Epithelial–mesenchymal transition related genes in unruptured aneurysms identified through weighted gene coexpression network analysis

Abstract

Intracranial aneurysm (IA) can cause fatal subarachnoid hemorrhage (SAH) after rupture, and identifying patients with unruptured IAs is essential for reducing SAH fatalities. The epithelial–mesenchymal transition (EMT) may be vital to IA progression. Here, identified key EMT-related genes in aneurysms and their pathogenic mechanisms via bioinformatic analysis. The GSE13353, GSE75436, and GSE54083 datasets from Gene Expression Omnibus were analyzed with limma to identify differentially expressed genes (DEGs) among unruptured aneurysms, ruptured aneurysms, and healthy samples. The results revealed that three EMT-related DEGs (ADIPOQ, WNT11, and CCL21) were shared among all groups. Coexpression modules and hub genes were identified via weighted gene co-expression network analysis, revealing two significant modules (red and green) and 14 EMT-related genes. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses suggested that cytokine interactions were closely related. Gene set enrichment analysis revealed that unruptured aneurysms were enriched for the terms “inflammatory response” and “vascular endothelial growth”. Protein–protein interaction analysis identified seven key genes, which were evaluated with the GSE54083 dataset to determine their sensitivity and specificity. In the external validation set, we verified the differential expression of seven genes in unruptured aneurysms and normal samples. Together, these findings indicate that FN1, and SPARC may help distinguish normal patients from patients with asymptomatic IAs.

Integrated analysis of single-cell sequencing and machine learning identifies a signature based on monocyte/macrophage hub genes to analyze the intracranial aneurysm associated immune microenvironment

“…The FN1 (fibronectin 1) is a crucial constituent of the extracellular matrix within the arterial wall, playing a pivotal role in pathological angiogenesis, embryonic blood vessel morphology, and maintenance of arterial wall integrity (39,40). The high expression and localization of FN1 in IAs have been demonstrated, and based on the ROC results, FN1 exhibits a remarkable sensitivity and specificity in samples from IAs patients (39,41,42). This suggests that FN1 may play a direct role in the initiation and progression of IAs, thereby providing a crucial foundation for guiding targeted therapy strategies.…”

Transcriptomic Studies on Intracranial Aneurysms

“…Along with the development of bioinformatic tools appeared a new type of study presenting re-analyzed data from available datasets, including expression data from the Gene Expression Omnibus (GEO). Approximately one third of these published secondary analyses utilized a single dataset [ 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 ] and two thirds leveraged data from two to eight datasets [ 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 ]. These studies did not provide any new additional clinical data but rather aimed to deepen the insight into molecular mechanisms of the IA pathophysiology by revealing key regulatory networks and interactions between investigated molecules.…”

“…Although differential expression and functional annotation were examined, further analyses of co-expression networks with identification of hub RNA molecules, competing endogenous RNA (ceRNA) networks, or protein–protein interaction (PPI) networks became a standard approach. In some of these studies, specific areas of interest were predefined, such as: epithelial–mesenchymal transition [ 78 ], endoplasmic reticulum stress [ 81 ], immune environment [ 79 , 83 ], or ferroptosis [ 84 , 85 ]. In three studies, an attempt was made to identify potential therapeutic targets [ 71 , 82 , 83 ].…”

Identification of key potassium channel genes of temporal lobe epilepsy by bioinformatics analyses and experimental verification

“…Various modules were identified using the dynamic tree-cutting method, with at least 30 genes in each module (14). Subsequently, to merge similar modules, the threshold was set as 0.2 (15,16). The associations between these modules the two clinical characteristics were further analyzed.…”