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Bioinformatics Analysis Reveals the Potential Diagnostic Biomarkers for Abdominal Aortic Aneurysm.

ABSTRACT: Objectives: Abdominal aortic aneurysms (AAAs) are associated with high mortality rates. The genes and pathways linked with AAA remain poorly understood. This study aimed to identify key differentially expressed genes (DEGs) linked to the progression of AAA using bioinformatics analysis. Methods: Gene expression profiles of the GSE47472 and GSE57691 datasets were acquired from the Gene Expression Omnibus (GEO) database. These datasets were merged and normalized using the "sva" R package, and DEGs were identified using the limma package in R. The functions of these DEGs were assessed using Cytoscape software. We analyzed the DEGs using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis. Protein-protein interaction networks were assembled using Cytoscape, and crucial genes were identified using the Cytoscape plugin, molecular complex detection. Data from GSE15729 and GSE24342 were also extracted to verify our findings. Results: We found that 120 genes were differentially expressed in AAA. Genes associated with inflammatory responses and nuclear-transcribed mRNA catabolic process were clustered in two gene modules in AAA. The hub genes of the two modules were IL6, RPL21, and RPL7A. The expression levels of IL6 correlated positively with RPL7A and negatively with RPL21. The expression of RPL21 and RPL7A was downregulated, whereas that of IL6 was upregulated in AAA. Conclusions: The expression of RPL21 or RPL7A combined with IL6 has a diagnostic value for AAA. The novel DEGs and pathways identified herein might provide new insights into the underlying molecular mechanisms of AAA.

SUBMITTER: Xie X

PROVIDER: S-EPMC8329524 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:PurposeFerroptosis plays a crucial role in the development and progression of abdominal aortic aneurysm (AAA). The aim of this study was to identify differentially expressed genes associated with ferroptosis in AAA through bioinformatics analysis combined with experimental validation.Materials and methodsFirstly, the mRNA expression profile datasets GSE57691 and GSE47472 from Gene Expression Omnibus database were screened, and principal component analysis was carried out. Next, the R software (version 4.0.0) was used to analyze potentially differentially expressed genes associated with AAA and ferroptosis. Subsequently, protein-protein interaction analysis, gene ontology enrichment analysis, and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis were performed on the selected candidate genes. Finally, quantitative real-time polymerase chain reaction (qRT-PCR) was used to detect the expression levels of the first five selected abnormal ferroptosis-related genes in clinical samples obtained from patients with AAA and healthy controls.ResultsBased on the information contained in the two datasets, a total of 20 differentially expressed ferroptosis-related genes (three upregulated genes and 17 downregulated genes) were selected. Protein-protein interaction analysis demonstrated interaction between these genes, while gene ontology enrichment analysis of ferroptosis genes with differential expression indicated that some enrichment items were associated with oxidative stress. The qRT-PCR results showed that the expression levels of interleukin-6 (IL-6), peroxiredoxin 1 (PRDX1), and stearoyl-CoA desaturase (SCD) were consistent with the bioinformatics prediction results obtained from the mRNA chip.ConclusionBioinformatics analysis identified 20 potential ferroptosis-related differentially expressed genes in AAA. Further verification by qRT-PCR showed that IL-6, PRXD1, and SCD might affect the process of AAA by regulating ferroptosis. Our results might assist in further understanding the pathogenesis of AAA and guiding treatment.

Project description: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.

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Bioinformatics Analysis Reveals the Potential Diagnostic Biomarkers for Abdominal Aortic Aneurysm.

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