Network Mining Indicated the Triglycerides as the Most Related Clinical Relevance to Age‑related Transcriptional Changes in the Aorta

Background: Aging is believed to be one of the main causes of cardiovascular diseases. The incidence of cardiovascular dysfunctions has increased substantially over the past few years. However, our understanding of molecular mechanisms of age‑related vascular disorders remains somehow unclear, and an effective treatment has not been developed. A biological network is a collection of interactions between molecular regulators and their targets in cells governing gene expression level that is usually built by employing omics data, facilitating the inference of molecular basis of complex diseases. Materials and Methods: GSE50833 series containing aorta samples of 6‑month‑old mice (n = 6) and 20‑month‑old mice (n = 6) obtained from Janvier labs (Saint Berthevin, France) were downloaded from Gene Expression Omnibus database and the verified Agilent probe IDs were subjected to build a weighted gene coexpression network by a bioinformatics tool known as Weighted Gene Coexpression Network Analysis. We then conducted a network‑driven integrative analysis to find significant modules and underlying pathways. Results: The unique genes extracted from normalized gene expression values were parsed into six modules. Among the incorporated clinical traits, the most significant module was associated with triglycerides enriched in biological terms, including proteolysis, blood circulation, and circulatory system process. Moreover, Enpp5, Fez1, Kif1a, F3, H2‑Q7, and Pa × 8 were taken as putative hallmark molecules by further screening. Conclusion: the main goal of this analysis was the prioritization of genes that likely play a role in the pathogenesis of vascular diseases. We attempted to provide a system understanding of the potential connections among these genes.