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Identification of DOK genes as lung tumor suppressors.

ABSTRACT: Genome-wide analyses of human lung adenocarcinoma have identified regions of consistent copy-number gain or loss, but in many cases the oncogenes and tumor suppressors presumed to reside in these loci remain to be determined. Here we identify the downstream of tyrosine kinase (Dok) family members Dok1, Dok2 and Dok3 as lung tumor suppressors. Single, double or triple compound loss of these genes in mice results in lung cancer, with penetrance and latency dependent on the number of lost Dok alleles. Cancer development is preceded by an aberrant expansion and signaling profile of alveolar type II cells and bronchioalveolar stem cells. In human lung adenocarcinoma, we identify DOK2 as a target of copy-number loss and mRNA downregulation and find that DOK2 suppresses lung cancer cell proliferation in vitro and in vivo. Given the genomic localization of DOK2, we propose it as an 8p21.3 haploinsufficient human lung tumor suppressor.

SUBMITTER: Berger AH

PROVIDER: S-EPMC2956443 | biostudies-literature | 2010 Mar

REPOSITORIES: biostudies-literature

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Identification of DOK genes as lung tumor suppressors.

Berger Alice H AH Niki Masaru M Morotti Alessandro A Taylor Barry S BS Socci Nicholas D ND Viale Agnes A Brennan Cameron C Szoke Janos J Motoi Noriko N Rothman Paul B PB Teruya-Feldstein Julie J Gerald William L WL Ladanyi Marc M Pandolfi Pier Paolo PP

Nature genetics 20100207 3

Genome-wide analyses of human lung adenocarcinoma have identified regions of consistent copy-number gain or loss, but in many cases the oncogenes and tumor suppressors presumed to reside in these loci remain to be determined. Here we identify the downstream of tyrosine kinase (Dok) family members Dok1, Dok2 and Dok3 as lung tumor suppressors. Single, double or triple compound loss of these genes in mice results in lung cancer, with penetrance and latency dependent on the number of lost Dok allel ...[more]

PMID: 20139980

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Project description:BackgroundLung adenocarcinoma (LAC) is the predominant histologic subtype of lung cancer and has a complicated pathogenesis with high mortality. The purpose of this study was to identify differentially expressed genes (DEGs) with prognostic value and determine their underlying mechanisms.MethodsGene expression data of GSE27262 and GSE118370 were acquired from the Gene Expression Omnibus database, enrolling 31 LAC and 31 normal tissues. Common DEGs between LAC and normal tissues were identified using the GEO2R tool and Venn diagram software. Next, the Database for Annotation, Visualization, and Integrated Discovery (DAVID) was used to analyze the Gene Ontology and Kyoto Encyclopedia of Gene and Genome (KEGG) pathways. Then, protein-protein interaction (PPI) network of DEGs was visualized by Cytoscape with Search Tool for the Retrieval of Interacting Genes and central genes were identified via Molecular Complex Detection. Furthermore, the expression and prognostic information of central genes were validated via Gene Expression Profiling Interactive Analysis (GEPIA) and Kaplan-Meier analysis, respectively. Finally, DAVID, real-time PCR and immunohistochemistry were applied to re-analyze the identified genes, which were also further validated in two additional datasets from ArrayExpress database.ResultsFirst, 189 common DEGs were identified among the two datasets, including 162 downregulated and 27 upregulated genes. Next, Gene Ontology and KEGG pathway analysis of the DEGs were conducted through DAVID. Then, PPI network of DEGs was constructed and 17 downregulated central genes were identified. Furthermore, the 17 downregulated central genes were validated via GEPIA and datasets from ArrayExpress, and 12 of them showed a significantly better prognosis. Finally, six genes were identified significantly enriched in neuroactive ligand-receptor interactions (EDNRB, RXFP1, P2RY1, CALCRL) and Rap1 signaling pathway (TEK, P2RY1, ANGPT1) via DAVID, which were further validated to be weakly expressed in LAC tissues via RNA quantification and immunohistochemistry analysis.ConclusionsThe low expression pattern and relation to prognosis indicated that the six genes were potential tumor suppressor genes in LAC. In conclusion, we identified six significantly downregulated DEGs as prognostic markers and potential tumor suppressor genes in LAC based on integrated bioinformatics methods, which could act as potential molecular markers and therapeutic targets for LAC patients.

Project description:The identification of genetic and epigenetic alterations from primary tumor cells has become a common method to identify genes critical to the development and progression of cancer. We seek to identify those genetic and epigenetic aberrations that have the most impact on gene function within the tumor. First, we perform a bioinformatic analysis of copy number variation (CNV) and DNA methylation covering the genetic landscape of ovarian cancer tumor cells. We separately examined CNV and DNA methylation for 42 primary serous ovarian cancer samples using MOMA-ROMA assays and 379 tumor samples analyzed by The Cancer Genome Atlas. We have identified 346 genes with significant deletions or amplifications among the tumor samples. Utilizing associated gene expression data we predict 156 genes with altered copy number and correlated changes in expression. Among these genes CCNE1, POP4, UQCRB, PHF20L1 and C19orf2 were identified within both data sets. We were specifically interested in copy number variation as our base genomic property in the prediction of tumor suppressors and oncogenes in the altered ovarian tumor. We therefore identify changes in DNA methylation and expression for all amplified and deleted genes. We statistically define tumor suppressor and oncogenic features for these modalities and perform a correlation analysis with expression. We predicted 611 potential oncogenes and tumor suppressors candidates by integrating these data types. Genes with a strong correlation for methylation dependent expression changes exhibited at varying copy number aberrations include CDCA8, ATAD2, CDKN2A, RAB25, AURKA, BOP1 and EIF2C3. We provide copy number variation and DNA methylation analysis for over 11,500 individual genes covering the genetic landscape of ovarian cancer tumors. We show the extent of genomic and epigenetic alterations for known tumor suppressors and oncogenes and also use these defined features to identify potential ovarian cancer gene candidates.

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Identification of DOK genes as lung tumor suppressors.

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Identification of DOK genes as lung tumor suppressors.

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