Unknown,Transcriptomics,Genomics,Proteomics

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Transcription profiling of human cell line and tumour breast cancer samples - functional ER alpha transcriptional regulatory network for cell cycle in an ER(+) breast cancer subgroup

ABSTRACT: To better characterize group IE like human breast cancer based on the gene profiles of estrogen actions through estrogen receptor alpha (ER alpha), we identified an ER alpha transcriptional regulatory network for cell cycle in silico. We used two datasets from cell line (Data 1) and clinical samples (Data 2), respectively. Analyses on Data 1 via trajectory clustering and Pathway-Express confirmed the significant estrogen effect on up-regulating cell cycle activities. The gene expression relationships between ER alpha and cell cycle genes were re-identified in Data 2 by three statistical methods – Galton-Pearson’s correlation coefficient, Student’s t-test and the coefficient of intrinsic dependence. They were mostly (56.09%)(46/82) re-confirmed by literature search. E2F1 was found to be the major ER alpha target in regulating cell cycle gene expressions (83.72%)(36/43) via suppressive mode. However, enhanced cell cycle progression via up-regulating some cell cycle genes was predicted in silico possibly involving E2F2, in part. Both tumorigenic and tumor suppressing activities indicated by this network were predicted. This network clearly provides a robust way for uncovering estrogen actions in an ER(+) subtype specific manner. Experiment Overall Design: Two clinical datasets were used in this study. One, the 37 clinical arrays (abbreviated as 37A) consist of 26 A for patients positive in estrogen receptor alpha (ER) and in progesterone receptor (PR) immunohistochemical stain (IHC) and 11A for patients negative in ER IHC. This dataset was designated as Data 2. The 31 clinical arrays (31A) consist of 20A for patients positive in ER status but negative in PR status and 11A which are the same as in 37A. This dataset was used for data comparison. All the signals from the mRNA profile of each sample in the experiments were normalized using the internal control RNA- Stratagene's human common reference RNA via statistical method 'rank consistant lowess. Finally, those ratios were transformed by Log2.

ORGANISM(S): Homo sapiens

SUBMITTER: Fon-Jou Hsieh

PROVIDER: E-GEOD-17040 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Project description:A variety of high-throughput techniques are now available for constructing comprehensive gene regulatory networks in systems biology. In this study, we report a new statistical approach for facilitating in silico inference of regulatory network structure. The new measure of association, coefficient of intrinsic dependence (CID), is model-free and can be applied to both continuous and categorical distributions. When given two variables X and Y, CID answers whether Y is dependent on X by examining the conditional distribution of Y given X. In this paper, we apply CID to analyze the regulatory relationships between transcription factors (TFs) (X) and their downstream genes (Y) based on clinical data. More specifically, we use estrogen receptor alpha (ERalpha) as the variable X, and the analyses are based on 48 clinical breast cancer gene expression arrays (48A). RESULTS: The analytical utility of CID was evaluated in comparison with four commonly used statistical methods, Galton-Pearson's correlation coefficient (GPCC), Student's t-test (STT), coefficient of determination (CoD), and mutual information (MI). When being compared to GPCC, CoD, and MI, CID reveals its preferential ability to discover the regulatory association where distribution of the mRNA expression levels on X and Y does not fit linear models. On the other hand, when CID is used to measure the association of a continuous variable (Y) against a discrete variable (X), it shows similar performance as compared to STT, and appears to outperform CoD and MI. In addition, this study established a two-layer transcriptional regulatory network to exemplify the usage of CID, in combination with GPCC, in deciphering gene networks based on gene expression profiles from patient arrays. CONCLUSION: CID is shown to provide useful information for identifying associations between genes and transcription factors of interest in patient arrays. When coupled with the relationships detected by GPCC, the association predicted by CID are applicable to the construction of transcriptional regulatory networks. This study shows how information from different data sources and learning algorithms can be integrated to investigate whether relevant regulatory mechanisms identified in cell models can also be partially re-identified in clinical samples of breast cancers. AVAILABILITY: the implementation of CID in R codes can be freely downloaded from (http://homepage.ntu.edu.tw/~lyliu/BC/). Experiment Overall Design: Total 48 clinical arrays (48A) used in this study can be found in GSE9309. We designed the experiments using a given breast cancer population with clear status of estrogen receptor alpha (ER), which were confirmed by immunochemical staining (If Â³10% immunopositive stain is found at tumor section, we designate it as ER(+). Otherwise, it is ER(-). ) in this study. 48A consist of 36A with positive in ER status and of 12A with negative in ER status.

Project description:Estrogen receptor-{alpha} (ER{alpha}) and its ligand estradiol play critical roles in breast cancer growth and are important therapeutic targets for this disease. Using chromatin immunoprecipitation (ChIP)-on-chip, ligand-bound ER{alpha} was recently found to function as a master transcriptional regulator via binding to many cis-acting sites genome-wide. Here, we used an alternative technology (ChIP cloning) and identified 94 ER{alpha} target loci in breast cancer cells. The ER{alpha}-binding sites contained both classic estrogen response elements and nonclassic binding sequences, showed specific transcriptional activity in reporter gene assay, and interacted with the key transcriptional regulators, including RNA polymerase II and nuclear receptor coactivator-3. The great majority of the binding sites were located in either introns or far distant to coding regions of genes. Forty-three percent of the genes that lie within 50 kb to an ER{alpha}-binding site were regulated by estradiol. Most of these genes are novel estradiol targets encoding receptors, signaling messengers, and ion binders/transporters. mRNA profiling in estradiol-treated breast cancer cell lines and tissues revealed that these genes are highly ER{alpha} responsive both in vitro and in vivo. Among estradiol-induced genes, Wnt11 was found to increase cell survival by significantly reducing apoptosis in breast cancer cells. Taken together, we showed novel genomic binding sites of ER{alpha} that regulate a novel set of genes in response to estradiol in breast cancer. Our findings suggest that at least a subset of these genes, including Wnt11, may play important in vivo and in vitro biological roles in breast cancer. Experiment Overall Design: This Series currently contains the gene expression data accompanying Zhihong Lin et al. Cancer Research 67,5017-5024(2007). MCF7 cells were treated with vehicle or E2 at a concentration of 10E-9 mol/L for 3 and 6 h. All experiments were performed in triplicate.

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Transcription profiling of human cell line and tumour breast cancer samples - functional ER alpha transcriptional regulatory network for cell cycle in an ER(+) breast cancer subgroup

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