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    eNOS gene polymorphisms in paraffin-embedded tissues of prostate cancer patients
    (TUBITAK SCIENTIFIC & TECHNICAL RESEARCH COUNCIL TURKEY, 2016) Polat, Fikriye; Turaclar, Nesrin; Yilmaz, Meral; Bingol, Gunsel; Cingilli Vural, Hasibe
    Background/aim: The purpose of the present study was to investigate whether endothelial nitric oxide synthase (eNOS) gene polymorphisms play a role in prostate cancer (PCa). Materials and methods: We examined three eNOS gene polymorphisms (T-786C promoter region, G894T, and Intron 4 VNTR 4a/b) at extracted DNAs from 50 formalin-fixed paraffin-embedded tissues of PCa patients. For the controls, blood samples obtained from 50 healthy men were studied. Genotyping of molecular variants was performed by PCR-RFLP technique. Results: We found that the TC genotype of the T-786C polymorphism was associated with PCa risk (OR: 3.325, CI: 1.350-8.188, P = 0.008). The eNOS G894T polymorphism was also associated with PCa. The frequency of the 894T allele was significantly higher in PCa patients. No association was identified between intron 4 VNTR polymorphism and PCa. Conclusion: We found significant differences in genotypic and allelic frequencies between PCa patients and controls for eNOS T-786C and G894T polymorphisms. The presence of the T-786C genotype and 894T allele in carriers increased the risk of PCa. No association was found between intron 4 VNTR polymorphism and PCa patients.
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    USE OF ARTIFICIAL IMMUNE SYSTEMS AND ARTIFICIAL NEURAL NETWORKS IN PROSTATE CANCER CLASSIFICATION
    (IEEE, 2014) Ozsen, Seral; Cingilli Vural, Hasibe
    Before analysing cells in Laboratory in prostate cancer detection, a classification system can give valuable information about the cancer. In this study, classification of prostate cancer classification was realized by Artificial Immune Systems (AIS) and Artificial Neural Networks (ANN). 50 data were used in which 17 data belong to healty persons, 33 data belong to patients. Training-test data which were divided by 3-fold cross validation were applied to both system and 97.92% classification accuracy was obtained by AIS while ANN reached an accuracy of 100%.