Dataset Information

Weighted Gene Co-Expression Network Analysis Reveals Key Genes and Potential Drugs in Abdominal Aortic Aneurysm.

ABSTRACT: Abdominal aortic aneurysm (AAA) is a prevalent aortic disease that causes high mortality due to asymptomatic gradual expansion and sudden rupture. The underlying molecular mechanisms and effective pharmaceutical therapy for preventing AAA progression have not been fully identified. In this study, we identified the key modules and hub genes involved in AAA growth from the GSE17901 dataset in the Gene Expression Omnibus (GEO) database through the weighted gene co-expression network analysis (WGCNA). Key genes were further selected and validated in the mouse dataset (GSE12591) and human datasets (GSE7084, GSE47472, and GSE57691). Finally, we predicted drug candidates targeting key genes using the Drug-Gene Interaction database. Overall, we identified key modules enriched in the mitotic cell cycle, GTPase activity, and several metabolic processes. Seven key genes (CCR5, ADCY5, ADCY3, ACACB, LPIN1, ACSL1, UCP3) related to AAA progression were identified. A total of 35 drugs/compounds targeting the key genes were predicted, which may have the potential to prevent AAA progression.

SUBMITTER: Kan KJ

PROVIDER: S-EPMC8152975 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature

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Weighted Gene Co-Expression Network Analysis Reveals Key Genes and Potential Drugs in Abdominal Aortic Aneurysm.

Kan Ke-Jia KJ Guo Feng F Zhu Lei L Pallavi Prama P Sigl Martin M Keese Michael M

Biomedicines 20210513 5

Abdominal aortic aneurysm (AAA) is a prevalent aortic disease that causes high mortality due to asymptomatic gradual expansion and sudden rupture. The underlying molecular mechanisms and effective pharmaceutical therapy for preventing AAA progression have not been fully identified. In this study, we identified the key modules and hub genes involved in AAA growth from the GSE17901 dataset in the Gene Expression Omnibus (GEO) database through the weighted gene co-expression network analysis (WGCNA ...[more]

PMID: 34068179

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Project description:BackgroundThere is still an unmet need for therapeutic drugs for patients with an abdominal aortic aneurysm (AAA), especially for candidates unsuitable for surgical or interventional repair. Therefore, the purpose of this in silico study is to identify significant genes and regulatory mechanisms in AAA patients to predicate the potential therapeutic compounds for significant genes.MethodsThe GSE57691 dataset was obtained from Gene Expression Omnibus (GEO) and used to identify the differentially expressed genes (DEGs) and weighted correlation network analysis (WGCNA). The biological function of DEGs was determined using gene ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG). AAA-related genes were obtained from the Comparative Toxicogenomics Database (CTD) using the keywords: aortic aneurysm and abdominal. The hub genes in AAA were obtained by overlapping DEGs, WGCNA-based hub genes, and CTD-based genes. The diagnostic values of hub genes were determined using ROC curve analysis. Hereby, a TF-miRNA-hub gene network was constructed based on the miRnet database. Using these data, potential therapeutic compounds for the therapy of AAA were predicted based on the Drug Gene Interaction Database (DGIdb).ResultsA total of 218 DEGs (17 upregulated and 201 downregulated) and their biological function were explored; 4093 AAA-related genes were derived by text mining. Three hub modules and 144 hub genes were identified by WGCNA. asparagine synthetase (ASNS), axin-related protein 2 (AXIN2), melanoma cell adhesion molecule (MCAM), and the testis-specific Y-encoded-like protein 1 (TSPYL1) were obtained as intersecting hub genes and the diagnostic values were confirmed with ROC curves. As potential compounds targeting the hub genes, asparaginase was identified as the target compound for ASNS. Prednisolone and abiraterone were identified as compounds targeting TSPYL1. For MCAM and TSPYL1, no potential therapeutic compound could be predicted.ConclusionUsing WGCNA analysis and text mining, pre-existing gene expression data were used to provide novel insight into potential AAA-related protein targets. For two of these targets, compounds could be predicted.

Project description:BackgroundThoracic aortic aneurysm (TAA) is a potentially life-threatening disease for which few medical therapies are available. Thus, it is critically important to investigate the underlying molecular mechanisms of TAA, and identify potential targets for TAA treatment.MethodsDifferentially expressed miRNAs (DEMs) and differentially expressed genes (DEGs) were screened, and a weighted correlation network analysis (WGCNA) was employed to construct a weighted miRNA co-expression network using GSE110527. The DEMs were then mapped into the whole co-expression network of all samples, and a DEM coexpression network was created. Molecular Complex Detection (MCODE) was used to identify crucial miRNAs. Target genes were predicted using the miRTarbase database, and further screened by identifying genes that overlapped with the DEGs of GSE26155. The screened target genes were validated using GSE9106, and the successfully validated genes were considered as crucial genes. Finally, a miRNA risk score for diagnosing TAA was calculated by undertaking a least absolute shrinkage and selection operator (LASSO) regression.ResultsThe tumor necrosis factor (TNF)-related apoptosis-inducing ligand (TRAIL) signaling pathway was found in DEM functional enrichment. Crucial miRNAs were identified and target genes were predicted and associated with the regulation of the TRAIL signaling pathway. Next, 113 important target genes were identified as overlapping with the DEGs of GSE26155. These genes were further validated, and 5 successfully validated genes were considered as crucial genes. Finally, the miRNA risk score calculated by the LASSO regression was shown to have potential diagnostic value.ConclusionsWe performed a WGCNA analysis to construct a weighted miRNA co-expression network, predicted target genes of crucial miRNAs, identified crucial genes, and finally calculated a miRNA risk score. The results showed that pathways and genes associated with apoptosis appear to play an important role in TAA pathogenesis, and that medications targeting apoptosis might slow TAA progression. Future in vitro and in vivo experimental studies need to be undertaken to further validate our findings and investigate the mechanistic details of these crucial miRNAs and crucial genes.

Project description:Approximately 13,000 people die of an abdominal aortic aneurysm (AAA) every year. This study aimed to identify the immune response-related genes that play important roles in AAA using bioinformatics approaches. We downloaded the GSE57691 and GSE98278 datasets related to AAA from the Gene Expression Omnibus database, which included 80 AAA and 10 normal vascular samples. CIBERSORT was used to analyze the samples and detect the infiltration of 22 types of immune cells and their differences and correlations. The principal component analysis showed significant differences in the infiltration of immune cells between normal vascular and AAA samples. High proportions of CD4+ T cells, activated mast cells, resting natural killer cells, and 12 other types of immune cells were found in normal vascular tissues, whereas high proportions of macrophages, CD8+ T cells, resting mast cells, and six other types of immune cells were found in AAA tissues. In the selected samples, we identified 39 upregulated (involved in growth factor activity, hormone receptor binding, and cytokine receptor activity) and 133 downregulated genes (involved in T cell activation, cell chemotaxis, and regulation of immune response mediators). The key differentially expressed immune response-related genes were screened using the STRING database and Cytoscape software. Two downregulated genes, PI3 and MAP2K1, and three upregulated genes, SSTR1, GPER1, and CCR10, were identified by constructing a protein-protein interaction network. Functional enrichment of the differentially expressed genes was analyzed, and the expression of the five key genes in AAA samples was verified using quantitative polymerase chain reaction, which revealed that MAP2K1 was downregulated in AAA, whereas SSTR1, GEPR1, and CCR10 were upregulated; there was no significant difference in PI3 expression. Our study shows that normal vascular and AAA samples can be distinguished via the infiltration of immune cells. Five genes, PI3, MAP2K1, SSTR1, GPER1, and CCR10, may play important roles in the development, diagnosis, and treatment of AAA.

Dataset Information

Weighted Gene Co-Expression Network Analysis Reveals Key Genes and Potential Drugs in Abdominal Aortic Aneurysm.

Publications

Weighted Gene Co-Expression Network Analysis Reveals Key Genes and Potential Drugs in Abdominal Aortic Aneurysm.

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