Dataset Information

Identification of key genes, transcription factors and microRNAs involved in intracranial aneurysm.

ABSTRACT: Intracranial aneurysm (IA) is a devastating disease, the pathogenesis of which remains to be elucidated. The present study aimed to determine the molecular mechanism of IA and to identify potential therapeutic targets using bioinformatics analysis. The GSE54083 dataset, which includes data from patients with ruptured IA and superficial temporal artery controls, was downloaded from the Gene Expression Omnibus, and differentially expressed genes (DEGs) were identified in the ruptured IA samples using the limma package in R. Subsequently, the Database for Annotation, Visualization and Integrated Discovery software was used to perform function and pathway enrichment analyses and the Search Tool for the Retrieval of Interacting Genes database was used to construct the protein?protein interaction (PPI) network. Then, microRNA (miRNA) target and transcription factor (TF) target pairs were identified using the miR2Disease, MiRwalk2, ITFP and TRANSFAC databases. Finally, an integrated network of TF?target?miRNAs was constructed using Cytoscape. A total of 402 upregulated DEGs and 375 downregulated DEGs were identified from the ruptured IA samples compared with the superficial temporal artery samples. The majority of the upregulated DEGs were significantly enriched in the immune system development category, including CD40 ligand (CD40LG) and CD40 and the downregulated DEGs, such as striatin (STRN), were enriched in neuron projection development. In addition, nitric oxide synthase 1 (NOS1), a target of miRNA?125b, and myosin heavy chain 11 (MYH11), a target of minichromosome maintenance complex component 4 (MCM4), had higher degree scores in the integrated network. These findings suggest that CD40, CD40LG, NOS1, STRN, MCM4, MYH11 and miR?125b may be potential therapeutic targets for the treatment of IA.

SUBMITTER: Wei L

PROVIDER: S-EPMC5780181 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Identification of key genes, transcription factors and microRNAs involved in intracranial aneurysm.

Wei Liang L Wang Qi Q Zhang Yanfei Y Yang Cheng C Guan Hongxin H Chen Yiming Y Sun Zhiyang Z

Molecular medicine reports 20171103 1

Intracranial aneurysm (IA) is a devastating disease, the pathogenesis of which remains to be elucidated. The present study aimed to determine the molecular mechanism of IA and to identify potential therapeutic targets using bioinformatics analysis. The GSE54083 dataset, which includes data from patients with ruptured IA and superficial temporal artery controls, was downloaded from the Gene Expression Omnibus, and differentially expressed genes (DEGs) were identified in the ruptured IA samples us ...[more]

PMID: 29115560

Similar Datasets

Project description:BackgroundRupture 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.MethodsWe 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.ResultsIn 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.ConclusionsUsing 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.

Project description:BackgroundLung adenocarcinoma (LUAD) is one of the most common cancers worldwide. The etiology and pathophysiology of LUAD remain unclear. The aim of the present study was to identify the key genes, miRNAs and transcription factors (TFs) associated with the pathogenesis and prognosis of LUAD.MethodsThree gene expression profiles (GSE43458, GSE32863, GSE74706) of LUAD were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified by GEO2R.The Gene Ontology (GO) terms, pathways, and protein-protein interactions (PPIs) of these DEGs were analyzed. Bases on DEGs, the miRNAs and TFs were predicted. Furthermore, TF-gene-miRNA co-expression network was constructed to identify key genes, miRNAs and TFs by bioinformatic methods. The expressions and prognostic values of key genes, miRNAs and TFs were carried out through The Cancer Genome Atlas (TCGA) database and Kaplan Meier-plotter (KM) online dataset.ResultsA total of 337 overlapped DEGs (75 upregulated and 262 downregulated) of LUAD were identified from the three GSE datasets. Moreover, 851 miRNAs and 29 TFs were identified to be associated with these DEGs. In total, 10 hub genes, 10 key miRNAs and 10 key TFs were located in the central hub of the TF-gene-miRNA co-expression network, and validated using The Cancer Genome Atlas (TCGA) database. Specifically, seven genes (PHACTR2, MSRB3, GHR, PLSCR4, EPB41L2, NPNT, FBXO32), two miRNAs (hsa-let-7e-5p, hsa-miR-17-5p) and four TFs (STAT6, E2F1, ETS1, JUN) were identified to be associated with prognosis of LUAD, which have significantly different expressions between LUAD and normal lung tissue. Additionally, the miRNA/gene co-expression analysis also revealed that hsa-miR-17-5p and PLSCR4 have a significant negative co-expression relationship (r=?0.33, P=1.67e-14) in LUAD.ConclusionsOur study constructed a regulatory network of TF-gene-miRNA in LUAD, which may provide new insights about the interaction between genes, miRNAs and TFs in the pathogenesis of LUAD, and identify potential biomarkers or therapeutic targets for LUAD.

Project description:Wilms tumor (WT) is one of the most common types of pediatric solid tumors; however, its molecular mechanisms remain unclear. The present study aimed to identify key genes and microRNAs (miRNAs), and to predict the underlying molecular mechanisms of WT using integrated bioinformatics analysis. Original gene expression profiles were downloaded from the Gene Expression Omnibus (GEO; accession, GSE66405) and The Cancer Genome Atlas (TCGA) databases. Similarly, miRNA expression patterns were downloaded from GEO (accession, GSE57370) and TCGA. R version 3.5.0 software was used to identify differentially expressed genes (DEGs) and differentially expressed miRNAs (DEMs) using the limma and edgeR packages. Kyoto Encyclopedia of Genes and Genomes pathway and Gene Ontology analyses were performed to examine the biological functions of the DEGs. Additionally, a protein-protein interaction (PPI) network was constructed to screen hub gene modules using Cytoscape software. By predicting target genes of the DEMs and integrating them with DEGs, the present study constructed a miRNA-mRNA regulatory network to predict the possible molecular mechanism of WT. Expression of hub genes was validated using the Oncomine database. A total of 613 genes and 29 miRNAs were identified to be differentially expressed in WT. By constructing a PPI network and screening hub gene modules, 5 upregulated genes, including BUB1 mitotic checkpoint serine/threonine kinase, BUB1B mitotic checkpoint serine/threonine kinase B, cell division cycle protein 45, cyclin B2 and pituitary tumor-transforming 1. These genes were identified to be associated with the cell cycle pathway, which suggested that these genes may serve important roles in WT. In addition, a miRNA-mRNA regulatory network was constructed and comprised 16 DEMs and 19 DEGs. In conclusion, key genes, miRNAs and the mRNA-miRNA regulatory network identified in the present study may improve understanding of the underlying molecular mechanisms in the occurrence and development of WT, and may aid the identification of potential biomarkers and therapeutic targets.

Dataset Information

Identification of key genes, transcription factors and microRNAs involved in intracranial aneurysm.

Publications

Identification of key genes, transcription factors and microRNAs involved in intracranial aneurysm.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets