| 1. |
- Brennan, Donal J, et al.
(författare)
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Altered cytoplasmic-to-nuclear ratio of survivin is a prognostic indicator in breast cancer
- 2008
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Ingår i: Clinical Cancer Research. - 1078-0432. ; 14:9, s. 2681-2689
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Tidskriftsartikel (refereegranskat)abstract
- Purpose: Survivin (BIRC5) is a promising tumor biomarker. Conflicting data exist on its prognostic effect in breast cancer. These data may at least be partly due to the manual interpretation of immunohistochemical staining, especially as survivin can be located in both the nucleus and cytoplasm. Quantitative determination of survivin expression using image analysis offers the opportunity to develop alternative scoring models for survivin immunohistochemistry. Here, we present such a model. Experimental Design: A breast cancer tissue microarray containing 102 tumors was stained with an anti-survivin antibody. Whole-slide scanning was used to capture high-resolution images. These images were analyzed using automated algorithms to quantify the staining. Results: Increased nuclear, but not cytoplasmic, survivin was associated with a reduced overall survival (OS; P = 0.038) and disease-specific survival (P = 0.0015). A high cytoplasmicto-nuclear ratio (CNR) of survivin was associated with improved OS (P = 0.005) and disease-specific survival (P = 0.05). Multivariate analysis revealed that the survivin CNR was an independent predictor of CIS (hazard ratio, 0.09; 95% confidence interval, 0.01-0.76; P = 0.027). A survivin CNR of >5 correlated positively with estrogen receptor (P = 0.019) and progesterone receptor (P = 0.033) levels, whereas it was negatively associated with Ki-67 expression (P = 0.04), p53 status (P = 0.005), and c-myc amplification (P = 0.016). Conclusion: Different prognostic information is supplied by nuclear and cytoplasmic survivin in breast cancer. Nuclear survivin is a poor prognostic marker in breast cancer. Moreover, CNR of survivin, as determined by image analysis, is an independent prognostic factor.
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| 2. |
- Madden, Stephen F., et al.
(författare)
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Detecting microRNA activity from gene expression data
- 2010
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Ingår i: BMC Bioinformatics. - : Springer Science and Business Media LLC. - 1471-2105. ; 11
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Tidskriftsartikel (refereegranskat)abstract
- Background: MicroRNAs (miRNAs) are non-coding RNAs that regulate gene expression by binding to the messenger RNA (mRNA) of protein coding genes. They control gene expression by either inhibiting translation or inducing mRNA degradation. A number of computational techniques have been developed to identify the targets of miRNAs. In this study we used predicted miRNA-gene interactions to analyse mRNA gene expression microarray data to predict miRNAs associated with particular diseases or conditions. Results: Here we combine correspondence analysis, between group analysis and co-inertia analysis (CIA) to determine which miRNAs are associated with differences in gene expression levels in microarray data sets. Using a database of miRNA target predictions from TargetScan, TargetScanS, PicTar4way PicTar5way, and miRanda and combining these data with gene expression levels from sets of microarrays, this method produces a ranked list of miRNAs associated with a specified split in samples. We applied this to three different microarray datasets, a papillary thyroid carcinoma dataset, an in-house dataset of lipopolysaccharide treated mouse macrophages, and a multi-tissue dataset. In each case we were able to identified miRNAs of biological importance. Conclusions: We describe a technique to integrate gene expression data and miRNA target predictions from multiple sources.
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| 3. |
- Mulrane, Laoighse, et al.
(författare)
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miR-187 Is an Independent Prognostic Factor in Breast Cancer and Confers Increased Invasive Potential In Vitro
- 2012
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Ingår i: Clinical Cancer Research. - 1078-0432. ; 18:24, s. 6702-6713
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Tidskriftsartikel (refereegranskat)abstract
- Purpose: Here, we describe an integrated bioinformatics, functional analysis, and translational pathology approach to identify novel miRNAs involved in breast cancer progression. Experimental Design: Coinertia analysis (CIA) was used to combine a database of predicted miRNA target sites and gene expression data. Using two independent breast cancer cohorts, CIA was combined with correspondence analysis and between group analysis to produce a ranked list of miRNAs associated with disease progression. Ectopic expression studies were carried out in MCF7 cells and miRNA expression evaluated in two additional cohorts of patients with breast cancer by in situ hybridization on tissue microarrays. Results: CIA identified miR-187 as a key miRNA associated with poor outcome in breast cancer. Ectopic expression of miR-187 in breast cancer cells resulted in a more aggressive phenotype. In a test cohort (n = 117), high expression of miR-187 was associated with a trend toward reduced breast cancer-specific survival (BCSS; P = 0.058), and a significant association with reduced BCSS in lymph node-positive patients (P = 0.036). In a validation cohort (n = 470), high miR-187 was significantly associated with reduced BCSS in the entire cohort (P = 0.021) and in lymph node-positive patients (P = 0.012). Multivariate Cox regression analysis revealed that miR-187 is an independent prognostic factor in both cohorts [cohort 1: HR, 7.37; 95% confidence interval (CI), 2.05-26.51; P = 0.002; cohort 2: HR, 2.80; 95% CI, 1.52-5.16; P = 0.001] and in lymph node-positive patients in both cohorts (cohort 1: HR, 13.74; 95% CI, 2.62-72.03; P = 0.002; cohort 2: HR, 2.77; 95% CI, 1.32-5.81; P = 0.007). Conclusions: miR-187 expression in breast cancer leads to a more aggressive, invasive phenotype and acts as an independent predictor of outcome. Clin Cancer Res; 18(24); 6702-13. (C) 2012 AACR.
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| 4. |
- O'Brien, Sallyarm L., et al.
(författare)
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CENP-F expression is associated with poor prognosis and chromosomal instability in patients with primary breast cancer
- 2007
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Ingår i: International Journal of Cancer. - : Wiley. - 0020-7136. ; 120:7, s. 1434-1443
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Tidskriftsartikel (refereegranskat)abstract
- DNA microarrays have the potential to classify tumors according to their transcriptome. Tissue microarrays (TMAs) facilitate the validation of biomarkers by offering a high-throughput approach to sample analysis. We reanalyzed a high profile breast cancer DNA microarray dataset containing 96 tumor samples using a powerful statistical approach, between group analyses. Among the genes we identified was centromere protein-F (CENP-F), a gene associated with poor prognosis. In a published follow-up breast cancer DNA microarray study, comprising 295 tumour samples, we found that CENP-F upregulation was significantly associated with worse overall survival (p < 0.001) and reduced metastasis-free survival (p < 0.001). To validate and expand upon these findings, we used 2 independent breast cancer patient cohorts represented on TMAs. CENP-F protein expression was evaluated by immunohistochemistry in 91 primary breast cancer samples from cohort I and 289 samples from cohort II. CENP-F correlated with markers of aggressive tumor behavior including ER negativity and high tumor grade. In cohort I, CENP-F was significantly associated with markers of CIN including cyclin E, increased telomerase activity, c-Myc amplification and aneuploidy. In cohort II CENP-F correlated with VEGFR2, phosphorylated Ets-2 and Ki67, and in multivariate analysis, was an independent predictor of worse breast cancer-specific survival (p = 0.036) and overall survival (p = 0.040). In conclusion, we identified CENP-F as a biomarker associated with poor outcome in breast cancer and showed several novel associations of biological significance. (c) 2007 Wiley-Liss, Inc.
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