No abstract

The present findings indicate that AEG-1 is overexpressed in a great portion of EOC patients with peritoneal dissemination and/or lymph node metastasis and may be clinically useful for predicting metastasis in EOC. Our findings might provide some benefits for metastatic EOC patients in the clinic.

Our findings indicate that tumour AEG-1 overexpression is associated with poor prognosis and cisplatin resistance in advanced serous ovarian cancer.

Cervical cancer is a clinical and pathological heterogeneity disease, which requires different types of treatments and leads to a variety of outcomes. In clinical practice, only some patients benefit from chemotherapy treatment. Identifying patients who will be responsive to chemotherapy could increase their survival time, which has important implications in personalized treatment and outcomes, while identifying non-responders may reduce the likelihood for these patients to receive ineffective treatment and thereby enable them to receive other potentially effective treatments. Plasma metabolite profiling was performed in this study to identify the potential biomarkers that could predict the response to neoadjuvant chemotherapy (NACT) for cervical cancer patients. The metabolic profiles of plasma from 38 cervical cancer patients with a complete, partial and non-response to NACT were studied using a combination of liquid chromatography coupled with mass spectrometry (LC/MS) and multivariate analysis methods. L-Valine and L-tryptophan were finally identified and verified as the potential biomarkers. A prediction model constructed with L-valine and L-tryptophan correctly identified approximately 80% of patients who were non-response to chemotherapy and 87% of patients who were had a pathologically complete response to chemotherapy. The model has an excellent discriminant performance with an AUC of 0.9407. These results show promise for larger studies that could produce more personalized treatment protocols for cervical cancer patients.

This paper reports the development of empirical models to explain spatial price variation in an urban area. Models are constructed for petrol price data collected in 1995 and 1997 in Sheffield, England. The 1995 data are modelled by using only supply-side predictors following the collection of supply-side information from field surveys of the retail sites, a site questionnaire survey, and interviews with site managers. The 1997 data are modelled by using supply-side predictors and demand-side predictors that relate to the economic characteristics of the population of consumers. This modelling is based on field surveys of the sites, a new site questionnaire survey, and a household survey. The purpose of this work is to assess supply-side and demand-side factors in explaining spatial price variation. Supply-side predictors are classified into site characteristics, location characteristics, and measures of spatial competition. We examine the relative importance of these different groups of supply-side variables in explaining price variation, with a particular interest in location and competition effects as these relate directly to the spatial and geographical aspects of the problem. Another contribution of the paper is to observe the stability of findings by contrasting the best-fitting models obtained for the 1995 price data to the best-fitting models obtained for the 1997 price data.We find that no demand-side factors are statistically significant. For 1995 a spatial competition variable and a location variable (whether a site is attached to a supermarket) are the consistently important supply-side variables. For 1997 all three categories of supply-side variables are important.