ArticleBMC genomics2009
Identification of genes associated with multiple cancers via integrative analysis.
Article in BMC genomics, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed, 19 citations in OpenAlex.
- Replicability in cancer omics data analysis: measures and empirical explorations.Briefings in bioinformatics · 2022Article
- Disease classification via gene network integrating modules and pathways.Royal Society open science · 2019Article
- miR-590-3p and Its Downstream Target Genes in HCC Cell Lines.Analytical cellular pathology (Amsterdam) · 2019Article
- Measures for the degree of overlap of gene signatures and applications to TCGA.Briefings in bioinformatics · 2015Article
- Similarity of markers identified from cancer gene expression studies: observations from GEO.Briefings in bioinformatics · 2014Article
- Integrative analysis of prognosis data on multiple cancer subtypes.Biometrics · 2014Article
- Integrative Analysis of Cancer Diagnosis Studies with Composite Penalization.Scandinavian journal of statistics, theory and applications · 2014Article
- Integrative analysis of multiple cancer genomic datasets under the heterogeneity model.Statistics in medicine · 2013Article
- Sparse group penalized integrative analysis of multiple cancer prognosis datasets.Genetics research · 2013Article
- Incorporating network structure in integrative analysis of cancer prognosis data.Genetic epidemiology · 2013Article
- Integrative analysis of cancer prognosis data with multiple subtypes using regularized gradient descent.Genetic epidemiology · 2012Article
- Probe mapping across multiple microarray platforms.Briefings in bioinformatics · 2012Article
- A Selective Review of Group Selection in High-Dimensional Models.Statistical science : a review journal of the Institute of Mathematical Statistics · 2012Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundAdvancement in gene profiling techniques makes it possible to measure expressions of thousands of genes and identify genes associated with development and progression of cancer. The identified cancer-associated genes can be used for diagnosis, prognosis prediction, and treatment selection. Most existing cancer microarray studies have been focusing on the identification of genes associated with a specific type of cancer. Recent biomedical studies suggest that different cancers may share common susceptibility genes. A comprehensive description of the associations between genes and cancers requires identification of not only multiple genes associated with a specific type of cancer but also genes associated with multiple cancers.
resultsIn this article, we propose the Mc.TGD (Multi-cancer Threshold Gradient Descent), an integrative analysis approach capable of analyzing multiple microarray studies on different cancers. The Mc.TGD is the first regularized approach to conduct "two-dimensional" selection of genes with joint effects on cancer development. Simulation studies show that the Mc.TGD can more accurately identify genes associated with multiple cancers than meta analysis based on "one-dimensional" methods. As a byproduct, identification accuracy of genes associated with only one type of cancer may also be improved. We use the Mc.TGD to analyze seven microarray studies investigating development of seven different types of cancers. We identify one gene associated with six types of cancers and four genes associated with five types of cancers. In addition, we also identify 11, 9, 18, and 17 genes associated with 4 to 1 types of cancers, respectively. We evaluate prediction performance using a Leave-One-Out cross validation approach and find that only 4 (out of 570) subjects cannot be properly predicted.
conclusionThe Mc.TGD can identify a short list of genes associated with one or multiple types of cancers. The identified genes are considerably different from those identified using meta analysis or analysis of marginal effects.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.