Uncovering of Key Pathways and miRNAs for Intracranial Aneurysm Based on Weighted Gene Co-Expression Network Analysis

Abstract

<b><i>Background:</i></b> Intracranial aneurysm (IA) is a serious cerebrovascular disease. The identification of key regulatory genes can provide research directions for early diagnosis and treatment of IA. <b><i>Methods:</i></b> Initially, the miRNA and mRNA data were downloaded from the Gene Expression Omnibus database. Subsequently, the limma package in R was used to screen for differentially expressed genes. In order to investigate the function of the differentially expressed genes, a functional enrichment analysis was performed. Moreover, weighted gene co-expression network analysis (WGCNA) was performed to identify the hub module and hub miRNAs. The correlations between miRNAs and mRNAs were assessed by constructing miRNA-mRNA regulatory networks. In addition, in vitro validation was performed. Finally, diagnostic analysis and electronic expression verification were performed on the GSE122897 dataset. <b><i>Results:</i></b> In the present study, 955 differentially expressed mRNAs (DEmRNAs, 480 with increased and 475 with decreased expression) and 46 differentially expressed miRNAs (DEmiRNAs, 36 with increased and 10 with decreased expression) were identified. WGCNA demonstrated that the yellow module was the hub module. Moreover, 16 hub miRNAs were identified. A total of 1,124 negatively regulated miRNA-mRNA relationship pairs were identified. Functional analysis demonstrated that DEmRNAs in the targeted network were enriched in vascular smooth muscle contraction and focal adhesion pathways. In addition, the area under the curve of 16 hub miRNAs was >0.8. It is implied that 16 hub miRNAs may be used as potential diagnostic biomarkers of IA. <b><i>Conclusion:</i></b> Hub miRNAs and key signaling pathways were identified by bioinformatics analysis. This evidence lays the foundation for understanding the underlying molecular mechanisms of IA and provided potential therapeutic targets for the treatment of this disease.

A novel machine learning model based on ubiquitin-related gene pairs and clinical features to predict prognosis and treatment effect in colon adenocarcinoma

“…For further investigation, the most important genes in each module were chosen. There are two limiting factors in this step: gene significance (GS) and module membership (MM) [ 15 ].…”

“…Then, with the help of the "glmnet" package in R software (version 4.0.4), the least absolute shrinkage, and selection operator (LASSO) Cox regression analysis was performed to determine the significant prognostic URGPs. Following that, a multivariate stepwise Cox regression proportional hazards regression model was developed to additionally identify URGPs and for model optimization [ 15 ]. Ultimately, a risk score equation was derived by combining the regression coefficients from the multivariate Cox analysis with the URGPs expression values that corresponded to those coefficients.…”

“…Besides, Pearson analysis was used to identify transcription factors (TFs) and enhancer RNAs (eRNAs) of interest (|Pearson cor|> 0.4 and p < 0.001), and a Sankey diagram was constructed to examine the potential relationship between URGs and TFs/eRNAs [ 15 ]. When conducting a survival analysis, it is a usual practice to stratify the data premised on both the median value and the optimum threshold value.…”

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.…”