Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

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Major gene partners of a favorable biomarker in ER(+) breast cancer

ABSTRACT: Background: We have established a statistical approach on building a transcriptional regulatory network in silico at a global transcriptome scale. Here, we characterize clinical relevant gene activities regulated by a transcription factor-estrogen-related receptor gamma (ESRRG) via such approach. Methods: We measured gene expression relationship between ESRRG and its target gene via univariate coefficient of intrinsic dependence and Galton Pearson’s correlation coefficient. A few more bioinformatics tools were used for further investigation on ESRRG. Results: ESRRG is a negative determinant of lymphovascular invasion, lymph nodal category, stage, histological grade, nuclear pleomorphism and tubule formation in ER(+) infiltrating ductal carcinoma of breast. Interestingly, ESRRG was the only one in the final intersection of Venn Diagram analyses for determinants of those clinical indices. We established an ESRRG core network consisting of 118 probes. Both MYB and ARNT2 were up-regulated by ESRRG and in two ESRRG subnetworks (29 probes, 89 probes), respectively. We observed genes for tumor suppressive activities and responsiveness to anticancer treatments within ESRRG core network and a conserved protective role of ESRRG. Conclusions: ESRRG is a tumor suppressor typically for ER(+) breast cancer and up-regulates tumor suppressor genes mostly within ERα transcriptional regulatory network. An ESRRG core network reveals ESRRG to be a favorable marker and MYB to be the main partner for its cancer protective role during early tumor promotion. But, ARNT2 is an additional partner of ESRRG during early tumor progression. Impact: A core ESRRG transcriptional regulatory network indicates the major clinical impact of ESRRG. 181 surgical specimens of primary infiltrating ductal carcinoma (IDC) were obtained from patients who underwent surgery at National Taiwan University Hospital (NTUH) between 1998 and 2007. They include 90 ER(+) IDCs and 91 ER(-) IDCs. Tissue samples were excised, snap frozen in liquid nitrogen, and stored at -80℃. Breast cancer samples containing relatively pure cancer as defined by greater than 50% tumor cells per high-power field examined in an adjacent section of tumor sample were for this study (Lien HC, et al. Oncogene 2007; 26: 7859-71). Twenty five non-tumor samples were surgically taken from breast cancer patients in 181 IDC patients to generate 25 gene expression profiles as a control in this study. All patients had given informed consent according to the guidelines approved by the Institutional Review Board at NTUH. The matrix file linked to the bottom of this Series includes reanalyzed data from the 119 Samples of this Series and 87 Samples from Series GSE9309 and GSE17040.

ORGANISM(S): Homo sapiens

SUBMITTER: Fon-Jou Hsieh

PROVIDER: E-GEOD-24124 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Project description:Ductal carcinoma in situ (DCIS) is a non-invasive form of breast cancer where cells restricted to the ducts exhibit an atypical phenotype. Some DCIS lesions are believed to rapidly transit to invasive ductal carcinomas (IDCs), while others remain unchanged. Existing classification systems for DCIS fail to identify those lesions that transit to IDC. We studied gene expression patterns of 31 pure DCIS, 36 pure invasive cancers and 42 cases of mixed diagnosis (invasive cancer with an in situ component) using Agilent Whole Human Genome Oligo Microarrays 44k. Six normal breast tissue samples were also included as controls. qRT-PCR was used for validation. All DCIS and invasive samples could be classified into the intrinsic molecular subtypes defined for invasive breast cancer. Hierarchical clustering establishes that samples group by intrinsic subtype, and not by diagnosis. We observed heterogeneity in the transcriptomes among DCIS of high histological grade and identified a distinct subgroup containing seven of the 31 DCIS samples with gene expression characteristics more similar to advanced tumours. A set of genes independent of grade, ER-status and HER2-status was identified by logistic regression that univariately classified a sample as belonging to this distinct DCIS subgroup. qRT-PCR of single markers clearly separated this DCIS subgroup from the other DCIS, and contains samples from several histopathological and intrinsic molecular subtypes. The genes that differentiate between these two types of DCIS suggest several processes related to the re-organisation of the microenvironment. This raises interesting possibilities for identification of DCIS lesions both with and without invasive characteristics, which potentially could be used in clinical assessment of a woman's risk of progression, and lead to improved management that would avoid the current over- and under-treatment of patients. Breast cancer samples, 31 pure DCIS patients, 36 IDC patients, 42 mixed and 6 normal.

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Major gene partners of a favorable biomarker in ER(+) breast cancer

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