Dataset Information

Using bioinformatics to predict the functional impact of SNVs.

ABSTRACT:

Motivation

The past decade has seen the introduction of fast and relatively inexpensive methods to detect genetic variation across the genome and exponential growth in the number of known single nucleotide variants (SNVs). There is increasing interest in bioinformatics approaches to identify variants that are functionally important from millions of candidate variants. Here, we describe the essential components of bioinformatics tools that predict functional SNVs.

Results

Bioinformatics tools have great potential to identify functional SNVs, but the black box nature of many tools can be a pitfall for researchers. Understanding the underlying methods, assumptions and biases of these tools is essential to their intelligent application.

SUBMITTER: Cline MS

PROVIDER: S-EPMC3105482 | biostudies-literature | 2011 Feb

REPOSITORIES: biostudies-literature

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Publications

Using bioinformatics to predict the functional impact of SNVs.

Cline Melissa S MS Karchin Rachel R

Bioinformatics (Oxford, England) 20101215 4

<h4>Motivation</h4>The past decade has seen the introduction of fast and relatively inexpensive methods to detect genetic variation across the genome and exponential growth in the number of known single nucleotide variants (SNVs). There is increasing interest in bioinformatics approaches to identify variants that are functionally important from millions of candidate variants. Here, we describe the essential components of bioinformatics tools that predict functional SNVs.<h4>Results</h4>Bioinform ...[more]

PMID: 21159622

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Project description:Background:Ranking fourth in the world in tumor incidence and second in cancer-related death worldwide, gastric cancer (GC) is one of the major malignant tumors, and has a very complicated pathogenesis. In the present study, we aimed to identify new biomarkers to predict the survival rate of GC patients. Methods:The differentially expressed genes (DEGs) between GC tissues and normal stomach tissues were obtained by using GEO2R, and overlapped DEGs were acquired with Venn diagrams. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were conducted with R software. Then, the protein-protein interaction (PPI) of these DEGs was visualized by Cytoscape. Gene Expression Profiling Interactive Analysis (GEPIA) was used to verify the expression differences of hub genes in gastric adenocarcinoma tissues and normal tissues. Overall survival (OS) of hub genes was calculated by Kaplan-Meier plotter. Results:There were a total of 128 consistently expressed genes in the two datasets: 85 upregulated genes were enriched in extra-cellular matrix (ECM)-receptor interaction, protein digestion and absorption, focal adhesion, gastric acid secretion, mineral absorption, systemic lupus erythematosus, amoebiasis, and PI3K-Akt signaling pathway, and 43 downregulated genes were enriched in palate development, blood coagulation, positive regulation of transcription from RNA polymerase II promoter, axonogenesis, receptor internalization, negative regulation of transcription from RNA polymerase II promoter, and in no significant signaling pathways. From the PPI network analyzed by Molecular Complex Detection (MCODE) plug-in, all 27 upregulated genes were selected. Furthermore, to analyze the OS among these genes, Kaplan-Meier analysis was conducted, and 25 genes were associated with remarkably worse survival. For validation in GEPIA, 11 of 25 genes were discovered to be highly expressed in GC tissues compared to normal OS tissues. Furthermore, in the re-analysis of the Database for Annotation, Visualization and Integrated Discovery (DAVID), three genes [G2/miotic-specific cyclin B1 (CCNB1), polo-like kinases 1 (PLK1), and pituitary tumor-transforming gene-1 (PTTG1)] were markedly enriched in the cell cycle pathway, particulary the G1-G1/S phase. Conclusions:Three remarkably upregulated DEGs with poor prognosis in GC were identified and may serve as new prognostic biomarkers and targets in GC therapy.

Project description:BACKGROUND As all we know, gastric cancer (GC) is a highly aggressive disease. Recently, circular RNA (circRNA) was found to play a vital role in regulation of GC. Some circRNAs could regulate messenger RNA (mRNA) expression by functioning as a microRNA (miRNA) sponge. Nevertheless, the circRNA-miRNA-mRNA regulatory network involved GC rarely has been explored and researched. MATERIAL AND METHODS All the differentially expressed circRNAs, miRNAs, and mRNA were derived from Gene Expression Omnibus (GEO) microarray data (GSE78092, GSE89143, GSE93415, and GSE54129). GC level 3 miRNA-sequencing data and clinical information were downloaded from The Cancer Genome Atlas (TCGA) database. Furthermore, a circRNA-miRNA-mRNA regulatory network was constructed by Cytoscape (version 3.6.1). Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway revealed the functions and signaling pathways associated with these target genes. Hub genes of protein-protein interaction (PPI) network were identified by STRING database and cytoHubba. RESULTS The regulatory network consists of 3 circRNAs, 22 miRNAs, and 128 mRNAs. Only 3 miRNAs of the network were consistent with the expression of TCGA and were associated with some clinical features. The results of the functional analysis of 128 mRNAs showed that GO analysis and KEGG pathways of inclusion criteria were 49 and 24, respectively. PPI network and Cytoscape showed that the top 10 hub genes were MYC, CTGF, TGFBR2, TGFBR1, SERPINE1, KRAS, ZEB1, THBS1, CDK6, and TNS1; 4 of which were verified by GEPIA based on TCGA. Highly expressed SERPINE1 had a poor OS (over survival) and DFS (disease-free survival), and TGFBR1 expression increased along with the increase of clinical stages. CONCLUSIONS This study looked at a circRNA-miRNA-mRNA regulatory network associated with GC and explored the potential functions of mRNA in the network, then identified a new molecular marker for prediction, prognosis, and therapeutic targets for clinical patients.

Dataset Information

Using bioinformatics to predict the functional impact of SNVs.

Motivation

Results

Publications

Using bioinformatics to predict the functional impact of SNVs.

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