Identification of immune-related signatures and pathogenesis differences between thoracic aortic aneurysm patients with bicuspid versus tricuspid valves via weighted gene co-expression network analysis

Abstract

Background Thoracic 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. Methods Publically-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. Results WGCNA 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. Conclusions TAA/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.