FOXC1 (forkhead box C1)

Review on FOXC1 (forkhead box C1), with data on DNA, on the protein encoded, and where the gene is implicated

Comparison of the Agilent, ROMA/NimbleGen and Illumina platforms for classification of copy number alterations in human breast tumors

<p>Abstract</p> <p>Background</p> <p>Microarray Comparative Genomic Hybridization (array CGH) provides a means to examine DNA copy number aberrations. Various platforms, brands and underlying technologies are available, facing the user with many choices regarding platform sensitivity and number, localization, and density distribution of probes.</p> <p>Results</p> <p>We evaluate three different platforms presenting different nature and arrangement of the probes: The Agilent Human Genome CGH Microarray 44 k, the ROMA/NimbleGen Representational Oligonucleotide Microarray 82 k, and the Illumina Human-1 Genotyping 109 k BeadChip, with Agilent being gene oriented, ROMA/NimbleGen being genome oriented, and Illumina being genotyping oriented. We investigated copy number changes in 20 human breast tumor samples representing different gene expression subclasses, using a suite of graphical and statistical methods designed to work across platforms. Despite substantial differences in the composition and spatial distribution of probes, the comparison revealed high overall concordance. Notably however, some short amplifications and deletions of potential biological importance were not detected by all platforms. Both correlation and cluster analysis indicate a somewhat higher similarity between ROMA/NimbleGen and Illumina than between Agilent and the other two platforms. The programs developed for the analysis are available from <url>http://www.ifi.uio.no/bioinf/Projects/</url>.</p> <p>Conclusion</p> <p>We conclude that platforms based on different technology principles reveal similar aberration patterns, although we observed some unique amplification or deletion peaks at various locations, only detected by one of the platforms. The correct platform choice for a particular study is dependent on whether the appointed research intention is gene, genome, or genotype oriented.</p

A polymorphism at the 3'-UTR region of the aromatase gene defines a subgroup of postmenopausal breast cancer patients with poor response to neoadjuvant letrozole

<p>Abstract</p> <p>Background</p> <p>Aromatase (<it>CYP19A1</it>) regulates estrogen biosynthesis. Polymorphisms in <it>CYP19A1 </it>have been related to the pathogenesis of breast cancer (BC). Inhibition of aromatase with letrozole constitutes the best option for treating estrogen-dependent BC in postmenopausal women. We evaluate a series of polymorphisms of <it>CYP19A1 </it>and their effect on response to neoadjuvant letrozole in early BC.</p> <p>Methods</p> <p>We analyzed 95 consecutive postmenopausal women with stage II-III ER/PgR [+] BC treated with neoadjuvant letrozole. Response to treatment was measured by radiology at 4^thmonth by World Health Organization (WHO) criteria. Three polymorphisms of <it>CYP19A1</it>, one in exon 7 (rs700519) and two in the 3'-UTR region (rs10046 and rs4646) were evaluated on DNA obtained from peripheral blood.</p> <p>Results</p> <p>Thirty-five women (36.8%) achieved a radiological response to letrozole. The histopathological and immunohistochemical parameters, including hormonal receptor status, were not associated with the response to letrozole. Only the genetic variants (AC/AA) of the rs4646 polymorphism were associated with poor response to letrozole (p = 0.03). Eighteen patients (18.9%) reported a progression of the disease. Those patients carrying the genetic variants (AC/AA) of rs4646 presented a lower progression-free survival than the patients homozygous for the reference variant (p = 0.0686). This effect was especially significant in the group of elderly patients not operated after letrozole induction (p = 0.009).</p> <p>Conclusions</p> <p>Our study reveals that the rs4646 polymorphism identifies a subgroup of stage II-III ER/PgR [+] BC patients with poor response to neoadjuvant letrozole and poor prognosis. Testing for the rs4646 polymorphism could be a useful tool in order to orientate the treatment in elderly BC patients.</p

Gene expression analyses in breast cancer epidemiology: the Norwegian Women and Cancer postgenome cohort study

Introduction The introduction of high-throughput technologies, also called -omics technologies, into epidemiology has raised the need for high-quality observational studies to reduce several sources of error and bias. Methods The Norwegian Women and Cancer (NOWAC) postgenome cohort study consists of approximately 50,000 women born between 1943 and 1957 who gave blood samples between 2003 and 2006 and filled out a two-page questionnaire. Blood was collected in such a way that RNA is preserved and can be used for gene expression analyses. The women are part of the NOWAC study consisting of 172,471 women 30 to 70 years of age at recruitment from 1991 to 2006 who answered one to three questionnaires on diet, medication use, and lifestyle. In collaboration with the Norwegian Breast Cancer Group, every NOWAC participant born between 1943 and 1957 who is admitted to a collaborating hospital for a diagnostic biopsy or for surgery of breast cancer will be asked to donate a tumor biopsy and two blood samples. In parallel, at least three controls are approached for each breast cancer case in order to obtain blood samples from at least two controls per case. The controls are drawn at random from NOWAC matched by time of follow-up and age. In addition, 400 normal breast tissues as well as blood samples will be collected among healthy women participating at the Norwegian Mammography Screening program at the Breast Imaging Center at the University Hospital of North-Norway, Tromsø. Results The NOWAC postgenome cohort offers a unique opportunity (a) to study blood-derived gene expression profiles as a diagnostic test for breast cancer in a nested case-control design with adjustment for confounding factors related to different exposures, (b) to improve the reliability and accuracy of this approach by adjusting for an individual's genotype (for example, variants in genes coding for hormone and drug-metabolizing and detoxifying enzymes), (c) to study gene expression profiles from peripheral blood as surrogate tissue to biomonitor defined exposure (for example, hormone) and its association with disease risk (that is, breast cancer), and (d) to study gene variants (single nucleotide polymorphisms and copy number variations) and environmental exposure (endogenous and exogenous hormones) and their influence on the incidence of different molecular subtypes of breast cancer. Conclusion The NOWAC postgenome cohort combining a valid epidemiological approach with richness of biological samples should make an important contribution to the study of the etiology and system biology of breast cancer

Comparison of scores for bimodality of gene expression distributions and genome-wide evaluation of the prognostic relevance of high-scoring genes

<p>Abstract</p> <p>Background</p> <p>A major goal of the analysis of high-dimensional RNA expression data from tumor tissue is to identify prognostic signatures for discriminating patient subgroups. For this purpose genome-wide identification of bimodally expressed genes from gene array data is relevant because distinguishability of high and low expression groups is easier compared to genes with unimodal expression distributions.</p> <p>Recently, several methods for the identification of genes with bimodal distributions have been introduced. A straightforward approach is to cluster the expression values and score the distance between the two distributions. Other scores directly measure properties of the distribution. The kurtosis, e.g., measures divergence from a normal distribution. An alternative is the outlier-sum statistic that identifies genes with extremely high or low expression values in a subset of the samples.</p> <p>Results</p> <p>We compare and discuss scores for bimodality for expression data. For the genome-wide identification of bimodal genes we apply all scores to expression data from 194 patients with node-negative breast cancer. Further, we present the first comprehensive genome-wide evaluation of the prognostic relevance of bimodal genes. We first rank genes according to bimodality scores and define two patient subgroups based on expression values. Then we assess the prognostic significance of the top ranking bimodal genes by comparing the survival functions of the two patient subgroups. We also evaluate the global association between the bimodal shape of expression distributions and survival times with an enrichment type analysis.</p> <p>Various cluster-based methods lead to a significant overrepresentation of prognostic genes. A striking result is obtained with the outlier-sum statistic (<it>p </it>< 10^-12). Many genes with heavy tails generate subgroups of patients with different prognosis.</p> <p>Conclusions</p> <p>Genes with high bimodality scores are promising candidates for defining prognostic patient subgroups from expression data. We discuss advantages and disadvantages of the different scores for prognostic purposes. The outlier-sum statistic may be particularly valuable for the identification of genes to be included in prognostic signatures. Among the genes identified as bimodal in the breast cancer data set several have not yet previously been recognized to be prognostic and bimodally expressed in breast cancer.</p

CYP17 5'-UTR MspA1 polymorphism and the risk of premenopausal breast cancer in a German population-based case–control study

INTRODUCTION: Studies on the association between the cytochrome P450c17α gene (CYP17) 5'-untranslated region MspA1 genetic polymorphism and breast cancer risk have yielded inconsistent results. Higher levels of estrogen have been reported among young nulliparous women with the A2 allele. Therefore we assessed the impact of CYP17 genotypes on the risk of premenopausal breast cancer, with emphasis on parity. METHODS: We used data from a population-based case–control study of women aged below 51 years conducted from 1992 to 1995 in Germany. Analyses were restricted to clearly premenopausal women with complete information on CYP17 and encompassed 527 case subjects and 904 controls, 99.5% of whom were of European descent. The MspA1 polymorphism was analyzed using PCR-RFLP (PCR–restriction fragment length polymorphism) assay. RESULTS: The frequencies of the variant allele among the cases and controls were 43% and 41%, respectively. Overall, CYP17 A1/A2 and A2/A2 genotypes compared with the A1/A1 genotype were not associated with breast cancer, with adjusted odds ratios (ORs) of 1.04 and 1.23, respectively. Among nulliparous women, however, breast cancer risk was elevated for the A1/A2 (OR = 1.31; 95% confidence interval (CI) 0.74 to 2.32) and the A2/A2 genotype (OR = 2.12; 95% CI 1.04 to 4.32) compared with the A1/A1 genotype, with a trend towards increasing risk associated with number of A2 alleles (P = 0.04). Otherwise, the CYP17 polymorphism was found neither to be an effect modifier of breast cancer risks nor to be associated with stage of disease. CONCLUSION: Our results do not indicate a major influence of CYP17 MspA1 polymorphism on the risk of premenopausal breast cancer, but suggest that it may have an impact on breast cancer risk among nulliparous women. The finding, however, needs to be confirmed in further studies

Risk Factors for Breast Cancer and Expression of Insulin-Like Growth Factor-2 (IGF-2) in Women with Breast Cancer in Wuhan City, China

PURPOSE: The purpose of this study was to explore the risk factors for breast cancer and establish the expression rate of IGF-2 in female patients. METHODS: A case control study with 500 people in case group and 500 people in control group. A self-administered questionnaire was used to investigate risk factors for breast cancer. All cases were interviewed during a household survey. Immune-histochemical method was used to inspect the expression of IGF-2 in different tissues (benign breast lesions, breast cancer and tumor-adjacent tissue). RESULTS: Multivariate adjusted odds ratios and 95% confidence intervals were calculated using unconditional logistic regression. High body mass index (OR = 1.012,95%CI = 1.008-1.016), working attributes (OR = 1.004, 95%CI = 1.002 = 1.006), long menstrual period (OR = 1.007, 95%CI = 1.005-1.009), high parity OR = 1.003, 95%CI = 1.001-1.005) , frequent artificial abortion (OR = 1.004, 95%CI = 1.001-1.005), family history of cancer (OR = 1.003, 95%CI = 1.000-1.005), period of night shift (OR = 1.003, 95%CI = 1.001-1.006), live in high risk environment (OR = 1.005, 95%CI = 1.002-1.008), and family problems (OR = 1.010, 95%CI = 1.005-1.014) were associated with increased risk for breast cancer. In this study, good sleeping status, positive coping strategies, subjective support, and utility degree of social support were associated with reduced risk for breast cancer (OR = 0.998, 0.997, 0.985, 0.998 respectively; 95%CI = 0.996-1.000, 0.994-1.000, 0.980-0.989, 0.996-1.000, respectively). In benign breast lesions, breast cancer and tumor-adjacent tissue, IGF-2 was mainly expressed in the cytoplasm, but its expression rate was different (p<0.05). CONCLUSIONS: The incidence of breast cancer is a common result of multiple factors. IGF-2 is involved in the development of breast cancer, and its expression varies in different tissues (benign breast lesions, breast cancer and tumor-adjacent tissue)