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Identification and validation of oncogenes in liver cancer using an integrative oncogenomic approach.

ABSTRACT: The heterogeneity and instability of human tumors hamper straightforward identification of cancer-causing mutations through genomic approaches alone. Herein we describe a mouse model of liver cancer initiated from progenitor cells harboring defined cancer-predisposing lesions. Genome-wide analyses of tumors in this mouse model and in human hepatocellular carcinomas revealed a recurrent amplification at mouse chromosome 9qA1, the syntenic region of human chromosome 11q22. Gene-expression analyses delineated cIAP1, a known inhibitor of apoptosis, and Yap, a transcription factor, as candidate oncogenes in the amplicon. In the genetic context of their amplification, both cIAP1 and Yap accelerated tumorigenesis and were required to sustain rapid growth of amplicon-containing tumors. Furthermore, cIAP1 and Yap cooperated to promote tumorigenesis. Our results establish a tractable model of liver cancer, identify two oncogenes that cooperate by virtue of their coamplification in the same genomic locus, and suggest an efficient strategy for the annotation of human cancer genes.

SUBMITTER: Zender L

PROVIDER: S-EPMC3026384 | biostudies-literature | 2006 Jun

REPOSITORIES: biostudies-literature

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Identification and validation of oncogenes in liver cancer using an integrative oncogenomic approach.

Zender Lars L Spector Mona S MS Xue Wen W Flemming Peer P Cordon-Cardo Carlos C Silke John J Fan Sheung-Tat ST Luk John M JM Wigler Michael M Hannon Gregory J GJ Mu David D Lucito Robert R Powers Scott S Lowe Scott W SW

Cell 20060601 7

The heterogeneity and instability of human tumors hamper straightforward identification of cancer-causing mutations through genomic approaches alone. Herein we describe a mouse model of liver cancer initiated from progenitor cells harboring defined cancer-predisposing lesions. Genome-wide analyses of tumors in this mouse model and in human hepatocellular carcinomas revealed a recurrent amplification at mouse chromosome 9qA1, the syntenic region of human chromosome 11q22. Gene-expression analyses ...[more]

PMID: 16814713

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Project description:More than one pathway is involved in disease development and progression, and two or more pathways may be interconnected to further affect the disease onset, as functional proteins participate in multiple pathways. Thus, identifying cross-talk among pathways is necessary to understand the molecular mechanisms of multiple myeloma (MM). Based on this, this paper looked at extracting potential pathway cross-talk in MM through an integrative approach using Monte Carlo cross-validation analysis. The gene expression library of MM (accession number: GSE6477) was downloaded from the Gene Expression Omnibus (GEO) database. The integrative approach was then used to identify potential pathway cross-talk, and included four steps: Firstly, differential expression analysis was conducted to identify differentially expressed genes (DEGs). Secondly, the DEGs obtained were mapped to the pathways downloaded from an ingenuity pathways analysis (IPA), to reveal the underlying relationship between the DEGs and pathways enriched by these DEGs. A subset of pathways enriched by the DEGs was then obtained. Thirdly, a discriminating score (DS) value for each paired pathway was computed. Lastly, random forest (RF) classification was used to identify the paired pathways based on area under the curve (AUC) and Monte Carlo cross-validation, which was repeated 50 times to explore the best paired pathways. These paired pathways were tested with another independently published MM microarray data (GSE85837), using in silico validation. Overall, 60 DEGs and 19 differential pathways enriched by DEGs were extracted. Each pathway was sorted based on their AUC values. The paired pathways, inhibition of matrix metalloproteases and EIF2 signaling pathway, indicated the best AUC value of 1.000. Paired pathways consisting of IL-8 and EIF2 signaling pathways with higher AUC of 0.975, were involved in 7 runs. Furthermore, it was validated consistently in separate microarray data sets (GSE85837). Paired pathways (inhibition of matrix metalloproteases and EIF2 signaling, IL-8 signaling and EIF2 signaling) exhibited the best AUC values and higher frequency of validation. Two paired pathways (inhibition of matrix metalloproteases and EIF2 signaling, IL-8 signaling and EIF2 signaling) were used to accurately classify MM and control samples. These paired pathways may be potential bio-signatures for diagnosis and management of MM.

Dataset Information

Identification and validation of oncogenes in liver cancer using an integrative oncogenomic approach.

Publications

Identification and validation of oncogenes in liver cancer using an integrative oncogenomic approach.

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