Dataset Information

Network-based Identification of novel cancer genes.

ABSTRACT: Genes involved in cancer susceptibility and progression can serve as templates for searching protein networks for novel cancer genes. To this end, we introduce a general network searching method, MaxLink, and apply it to find and rank cancer gene candidates by their connectivity to known cancer genes. Using a comprehensive protein interaction network, we searched for genes connected to known cancer genes. First, we compiled a new set of 812 genes involved in cancer, more than twice the number in the Cancer Gene Census. Their network neighbors were then extracted. This candidate list was refined by selecting genes with unexpectedly high levels of connectivity to cancer genes and without previous association to cancer. This produced a list of 1891 new cancer candidates with up to 55 connections to known cancer genes. We validated our method by cross-validation, Gene Ontology term bias, and differential expression in cancer versus normal tissue. An example novel cancer gene candidate is presented with detailed analysis of the local network and neighbor annotation. Our study provides a ranked list of high priority targets for further studies in cancer research. Supplemental material is included.

SUBMITTER: Ostlund G

PROVIDER: S-EPMC2860235 | biostudies-literature | 2010 Apr

REPOSITORIES: biostudies-literature

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Network-based Identification of novel cancer genes.

Ostlund Gabriel G Lindskog Mats M Sonnhammer Erik L L EL

Molecular & cellular proteomics : MCP 20091203 4

Genes involved in cancer susceptibility and progression can serve as templates for searching protein networks for novel cancer genes. To this end, we introduce a general network searching method, MaxLink, and apply it to find and rank cancer gene candidates by their connectivity to known cancer genes. Using a comprehensive protein interaction network, we searched for genes connected to known cancer genes. First, we compiled a new set of 812 genes involved in cancer, more than twice the number in ...[more]

PMID: 19959820

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Project description:The aim of the present study was to identify hub genes and signaling pathways associated with bladder cancer (BC) utilizing centrality analysis and pathway enrichment analysis. The differentially expressed genes (DEGs) were screened from the ArrayExpress database between normal subjects and BC patients. Co-expression networks of BC were constructed using differentially co-expressed genes and links, and hub genes were investigated by degree centrality analysis of co-expression networks in BC. The enriched signaling pathways were investigated by Kyoto Encyclopedia of Genes and Genomes database analysis based on the DEGs. The hub gene expression in BC tissues was validated using reverse transcription-quantitative polymerase chain reaction (RT-qPCR) and western blotting. A total of 329 DEGs were screened, including 147 upregulated and 182 downregulated genes. The co-expression network constructed between BC and normal controls consisted of 182 nodes and 434 edges, and the two genes in each gene pair were differentially co-expressed genes. Centrality analysis of co-expression networks suggested that the top 5 hub genes with high degree included lectin, galactoside-binding, soluble, 4 (LGALS4), protein tyrosine phosphatase, receptor type N2 (PTPRN2), transmembrane protease, serine 11E (TMPRSS11E), tripartite motif containing 31 (TRIM31) and potassium voltage-gated channel subfamily D member 3 (KCND3). Pathway analysis revealed that the 329 DEGs were significantly enriched in 5 terms (cell cycle, DNA replication, oocyte meiosis, p53 signaling pathway and peroxisome proliferator-activated receptor signaling pathway). According to RT-qPCR and western blot analysis, 4/5 hub genes were significantly expressed, including LGALS4, PTPRN2, TMPRSS11E, TRIM31; however, KCND3 was not significantly expressed. In the present study, 5 hub genes were successfully identified (LGALS4, PTPRN2, TMPRSS11E, TRIM31 and KCND3) and 5 biological pathways that may be underlying biomarkers for early diagnosis and treatment associated with bladder cancer were revealed.

Project description:ObjectiveEvidence increasingly shows that circular RNAs (circRNAs) are closely associated with tumorigenesis and cancer progression. However, the roles of circRNAs and the underlying mechanism behind these circRNAs in gastric cancer (GC) remain to be elucidated. This study is aimed at conferring a better understanding of GC pathogenesis with a specific focus on circRNA-based ceRNA action.MethodscircRNA expression profiles were downloaded from two Gene Expression Omnibus (GEO) microarray datasets, GSE152309 and GSE121445. Expression profiles of miRNAs and mRNAs were collected from The Cancer Genome Atlas (TCGA) database. The ceRNA network was constructed based on circRNA-miRNA pairs and miRNA-mRNA pairs. Functional and pathway enrichment analyses were performed to evaluate functional pathways of differentially expressed mRNAs (DEmRNAs). The PPI network was constructed by mapping DEmRNAs into the STRING database to identify hub genes, and then the DEcircRNA-DEmiRNA-hub gene subnetwork was constructed. The expression levels of candidate differentially expressed circRNAs (DEcircRNAs) in cancerous and matched noncancerous gastric tissues surgically resected from 52 GC patients were determined by the RT-qPCR analysis.ResultsDifferential expression analysis with Venn diagram analysis showed 11 overlapped DEcircRNAs (7 upregulated circRNAs and 4 downregulated ones) between cancerous tissues and noncancerous gastric tissues. The DEcircRNA-DEmiRNA-DEmRNA network was constructed, consisting of 2 DEcircRNAs, 7 DEmiRNAs, and 104 DEmRNAs. GO and KEGG pathway analyses revealed that 104 DEmRNAs might be associated with GC development and progression. The PPI network was constructed, yielding 16 hub genes, TFDP1, KRAS, LMNB1, MET, MYBL2, CDC25A, E2F5, HMGA1, HMGA2, CBFB, CBX3, CDC7, IGF2BP3, KIF11, PDGFB, and SMC1A, which were all upregulated in GC tissues compared with adjacent tumor-free gastric tissues. Based on the above hub genes in GC, the DEcircRNA-DEmiRNA-hub gene subnetwork was reconstructed based on hsa_circ_0000384 and hsa_circ_0000043, including 22 pairs of the upcircRNA-downmiRNA-upmRNA network. The expression levels of hsa_circ_0000384 and hsa_circ_0000043 were remarkably higher in GC tissues than those in matched adjacent tumor-free gastric tissues (p < 0.001), concurring with the bioinformatics results.ConclusionOur study offers a better understanding of circRNA-related ceRNA regulatory mechanism in the pathogenesis of GC, highlighting two ceRNA networks based on hsa_circ_0000384 and hsa_circ_0000043.

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Network-based Identification of novel cancer genes.

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Network-based Identification of novel cancer genes.

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