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MicroRNA-Regulated Protein-Protein Interaction Networks and Their Functions in Breast Cancer  [PDF]
Chia-Hsien Lee,Wen-Hong Kuo,Chen-Ching Lin,Yen-Jen Oyang,Hsuan-Cheng Huang,Hsueh-Fen Juan
International Journal of Molecular Sciences , 2013, DOI: 10.3390/ijms140611560
Abstract: MicroRNAs, which are small endogenous RNA regulators, have been associated with various types of cancer. Breast cancer is a major health threat for women worldwide. Many miRNAs were reported to be associated with the progression and carcinogenesis of breast cancer. In this study, we aimed to discover novel breast cancer-related miRNAs and to elucidate their functions. First, we identified confident miRNA-target pairs by combining data from miRNA target prediction databases and expression profiles of miRNA and mRNA. Then, miRNA-regulated protein interaction networks (PINs) were constructed with confident pairs and known interaction data in the human protein reference database (HPRD). Finally, the functions of miRNA-regulated PINs were elucidated by functional enrichment analysis. From the results, we identified some previously reported breast cancer-related miRNAs and functions of the PINs, e.g., miR-125b, miR-125a, miR-21, and miR-497. Some novel miRNAs without known association to breast cancer were also found, and the putative functions of their PINs were also elucidated. These include miR-139 and miR-383. Furthermore, we validated our results by receiver operating characteristic (ROC) curve analysis using our miRNA expression profile data, gene expression-based outcome for breast cancer online (GOBO) survival analysis, and a literature search. Our results may provide new insights for research in breast cancer-associated miRNAs.
Most Random Gene Expression Signatures Are Significantly Associated with Breast Cancer Outcome  [PDF]
David Venet,Jacques E. Dumont,Vincent Detours
PLOS Computational Biology , 2011, DOI: 10.1371/journal.pcbi.1002240
Abstract: Bridging the gap between animal or in vitro models and human disease is essential in medical research. Researchers often suggest that a biological mechanism is relevant to human cancer from the statistical association of a gene expression marker (a signature) of this mechanism, that was discovered in an experimental system, with disease outcome in humans. We examined this argument for breast cancer. Surprisingly, we found that gene expression signatures—unrelated to cancer—of the effect of postprandial laughter, of mice social defeat and of skin fibroblast localization were all significantly associated with breast cancer outcome. We next compared 47 published breast cancer outcome signatures to signatures made of random genes. Twenty-eight of them (60%) were not significantly better outcome predictors than random signatures of identical size and 11 (23%) were worst predictors than the median random signature. More than 90% of random signatures >100 genes were significant outcome predictors. We next derived a metagene, called meta-PCNA, by selecting the 1% genes most positively correlated with proliferation marker PCNA in a compendium of normal tissues expression. Adjusting breast cancer expression data for meta-PCNA abrogated almost entirely the outcome association of published and random signatures. We also found that, in the absence of adjustment, the hazard ratio of outcome association of a signature strongly correlated with meta-PCNA (R2 = 0.9). This relation also applied to single-gene expression markers. Moreover, >50% of the breast cancer transcriptome was correlated with meta-PCNA. A corollary was that purging cell cycle genes out of a signature failed to rule out the confounding effect of proliferation. Hence, it is questionable to suggest that a mechanism is relevant to human breast cancer from the finding that a gene expression marker for this mechanism predicts human breast cancer outcome, because most markers do. The methods we present help to overcome this problem.
Hierarchy of Gene Expression Data is Predictive of Future Breast Cancer Outcome  [PDF]
Man Chen,Michael W. Deem
Quantitative Biology , 2014, DOI: 10.1088/1478-3975/10/5/056006
Abstract: We calculate measures of hierarchy in gene and tissue networks of breast cancer patients. We find that the likelihood of metastasis in the future is correlated with increased values of network hierarchy for expression networks of cancer-associated genes, due to correlated expression of cancer-specific pathways. Conversely, future metastasis and quick relapse times are negatively correlated with values of network hierarchy in the expression network of all genes, due to dedifferentiation of gene pathways and circuits. These results suggest that hierarchy of gene expression may be useful as an additional biomarker for breast cancer prognosis.
Accuracy validation of adjuvant! online in Taiwanese breast cancer patients - a 10-year analysis  [cached]
Yao-Lung Kuo,Dar-Ren Chen,Tsai-Wang Chang
BMC Medical Informatics and Decision Making , 2012, DOI: 10.1186/1472-6947-12-108
Abstract: Background Adjuvant! Online ( http://www.adjuvantonline.com) is an Internet-based software program that allows clinicians to make predictions about the benefits of adjuvant therapy and 10-year survival probability for early-stage breast cancer patients. This model has been validated in Western countries such as the United States, United Kingdom, Canada, Germany, and Holland. The aim of our study was to investigate the performance and accuracy of Adjuvant! Online in a cohort of Taiwanese breast cancer patients. Methods Data on the prognostic factors and clinical outcomes of 559 breast cancer patients diagnosed at the National Cheng Kung University Hospital in Tainan between 1992 and 2001 were enrolled in the study. Comprehensive demographic, clinical outcome data, and adjuvant treatment data were entered into the Adjuvant! Online program. The outcome prediction at 10 years was compared with the observed and predicted outcomes using Adjuvant! Online. Results Comparison between low- and high-risk breast cancer patient subgroups showed significant differences in tumor grading, tumor size, and lymph node status (p < 0.0001). The mean 10-year predicted death probability in 559 patients was 19.44%, and the observed death probability was 15.56%. Comparison with the Adjuvant! Online-predicted breast cancer-specific survival (BCSS) showed significant differences in the whole cohort (p < 0.001). In the low-risk subgroup, the predicted and observed outcomes did not differ significantly (3.69% and 3.85%, respectively). In high-risk patients, Adjuvant! Online overestimated breast cancer-specific survival (p = 0.016); the predicted and observed outcomes were 21.99% and 17.46%, respectively. Conclusions Adjuvant! Online accurately predicted 10-year outcomes and assisted in decision making about adjuvant treatment in low-risk breast cancer patients in our study, although the results were less accurate in the high-risk subgroup. Development of a prognostic program based on a national database should be considered, especially for high-risk breast cancer patients in Taiwan.
Identification of a robust gene signature that predicts breast cancer outcome in independent data sets
James E Korkola, Ekaterina Blaveri, Sandy DeVries, Dan H Moore, E Shelley Hwang, Yunn-Yi Chen, Anne LH Estep, Karen L Chew, Ronald H Jensen, Frederic M Waldman
BMC Cancer , 2007, DOI: 10.1186/1471-2407-7-61
Abstract: We profiled 162 breast tumors using expression microarrays to stratify tumors based on gene expression. A subset of 55 tumors with extensive follow-up was used to identify gene sets that predicted outcome. The predictive gene set was further tested in previously published data sets.We used different statistical methods to identify three gene sets associated with disease free survival. A fourth gene set, consisting of 21 genes in common to all three sets, also had the ability to predict patient outcome. To validate the predictive utility of this derived gene set, it was tested in two published data sets from other groups. This gene set resulted in significant separation of patients on the basis of survival in these data sets, correctly predicting outcome in 62–65% of patients. By comparing outcome prediction within subgroups based on ER status, grade, and nodal status, we found that our gene set was most effective in predicting outcome in ER positive and node negative tumors.This robust gene selection with extensive validation has identified a predictive gene set that may have clinical utility for outcome prediction in breast cancer patients.The application of expression microarray profiling technology promises to change both our understanding of tumor biology and our clinical practices. Expression arrays have proven useful in a variety of fields, allowing us to examine gene expression dynamics during complex processes such as growth and proliferation [1], as well as to identify gene function by expression patterns [2]. In particular, cancer researchers have made use of this technology to distinguish distinct subsets of cancer, predict patient outcome, and identify genes with clinical relevance [3-8]. Breast cancer has been one of the diseases most extensively studied with microarrays.Breast cancer is a heterogeneous disease [9], making it an ideal disease to study using microarrays since different expression patterns can be identified within distinct tumor groups. E
Gene expression in extratumoral microenvironment predicts clinical outcome in breast cancer patients
Erick Román-Pérez, Patricia Casbas-Hernández, Jason R Pirone, Jessica Rein, Lisa A Carey, Ronald A Lubet, Sendurai A Mani, Keith D Amos, Melissa A Troester
Breast Cancer Research , 2012, DOI: 10.1186/bcr3152
Abstract: Gene expression was evaluated in 72 patient-derived breast tissue samples adjacent to invasive breast cancer or ductal carcinoma in situ. Unsupervised clustering identified two distinct gene expression subgroups that differed in expression of genes involved in activation of fibrosis, cellular movement, cell adhesion and cell-cell contact. We evaluated the prognostic relevance of extratumoral subtype (comparing the Active group, defined by high expression of fibrosis and cellular movement genes, to the Inactive group, defined by high expression of claudins and other cellular adhesion and cell-cell contact genes) using clinical data. To establish the biological characteristics of these subtypes, gene expression profiles were compared against published and novel tumor and tumor stroma-derived signatures (Twist-related protein 1 (TWIST1) overexpression, transforming growth factor beta (TGF-β)-induced fibroblast activation, breast fibrosis, claudin-low tumor subtype and estrogen response). Histological and immunohistochemical analyses of tissues representing each microenvironment subtype were performed to evaluate protein expression and compositional differences between microenvironment subtypes.Extratumoral Active versus Inactive subtypes were not significantly associated with overall survival among all patients (hazard ratio (HR) = 1.4, 95% CI 0.6 to 2.8, P = 0.337), but there was a strong association with overall survival among estrogen receptor (ER) positive patients (HR = 2.5, 95% CI 0.9 to 6.7, P = 0.062) and hormone-treated patients (HR = 2.6, 95% CI 1.0 to 7.0, P = 0.045). The Active subtype of breast microenvironment is correlated with TWIST-overexpression signatures and shares features of claudin-low breast cancers. The Active subtype was also associated with expression of TGF-β induced fibroblast activation signatures, but there was no significant association between Active/Inactive microenvironment and desmoid type fibrosis or estrogen response gene expressio
Integration of Clinical and Gene Expression Data Has a Synergetic Effect on Predicting Breast Cancer Outcome  [PDF]
Martin H. van Vliet, Hugo M. Horlings, Marc J. van de Vijver, Marcel J. T. Reinders, Lodewyk F. A. Wessels
PLOS ONE , 2012, DOI: 10.1371/journal.pone.0040358
Abstract: Breast cancer outcome can be predicted using models derived from gene expression data or clinical data. Only a few studies have created a single prediction model using both gene expression and clinical data. These studies often remain inconclusive regarding an obtained improvement in prediction performance. We rigorously compare three different integration strategies (early, intermediate, and late integration) as well as classifiers employing no integration (only one data type) using five classifiers of varying complexity. We perform our analysis on a set of 295 breast cancer samples, for which gene expression data and an extensive set of clinical parameters are available as well as four breast cancer datasets containing 521 samples that we used as independent validation.mOn the 295 samples, a nearest mean classifier employing a logical OR operation (late integration) on clinical and expression classifiers significantly outperforms all other classifiers. Moreover, regardless of the integration strategy, the nearest mean classifier achieves the best performance. All five classifiers achieve their best performance when integrating clinical and expression data. Repeating the experiments using the 521 samples from the four independent validation datasets also indicated a significant performance improvement when integrating clinical and gene expression data. Whether integration also improves performances on other datasets (e.g. other tumor types) has not been investigated, but seems worthwhile pursuing. Our work suggests that future models for predicting breast cancer outcome should exploit both data types by employing a late OR or intermediate integration strategy based on nearest mean classifiers.
The estrogen and c-Myc target gene HSPC111 is over-expressed in breast cancer and associated with poor patient outcome
Alison J Butt, C Marcelo Sergio, Claire K Inman, Luke R Anderson, Catriona M McNeil, Amanda J Russell, Marco Nousch, Thomas Preiss, Andrew V Biankin, Robert L Sutherland, Elizabeth A Musgrove
Breast Cancer Research , 2008, DOI: 10.1186/bcr1985
Abstract: We used a transcript profiling approach to identify targets of estrogen and c-Myc in breast cancer cells. One previously uncharacterized gene, namely HBV pre-S2 trans-regulated protein 3 (HSPC111), was acutely upregulated after estrogen treatment or inducible expression of c-Myc, and was selected for further functional analysis using over-expression and knock-down strategies. HSPC111 expression was also analyzed in relation to MYC expression and outcome in primary breast carcinomas and published gene expression datasets.Pretreatment of cells with c-Myc small interfering RNA abrogated estrogen induction of HSPC111, identifying HSPC111 as a potential c-Myc target gene. This was confirmed by the demonstration of two functional E-box motifs upstream of the transcription start site. HSPC111 mRNA and protein were over-expressed in breast cancer cell lines and primary breast carcinomas, and this was positively correlated with MYC mRNA levels. HSPC111 is present in a large, RNA-dependent nucleolar complex, suggesting a possible role in ribosomal biosynthesis. Neither over-expression or small interfering RNA knock-down of HSPC111 affected cell proliferation rates or sensitivity to estrogen/antiestrogen treatment. However, high expression of HSPC111 mRNA was associated with adverse patient outcome in published gene expression datasets.These data identify HSPC111 as an estrogen and c-Myc target gene that is over-expressed in breast cancer and is associated with an adverse patient outcome.Breast cancer is the major contributor to cancer incidence and mortality in women in the Western world. Although the genetic and environmental factors that lead to the initiation of breast cancer remain unclear, it is known that exposure to estrogens plays a crucial role in the development and progression of this disease [1]. It has been proposed that the causative link between estrogen and breast cancer is due to its potent mitogenic and antiapoptotic effects [2]. However, it is not fully und
Selective gene-expression profiling of migratory tumor cells in vivo predicts clinical outcome in breast cancer patients
Antonia Patsialou, Yarong Wang, Juan Lin, Kathleen Whitney, Sumanta Goswami, Paraic A Kenny, John S Condeelis
Breast Cancer Research , 2012, DOI: 10.1186/bcr3344
Abstract: In the past, we developed an in vivo invasion assay that can capture specifically the highly motile tumor cells in the act of migrating inside living tumors. Here, we used this assay in orthotopic xenografts of human MDA-MB-231 breast cancer cells to isolate selectively the migratory cell subpopulation of the primary tumor for gene-expression profiling. In this way, we derived a gene signature specific to breast cancer migration and invasion, which we call the Human Invasion Signature (HIS).Unsupervised analysis of the HIS shows that the most significant upregulated gene networks in the migratory breast tumor cells include genes regulating embryonic and tissue development, cellular movement, and DNA replication and repair. We confirmed that genes involved in these functions are upregulated in the migratory tumor cells with independent biological repeats. We also demonstrate that specific genes are functionally required for in vivo invasion and hematogenous dissemination in MDA-MB-231, as well as in patient-derived breast tumors. Finally, we used statistical analysis to show that the signature can significantly predict risk of breast cancer metastasis in large patient cohorts, independent of well-established prognostic parameters.Our data provide novel insights into, and reveal previously unknown mediators of, the metastatic steps of invasion and dissemination in human breast tumors in vivo. Because migration and invasion are the early steps of metastatic progression, the novel markers that we identified here might become valuable prognostic tools or therapeutic targets in breast cancer.Breast cancer is one of the most frequent malignant neoplasms occurring in women in developed countries, and metastasis is the main cause of cancer-related death in these patients. The idea of personalized medicine and molecular profiling for prognostic tests has led to a plethora of studies in the past 10 years in search of genetic determinants of metastasis. Such studies have identi
GnRH and LHR gene variants predict adverse outcome in premenopausal breast cancer patients
Djura Piersma, Axel PN Themmen, Maxime P Look, Jan GM Klijn, John A Foekens, André G Uitterlinden, Huibert AP Pols, Els MJJ Berns
Breast Cancer Research , 2007, DOI: 10.1186/bcr1756
Abstract: We have investigated the prognostic significance of two variants of genes involved in the HPG-axis, the GnRH (encoding gonadotropin-releasing hormone) 16Trp/Ser genotype and the LHR (encoding the luteinizing hormone receptor) insLQ variant, in retrospectively collected premenopausal breast cancer patients with a long follow-up (median follow-up of 11 years for living patients).Carriership was not related with breast cancer risk (the case control study encompassed 278 premenopausal cases and 1,758 premenopausal controls). A significant adverse relationship of the LHR insLQ and GnRH 16Ser genotype with disease free survival (DFS) was observed in premenopausal (hormone receptor positive) breast cancer patients. In particular, those patients carrying both the GnRH 16Ser and LHR insLQ allele (approximately 25%) showed a significant increased risk of relapse, which was independent of traditional prognostic factors (hazard ratio 2.14; 95% confidence interval 1.32 to 3.45; P = 0.002).We conclude that the LHR insLQ and GnRH 16Ser alleles are independently associated with shorter DFS in premenopausal patients. When validated, these findings may provide a lead in the development of tailored treatment for breast cancer patients carrying both polymorphisms.The diagnosis of breast cancer is made one million times each year worldwide. About one-quarter of these women are premenopausal at time of diagnosis, which is associated with poor prognosis compared to postmenopausal women [1,2]. It is anticipated that, as a result of changing demographic and lifestyle factors, more and more women will be diagnosed at a younger age with breast cancer [3,4]. In addition to age and family history, several factors relating to increased or prolonged cumulative estrogen exposure have been identified as important risk factors for breast cancer development and progression [5,6]. Polymorphic variation in genes regulating estrogen production may partly explain differences in susceptibility, clinical p
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