Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

Molecular profiling of human hepatocarcinogenesis

ABSTRACT: Comparison of hepatocellular carcinoma with non-cancerous liver tissue. Keywords: repeat sample

ORGANISM(S): Homo sapiens

SUBMITTER: Xin Wang

PROVIDER: E-GEOD-364 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Publications

Predicting hepatitis B virus-positive metastatic hepatocellular carcinomas using gene expression profiling and supervised machine learning.

Ye Qing-Hai QH Qin Lun-Xiu LX Forgues Marshonna M He Ping P Kim Jin Woo JW Peng Amy C AC Simon Richard R Li Yan Y Robles Ana I AI Chen Yidong Y Ma Zeng-Chen ZC Wu Zhi-Quan ZQ Ye Sheng-Long SL Liu Yin-Kun YK Tang Zhao-You ZY Wang Xin Wei XW

Nature medicine 20030317 4

Hepatocellular carcinoma (HCC) is one of the most common and aggressive human malignancies. Its high mortality rate is mainly a result of intra-hepatic metastases. We analyzed the expression profiles of HCC samples without or with intra-hepatic metastases. Using a supervised machine-learning algorithm, we generated for the first time a molecular signature that can classify metastatic HCC patients and identified genes that were relevant to metastasis and patient survival. We found that the gene e ...[more]

PMID: 12640447

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Project description:Hepatocarcinogenesis is a multi-stage process in which precursor lesions progress into early hepatocellular carcinomas (eHCC) by sequential accumulation of multiple genetic and epigenetic alterations. To decode the molecular events during early stages of liver carcinogenesis, we performed gene expression profiling on cirrhotic (regenerative) and dysplastic nodules (DN) as well as eHCC. Although considerable heterogeneity was observed at the regenerative and dysplastic stages, clear differences were detected between DN and eHCC which included 460 differentially expressed genes. Functional analysis of the significant gene set identified the MYC oncogene as a plausible driver gene for malignant conversion of the dysplastic nodules. In addition, gene set enrichment analysis (GSEA) revealed a remarkable enrichment of MYC up-regulated gene set in eHCC versus dysplasia. Presence of the MYC signature significantly correlated with increased expression of CSN5 as well as with the higher overall transcription rate of genes located in the 8q chromosome region. Furthermore, a classifier constructed from MYC target genes could robustly discriminate eHCC from high- and low-grade dysplastic nodules. In conclusion, our study identified unique expression patterns associated with the transition of high-grade dysplastic nodules to early HCC and demonstrated that activation of the MYC transcription signature is critical for the malignant conversion of pre-neoplastic liver lesions. Samples from forty-nine nodular liver lesions including 24 regenerative (cirrhotic) nodules (CN), 3 low-grade (LGDN), 12 high-grade dysplastic nodules (HGDN) and 10 early hepatocellular carcinomas (Early HCC) were used. The Human Operon V2 oligonucleotide library containing 22K features representing expressed sequences was printed to glass arrays in the Advanced Technology Center (National Cancer Institute, Gaithersburg, MD). The aRNA probes were fragmented and hybridized to the microarray slides following the standard procedures. All samples were hybridized against a common amplified reference RNA pooled from normal liver samples. Experimental duplicates were prepared following a reverse-flour design.

Dataset Information

Molecular profiling of human hepatocarcinogenesis

Publications

Predicting hepatitis B virus-positive metastatic hepatocellular carcinomas using gene expression profiling and supervised machine learning.

Similar Datasets

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