Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

Transcription profiling of human colon samples from patients who have colorectal cancer recurrence or are recurrence-free to identify gene expression classifiers of clinical outcome

ABSTRACT: A common reference experiment design was performed. mRNAs from 149 New Zealand colorectal cancer samples were labeled with Cy5 and a common reference RNA from 12 cell lines were labeled with Cy3. Data were log2 transformed and normalized. Class prediction was performed to identify genes capable of predicting future samples.

INSTRUMENT(S): GenePix 4000B [Axon Instruments]

ORGANISM(S): Homo sapiens

DISEASE(S): colorectal cancer recurrence-free

SUBMITTER: yu-hsin lin

PROVIDER: E-MEXP-1245 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Publications

Multiple gene expression classifiers from different array platforms predict poor prognosis of colorectal cancer.

Lin Yu-Hsin YH Friederichs Jan J Black Michael A MA Mages Jörg J Rosenberg Robert R Guilford Parry J PJ Phillips Vicky V Thompson-Fawcett Mark M Kasabov Nikola N Toro Tumi T Merrie Arend E AE van Rij Andre A Yoon Han-Seung HS McCall John L JL Siewert Jörg Rüdiger JR Holzmann Bernhard B Reeve Anthony E AE

Clinical cancer research : an official journal of the American Association for Cancer Research 20070101 2 Pt 1

<h4>Purpose</h4>This study aimed to develop gene classifiers to predict colorectal cancer recurrence. We investigated whether gene classifiers derived from two tumor series using different array platforms could be independently validated by application to the alternate series of patients.<h4>Experimental design</h4>Colorectal tumors from New Zealand (n = 149) and Germany (n = 55) patients had a minimum follow-up of 5 years. RNA was profiled using oligonucleotide printed microarrays (New Zealand ...[more]

PMID: 17255271

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Project description:In this study, we employ high-density oligonucleotide microarrays to characterize the MutaMouse FE1 cell line at various stages of cell growth, in primary MutaMouse lung epithelial cell cultures, and in whole lung. Global transcriptional analysis and real-time RT-PCR was applied to (1) further define the cellular origin of the FE1 cell line and its responses under different culture conditions (media and substratum), (2) provide insight into the transcriptional differences in cellular processes between FE1 cultures compared to whole lung tissues, more specifically in toxicological response, and (3) preliminarily examine FE1 culture response to exposure of benzo(a)pyrene compared to whole animals. Total RNA samples from 3 cell culture types (50% FE1, %100 FE1, and Primary lung) or MutaMouse lung were labeled with Cyanine 5-CTP, and universal reference total RNA (Stratagene, CA, USA) was labeled with Cyanine 3-CTP (Perkin Elmer Life Sciences, Woodbridge, ON, Canada) using Agilent linear Amplification kits (Agilent Tech. Inc. Mississauga, ON, Canada) following the manufacturer's instruction. Briefly, double-stranded cDNA was synthesized using MMLV-RT with T7 promoter primer, starting with 5 ug total RNA. Cyanine-labeled cRNA targets were in vitro transcribed using T7 RNA polymerase. The synthesized cRNA was precipitated by LiCl and fragmented at 60 degrees C for 30 min with fragmentation solution. Cy5- sample cRNA and Cy3- reference cRNA were hybridized to Agilent mouse development microarrays (containing ~20,000 unique 60 mer oligonucleotides; Agilent Tech. Inc. Mississauga, ON, Canada) at 60 degrees C overnight with Agilent hybridization solution and washed according to manufacturer's instruction. Arrays were scanned on a VersArray ChipReader (BioRad Laboratories Ltd., Waterloo, Ontario, Canada), and data were acquired with ImaGene 5.5 (BioDiscovery, Inc. CA, USA). Present calls were determined as signals that were greater than the mean plus three times the standard deviation of the average of the negative control spots.

Dataset Information

Transcription profiling of human colon samples from patients who have colorectal cancer recurrence or are recurrence-free to identify gene expression classifiers of clinical outcome

Publications

Multiple gene expression classifiers from different array platforms predict poor prognosis of colorectal cancer.

Similar Datasets

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