Abstract
BackgroundHuman disease often arises as a consequence of alterations in a set of associated genes rather than alterations to a set of unassociated individual genes. Most previous microarray-based meta-analyses identified disease-associated genes or biomarkers independent of genetic interactions. Therefore, in this study, we present the first meta-analysis method capable of taking gene combination effects into account to efficiently identify associated biomarkers (ABs) across different microarray platforms.ResultsWe propose a new meta-analysis approach called MiningABs to mine ABs across different array-based datasets. The similarity between paired probe sequences is quantified as a bridge to connect these datasets together. The ABs can be subsequently identified from an “improved” common logit model (c-LM) by combining several sibling-like LMs in a heuristic genetic algorithm selection process. Our approach is evaluated with two sets of gene expression datasets: i) 4 esophageal squamous cell carcinoma and ii) 3 hepatocellular carcinoma datasets. Based on an unbiased reciprocal test, we demonstrate that each gene in a group of ABs is required to maintain high cancer sample classification accuracy, and we observe that ABs are not limited to genes common to all platforms. Investigating the ABs using Gene Ontology (GO) enrichment, literature survey, and network analyses indicated that our ABs are not only strongly related to cancer development but also highly connected in a diverse network of biological interactions.ConclusionsThe proposed meta-analysis method called MiningABs is able to efficiently identify ABs from different independently performed array-based datasets, and we show its validity in cancer biology via GO enrichment, literature survey and network analyses. We postulate that the ABs may facilitate novel target and drug discovery, leading to improved clinical treatment. Java source code, tutorial, example and related materials are available at “http://sourceforge.net/projects/miningabs/”.
Highlights
Human disease often arises as a consequence of alterations in a set of associated genes rather than alterations to a set of unassociated individual genes
Our method addresses all of these issues and is evaluated with two publicly available cancer microarray datasets: i) 4 gene expression microarray datasets conducted by 3 independent research groups in human esophageal squamous cell carcinoma [5,6,7] and ii) 3 gene expression microarray datasets in human hepatocellular carcinoma [8,9]
ABs are highly related to cancer development and connected in network In addition to using the reciprocal tests to repetitively examine the improved c-logit model (LM) in different cancer-related datasets, we tested whether the associated biomarkers (ABs) we discovered possessed biological insights into cancer development using a GO (Gene Ontology) enrichment analysis [33]
Summary
Human disease often arises as a consequence of alterations in a set of associated genes rather than alterations to a set of unassociated individual genes. In this study, we present the first meta-analysis method capable of taking gene combination effects into account to efficiently identify associated biomarkers (ABs) across different microarray platforms. Many clinical diseases such as cancer arise as a consequence of massive alterations in gene activity. Microarray techniques have been widely used to detect large-scale molecular changes in many biological events such as alterations in gene expression for human tumorigenesis [5,6,7,8,9] These approaches identified some important cancer-associated genes and cellular pathways. These results motivated others to develop meta-analysis methods to discover reliable common patterns across different individually performed experiments
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