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.

Comprehensive RNA Expression Analysis Revealed Biological Functions of Key Gene Sets and Identified Disease-Associated Cell Types Involved in Rat Traumatic Brain Injury

“…The correlation between modules and sample traits was estimated so that modules that are highly correlated with sample traits could be identified and gene functions of related modules could be further studied. WGCNA significantly reduces errors caused by multiple testing problems inherent in microarray data, while maximizing the use of all data, as it uses all gene expression data from samples—instead of focusing only on differentially expressed genes—to construct the scale-free weighted network [ 35 ]. Furthermore, the scale-free weighted network has a high degree of robustness, which means that the errors in individual genes will not affect the overall results.…”

Identification of key monocytes/macrophages related gene set of the early-stage abdominal aortic aneurysm by integrated bioinformatics analysis and experimental validation

“…WGCNA is one of the most used methods for inferencing gene networks from transcriptomic data. Previous studies have used WGCNA to identify hub genes for AAA based on bulk transcriptomic data ( 19 , 20 ). But the same gene may have different effects on AAA progression in different cell types.…”

Aortic Aneurysm: Finding the Right Target

“…Kan K.J. et al [6] further identified significant genes in abdominal AA patients. Moreover, the authors could predict the potential therapeutic compounds for these genes.…”