RESUMO
BACKGROUND: In search of novel prognostic biomarkers for clear cell renal carcinoma (ccRCC), we analysed the expression of several proteins related to angiogenesis and hypoxia. METHODS: A monocentric study on 30 consecutive surgical samples from surgically-treated ccRCC patients with a 10-year follow up was performed. The following proteins were analysed by immunohistochemistry: Vascular Endothelial Growth Factor- A (VEGF-A), Platelet-Derived Growth Factor ß Receptor (PDGFRß), VEGF-receptor 1 (Flt1), VEGF-receptor 2 (KDR), Glucose Transporter 1 (GLUT1), Carbonic anhydrase IX (CA-IX) and the hERG1 potassium channel. Data were analysed in conjunction with the clinico-pathological characteristics of the patients and follow up. RESULTS: All the proteins were expressed in the samples, with statistically significant associations of VEGF-A with PDGFRß and Flt1 and hERG1 with CA IX. Notably, hERG1 and CAIX co-immunoprecipitated in primary ccRCC samples and survival analysis showed that the positivity for hERG1 and CA IX had a negative impact on Recurrence Free Survival (RFS) at the univariate analysis. At the multivariate analysis only hERG1 maintained its statistically significant negative impact. CONCLUSIONS: hERG1 expression can be exploited to predict recurrence in surgically-treated ccRCC patients. hERG1 channels form a multiprotein complex with the pH regulator CA IX in primary ccRCC samples their potential use as therapeutic target might be suggested.
Assuntos
Biomarcadores Tumorais/metabolismo , Anidrase Carbônica IX/metabolismo , Carcinoma de Células Renais/cirurgia , Canais de Potássio Éter-A-Go-Go/metabolismo , Neoplasias Renais/cirurgia , Recidiva Local de Neoplasia/metabolismo , Idoso , Carcinoma de Células Renais/metabolismo , Carcinoma de Células Renais/mortalidade , Carcinoma de Células Renais/patologia , Feminino , Humanos , Itália , Neoplasias Renais/metabolismo , Neoplasias Renais/mortalidade , Neoplasias Renais/patologia , Masculino , Pessoa de Meia-Idade , Estadiamento de Neoplasias , Nefrectomia/métodos , Prognóstico , Taxa de SobrevidaRESUMO
The complementary strands of DNA molecules can be separated when stretched apart by a force; the unzipping signal is correlated to the base content of the sequence but is affected by thermal and instrumental noise. We consider here the ideal case where opening events are known to a very good time resolution (very large bandwidth), and study how the sequence can be reconstructed from the unzipping data. Our approach relies on the use of statistical Bayesian inference and of Viterbi decoding algorithm. Performances are studied numerically on Monte Carlo generated data, and analytically. We show how multiple unzippings of the same molecule may be exploited to improve the quality of the prediction, and calculate analytically the number of required unzippings as a function of the bandwidth, the sequence content, and the elasticity parameters of the unzipped strands.
Assuntos
Biofísica/métodos , DNA/química , Conformação de Ácido Nucleico , Algoritmos , Sequência de Bases , Teorema de Bayes , Elasticidade , Entropia , Modelos Estatísticos , Modelos Teóricos , Dados de Sequência Molecular , Método de Monte Carlo , Probabilidade , Termodinâmica , Fatores de TempoRESUMO
We describe some recent enhancements introduced in C-ImmSim, a simulator of the immune system response that we have been developing for a number of years along with preliminary results produced by the simulation of the Highly Active Anti-Retroviral Therapy in HIV-1 infected patients.
Assuntos
Terapia Antirretroviral de Alta Atividade/métodos , Infecções por HIV/tratamento farmacológico , Infecções por HIV/imunologia , HIV-1/crescimento & desenvolvimento , Modelos Imunológicos , Simulação por Computador , Infecções por HIV/virologia , HIV-1/genética , Humanos , RNA Viral/sangueRESUMO
The performances of Bayesian inference to predict the sequence of DNA molecules from fixed-force unzipping experiments are investigated. We show that the probability of misprediction decreases exponentially with the amount of collected data. The decay rate is calculated as a function of biochemical parameters (binding free energies), the sequence content, the applied force, the elastic properties of a DNA single strand, and time resolution.