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Overexpression of E2F mRNAs associated with gastric cancer progression identified by the transcription factor and miRNA co-regulatory network analysis.

ABSTRACT: Gene expression is regulated at the transcription and translation levels; thus, both transcription factors (TFs) and microRNAs (miRNA) play roles in regulation of gene expression. This study profiled differentially expressed mRNAs and miRNAs in gastric cancer tissues to construct a TF and miRNA co-regulatory network in order to identify altered genes in gastric cancer progression. A total of 70 cases gastric cancer and paired adjacent normal tissues were subjected to cDNA and miRNA microarray analyses. We obtained 887 up-regulated and 93 down-regulated genes and 41 down-regulated and 4 up-regulated miRNAs in gastric cancer tissues. Using the Transcriptional Regulatory Element Database, we obtained 105 genes that are regulated by the E2F family of genes and using Targetscan, miRanda, miRDB and miRWalk tools, we predicted potential targeting genes of these 45 miRNAs. We then built up the E2F-related TF and miRNA co-regulatory gene network and identified 9 hub-genes. Furthermore, we found that levels of E2F1, 2, 3, 4, 5, and 7 mRNAs associated with gastric cancer cell invasion capacity, and has associated with tumor differentiation. These data showed Overexpression of E2F mRNAs associated with gastric cancer progression.

SUBMITTER: Zhang X

PROVIDER: S-EPMC4315469 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Overexpression of E2F mRNAs associated with gastric cancer progression identified by the transcription factor and miRNA co-regulatory network analysis.

Zhang XiaoTian X Ni ZhaoHui Z Duan ZiPeng Z Xin ZhuoYuan Z Wang HuaiDong H Tan JiaYi J Wang GuoQing G Li Fan F

PloS one 20150203 2

Gene expression is regulated at the transcription and translation levels; thus, both transcription factors (TFs) and microRNAs (miRNA) play roles in regulation of gene expression. This study profiled differentially expressed mRNAs and miRNAs in gastric cancer tissues to construct a TF and miRNA co-regulatory network in order to identify altered genes in gastric cancer progression. A total of 70 cases gastric cancer and paired adjacent normal tissues were subjected to cDNA and miRNA microarray an ...[more]

PMID: 25646628

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Project description:Background and aimsGastric cancer (GC) is one of the most common cancers worldwide, and its pathogenesis is related to a complex network of gene interactions. The aims of our study were to find hub genes associated with the progression and prognosis of GC and illustrate the underlying mechanisms.MethodsWeighted gene co-expression network analysis (WGCNA) was conducted using the microarray dataset and clinical data of GC patients from Gene Expression Omnibus (GEO) database to identify significant gene modules and hub genes associated with TNM stage in GC. Functional enrichment analysis and protein-protein interaction network analysis were performed using the significant module genes. We regarded the common hub genes in the co-expression network and protein-protein interaction (PPI) network as "real" hub genes for further analysis. Hub gene was validated in another independent dataset and The Cancer Genome Atlas (TCGA) dataset.ResultsIn the significant purple module (R 2=0.35), a total of 12 network hub genes were identified, among which six were also hub nodes in the PPI network of the module genes. Functional annotation revealed that the genes in the purple module focused on the biological processes of system development, biological adhesion, extracellular structure organization and metabolic process. In terms of validation, CDH11 had a higher correlation with the TNM stage than other hub genes and was strongly correlated with biological adhesion based on GO functional enrichment analysis. Data obtained from the Gene Expression Profiling Interactive Analysis (GEPIA) showed that CDH11 expression had a strong positive correlation with GC stages (P<0.0001). In the testing set and Oncomine dataset, CDH11 was highly expressed in GC tissues (P<0.0001). Survival analysis indicated that samples with a high CDH11 expression showed a poor prognosis. Cox regression analysis demonstrated an independent predictor of CDH11 expression in GC prognosis (HR=1.482, 95% CI: 1.015-2.164). Furthermore, gene set enrichment analysis (GSEA) demonstrated that multiple tumor-related pathways, especially focal adhesion, were enriched in CDH11 highly expressed samples.ConclusionCDH11 was identified and validated in association with progression and prognosis in GC, probably by regulating biological adhesion and focal adhesion-related pathways.

Project description:BackgroundAs one of the most common types of co-regulatory motifs, feed-forward loops (FFLs) control many cell functions and play an important role in human cancers. Therefore, it is crucial to reconstruct and analyze cancer-related FFLs that are controlled by transcription factor (TF) and microRNA (miRNA) simultaneously, in order to find out how miRNAs and TFs cooperate with each other in cancer cells and how they contribute to carcinogenesis. Current FFL studies rely on predicted regulation information and therefore suffer the false positive issue in prediction results. More critically, FFLs generated by existing approaches cannot represent the dynamic and conditional regulation relationship under different experimental conditions.Methodology/principal findingsIn this study, we proposed a novel filter-wrapper feature selection method to accurately identify co-regulatory mechanism by incorporating prior information from predicted regulatory interactions with parallel miRNA/mRNA expression datasets. By applying this method, we reconstructed 208 and 110 TF-miRNA co-regulatory FFLs from human pan-cancer and prostate datasets, respectively. Further analysis of these cancer-related FFLs showed that the top-ranking TF STAT3 and miRNA hsa-let-7e are key regulators implicated in human cancers, which have regulated targets significantly enriched in cellular process regulations and signaling pathways that are involved in carcinogenesis.Conclusions/significanceIn this study, we introduced an efficient computational approach to reconstruct co-regulatory FFLs by accurately identifying gene co-regulatory interactions. The strength of the proposed feature selection method lies in the fact it can precisely filter out false positives in predicted regulatory interactions by quantitatively modeling the complex co-regulation of target genes mediated by TFs and miRNAs simultaneously. Moreover, the proposed feature selection method can be generally applied to other gene regulation studies using parallel expression data with respect to different biological contexts.

Dataset Information

Overexpression of E2F mRNAs associated with gastric cancer progression identified by the transcription factor and miRNA co-regulatory network analysis.

Publications

Overexpression of E2F mRNAs associated with gastric cancer progression identified by the transcription factor and miRNA co-regulatory network analysis.

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