical and experimental studies.

Identification of potential novel biomarkers for abdominal aortic aneurysm based on comprehensive analysis of circRNA-miRNA-mRNA networks

Abdominal aortic aneurysm (AAA) is a life-threatening disorder and, therefore, investigation into its underlying mechanisms in light of the competing endogenous RNAs (ceRNAs) hypothesis has gradually increased. However, there is still lacking systematic analysis on AAA-associated circular RNA (circRNA)-microRNA (miRNA/miR)-messenger RNA (mRNA) interaction networks based on bioinformatics methods. The present study attempted to identify novel molecular biomarkers for AAA by profiling circRNA-miRNA-mRNA networks using three public microarray datasets (GSE7084, GSE57691 and GSE144431). A total of 135 differentially expressed genes (DEGs) and 142 differentially expressed circRNAs were detected using the limma R package with the statistical threshold of P<0.05 and |log₂fold change (FC)| >1.5. In addition, 12 circRNA-miRNA-mRNA axes were identified to construct upregulated and downregulated ceRNA networks using Cytoscape. Based on molecular complex detection algorithm, (hsa_circ_0057691/0092108/0006845/0082182)- miR-330-5p-calponin 1 (CNN1) and (hsa_circ_0061482/0011450/0008351/0004121)-miR-326-CD8a molecule (CD8A) were recognized as the center axes in ceRNA networks. Reverse transcription-quantitative PCR results verified the significant downregulation of CNN1 and upregulation of CD8A in human AAA tissues (P<0.05). In addition, four upregulated circRNA/mRNA axes, and five downregulated circRNA/mRNA axes were revealed to have possible biological functions in the pathogenesis of AAA using the Cytoscape software. Receiver operating characteristic analysis demonstrated the accuracy of these nine DEGs involved in these axes for AAA diagnosis with area under the curves >0.80. The present study revealed novel circRNA-miRNA-mRNA networks associated with AAA, especially for CNN1 and CD8A axes with the potential function of ‘focal adhesion’ and ‘immune response’, respectively. Overall, the present findings may provide evidence to explore the implicated ceRNAs in the molecular mechanisms and as novel biomarkers for AAA.

Expression Pattern and Clinical Value of Key m6A RNA Modification Regulators in Abdominal Aortic Aneurysm

Background

Aberrant expression of N6-methyladenosine (m6A) RNA modification regulators plays a critical role in a variety of human diseases. However, their implication in abdominal aortic aneurysm (AAA) remains largely unknown. Herein, we sought to explore the general expression pattern and potential functions of m6A regulators in AAA.

Methods

We analyzed gene expression data of m6A regulators in human AAA and normal tissues from public GEO database. The R package and other tools such as m6A2Target database, Gene ontology (GO) functional and Kyoto encyclopedia of genes and genomes (KEGG) pathway analyses, gene set variation analysis (GSVA), Search Tool for the Retrieval of Interacting Genes (STRING), starBase, miRDB and Cytoscape software were applied for bioinformatics analysis to investigate the downstream molecular mechanisms and upstream regulatory mechanisms for distinctly expressed regulators. Quantitative real-time PCR (qRT-PCR) and enzyme-linked immunosorbent assay (ELISA) were performed to validate the expression of key m6A regulators in our collected human AAA specimens.

Results

We found that METTL14 and HNRNPC were the downregulated m6A regulators, and RBM15B was the upregulated methylation transferase in human AAA. The modified genes were primarily enriched in RNA catabolic process, regulation of translation, focal adhesion, transcription coregulator activity, ribosome, RNA transport, cell cycle, et al. METTL14, HNRNPC and RBM15B levels were correlated with the immune infiltration degree of Tcm, macrophages, mast cells, Tgd and NK CD56bright cells. A total of 154 and 76 target genes of three regulators were separately involved in body metabolism and autophagy in AAA disease, and their interactive relationships and hub genes were identified. The lncRNA-miRNA-mRNA interaction regulatory networks were also constructed for METTL14, HNRNPC and RBM15B. Based on our clinical tissue and serum samples, METTL14 exhibited lower expression levels in AAA and its rupture type, and low METTL14 expression was associated with high levels of WBC and CRP (all P < 0.05).

Conclusion

Our study presents an overview of the expression pattern and functional significance of m6A regulators in human AAA. Our findings will provide a valuable resource that may guide both mechanistic and therapeutic analyses about the role of key m6A regulators in AAA.