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 ... -  《-》

Identifying and validating key genes mediating intracranial aneurysm rupture using weighted correlation network analysis and exploration of personalized treatment.

Intracranial aneurysmal subarachnoid hemorrhage (aSAH) is a dangerous and highly fatal condition if ruptured. Significant advances have been made in the treatment of unruptured intracranial aneurysms (UIAs), but risk assessment methods for early diagnosis of intracranial aneurysm (IA) rupture remain limited. The datasets of IA GSE13353, GSE15629, and GSE54083 were downloaded through the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) in unruptured and ruptured aneurysms were identified by R software using methods such as gene set enrichment analysis (GSEA) and weighted gene co-expression network analysis (WGCNA). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were performed on the DEGs, and logistic regression models were used to construct a prediction model to discriminate UIA from healthy samples. We then performed GSEA on the genes in the model, followed by model validation using the GSE54083 dataset. Finally, we used the single-sample (ss)GSEA method to investigate the relationship between the diagnostic model genes and immune cells and immune function. A total of 79 DEGs were obtained in patients with IA rupture compared to unruptured controls. The results of KEGG and GO enrichment analysis showed that neutrophil activation is involved in immune response, neutrophil mediated immunity, and positive regulation of angiogenesis. Interestingly, the results of immunoassays demonstrated that the break in IA may be associated with immune T cells. We used DEGs and WGCNA to determine common genes. The logistic regression model was trained based on 24 intersecting genes, and eventually retained 2 genes, KIAA0226L and UPP1, which we found to be reliable using the validation set, and GSEA revealed that the diagnostic model was associated with the Hippo signaling pathway and vascular smooth muscle contraction, and viral protein interaction with cytokine and cytokine were also associated. Finally explored using the CMap database, Tivozanib could be a potential small molecule drug for the treatment of ruptured intracranial aneurysms (RIAs). We identified new diagnostic genes associated with IA rupture, which may provide a new way of aneurysm diagnosis.

Wu J ，Chen ZJ ，Liang J ，Lai CS ，Li XY ，Yang ZJ ... -  《-》

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 potential key pathways, genes and circulating markers in the development of intracranial aneurysm based on weighted gene co-expression network analysis.

Du G ，Geng D ，Zhou K ，Fan Y ，Su R ，Zhou Q ，Liu B ，Duysenbi S ... -  《-》

Potential Role of the Chemotaxis System in Formation and Progression of Intracranial Aneurysms Through Weighted Gene Co-Expression Network Analysis.

Intracranial aneurysm (IA) is the most common and is the main cause of spontaneous subarachnoid hemorrhage (SAH). The underlying molecular mechanisms for preventing IA progression have not been fully identified. Our research aimed to identify the key genes and critical pathways of IA through gene co-expression networks. Gene Expression Omnibus (GEO) datasets GSE13353, GSE54083 and GSE75436 were used in the study. The genetic data were analyzed by weighted gene co-expression network analysis (WGCNA). Then the clinically significant modules were identified and the differentially expressed genes (DEGs) with the genes were intersected in these modules. GO (gene ontology) and KEGG (Kyoto Gene and Genomic Encyclopedia) were used for gene enrichment analysis to determine the function or pathway. In addition, the composition of immune cells was analyzed by CIBERSORT algorithm. Finally, the hub genes and key genes were identified by GSE122897. A total of 266 DEGs and two modules with clinical significance were identified. The inflammatory response and immune response were identified by GO and KEGG. CCR5, CCL4, CCL20, and FPR3 were the key genes in the module correlated with IA. The proportions of infiltrating immune cells in IA and normal tissues were different, especially in terms of macrophages and mast cells. The chemotactic system has been identified as a key pathway of IA, and interacting macrophages may regulate this pathological process.

Zhu H ，Tan J ，Zhao Y ，Wang Z ，Wu Z ，Li M ... -  《International Journal of General Medicine》