Dataset Information

Weighted miRNA co-expression network reveals potential roles of apoptosis related pathways and crucial genes in thoracic aortic aneurysm.

ABSTRACT:

Background

Thoracic 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.

Methods

Differentially 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.

Results

The 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.

Conclusions

We 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.

SUBMITTER: Chen S

PROVIDER: S-EPMC8182548 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundThoracic aortic aneurysm (TAA) occurs due to pathological aortal dilation, and both individuals with normal tricuspid aortic valves (TAV) or abnormal bicuspid aortic valves (BAV), the latter being a congenital condition, are at risk. However, some differences are present between TAA/BAV and TAA/TAV with respect to their pathophysiological processes and molecular mechanisms, but their exact nature is still mostly unknown. Therefore, it is necessary to elucidate TAA developmental differences among BAV vs. TAV patients.MethodsPublically-available gene expression datasets, aortic tissue derived from TAA/BAV and TAA/TAV individuals, were analyzed by weighted gene co-expression network analysis (WGCNA) to identify gene modules associated with those conditions. Gene Ontology (GO) enrichment analysis was performed on those modules to identify the enriched genes within those modules, which were verified by Gene Set Variation Analysis (GSVA) on a dataset derived from aortic smooth muscle cell gene expression between TAA/TAV and TAV/BAV patients. Immune cell infiltration patterns were then analyzed by CIBERSORT, and a protein-protein interaction (PPI) network was constructed based on WGCNA and enrichment analysis results to identify hub genes, followed by validation via stepwise regression analysis. Three signatures most strongly associated with TAA/TAV were confirmed by receiver operating characteristic (ROC) and decision curve analyses (DCA) between prior-established training and testing gene sets.ResultsWGCNA delineated 2 gene modules being associated with TAA/TAV vs. TAA/BAV; both were enriched for immune-associated genes, such as those relating to immune responses, etc., under enrichment analysis. TAA/TAV and TAA/BAV tissues also had differing infiltrating immune cell proportions, particularly with respect to dendritic, mast and CD4 memory T cells. Identified three signatures, CD86, integrin beta 2 (ITGB2) and alpha M (ITGAM), as yielding the strongest associations with TAA/TAV onset, which was verified by areas under the curve (AUC) at levels approximating 0.8 or above under ROC analysis, indicating their predictive value for TAA/TAV onset. However, we did not examine possible confounding variables, so there are many alternative explanations for this association.ConclusionsTAA/TAV pathogenesis was found to be more associated with immune-related gene expression compared to TAA/BAV, and the identification of three strongly-associated genes could facilitate their usage as future biomarkers for diagnosing the likelihood of TAA/TAV onset vs. TAA/BAV, as well as for developing future treatments.