Gene expression profile and cancer-associated pathways linked to progesterone receptor isoform a (PRA) predominance in transgenic mouse mammary glands (original) (raw)
Related papers
Identification of gene expression signature in estrogen receptor positive breast carcinoma
Biomark. …, 2010
A significant group of patient with estrogen receptor (ER) α positive breast tumors fails to appreciably respond to endocrine therapy. An increased understanding of the molecular basis of estrogen-mediated signal transduction and resultant gene expression may lead to novel strategies for treating breast cancer. In this study, we sought to identify the dysregulated genes in breast tumors related to ERα status. Microarray analyses of 31 tumor samples showed 108 genes differentially expressed in ERα (+) and ERα (-) primary breast tumors. Further analyses of gene lists indicated that a significant number of dysregulated genes were involved in mRNA transcription and cellular differentiation. The majority of these genes were found to have promoter-binding sites for E74-like factor 5 (ELF5; 54.6% genes), E2F transcription factor 1 (E2F1; 22.2% genes), and nuclear transcription factor Y alpha (NFYA; 32.4% genes). Six candidate genes (NTN4, SLC7A8, MLPH, ENPP1, LAMB2, and PLAT) with differential expression were selected for further validation studies using RT-qPCR (76 clinical specimen) and immunohistochemistry (48 clinical specimen). Our studies indicate significant overexpression of all the six genes in ERα (+) breast tumors as compared to ERα (-) breast tumors. In vitro studies using T-47D breast cancer cell line confirmed the estrogen dependant expression of four of the above six genes (SLC7A8, ENPP1, LAMB2, and PLAT). Collectively, our study provides further insights into the molecular basis of estrogen-dependent breast cancer and identifies "candidate biomarkers" that could be useful for predicting endocrine responsiveness.
Gene Expression Profiles in Breast Cancer to Identify Estrogen Receptor Target Genes
Mini-Reviews in Medicinal Chemistry, 2008
The estrogens play important role in the homeostatic maintenance of several target tissues including those in the mammary gland, uterus, bone, cardiovascular system, and brain. Most of estrogen's action is thought to be mediated through its nuclear estrogen receptors, ER and ER , which are members of the nuclear receptor superfamily that act as ligand-induced transcription factors. Acting via its receptors, estrogen also plays an essential role in the development and progression of human breast cancer. The ER and progesterone receptor (PR), which are regulated by estrogen via ER, have been used as prognostic markers in the clinical management of breast cancer patients. However, the prognosis of a patient with ER+/PR+ breast cancer can be highly variable and a significant proportion of hormone receptor positive breast cancers does not respond to endocrine therapy. The identification of estrogen receptor target genes may improve our understanding of the role played by estrogens in breast cancer making it possible to better tailor hormone treatments and improve a patient's response to hormonal therapy. In this review, we explore the literature for data regarding the identification of estrogen receptor-regulated genes in breast cancer cell lines and breast tumor biopsies using high throughput technologies such as serial analysis of gene expression (SAGE) and cDNA microarrays.
Transcriptomic landscape of breast cancers through mRNA sequencing
Scientific Reports, 2012
B reast cancer is the leading cause of cancer death among women, accounting for 23% of the total cancer cases 1. The major treatment challenge remains at the level of defining the specific types and associated biology behind the disease 2-4. Breast cancer is known to be a heterogeneous disease with a variety of morphological features and clinical manifestations due to genetic, epigenetic, and transcriptomic alterations 3-7. This phenotypic diversity severely affects the diagnosis and prognosis of breast cancer. The main difficulties in resolving these issues include the complexities of determining specific markers and the lack of a complete understanding of the cellular hierarchy of the mammary epithelium 5,7-11. In addition, the remarkable variations in response to therapy 12,13 also emphasise the pressing need for further understanding of breast cancer evolution, the genomic basis of heterogeneity, and the biological basis of this disease. Numerous reports have demonstrated that the metastatic status, histological grade, tumour stage, size, and receptor expression are the main critical determinants of breast cancer treatment 14-18. Seminal gene expression studies by Perou et al. (2000) and Sorlie et al. (2001) have established a classification of breast cancer into five broad ''intrinsic phenotypic subtypes'' 19,20. These subtypes include Luminal A, Luminal B, Human Epidermal Growth Factor Receptor 2 (HER2)-positive, basal-like and normal breast-like breast cancers 19-22. Correlating these subtypes with the traditional tumour histology provided a paradigm shift in breast cancer diagnostics. Furthermore, microarray investigations have offered an initial basis for treatment prediction 22-26 and identification of the different breast tumour stages that are critical for breast cancer treatment 27,28,11,29. However, translating molecular profiling into clinical practice has proven to be a formidable challenge as a result of complex heterogeneity 30,31. Immunohistochemically, three broad types of breast tumours have been classified by the status of therapeutically significant components, the Estrogen receptor ER, the progesterone-receptor (PR) and the HER2 3,32. Breast tumours lacking expression of all three receptors are defined as triple-negative breast cancer (TNBC) 33-36. TNBC is often classified as basal-like breast cancer, which represents 10-25% of all tumours and is presumed to be derived from a distinct cell type and a specific developmental stage of mammary epithelial cell development 19,22,34,36. In contrast, the gene expression profiles of HER2-positive (ER and PR negative) and Non-TNBC (positive for all three receptors) tumours belong to the luminal-like subgroups, representing approximately 15% of patients 37,38. The main characteristics of TNBC are frequent occurrence in younger patients (,50), increased
Hormone Receptor and ERBB2 Status in Gene Expression Profiles of Human Breast Tumor Samples
PLoS ONE, 2011
The occurrence of large publically available repositories of human breast tumor gene expression profiles provides an important resource to discover new breast cancer biomarkers and therapeutic targets. For example, knowledge of the expression of the estrogen and progesterone hormone receptors (ER and PR), and that of the ERBB2 in breast tumor samples enables choice of therapies for the breast cancer patients that express these proteins. Identifying new biomarkers and therapeutic agents affecting the activity of signaling pathways regulated by the hormone receptors or ERBB2 might be accelerated by knowledge of their expression levels in large gene expression profiling data sets. Unfortunately, the status of these receptors is not invariably reported in public databases of breast tumor gene expression profiles. Attempts have been made to employ a single probe set to identify ER, PR and ERBB2 status, but the specificity or sensitivity of their prediction is low. We enquired whether estimation of ER, PR and ERBB2 status of profiled tumor samples could be improved by using multiple probe sets representing these three genes and others with related expression. We used 8 independent datasets of human breast tumor samples to define gene expression signatures comprising 24, 51 and 14 genes predictive of ER, PR and ERBB2 status respectively. These signatures, as demonstrated by sensitivity and specificity measures, reliably identified hormone receptor and ERBB2 expression in breast tumors that had been previously determined using protein and DNA based assays. Our findings demonstrate that gene signatures can be identified which reliably predict the expression status of the estrogen and progesterone hormone receptors and that of ERBB2 in publically available gene expression profiles of breast tumor samples. Using these signatures to query transcript profiles of breast tumor specimens may enable discovery of new biomarkers and therapeutic targets for particular subtypes of breast cancer.
Gene Expression Profiles Reveal Potential Targets for Breast Cancer Diagnosis and Treatment
Figuring out the molecular mechanisms underlying breast cancer is essential for the diagnosis and treatment of this invasive disorder. Hence it is important to identify the most significant genes correlated with molecular events and to study their interactions in order to identify breast cancer mechanisms. Here we focus on the gene expression profiles, which we have detected in breast cancer. High-throughput genomic innovations such as microarray have helped us understand the complex dynamics of multisystem diseases such as diabetes and cancer. We performed an analysis using microarray datasets by the Networkanalyst bioinformatics tool, based on a random effect model (REM). We achieved pivotal differential expressed genes like ADAMTS5, SCARA5, IGSF10, and C2orf40 that had the most down-regulation, and also COL10A1, COL11A1, and UHRF1 that they had the most up-regulation in four-stage of breast cancer. We used CentiScape and AllegroMCODE plugins in CytoScape software in order to figu...
International Journal of Cancer, 2004
Estrogen acts via its receptor (ER) to stimulate cell growth and differentiation in the mammary gland. ER and progesterone receptor (PR), which is regulated by estrogen via ER, have been used as prognostic markers in clinical management of breast cancer patients. Patients with ER− breast tumors have a poorer prognosis than patients with ER+ tumors. The aim of the present study was the identification of tumor-associated genes differentially expressed in breast tumors regarding the presence or absence of ER and PR hybridized with cDNA microarrays containing 4,500 tumor-derived expressed sequence tags generated using the ORESTES technique. Samples of human primary breast carcinomas from 38 patients were analyzed. The experiments were performed in triplicates and data from each element were acquired by phosphoimage scanning. Data acquisition was performed using the ArrayVision software. After normalization statistical analysis was applied. In a preliminary analysis, 98 differentially expressed transcripts were identified, 46 were found to be more expressed in ER+/PR+ and 52 were found to be more expressed in ER−/PR− breast tumors. The biochemical functions of the genes in the reported expression profile are diverse and include metabolic enzymes, protein kinases, helicases, transcription factors, cell cycle regulators and apoptotic factors. ER−/PR− breast tumors displayed increased levels of transcripts of genes associated with neurodegeneration and genes associated with proliferation were found in ER+/PR+ tumors. © 2004 Wiley-Liss, Inc.
International Journal of Cancer, 2008
Gene expression profiles were studied by microarray analysis in 2 sets of archival breast cancer tissues from patients with distinct clinical outcome. Seventy-seven differentially expressed genes were identified when comparing 30 cases with relapse and 30 cases without relapse within 72 months from surgery. These genes had a specific ontological distribution and some of them have been linked to breast cancer in previous studies: AIB1, the two keratin genes KRT5 and KRT15, RAF1, WIF1 and MSH6. Seven out of 77 differentially expressed genes were selected and analyzed by qRT-PCR in 127 cases of breast cancer. The expression levels of 6 upregulated genes (CKMT1B, DDX21, PRKDC, PTPN1, SLPI, YWHAE) showed a significant association to both disease-free and overall survival. Multivariate analysis using the significant factors (i.e., estrogen receptor and lymph node status) as covariates confirmed the association with survival. There was no correlation between the expression level of these genes and other clinical parameters. In contrast, SERPINA3, the only downregulated gene examined, was not associated with survival, but correlated with steroid receptor status. An indirect validation of our genes was provided by calculating their association with survival in 3 publicly available microarray datasets. CKMT1B expression was an independent prognostic marker in all 3 datasets, whereas other genes confirmed their association with disease-free survival in at least 1 dataset. This work provides a novel set of genes that could be used as independent prognostic markers and potential drug targets for breast cancer.
A Transcriptional Fingerprint of Estrogen in Human Breast Cancer Predicts Patient Survival
Neoplasia, 2008
Estrogen signaling plays an essential role in breast cancer progression, and estrogen receptor (ER) status has long been a marker of hormone responsiveness. However, ER status alone has been an incomplete predictor of endocrine therapy, as some ER+ tumors, nevertheless, have poor prognosis. Here we sought to use expression profiling of ER+ breast cancer cells to screen for a robust estrogen-regulated gene signature that may serve as a better indicator of cancer outcome. We identified 532 estrogen-induced genes and further developed a 73-gene signature that best separated a training set of 286 primary breast carcinomas into prognostic subtypes by stepwise cross-validation. Notably, this signature predicts clinical outcome in over 10 patient cohorts as well as their respective ER+ subcohorts. Further, this signature separates patients who have received endocrine therapy into two prognostic subgroups, suggesting its specificity as a measure of estrogen signaling, and thus hormone sensitivity. The 73-gene signature also provides additional predictive value for patient survival, independent of other clinical parameters, and outperforms other previously reported molecular outcome signatures. Taken together, these data demonstrate the power of using cell culture systems to screen for robust gene signatures of clinical relevance.
Molecular profiling of breast cancer: transcriptomic studies and beyond
Cell Mol Life Sci, 2007
Abstract. Utilisation of omics technologies, in particular gene expression profiling, has increased dramatically in recent years. In basic research, highthroughput profiling applications are increasingly used and may now even be considered standard research tools. In the clinic, there is a need for better and more accurate diagnosis, prognosis and treatment response indicators. As such, clinicians have looked to omics technologies for potential biomarkers. These prediction profiling studies have in turn attracted the attention ...