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      Selection of DDX5 as a novel internal control for Q-RT-PCR from microarray data using a block bootstrap re-sampling scheme

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          Abstract

          Background

          The development of microarrays permits us to monitor transcriptomes on a genome-wide scale. To validate microarray measurements, quantitative-real time-reverse transcription PCR (Q-RT-PCR) is one of the most robust and commonly used approaches. The new challenge in gene quantification analysis is how to explicitly incorporate statistical estimation in such studies. In the realm of statistical analysis, the various available methods of the probe level normalization for microarray analysis may result in distinctly different target selections and variation in the scores for the correlation between microarray and Q-RT-PCR. Moreover, it remains a major challenge to identify a proper internal control for Q-RT-PCR when confirming microarray measurements.

          Results

          Sixty-six Affymetrix microarray slides using lung adenocarcinoma tissue RNAs were analyzed by a statistical re-sampling method in order to detect genes with minimal variation in gene expression. By this approach, we identified DDX5 as a novel internal control for Q-RT-PCR. Twenty-three genes, which were differentially expressed between adjacent normal and tumor samples, were selected and analyzed using 24 paired lung adenocarcinoma samples by Q-RT-PCR using two internal controls, DDX5 and GAPDH. The percentage correlation between Q-RT-PCR and microarray were 70% and 48% by using DDX5 and GAPDH as internal controls, respectively.

          Conclusion

          Together, these quantification strategies for Q-RT-PCR data processing procedure, which focused on minimal variation, ought to significantly facilitate internal control evaluation and selection for Q-RT-PCR when corroborating microarray data.

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          Most cited references35

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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            Classification of human lung carcinomas by mRNA expression profiling reveals distinct adenocarcinoma subclasses.

            We have generated a molecular taxonomy of lung carcinoma, the leading cause of cancer death in the United States and worldwide. Using oligonucleotide microarrays, we analyzed mRNA expression levels corresponding to 12,600 transcript sequences in 186 lung tumor samples, including 139 adenocarcinomas resected from the lung. Hierarchical and probabilistic clustering of expression data defined distinct subclasses of lung adenocarcinoma. Among these were tumors with high relative expression of neuroendocrine genes and of type II pneumocyte genes, respectively. Retrospective analysis revealed a less favorable outcome for the adenocarcinomas with neuroendocrine gene expression. The diagnostic potential of expression profiling is emphasized by its ability to discriminate primary lung adenocarcinomas from metastases of extra-pulmonary origin. These results suggest that integration of expression profile data with clinical parameters could aid in diagnosis of lung cancer patients.
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              Gene-expression profiles predict survival of patients with lung adenocarcinoma.

              Histopathology is insufficient to predict disease progression and clinical outcome in lung adenocarcinoma. Here we show that gene-expression profiles based on microarray analysis can be used to predict patient survival in early-stage lung adenocarcinomas. Genes most related to survival were identified with univariate Cox analysis. Using either two equivalent but independent training and testing sets, or 'leave-one-out' cross-validation analysis with all tumors, a risk index based on the top 50 genes identified low-risk and high-risk stage I lung adenocarcinomas, which differed significantly with respect to survival. This risk index was then validated using an independent sample of lung adenocarcinomas that predicted high- and low-risk groups. This index included genes not previously associated with survival. The identification of a set of genes that predict survival in early-stage lung adenocarcinoma allows delineation of a high-risk group that may benefit from adjuvant therapy.
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                Author and article information

                Journal
                BMC Genomics
                BMC Genomics
                BioMed Central (London )
                1471-2164
                2007
                1 June 2007
                : 8
                : 140
                Affiliations
                [1 ]Institute of Cancer Research, National Health Research Institutes, Taipei 114, Taiwan
                [2 ]Department of Surgery, Veterans General Hospital, Taipei 112, Taiwan
                [3 ]Institute of Statistical Science, Academia Sinica, Taipei 115, Taiwan
                [4 ]Department of Chemical Engineering, National Chung Cheng University, Chia-Yi 621, Taiwan
                [5 ]Institute of Microbiology and Immunology, National Yang-Ming University, Taipei 112, Taiwan
                [6 ]Department of Education and Research, Taichung Veterans General Hospital, Taichung 407, Taiwan
                [7 ]Institute of Epidemiology, National Taiwan University, Taipei 100, Taiwan
                [8 ]Institute of Bio-Pharmaceutical Sciences, National Yang-Ming University, Taipei 112, Taiwan
                [9 ]Institute of Biotechnology in Medicine, National Yang-Ming University, Taipei 112, Taiwan
                [10 ]Department of Computer Science and Information Engineering, National Taiwan University, Taipei 106, Taiwan
                Article
                1471-2164-8-140
                10.1186/1471-2164-8-140
                1894975
                17540040
                02426082-6d97-4a26-8e6d-a19fe8d2946c
                Copyright © 2007 Su et al; licensee BioMed Central Ltd.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 11 January 2007
                : 1 June 2007
                Categories
                Methodology Article

                Genetics
                Genetics

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