ArticleBMC bioinformatics2007
Meta-analysis of several gene lists for distinct types of cancer: a simple way to reveal common prognostic markers.
Article in BMC bioinformatics, 2007. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 3 of them syntheses that pooled it.
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Who cites it
17 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Meta-analysis of gene expression signatures reveals hidden links among diverse biological processes in Arabidopsis.PloS one · 2014Pooled it
- A global meta-analysis of microarray expression data to predict unknown gene functions and estimate the literature-data divide.Bioinformatics (Oxford, England) · 2009Pooled it
- Meta-analysis of genome-wide expression patterns associated with behavioral maturation in honey bees.BMC genomics · 2008Pooled it
- Prognostic significance of alpha-2-macrglobulin and low-density lipoprotein receptor-related protein-1 in various cancers.American journal of cancer research · 2024Article
- Designing Dietary Recommendations Using System Level Interactomics Analysis and Network-Based Inference.Frontiers in physiology · 2017Article
- Identification of epigenetic modifications that contribute to pathogenesis in therapy-related AML: Effective integration of genome-wide histone modification with transcriptional profiles.BMC medical genomics · 2015Article
- AAPL: Assessing Association between P-value Lists.Statistical analysis and data mining · 2013Article
- Comprehensive literature review and statistical considerations for microarray meta-analysis.Nucleic acids research · 2012Review
- Algebraic comparison of partial lists in bioinformatics.PloS one · 2012Article
- High-throughput processing and normalization of one-color microarrays for transcriptional meta-analyses.BMC bioinformatics · 2011Article
- Using the ratio of means as the effect size measure in combining results of microarray experiments.BMC systems biology · 2009Article
- Mechanism-anchored profiling derived from epigenetic networks predicts outcome in acute lymphoblastic leukemia.BMC bioinformatics · 2009Article
- Data integration in genetics and genomics: methods and challenges.Human genomics and proteomics : HGP · 2009Article
- Clinical biomarkers of angiogenesis inhibition.Cancer metastasis reviews · 2008Review
- Connecting genes, coexpression modules, and molecular signatures to environmental stress phenotypes in plants.BMC systems biology · 2008Article
- Biomarkers for malignant pleural mesothelioma: current status.Molecular diagnosis & therapy · 2008Review
- Accessing and integrating data and knowledge for biomedical research.Yearbook of medical informatics · 2008Review
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAlthough prognostic biomarkers specific for particular cancers have been discovered, microarray analysis of gene expression profiles, supported by integrative analysis algorithms, helps to identify common factors in molecular oncology. Similarities of Ordered Gene Lists (SOGL) is a recently proposed approach to meta-analysis suitable for identifying features shared by two data sets. Here we extend the idea of SOGL to the detection of significant prognostic marker genes from microarrays of multiple data sets. Three data sets for leukemia and the other six for different solid tumors are used to demonstrate our method, using established statistical techniques.
resultsWe describe a set of significantly similar ordered gene lists, representing outcome comparisons for distinct types of cancer. This kind of similarity could improve the diagnostic accuracies of individual studies when SOGL is incorporated into the support vector machine algorithm. In particular, we investigate the similarities among three ordered gene lists pertaining to mesothelioma survival, prostate recurrence and glioma survival. The similarity-driving genes are related to the outcomes of patients with lung cancer with a hazard ratio of 4.47 (p = 0.035). Many of these genes are involved in breakdown of EMC proteins regulating angiogenesis, and may be used for further research on prognostic markers and molecular targets of gene therapy for cancers.
conclusionThe proposed method and its application show the potential of such meta-analyses in clinical studies of gene expression profiles.
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