Identification of crucial genes in abdominal aortic aneurysm by WGCNA.

Abdominal aortic aneurysm (AAA) is the full thickness dilation of the abdominal aorta. However, few effective medical therapies are available. Thus, elucidating the molecular mechanism of AAA pathogenesis and exploring the potential molecular target of medical therapies for AAA is of vital importance. Three expression datasets (GSE7084, GSE47472 and GSE57691) were downloaded from the Gene Expression Omnibus (GEO). These datasets were merged and then normalized using the "sva" R package. Differential expressed gene (DEG) analysis and weighted gene co-expression network analysis (WGCNA) were conducted. We compared the co-expression patterns between AAA and normal conditions, and hub genes of each functional module were identified. DEGs were mapped to co-expression network under AAA condition and a DEG co-expression network was generated. Crucial genes were identified using molecular complex detection (MCODE) (a plugin in Cytoscape). In our study, 6 and 10 gene modules were detected for the AAA and normal conditions, respectively, while 143 DEGs were screened. Compared to the normal condition, genes associated with immune response, inflammation and muscle contraction were clustered in three gene modules respectively under the AAA condition; the hub genes of the three modules were MAP4K1, NFIB and HPK1, respectively. A DEG co-expression network with 102 nodes and 303 edges was identified, and a hub gene cluster with 10 genes from the DEG co-expression network was detected. YIPF6, RABGAP1, ANKRD6, GPD1L, PGRMC2, HIGD1A, GMDS, MGP, SLC25A4 and FAM129A were in the cluster. The expression levels of these 10 genes showed potential diagnostic value. Based on WGCNA, we detected 6 modules under the AAA condition and 10 modules in the normal condition. Hub genes of each module and hub gene clusters of the DEG co-expression network were identified. These genes may act as potential targets for medical therapy and diagnostic biomarkers. Further studies are needed to elucidate the detailed biological function of these genes in the pathogenesis of AAA.

Chen S ，Yang D ，Lei C ，Li Y ，Sun X ，Chen M ，Wu X ，Zheng Y ... -  《PeerJ》

Identification of crucial genes mediating abdominal aortic aneurysm pathogenesis based on gene expression profiling of perivascular adipose tissue by WGCNA.

With a mortality rate of 65-85%, a ruptured abdominal aortic aneurysm (AAA) can have catastrophic consequences for patients. However, few effective pharmaceutical treatments are available to treat this condition. Therefore, elucidating the pathogenesis of AAA and finding the potential molecular targets for medical therapies are vital lines of research. An mRNA microarray dataset of perivascular adipose tissue (PVAT) in AAA patients was downloaded and differentially expressed gene (DEG) screening was performed. Weighted gene co-expression networks for dilated and non-dilated PVAT samples were constructed via weighted correlation network analysis (WGCNA) and used to detect gene modules. Functional annotation analysis was performed for the DEGs and gene modules. We identified the hub genes of the modules and created a DEG co-expression network. We then mined crucial genes based on this network using Molecular Complex Detection (MCODE) in Cytoscape. Crucial genes with top-6 degree in the crucial gene cluster were visualized, and their potential clinical significance was determined. Of the 173 DEGs screened, 99 were upregulated and 74 were downregulated. Co-expression networks were built and we detected 6 and 5 modules for dilated and non-dilated PVAT samples, respectively. The turquoise and black modules for dilated PVAT samples were related to inflammation and immune response. MAP4K1 and PROK2 were the hub genes of these 2 modules, respectively. Then a DEG co-expression network with 112 nodes and 953 edges was created. PLAU was the crucial gene with the highest connectivity and showed potential clinical significance. Using WGCNA, gene modules were detected and hub genes and crucial genes were identified. These crucial genes might be potential targets for pharmaceutic therapies and have potential clinical significance. Future in vitro and in vivo experiments are required to more comprehensively explore the biological mechanisms by which these genes affect AAA pathogenesis.

Chen S ，Yang D ，Liu B ，Chen Y ，Ye W ，Chen M ，Zheng Y ... -  《-》

Identification and validation of key genes mediating intracranial aneurysm rupture by weighted correlation network analysis.

Rupture of intracranial aneurysm (IA) is the leading cause of subarachnoid hemorrhage. However, there are few pharmacological therapies available for the prevention of IA rupture. Therefore, exploring the molecular mechanisms which underlie IA rupture and identifying the potential molecular targets for preventing the rupture of IA is of vital importance. We used the Gene Expression Omnibus (GEO) datasets GSE13353, GSE15629, and GSE54083 in our study. The 3 datasets were merged and normalized. Differentially expressed gene (DEG) screening and weighted correlation network analysis (WGCNA) were conducted. The co-expression patterns between ruptured IA samples and unruptured IA samples were compared. Then, the DEGs were mapped into the whole co-expression network of ruptured IA samples, and a DEG co-expression network was generated. Molecular Complex Detection (MCODE) (http://baderlab.org/Software/MCODE) was used to identify key genes based on the DEG co-expression network. Finally, key genes were validated using another GEO dataset (GSE122897), and their potential diagnostic values were shown using receiver operating characteristic (ROC) analysis. In our study, 49 DEGs were screened while 8 and 6 gene modules were detected based on ruptured IA samples and unruptured IA samples, respectively. Pathways associated with inflammation and immune response were clustered in the salmon module of ruptured IA samples. The DEG co-expression network with 35 nodes and 168 edges was generated, and 14 key genes were identified based on this DEG co-expression network. The gene with the highest degree in the key gene cluster was CXCR4. All key genes were validated using GSE122897, and they all showed the potential diagnostic value in predicting IA rupture. Using a weighted gene co-expression network approach, we identified 8 and 6 modules for ruptured IA and unruptured IA, respectively. After that, we identified the hub genes for each module and key genes based on the DEG co-expression network. All these key genes were validated by another GEO dataset and might serve as potential targets for pharmacological therapies and diagnostic markers in predicting IA rupture. Further studies are needed to elucidate the detailed molecular mechanisms and biological functions of these key genes which underlie the rupture of IA.

Chen S ，Yang D ，Liu B ，Wang L ，Chen Y ，Ye W ，Liu C ，Ni L ，Zhang X ，Zheng Y ... -  《-》

Identification of crucial genes involved in pathogenesis of regional weakening of the aortic wall.

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. 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. 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. 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.

Zu HL ，Liu HW ，Wang HY 《HEREDITAS》

Interleukin 2 receptor subunit beta as a novel hub gene plays a potential role in the immune microenvironment of abdominal aortic aneurysms.

Abdominal aortic aneurysm (AAA) is potentially life threatening and characterized by immune-inflammatory cell infiltration and extracellular matrix degradation. Currently, pharmacotherapy mainly aims to control risk factors without reversion of the dilated aorta. This study analyzed the immune-inflammatory response and identified the immune-related hub genes of AAA. Gene Expression Omnibus datasets (GSE57691, GSE47472 and GSE7084) were downloaded. After identification of GSE57691 differentially expressed genes (DEGs), weighted gene co-expression network analysis of the DEGs was performed. Through enrichment analysis of each module and screening in Immunology Database and Analysis Portal, immune-related hub genes were identified via protein-protein interaction (PPI) network construction and lasso regression. CIBERSORT was utilized to analyze AAA immune infiltration. The correlations between the immune-related hub genes and infiltrating immune cells were investigated. Receiver operating characteristic (ROC) curve analysis was performed to determine immune-related hub gene cutoff values, which were validated in GSE47472 and GSE7084. In GSE57691, 1,018 DEGs were identified. Five modules were identified in the co-expression network. The blue and green modules were found to be related to immune-inflammatory responses, and 61 immune-related genes were identified. PPI and lasso regression analyses identified FOS, IL-6 and IL2RB as AAA immune-related hub genes. CIBERSORT analysis indicated significantly increased infiltration of naive B cells, memory activated CD4 T cells, follicular helper T cells, monocytes and M1 macrophages and significantly decreased infiltration of M2 macrophages in AAA compared with normal samples. IL2RB was more strongly associated with immune infiltration in AAA than were FOS and IL6. The IL2RB area under the ROC curve (AUC) value was > 0.9 in both the training and validation set, demonstrating its strong, stable diagnostic value in AAA. AAA and normal samples had different immune infiltration statuses. IL2RB was identified as an immune-related hub gene and a potential hub gene with significant diagnostic value in AAA.

Gao H ，Wang L ，Ren J ，Liu Y ，Liang S ，Zhang B ，Sun X ... -  《-》