Dataset Information

Identification of potential driver genes in human liver carcinoma by genomewide screening.

ABSTRACT: Genomic copy number aberrations and corresponding transcriptional deregulation in the cancer genome have been suggested to have regulatory roles in cancer development and progression. However, functional evaluation of individual genes from lengthy lists of candidate genes from genomic data sets presents a significant challenge. Here, we report effective gene selection strategies to identify potential driver genes based on systematic integration of genome scale data of DNA copy numbers and gene expression profiles. Using regional pattern recognition approaches, we discovered the most probable copy number-dependent regions and 50 potential driver genes. At each step of the gene selection process, the functional relevance of the selected genes was evaluated by estimating the prognostic significance of the selected genes. Further validation using small interference RNA-mediated knockdown experiments showed proof-of-principle evidence for the potential driver roles of the genes in hepatocellular carcinoma progression (i.e., NCSTN and SCRIB). In addition, systemic prediction of drug responses implicated the association of the 50 genes with specific signaling molecules (mTOR, AMPK, and EGFR). In conclusion, the application of an unbiased and integrative analysis of multidimensional genomic data sets can effectively screen for potential driver genes and provides novel mechanistic and clinical insights into the pathobiology of hepatocellular carcinoma.

SUBMITTER: Woo HG

PROVIDER: S-EPMC2750086 | biostudies-literature | 2009 May

REPOSITORIES: biostudies-literature

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Identification of potential driver genes in human liver carcinoma by genomewide screening.

Woo Hyun Goo HG Park Eun Sung ES Lee Ju-Seog JS Lee Yun-Han YH Ishikawa Tsuyoshi T Kim Yoon Jun YJ Thorgeirsson Snorri S SS

Cancer research 20090414 9

Genomic copy number aberrations and corresponding transcriptional deregulation in the cancer genome have been suggested to have regulatory roles in cancer development and progression. However, functional evaluation of individual genes from lengthy lists of candidate genes from genomic data sets presents a significant challenge. Here, we report effective gene selection strategies to identify potential driver genes based on systematic integration of genome scale data of DNA copy numbers and gene e ...[more]

PMID: 19366792

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Project description:Objective: Adrenocortical carcinoma (ACC) is a rare but aggressive malignant cancer that has been attracting growing attention over recent decades. This study aims to integrate protein interaction networks with gene expression profiles to identify potential biomarkers with prognostic value in silico. Methods: Three microarray data sets were downloaded from the Gene Expression Omnibus (GEO) database to identify differentially expressed genes (DEGs) according to the normalization annotation information. Enrichment analyses were utilized to describe biological functions. A protein-protein interaction network (PPI) of the DEGs was developed, and the modules were analyzed using STRING and Cytoscape. LASSO Cox regression was used to identify independent prognostic factors. The Kaplan-Meier method for the integrated expression score was applied to analyze survival outcomes. A receiver operating characteristic (ROC) curve was constructed with area under curve (AUC) analysis to determine the diagnostic ability of the candidate biomarkers. Results: A total of 150 DEGs and 24 significant hub genes with functional enrichment were identified as candidate prognostic biomarkers. LASSO Cox regression suggested that ZWINT, PRC1, CDKN3, CDK1 and CCNA2 were independent prognostic factors in ACC. In multivariate Cox analysis, the integrated expression scores of the modules showed statistical significance in predicting disease-free survival (DFS, P = 0.019) and overall survival (OS, P < 0.001). Meanwhile, ROC curves were generated to validate the ability of the Cox model to predict prognosis. The AUC index for the integrated genes scores was 0.861 (P < 0.0001). Conclusion: In conclusion, the present study identifies DEGs and hub genes that may be involved in poor prognosis and early recurrence of ACC. The expression levels of ZWINT, PRC1, CDKN3, CDK1 and CCNA2 are of high prognostic value, and may help us understand better the underlying carcinogenesis or progression of ACC. Further studies are required to elucidate molecular pathogenesis and alteration in signaling pathways for these genes in ACC.

Dataset Information

Identification of potential driver genes in human liver carcinoma by genomewide screening.

Publications

Identification of potential driver genes in human liver carcinoma by genomewide screening.

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