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Identification of genes associated with multiple cancers via integrative analysis.

ABSTRACT: Advancement in gene profiling techniques makes it possible to measure expressions of thousands of genes and identify genes associated with development and progression of cancer. The identified cancer-associated genes can be used for diagnosis, prognosis prediction, and treatment selection. Most existing cancer microarray studies have been focusing on the identification of genes associated with a specific type of cancer. Recent biomedical studies suggest that different cancers may share common susceptibility genes. A comprehensive description of the associations between genes and cancers requires identification of not only multiple genes associated with a specific type of cancer but also genes associated with multiple cancers.In this article, we propose the Mc.TGD (Multi-cancer Threshold Gradient Descent), an integrative analysis approach capable of analyzing multiple microarray studies on different cancers. The Mc.TGD is the first regularized approach to conduct "two-dimensional" selection of genes with joint effects on cancer development. Simulation studies show that the Mc.TGD can more accurately identify genes associated with multiple cancers than meta analysis based on "one-dimensional" methods. As a byproduct, identification accuracy of genes associated with only one type of cancer may also be improved. We use the Mc.TGD to analyze seven microarray studies investigating development of seven different types of cancers. We identify one gene associated with six types of cancers and four genes associated with five types of cancers. In addition, we also identify 11, 9, 18, and 17 genes associated with 4 to 1 types of cancers, respectively. We evaluate prediction performance using a Leave-One-Out cross validation approach and find that only 4 (out of 570) subjects cannot be properly predicted.The Mc.TGD can identify a short list of genes associated with one or multiple types of cancers. The identified genes are considerably different from those identified using meta analysis or analysis of marginal effects.

SUBMITTER: Ma S

PROVIDER: S-EPMC2785840 | biostudies-other | 2009 Nov

REPOSITORIES: biostudies-other

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Identification of genes associated with multiple cancers via integrative analysis.

Ma Shuangge S Huang Jian J Moran Meena S MS

BMC genomics 20091117

<h4>Background</h4>Advancement in gene profiling techniques makes it possible to measure expressions of thousands of genes and identify genes associated with development and progression of cancer. The identified cancer-associated genes can be used for diagnosis, prognosis prediction, and treatment selection. Most existing cancer microarray studies have been focusing on the identification of genes associated with a specific type of cancer. Recent biomedical studies suggest that different cancers ...[more]

PMID: 19919702

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Project description:Multiple organ dysfunction syndrome (MODS) is characterized by the development of progressive physiological dysfunction of ?2 organs or organ systems and is responsible for the majority of the morbidity and mortality among patients in intensive care units. The aim of the present study was to investigate the potential genes and pathways associated with MODS. The microarray dataset GSE60088 was downloaded from the Gene Expression Omnibus and used to identify differentially expressed genes (DEGs) between organ tissues (lung, liver and kidney) obtained from a murine model of MODS and healthy controls. The interactions between DEGs in lungs, liver and kidneys were revealed by Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis. Furthermore, protein?protein interaction (PPI) data for DEGs were obtained from the Search Tool for the Retrieval of Interacting Genes and a PPI network was constructed. Additionally, DEGs that were common among the three organs were screened and transcription factors that regulated them were predicted using the iRegulon plugin. A total of 943, 267 and 227 DEGs were identified in lung, liver and kidney samples, respectively, between mice with MODS and healthy controls. In lung and liver samples, two pathways that were enriched with DEGs were identified and were common between lung and liver samples, including 'cytokine?cytokine receptor interaction' and 'Jnk?STAT signaling pathway', and examples of DEGs associated with these pathways include C?X?C motif chemokine ligand (Cxcl)1 and Cxcl10, and signal transducer and activator of transcription (Stat)1, respectively. Furthermore, two common pathways were identified in liver and kidney samples, which included 'MAPK signaling pathway' and 'p53 signaling pathway', and DEGs associated with these pathways included growth arrest and DNA damage?inducible ?. A total of 18 DEGs were common among lung, liver and kidney tissues, including CCAAT/enhancer binding protein ? (Cebpb) and olfactomedin?like 1 (Olfml1). Cebpb modulated various other DEGs, such as Cxcl1, and Olfml1 was regulated by Stat5A. These genes and pathways may serve roles in the progression of MODS and may be considered to be potential therapy targets for MODS.

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Identification of genes associated with multiple cancers via integrative analysis.

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Identification of genes associated with multiple cancers via integrative analysis.

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