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Expression data from cirrhosis and HCC tissue samples

ABSTRACT: Hepatocellular carcinoma (HCC) is the fifth most-common cancer worldwide causing nearly 600,000 deaths esch year. Approximately 80% of HCC develops on the background of cirrhosis.It is necessary to identify novel genes involved in HCC to implement new diagnostic and treatment options. However, the molecular pathogenesis of HCC largely remains unsolved. Only a few genetic alterations, namely those affecting p53, β-catenin and p16INK4a have been implicated at moderate frequencies of these cancers. Early detection of HCC with appropriate treatment can decrease tumor-related deaths We used microarrays to detail the global programme of gene expression profiles of cirrhosis and tumor liver samples. 15 cirrhosis and 15 HCC samples were collected with informed constent of each patient. Surgically resected tissues were snap frozen in liquid nitrogen and stored at -80C, and subjected to RNA extraction. Histology slides were prepared for all samples and scored by an experienced scientist. For the tumor samples, tissues were resected from inside the tumor, while tissues were resected from adjacent tissue sorrounding the tumor for non-tumor samples.

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PROVIDER: S-DIXA-D-1040 | biostudies-other |

REPOSITORIES: biostudies-other

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Expression data from cirrhosis and HCC tissue samples

2013-05-23 | GSE17548 | GEO

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Project description: Not available

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In silico estimates of tissue components in surgical samples based on expression profiling data.

Project description:Tissue samples from many diseases have been used for gene expression profiling studies, but these samples often vary widely in the cell types they contain. Such variation could confound efforts to correlate expression with clinical parameters. In principle, the proportion of each major tissue component can be estimated from the profiling data and used to triage samples before studying correlations with disease parameters. Four large gene expression microarray data sets from prostate cancer, whose tissue components were estimated by pathologists, were used to test the performance of multivariate linear regression models for in silico prediction of major tissue components. Ten-fold cross-validation within each data set yielded average differences between the pathologists' predictions and the in silico predictions of 8% to 14% for the tumor component and 13% to 17% for the stroma component. Across independent data sets that used similar platforms and fresh frozen samples, the average differences were 11% to 12% for tumor and 12% to 17% for stroma. When the models were applied to 219 arrays of "tumor-enriched" samples in the literature, almost one quarter were predicted to have 30% or less tumor cells. Furthermore, there was a 10.5% difference in the average predicted tumor content between 37 recurrent and 42 nonrecurrent cancer patients. As a result, genes that correlated with tissue percentage generally also correlated with recurrence. If such a correlation is not desired, then some samples might be removed to rebalance the data set or tissue percentages might be incorporated into the prediction algorithm. A web service, "CellPred," has been designed for the in silico prediction of sample tissue components based on expression data.

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Statistical expression deconvolution from mixed tissue samples.

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