Dataset Information

Ruguo key genes and tumor driving factors identification of bladder cancer based on the RNA-seq profile.

ABSTRACT: AIM:This study aimed to select several signature genes associated with bladder cancer, thus to investigate the possible mechanism in bladder cancer. METHODS:The mRNA expression profile data of GSE31614, including ten bladder tissues and ten control samples, was downloaded from the Gene Expression Omnibus. The differentially expressed genes (DEGs) in bladder cancer samples compared with the control samples were screened using the Student's t-test method. Functional analysis for the DEGs was analyzed using the Database for Annotation, Visualization, and Integrated Discovery from the Gene Ontology database, followed by the transcription function annotation of DEGs from Tumor-Associated Gene database. Motifs of genes that had transcription functions in promoter region were analyzed using the Seqpos. RESULTS:A total of 1,571 upregulated and 1,507 downregulated DEGs in the bladder cancer samples were screened. ELF3 and MYBL2 involved in cell cycle and DNA replication were tumor suppressors. MEG3, APEX1, and EZH2 were related with the cell epigenetic regulation in bladder cancer. Moreover, HOXB9 and EN1 that have their own motif were the transcription factors. CONCLUSION:Our study has identified several key genes involved in bladder cancer. ELF3 and MYBL2 are tumor suppressers, HOXB9 and EN1 are the main regulators, while MEG3, APEX1, and EZH2 are driving factors for bladder cancer progression.

SUBMITTER: Zhang M

PROVIDER: S-EPMC4863592 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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Ruguo key genes and tumor driving factors identification of bladder cancer based on the RNA-seq profile.

Zhang Minglei M Li Hongyan H Zou Di D Gao Ji J

OncoTargets and therapy 20160505

<h4>Aim</h4>This study aimed to select several signature genes associated with bladder cancer, thus to investigate the possible mechanism in bladder cancer.<h4>Methods</h4>The mRNA expression profile data of GSE31614, including ten bladder tissues and ten control samples, was downloaded from the Gene Expression Omnibus. The differentially expressed genes (DEGs) in bladder cancer samples compared with the control samples were screened using the Student's t-test method. Functional analysis for the ...[more]

PMID: 27217782

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Project description:BackgroundBladder cancer is a malignant tumor in the urinary system with high mortality and recurrence rates. However, the causes and recurrence mechanism of bladder cancer are not fully understood. In this study, we used integrated bioinformatics to screen for key genes associated with the development of bladder cancer and reveal their potential molecular mechanisms.MethodsThe GSE7476, GSE13507, GSE37815 and GSE65635 expression profiles were downloaded from the Gene Expression Omnibus database, and these datasets contain 304 tissue samples, including 81 normal bladder tissue samples and 223 bladder cancer samples. The RobustRankAggreg (RRA) method was utilized to integrate and analyze the four datasets to obtain integrated differentially expressed genes (DEGs), and the gene ontology (GO) functional annotation and Kyoto encyclopedia of genes and genomes (KEGG) pathway analysis were performed. Protein-protein interaction (PPI) network and module analyses were performed using Cytoscape software. The OncoLnc online tool was utilized to analyze the relationship between the expression of hub genes and the prognosis of bladder cancer.ResultsIn total, 343 DEGs, including 111 upregulated and 232 downregulated genes, were identified from the four datasets. GO analysis showed that the upregulated genes were mainly involved in mitotic nuclear division, the spindle and protein binding. The downregulated genes were mainly involved in cell adhesion, extracellular exosomes and calcium ion binding. The top five enriched pathways obtained in the KEGG pathway analysis were focal adhesion (FA), PI3K-Akt signaling pathway, proteoglycans in cancer, extracellular matrix (ECM)-receptor interaction and vascular smooth muscle contraction. The top 10 hub genes identified from the PPI network were vascular endothelial growth factor A (VEGFA), TOP2A, CCNB1, Cell division cycle 20 (CDC20), aurora kinase B, ACTA2, Aurora kinase A, UBE2C, CEP55 and CCNB2. Survival analysis revealed that the expression levels of ACTA2, CCNB1, CDC20 and VEGFA were related to the prognosis of patients with bladder cancer. In addition, a KEGG pathway analysis of the top 2 modules identified from the PPI network revealed that Module 1 mainly involved the cell cycle and oocyte meiosis, while the analysis in Module 2 mainly involved the complement and coagulation cascades, vascular smooth muscle contraction and FA.ConclusionsThis study identified key genes and pathways in bladder cancer, which will improve our understanding of the molecular mechanisms underlying the development and progression of bladder cancer. These key genes might be potential therapeutic targets and biomarkers for the treatment of bladder cancer.

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Ruguo key genes and tumor driving factors identification of bladder cancer based on the RNA-seq profile.

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Ruguo key genes and tumor driving factors identification of bladder cancer based on the RNA-seq profile.

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