RESULTS: iCLIP analysis found SAFB1 binding was enriched, specifically in exons, ncRNAs, 3' and 5' untranslated regions. SAFB1 was found to recognise a purine-rich GAAGA motif with the highest frequency and it is therefore likely to bind core AGA, GAA, or AAG motifs. Confirmatory RT-PCR experiments showed that the expression of coding and non-coding genes with SAFB1 cross-link sites was altered by SAFB1 knockdown. For example, we found that the isoform-specific expression of neural cell adhesion molecule (NCAM1) and ASTN2 was influenced by SAFB1 and that the processing of miR-19a from the miR-17-92 cluster was regulated by SAFB1. These data suggest SAFB1 may influence alternative splicing and, using an NCAM1 minigene, we showed that SAFB1 knockdown altered the expression of two of the three NCAM1 alternative spliced isoforms. However, when the AGA, GAA, and AAG motifs were mutated, SAFB1 knockdown no longer mediated a decrease in the NCAM1 9-10 alternative spliced form. To further investigate the association of SAFB1 with splicing we used exon array analysis and found SAFB1 knockdown mediated the statistically significant up- and downregulation of alternative exons. Further analysis using RNAmotifs to investigate the frequency of association between the motif pairs (AGA followed by AGA, GAA or AAG) and alternative spliced exons found there was a highly significant correlation with downregulated exons. Together, our data suggest SAFB1 will play an important physiological role in the central nervous system regulating synaptic function. We found that SAFB1 regulates dendritic spine density in hippocampal neurons and hence provide empirical evidence supporting this conclusion.
CONCLUSIONS: iCLIP showed that SAFB1 has previously uncharacterised specific RNA binding properties that help coordinate the isoform-specific expression of coding and non-coding genes. These genes regulate splicing, axonal and synaptic function, and are associated with neuropsychiatric disease, suggesting that SAFB1 is an important regulator of key neuronal processes.
OBJECTIVE: This study aimed to investigate the association between vegetable and fruit intake and steroid hormone receptor-defined breast cancer risk.
DESIGN: A total of 335,054 female participants in the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort were included in this study (mean ± SD age: 50.8 ± 9.8 y). Vegetable and fruit intake was measured by country-specific questionnaires filled out at recruitment between 1992 and 2000 with the use of standardized procedures. Cox proportional hazards models were stratified by age at recruitment and study center and were adjusted for breast cancer risk factors.
RESULTS: After a median follow-up of 11.5 y (IQR: 10.1-12.3 y), 10,197 incident invasive breast cancers were diagnosed [3479 estrogen and progesterone receptor positive (ER+PR+); 1021 ER and PR negative (ER-PR-)]. Compared with the lowest quintile, the highest quintile of vegetable intake was associated with a lower risk of overall breast cancer (HRquintile 5-quintile 1: 0.87; 95% CI: 0.80, 0.94). Although the inverse association was most apparent for ER-PR- breast cancer (ER-PR-: HRquintile 5-quintile 1: 0.74; 95% CI: 0.57, 0.96; P-trend = 0.03; ER+PR+: HRquintile 5-quintile 1: 0.91; 95% CI: 0.79, 1.05; P-trend = 0.14), the test for heterogeneity by hormone receptor status was not significant (P-heterogeneity = 0.09). Fruit intake was not significantly associated with total and hormone receptor-defined breast cancer risk.
CONCLUSION: This study supports evidence that a high vegetable intake is associated with lower (mainly hormone receptor-negative) breast cancer risk.
MATERIALS AND METHOD: We evaluated cytoplasmic expression of MMP-13 based on staining index using immunohistochemistry (IHC) in epithelial cells, stromal fibroblasts of IDC (n=90) and benign epithelial breast (n=90) lesions. Correlation between IHC and tumor size, lymph node status, distance metastasis, estrogen receptor (ER), progesterone receptor (PR) and Her-2/neu was assessed.
RESULTS: MMP-13 expression was 45% and 38.8% in malignant epithelial cells and peritumoral fibroblasts, respectively. Only low level of MMP-13 expression was seen in benign breast lesions (8.8% in epithelial component and 2.2% in stromal fibroblasts), while high level of MMP-13 expression was noted in malignant tumors, mainly grade II or III. Cytoplasmic MMP-13 expressions in epithelial tumor cells was correlated significantly with peritumoral fibroblasts. MMP-13 expression was directly correlated with distant metastasis and tumor stage in epithelial tumoral cells and was inversely correlated with progesterone expression in both tumoral and stromal cells.
CONCLUSION: This study showed that MMP-13 was a moderator for tumor invasion and metastasis and could be an independent predictor of poor prognosis in breast cancer. The role of MMP-13 in predicting the risk of malignant transformation in benign lesions should be further investigated.
METHODS: We built two models, for ER+ (ModelER+) and ER- tumors (ModelER-), respectively, in 281,330 women (51% postmenopausal at recruitment) from the European Prospective Investigation into Cancer and Nutrition cohort. Discrimination (C-statistic) and calibration (the agreement between predicted and observed tumor risks) were assessed both internally and externally in 82,319 postmenopausal women from the Women's Health Initiative study. We performed decision curve analysis to compare ModelER+ and the Gail model (ModelGail) regarding their applicability in risk assessment for chemoprevention.
RESULTS: Parity, number of full-term pregnancies, age at first full-term pregnancy and body height were only associated with ER+ tumors. Menopausal status, age at menarche and at menopause, hormone replacement therapy, postmenopausal body mass index, and alcohol intake were homogeneously associated with ER+ and ER- tumors. Internal validation yielded a C-statistic of 0.64 for ModelER+ and 0.59 for ModelER-. External validation reduced the C-statistic of ModelER+ (0.59) and ModelGail (0.57). In external evaluation of calibration, ModelER+ outperformed the ModelGail: the former led to a 9% overestimation of the risk of ER+ tumors, while the latter yielded a 22% underestimation of the overall BC risk. Compared with the treat-all strategy, ModelER+ produced equal or higher net benefits irrespective of the benefit-to-harm ratio of chemoprevention, while ModelGail did not produce higher net benefits unless the benefit-to-harm ratio was below 50. The clinical applicability, i.e. the area defined by the net benefit curve and the treat-all and treat-none strategies, was 12.7 × 10- 6 for ModelER+ and 3.0 × 10- 6 for ModelGail.
CONCLUSIONS: Modeling heterogeneous epidemiological risk factors might yield little improvement in BC risk prediction. Nevertheless, a model specifically predictive of ER+ tumor risk could be more applicable than an omnibus model in risk assessment for chemoprevention.
MATERIALS AND METHODS: An extensive systematic electronic review (PUBMED, CINAHL, PsyINFO and Ovid) and handsearch were carried out to retrieve published articles up to November 2012, using Depression OR Dysthymia AND (Cancer OR Tumor OR Neoplasms as the keywords. Information about the design of the studies, measuring scale, characteristics of the participants, prevalence of depression and its associated factors from the included studies were extracted and summarized.
RESULTS: We identified 32 eligible studies that recruited 10,826 breast cancer survivors. Most were cross-sectional or prospective designed. The most frequent instrument used to screen depression was the Center for Epidemiological Studies for Depression (CES-D, n=11 studies) followed by the Beck Depression Inventory (BDI, n=6 studies) and the Hospital Anxiety and Depression Scale (HADS, n=6 studies). CES-D returned about similar prevalence of depression (median=22%, range=13-56%) with BDI (median=22%, range=17-48%) but higher than HADS (median=10%, range=1-22%). Depression was associated with several socio-demographic variables, cancer-related factors, treatment-related factors, subject psychological factors, lifestyle factors, social support and quality of life.
CONCLUSIONS: Breast cancer survivors are at risk for depression so that detection of associated factors is important in clinical practice.