Dataset Information

Multi-level reproducibility of signature hubs in human interactome for breast cancer metastasis.

ABSTRACT:

Background

It has been suggested that, in the human protein-protein interaction network, changes of co-expression between highly connected proteins ("hub") and their interaction neighbours might have important roles in cancer metastasis and be predictive disease signatures for patient outcome. However, for a cancer, such disease signatures identified from different studies have little overlap.

Results

Here, we propose a systemic approach to evaluate the reproducibility of disease signatures at multiple levels, on the basis of some statistically testable biological models. Using two datasets for breast cancer metastasis, we showed that different signature hubs identified from different studies were highly consistent in terms of significantly sharing interaction neighbours and displaying consistent co-expression changes with their overlapping neighbours, whereas the shared interaction neighbours were significantly over-represented with known cancer genes and enriched in pathways deregulated in breast cancer pathogenesis. Then, we showed that the signature hubs identified from the two datasets were highly reproducible at the protein interaction and pathway levels in three other independent datasets.

Conclusions

Our results provide a possible biological model that different signature hubs altered in different patient cohorts could disturb the same pathways associated with cancer metastasis through their interaction neighbours.

SUBMITTER: Yao C

PROVIDER: S-EPMC2990745 | biostudies-literature | 2010 Nov

REPOSITORIES: biostudies-literature

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Publications

Multi-level reproducibility of signature hubs in human interactome for breast cancer metastasis.

Yao Chen C Li Hongdong H Zhou Chenggui C Zhang Lin L Zou Jinfeng J Guo Zheng Z

BMC systems biology 20101109

<h4>Background</h4>It has been suggested that, in the human protein-protein interaction network, changes of co-expression between highly connected proteins ("hub") and their interaction neighbours might have important roles in cancer metastasis and be predictive disease signatures for patient outcome. However, for a cancer, such disease signatures identified from different studies have little overlap.<h4>Results</h4>Here, we propose a systemic approach to evaluate the reproducibility of disease ...[more]

PMID: 21059271

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Project description:BACKGROUND:We have previously identified genome-wide DNA methylation changes in a cell line model of breast cancer metastasis. These complex epigenetic changes that we observed, along with concurrent karyotype analyses, have led us to hypothesize that complex genomic alterations in cancer cells (deletions, translocations and ploidy) are superimposed over promoter-specific methylation events that are responsible for gene-specific expression changes observed in breast cancer metastasis. METHODOLOGY/PRINCIPAL FINDINGS:We undertook simultaneous high-resolution, whole-genome analyses of MDA-MB-468GFP and MDA-MB-468GFP-LN human breast cancer cell lines (an isogenic, paired lymphatic metastasis cell line model) using Affymetrix gene expression (U133), promoter (1.0R), and SNP/CNV (SNP 6.0) microarray platforms to correlate data from gene expression, epigenetic (DNA methylation), and combination copy number variant/single nucleotide polymorphism microarrays. Using Partek Software and Ingenuity Pathway Analysis we integrated datasets from these three platforms and detected multiple hypomethylation and hypermethylation events. Many of these epigenetic alterations correlated with gene expression changes. In addition, gene dosage events correlated with the karyotypic differences observed between the cell lines and were reflected in specific promoter methylation patterns. Gene subsets were identified that correlated hyper (and hypo) methylation with the loss (or gain) of gene expression and in parallel, with gene dosage losses and gains, respectively. Individual gene targets from these subsets were also validated for their methylation, expression and copy number status, and susceptible gene pathways were identified that may indicate how selective advantage drives the processes of tumourigenesis and metastasis. CONCLUSIONS/SIGNIFICANCE:Our approach allows more precisely profiling of functionally relevant epigenetic signatures that are associated with cancer progression and metastasis.

Project description:BackgroundVisceral organ metastasis is associated with poor survival outcomes in terms of metastasis free- and overall survival in breast carcinomas. Identification of a gene expression profile in tumours that selects a subpopulation of patients that is more likely to develop visceral organ metastases will help elucidate mechanisms for the development of distant metastases and could be of clinical value. With this study we aimed to determine genomic predictors that would help to distinguish breast cancer patients with more likelihood to develop visceral metastasis.MethodsGene expression profiling data of 157 primary tumours from breast cancer patients who developed distant metastases were analyzed and differentially expressed genes between the group of tumours with visceral metastasis and the those without visceral metastases were identified. Published data were used to validate our findings. Multivariate logistic regression tests were applied to further investigate the association between the gene-expression-signature and clinical variables. Survival analyses were performed by the Kaplan-Meier method.ResultsFourteen differentially expressed genes (WDR6, CDYL, ATP6V0A4, CHAD, IDUA, MYL5, PREP, RTN4IP1, BTG2, TPRG1, ABHD14A, KIF18A, S100PBP and BEND3) were identified between the group of tumours with and without visceral metastatic disease. Five of these genes (CDYL, ATP6V0A4, PREP, RTN4IP1 and KIF18A) were up-regulated and the other genes were down-regulated. This gene expression signature was validated in the training and in the independent data set (p 2.13e- 08 and p 9.68e- 06, respectively). Multivariate analyses revealed that the 14-gene-expression-signature was associated with visceral metastatic disease (p 0.001, 95% CI 1.43-4.27), independent of other clinicopathologic features. This signature has been also found to be associated with survival status of the patients (p < .001).ConclusionWe have identified an unique gene expression signature which is specific to visceral metastasis. This 14-gene-expression-signature may play a role in identifying the subgroup of patients with potential to develop visceral metastasis.

Dataset Information

Multi-level reproducibility of signature hubs in human interactome for breast cancer metastasis.

Background

Results

Conclusions

Publications

Multi-level reproducibility of signature hubs in human interactome for breast cancer metastasis.

Similar Datasets

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