Dataset Information

Differential network analysis reveals the genome-wide landscape of estrogen receptor modulation in hormonal cancers.

ABSTRACT: Several mutual information (MI)-based algorithms have been developed to identify dynamic gene-gene and function-function interactions governed by key modulators (genes, proteins, etc.). Due to intensive computation, however, these methods rely heavily on prior knowledge and are limited in genome-wide analysis. We present the modulated gene/gene set interaction (MAGIC) analysis to systematically identify genome-wide modulation of interaction networks. Based on a novel statistical test employing conjugate Fisher transformations of correlation coefficients, MAGIC features fast computation and adaption to variations of clinical cohorts. In simulated datasets MAGIC achieved greatly improved computation efficiency and overall superior performance than the MI-based method. We applied MAGIC to construct the estrogen receptor (ER) modulated gene and gene set (representing biological function) interaction networks in breast cancer. Several novel interaction hubs and functional interactions were discovered. ER+ dependent interaction between TGF? and NF?B was further shown to be associated with patient survival. The findings were verified in independent datasets. Using MAGIC, we also assessed the essential roles of ER modulation in another hormonal cancer, ovarian cancer. Overall, MAGIC is a systematic framework for comprehensively identifying and constructing the modulated interaction networks in a whole-genome landscape. MATLAB implementation of MAGIC is available for academic uses at https://github.com/chiuyc/MAGIC.

SUBMITTER: Hsiao TH

PROVIDER: S-EPMC4789788 | biostudies-literature | 2016 Mar

REPOSITORIES: biostudies-literature

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Differential network analysis reveals the genome-wide landscape of estrogen receptor modulation in hormonal cancers.

Hsiao Tzu-Hung TH Chiu Yu-Chiao YC Hsu Pei-Yin PY Lu Tzu-Pin TP Lai Liang-Chuan LC Tsai Mong-Hsun MH Huang Tim H-M TH Chuang Eric Y EY Chen Yidong Y

Scientific reports 20160314

Several mutual information (MI)-based algorithms have been developed to identify dynamic gene-gene and function-function interactions governed by key modulators (genes, proteins, etc.). Due to intensive computation, however, these methods rely heavily on prior knowledge and are limited in genome-wide analysis. We present the modulated gene/gene set interaction (MAGIC) analysis to systematically identify genome-wide modulation of interaction networks. Based on a novel statistical test employing c ...[more]

PMID: 26972162

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Project description:INTRODUCTION: Breast tumors lacking the estrogen receptor-alpha (ER-alpha) have increased incidence of resistance to therapy and poorer clinical prognosis. METHODS: Whole tissue sections from 16 cryopreserved breast cancer tumors that were either positive or negative for the ER (eight ER positive and eight ER negative) were differentially analyzed by multiplex imaging of two-dimensional PAGE gels using 54 cm isoelectric focusing. Differentially detected spots of Progesterone Receptor Membrane Component 1 (PGRMC1) were shown to differ in phosphorylation status by differential two dimensional polyacrylamide gel electrophoresis of phosphatase-treated tumor proteins. Site directed mutagenesis was used to create putative phosphorylation site point mutants in PGRMC1. Stable transfectants of these mutants in MCF7 cells were assayed for their survival after oxidative stress, and for AKT kinase phosphorylation. Immune fluorescence using anti-PGRMC1 monoclonal antibody 5G7 was performed on breast cancer tissue microarrays. RESULTS: Proteins significantly differentially abundant between estrogen receptor negative and estrogen receptor positive tumors at the 0.1% level were consistent with published profiles, suggesting an altered keratin pool, and increased inflammation and wound responses in estrogen receptor negative tumors. Two of three spots of PGRMC1 were more abundant in estrogen receptor negative tumors. Phosphatase treatment of breast tumor proteins indicated that the PGRMC1 isoforms differed in their phosphorylation status. Simultaneous mutation of PGRMC1 serine-56 and serine-180 [corrected] fully abrogated the sensitivity of stably transfected MCF7 breast cancer cells to peroxide-induced cell death. Immune fluorescence revealed that PGRMC1 was primarily expressed in ER-negative basal epithelial cells of mammary ductules. Even in advanced tumors, high levels of ER or PGRMC1 were almost mutually exclusive in individual cells. In five out of five examined ductal in situ breast cancers of comedo type, PGRMC1 was expressed in glucose transporter 1 negative or positive poorly oxygenated cells surrounding the necrotic core, surrounded by a more distal halo of ER-positive cells. CONCLUSIONS: PGRMC1 phosphorylation may be involved in the clinical differences that underpin breast tumors of differing ER status.

Project description:BackgroundDirectly comparing gene expression profiles of estrogen receptor-positive (ER+) and estrogen receptor-negative (ER-) breast cancers cannot determine whether differentially expressed genes between these two subtypes result from dysregulated expression in ER+ cancer or ER- cancer versus normal controls, and thus would miss critical information for elucidating the transcriptomic difference between the two subtypes.Principal findingsUsing microarray datasets from TCGA, we classified the genes dysregulated in both ER+ and ER- cancers versus normal controls into two classes: (i) genes dysregulated in the same direction but to a different extent, and (ii) genes dysregulated to opposite directions, and then validated the two classes in RNA-sequencing datasets of independent cohorts. We showed that the genes dysregulated to a larger extent in ER+ cancers than in ER- cancers enriched in glycerophospholipid and polysaccharide metabolic processes, while the genes dysregulated to a larger extent in ER- cancers than in ER+ cancers enriched in cell proliferation. Phosphorylase kinase and enzymes of glycosylphosphatidylinositol (GPI) anchor biosynthesis were upregulated to a larger extent in ER+ cancers than in ER- cancers, whereas glycogen synthase and phospholipase A2 were downregulated to a larger extent in ER+ cancers than in ER- cancers. We also found that the genes oppositely dysregulated in the two subtypes significantly enriched with known cancer genes and tended to closely collaborate with the cancer genes. Furthermore, we showed the possibility that these oppositely dysregulated genes could contribute to carcinogenesis of ER+ and ER- cancers through rewiring different subpathways.ConclusionsGPI-anchor biosynthesis and glycogenolysis were elevated and hydrolysis of phospholipids was depleted to a larger extent in ER+ cancers than in ER- cancers. Our findings indicate that the genes oppositely dysregulated in the two subtypes are potential cancer genes which could contribute to carcinogenesis of both ER+ and ER- cancers through rewiring different subpathways.

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Dataset Information

Differential network analysis reveals the genome-wide landscape of estrogen receptor modulation in hormonal cancers.

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Differential network analysis reveals the genome-wide landscape of estrogen receptor modulation in hormonal cancers.

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