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Discovery of multi-dimensional modules by integrative analysis of cancer genomic data.

ABSTRACT: Recent technology has made it possible to simultaneously perform multi-platform genomic profiling (e.g. DNA methylation (DM) and gene expression (GE)) of biological samples, resulting in so-called 'multi-dimensional genomic data'. Such data provide unique opportunities to study the coordination between regulatory mechanisms on multiple levels. However, integrative analysis of multi-dimensional genomics data for the discovery of combinatorial patterns is currently lacking. Here, we adopt a joint matrix factorization technique to address this challenge. This method projects multiple types of genomic data onto a common coordinate system, in which heterogeneous variables weighted highly in the same projected direction form a multi-dimensional module (md-module). Genomic variables in such modules are characterized by significant correlations and likely functional associations. We applied this method to the DM, GE, and microRNA expression data of 385 ovarian cancer samples from the The Cancer Genome Atlas project. These md-modules revealed perturbed pathways that would have been overlooked with only a single type of data, uncovered associations between different layers of cellular activities and allowed the identification of clinically distinct patient subgroups. Our study provides an useful protocol for uncovering hidden patterns and their biological implications in multi-dimensional 'omic' data.

SUBMITTER: Zhang S

PROVIDER: S-EPMC3479191 | biostudies-literature | 2012 Oct

REPOSITORIES: biostudies-literature

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Discovery of multi-dimensional modules by integrative analysis of cancer genomic data.

Zhang Shihua S Liu Chun-Chi CC Li Wenyuan W Shen Hui H Laird Peter W PW Zhou Xianghong Jasmine XJ

Nucleic acids research 20120808 19

Recent technology has made it possible to simultaneously perform multi-platform genomic profiling (e.g. DNA methylation (DM) and gene expression (GE)) of biological samples, resulting in so-called 'multi-dimensional genomic data'. Such data provide unique opportunities to study the coordination between regulatory mechanisms on multiple levels. However, integrative analysis of multi-dimensional genomics data for the discovery of combinatorial patterns is currently lacking. Here, we adopt a joint ...[more]

PMID: 22879375

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Project description:BackgroundNeuroinflammation is one of the key factors leading to neuron death and synapse dysfunction in Alzheimer's disease (AD). Amyloid-β (Aβ) is thought to have an association with microglia activation and trigger neuroinflammation in AD. However, inflammation response in brain disorders is heterogenous, and thus, it is necessary to unveil the specific gene module of neuroinflammation caused by Aβ in AD, which might provide novel biomarkers for AD diagnosis and help understand the mechanism of the disease.MethodsTranscriptomic datasets of brain region tissues from AD patients and the corresponding normal tissues were first used to identify gene modules through the weighted gene co-expression network analysis (WGCNA) method. Then, key modules highly associated with Aβ accumulation and neuroinflammatory response were pinpointed by combining module expression score and functional information. Meanwhile, the relationship of the Aβ-associated module to the neuron and microglia was explored based on snRNA-seq data. Afterward, transcription factor (TF) enrichment and the SCENIC analysis were performed on the Aβ-associated module to discover the related upstream regulators, and then a PPI network proximity method was employed to repurpose the potential approved drugs for AD.ResultsA total of 16 co-expression modules were primarily obtained by the WGCNA method. Among them, the green module was significantly correlated with Aβ accumulation, and its function was mainly involved in neuroinflammation response and neuron death. Thus, the module was termed the amyloid-β induced neuroinflammation module (AIM). Moreover, the module was negatively correlated with neuron percentage and showed a close association with inflammatory microglia. Finally, based on the module, several important TFs were recognized as potential diagnostic biomarkers for AD, and then 20 possible drugs including ibrutinib and ponatinib were picked out for the disease.ConclusionIn this study, a specific gene module, termed AIM, was identified as a key sub-network of Aβ accumulation and neuroinflammation in AD. Moreover, the module was verified as having an association with neuron degeneration and inflammatory microglia transformation. Moreover, some promising TFs and potential repurposing drugs were presented for AD based on the module. The findings of the study shed new light on the mechanistic investigation of AD and might make benefits the treatment of the disease.

Dataset Information

Discovery of multi-dimensional modules by integrative analysis of cancer genomic data.

Publications

Discovery of multi-dimensional modules by integrative analysis of cancer genomic data.

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