Large-scale meta-analysis of cancer microarray data identifies common transcriptional profiles of neoplastic transformation and progression.

By Daniel R. Rhodes, Jianjun Yu, K. Shanker, Nandan P. Deshpande, Radhika Varambally, Debashis Ghosh, T. Barrette, Akhilesh Pandey, Arul M. Chinnaiyan, Patrick O. Brown. Proceedings of the National Academy of Sciences of the United States of America, 2004

1,021 citations recorded by the source corpus.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/49409df130f420165422e5ba0798d31e76e9a870

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Citations in the corpus

Gene expression profiling spares early breast cancer patients from adjuvant therapy: derived and validated in two population-based cohorts (2005, background): “The TOP2A gene, which was associated with the high-risk tumors, was recently identified as one of the genes of a general cancer metasignature [35].”
Batch effect removal methods for microarray gene expression data integration: a survey (2013, background): “Several large-scale meta-analysis of MAGE datasets have already been performed based on the above mentioned assumption and reported in the literature [1, 8, 9].”
PAGE: Parametric Analysis of Gene Set Enrichment (2005, neutral): “Each approach was shown to lead to better congruency among diverse data sets than would be achieved by direct comparison of data sets, whether in demonstrating common transcriptional profiles of prostate cancer [34], common molecular markers of lung cancer [15], or a common biological theme in diabe”
Network-Based Elucidation of Human Disease Similarities Reveals Common Functional Modules Enriched for Pluripotent Drug Targets (2010, background): “Although the 54 diseases considered in this study cover many categories of diseases ranging from cancers to cardiomyopathies, some categories of diseases such as cancer are over-represented as opposed to others such as infectious diseases. Ideally, we would like to explicitly correct for this bias b”
Comprehensive literature review and statistical considerations for microarray meta-analysis (2012, methodology): “For all the ﬁve methods, statistical inference can be performed parametrically under the assumption that P -values are uniformly distributed under the null hypothesis or can be done non-parametrically by permutation-based analysis (109,112).”
From signatures to models: understanding cancer using microarrays (2005, background): “These results and others 8,9,11–13 illustrate the value of analyzing complex processes such as tumorigenesis in terms of higher-level gene modules and biological processes.”
Integrative analysis of the cancer transcriptome (2005, methodology): “We also carried out meta-analysis in search of a common signatures across distinct cancer type”
Microarray analysis identifies a death-from-cancer signature predicting therapy failure in patients with multiple types of cancer. (2005, background): “Perhaps inclusion of a relevant biological model in the signature discovery protocol was an essential component of the successful hit selection, since recent metaanalysis of cancer microarray data that was based solely on statistical approaches did not identify an outcome signature common to multipl”
Probabilistic model of the human protein-protein interaction network (2005, methodology): “We identified coexpressed genes from 65 genome-wide gene expression data sets present in the Oncomine Cancer Microarray Database 3,21 .”
A module map showing conditional activity of expression modules in cancer (2004, background): “DLX genes as targets of ALL-1: DLX 2,3,4 down-regulation in t(4;11) acute lymphoblastic leukemias.”
Machine Learning Meta-analysis of Large Metagenomic Datasets: Tools and Biological Insights (2016, methodology): “The general objectives of meta-analysis include proposing new classifiers [10], comparing different classification methods [11], finding a common transcriptional profile [12], and evaluating generalization of prediction models across different studies”