Background The diameter of the abdominal aortic aneurysm (AAA) is the most commonly used parameter for the prediction of occurrence of AAA rupture. However, the most vulnerable region of the aortic wall may be different from the most dilated region of AAA under pressure. The present study is the first to use weighted gene coexpression network analysis (WGCNA) to detect the coexpressed genes that result in regional weakening of the aortic wall. Methods The GSE165470 raw microarray dataset was used in the present study. Differentially expressed genes (DEGs) were filtered using the “limma” R package. DEGs were assessed by Gene Ontology biological process (GO-BP) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. WGCNA was used to construct the coexpression networks in the samples with regional weakening of the AAA wall and in the control group to detect the gene modules. The hub genes were defined in the significant functional modules, and a hub differentially expressed gene (hDEG) coexpression network was constructed with the highest confidence based on protein–protein interactions (PPIs). Molecular compound detection (MCODE) was used to identify crucial genes in the hDEG coexpression network. Crucial genes in the hDEG coexpression network were validated using the GSE7084 and GSE57691 microarray gene expression datasets. Result A total of 350 DEGs were identified, including 62 upregulated and 288 downregulated DEGs. The pathways were involved in immune responses, vascular smooth muscle contraction and cell–matrix adhesion of DEGs in the samples with regional weakening in AAA. Antiquewhite3 was the most significant module and was used to identify downregulated hDEGs based on the result of the most significant modules negatively related to the trait of weakened aneurysm walls. Seven crucial genes were identified and validated: ACTG2, CALD1, LMOD1, MYH11, MYL9, MYLK, and TPM2. These crucial genes were associated with the mechanisms of AAA progression. Conclusion We identified crucial genes that may play a significant role in weakening of the AAA wall and may be potential targets for medical therapies and diagnostic biomarkers. Further studies are required to more comprehensively elucidate the functions of crucial genes in the pathogenesis of regional weakening in AAA.

Objective To explore the candidate genes that play significant roles in the interconnection of the abdominal aortic aneurysm (AAA) and diabetes mellitus (DM). Methods We used the Biomedical Discovery Support System (BITOLA) to screen out the candidate intermediate molecular (CIM) "Gene or Gene Product" that are related to AAA and DM. The dataset of GSE13760, GSE7084, GSE57691, GSE47472 were used to analyze differentially expressed genes (DEGs) of AAA and DM compared to the healthy status. We use the online tool of Venny 2.1 assisted by manual checking to identify the overlapped DEGs with the CIMs, and the Human eFP Browser examine the tissue specific expression levels of the detected genes in order to recognize strong expressed genes in both human artery and pancreatic tissue. Results There are 86 CIMs suggested by the closed BITOLA system. Among variety of DEGs of AAA and DM, 8 genes in GSE7084 (ISG20, ITGAX, DSTN, CCL5, CCR5, AGTR1, CD19, CD44) and 2 genes (PSMD12, FAS) in GSE13760 were found to be overlapped with the 86 CIM. By manual checking and comparing with tissue-specific gene data through Human eFP Browser, the gene PSMD12 (proteasome 26S subunit, non-ATPase 12) was recognized to be strongly expressed in both the aorta and pancreatic tissue. Conclusion We proposed a hypothesis through text mining that PSMD12 is involved or potentially involved in the interconnection of AAA with DM, which may provide a new clue for study novel therapeutic strategy for these two diseases.