RESUMO
BACKGROUND: Breast cancer is among the five most common cancers and ranks first among cancers diagnosed in Iranian women. Screening and treatment of this disease with molecular methods, especially regarding high incidences at early age and advanced stage, is essential. Several genes with altered expression have been identified by cDNA microarray studies in breast cancer, with the Bcl-2 gene indicated as a likely candidate. In this study, we studied Bcl-2 gene expression levels in parallel tumor and non-tumor breast tissues. MATERIALS AND METHODS: Forty samples including 21 tumor, 16 non tumor (marginal) and 3 benign breast tissues which were all pathologically diagnosed, were subjected to RNA extraction and polyA RT-PCR with the expression level of Bcl-2 quantified using real-time PCR. RESULTS: There is higher expression levels of the Bcl-2 gene in tumor samples compared with marginal samples, but not attaining significance(p>0.05). Bcl-2 expression in 14 (66.7%) of the cases of tumor samples and 9 (56.3%) cases of the marginal samples were positive. Comparison of the expression of the Bcl-2 gene in histological grade showed that a high expression of Bcl-2 was associated with a high histological grade (p<0.41). CONCLUSIONS: Our data suggests that dysregulated Bcl-2 gene expression is potentially involved in the pathogenesis of breast cancer. Using gene expression analysis may significantly improve our ability for screening cancer patients and will prove a powerful tool in the diagnosis and prognostic evaluation of the disease whilst aiding the cooperative group trials in the Bcl-2 based therapy project.
Assuntos
Neoplasias da Mama/genética , Carcinoma Ductal de Mama/genética , Carcinoma Intraductal não Infiltrante/genética , Carcinoma Lobular/genética , Regulação Neoplásica da Expressão Gênica , Proteínas Proto-Oncogênicas c-bcl-2/genética , Neoplasias da Mama/patologia , Carcinoma Ductal de Mama/secundário , Carcinoma Intraductal não Infiltrante/secundário , Carcinoma Lobular/secundário , Estudos de Casos e Controles , Feminino , Seguimentos , Humanos , Irã (Geográfico) , Pessoa de Meia-Idade , Gradação de Tumores , Metástase Neoplásica , Análise de Sequência com Séries de Oligonucleotídeos , Prognóstico , Reação em Cadeia da Polimerase em Tempo Real , Reação em Cadeia da Polimerase Via Transcriptase ReversaRESUMO
Prediction of the amount of hospital waste production will be helpful in the storage, transportation and disposal of hospital waste management. Based on this fact, two predictor models including artificial neural networks (ANNs) and multiple linear regression (MLR) were applied to predict the rate of medical waste generation totally and in different types of sharp, infectious and general. In this study, a 5-fold cross-validation procedure on a database containing total of 50 hospitals of Fars province (Iran) were used to verify the performance of the models. Three performance measures including MAR, RMSE and R(2) were used to evaluate performance of models. The MLR as a conventional model obtained poor prediction performance measure values. However, MLR distinguished hospital capacity and bed occupancy as more significant parameters. On the other hand, ANNs as a more powerful model, which has not been introduced in predicting rate of medical waste generation, showed high performance measure values, especially 0.99 value of R(2) confirming the good fit of the data. Such satisfactory results could be attributed to the non-linear nature of ANNs in problem solving which provides the opportunity for relating independent variables to dependent ones non-linearly. In conclusion, the obtained results showed that our ANN-based model approach is very promising and may play a useful role in developing a better cost-effective strategy for waste management in future.