Recently, we have shown that the new G-protein-coupled estrogen receptor GPR30 plays an important role in the development of tamoxifen resistance in vitro. This study was undertaken to evaluate the correlation between GPR30 and tamoxifen resistance in breast cancer patients. GPR30 protein expression was evaluated by immunohistochemical analysis in 323 patients with primary operable breast cancer. The association between GPR30 expression and tamoxifen resistance was confirmed in a second cohort of 103 patients treated only with tamoxifen. Additionally, we evaluated GPR30 expression in 33 primary tumors and in recurrent tumors from the same patients. GPR30 expression was detected in 56.7% of the breast cancer specimens investigated and it correlated with overexpression of HER-2 (P = 0.021), EGFR (P = 0.024) and lymph node status (P = 0.047). In a first cohort, survival analysis showed that GPR30 was negatively correlated with relapse-free survival (RFS) only in patients treated with tamoxifen (tamoxifen with or without chemotherapy). GPR30 expression was associated with shorter RFS (HR = 1.768; 95% CI, 1.156-2.703; P = 0.009). In a subset of patients treated only with tamoxifen, multivariate analysis revealed that GPR30 expression is an independent unfavorable factor for RFS (HR = 4.440; 95% CI, 1.408-13.997; P = 0.011). In contrast, GPR30 tended to be a favorable factor regarding RFS in patients who did not receive tamoxifen. In 33 paired biopsies obtained before and after adjuvant therapy, GPR30 expression significantly increased only under tamoxifen treatment (P = 0.001). GPR30 expression in breast cancer independently predicts a poor RFS in patients treated with tamoxifen.

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BackgroundComputational intelligence methods, including non-linear classification algorithms, can be used in medical research and practice as a decision making tool. This study aimed to evaluate the usefulness of artificial intelligence models for 5–year overall survival prediction in patients with cervical cancer treated by radical hysterectomy.MethodsThe data set was collected from 102 patients with cervical cancer FIGO stage IA2-IIB, that underwent primary surgical treatment. Twenty-three demographic, tumor-related parameters and selected perioperative data of each patient were collected. The simulations involved six computational intelligence methods: the probabilistic neural network (PNN), multilayer perceptron network, gene expression programming classifier, support vector machines algorithm, radial basis function neural network and k-Means algorithm. The prediction ability of the models was determined based on the accuracy, sensitivity, specificity, as well as the area under the receiver operating characteristic curve. The results of the computational intelligence methods were compared with the results of linear regression analysis as a reference model.ResultsThe best results were obtained by the PNN model. This neural network provided very high prediction ability with an accuracy of 0.892 and sensitivity of 0.975. The area under the receiver operating characteristics curve of PNN was also high, 0.818. The outcomes obtained by other classifiers were markedly worse.ConclusionsThe PNN model is an effective tool for predicting 5–year overall survival in cervical cancer patients treated with radical hysterectomy.

Our results are the first to suggest an association between renalase gene polymorphisms analysed and hypertension in dialysed patients. It may be an important step towards gaining a deeper insight into cardiovascular pathophysiology. Furthermore, it might provide an optimal treatment and better prognosis for patients with chronic kidney disease.

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Objective: We assessed the prognostic factors related to 10-year overall survival and disease-free survival in cervical cancer patients that underwent primary surgical protocols in 1 institution. Materials and Methods: A total of 102 patients with uterine cervical cancer at FIGO stages IA2-IIB that underwent a Piver type III radical hysterectomy and pelvic lymphadenectomy between 1998 and 2001 were included. Univariate and multivariate analyses of 10-year overall survival and 10-year disease-free survival were performed. Results: Univariate analysis revealed that only lymphovascular space invasion significantly affected 10-year overall survival (p = 0.04), but it had no effect on the 10-year disease-free survival rate. Multivariate analysis demonstrated that survival rates were significantly affected by FIGO stage (p = 0.02, 95% CI: 1.18-5.55, for 10-year overall survival; p = 0.03, 95% CI: 1.07-6.12, for 10-year disease-free survival) and metastases to the pelvic lymph nodes (p = 0.0005, 95% CI: 1.81-8.53, for 10-year overall survival; p = 0.01, 95% CI: 1.26-7.24, for 10-year disease-free survival). Conclusions: The only independent prognostic factors for 10-year survival rates in patients with cervical cancer at FIGO stages IA2-IIB were clinical stage and presence of metastases to the pelvic lymph nodes. The presence of lymphovascular space invasion adversely affected 10-year overall survival.

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