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Article
The Significance of TP53 Gene Polymorphisms as A Risk Factor For Non-Hodgkin’s Lymphoma in Iraqi Patients

Authors: Maysoon Abdul-Ameer --- Ahmed Al-Salman
Journal: Medical Journal of Babylon مجلة بابل الطبية ISSN: 1812156X 23126760 Year: 2016 Volume: 13 Issue: 4 Pages: 778 -785
Publisher: Babylon University جامعة بابل

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Abstract

Genetic factors including single nucleotide polymorphisms have been implicated as predisposing factors for large numbers of malignancies. When genetic disorders occur in a tumor suppressor gene, like TP53 gene, the results are expected to have devastating effects. The current study aimed to assess the effect of certain polymorphisms in TP53 genein the individual’s susceptibility to non- Hodgkin’s lymphomas among Iraqi patients. A total of 62 patients with these malignancies and 34 apparently healthy individuals were enrolled for this study. DNA was extracted from blood samples and fragment in TP53 corresponding for TP53 p.Arg72Pro, TP53 p.Pro47Ser and r.13494 G>A polymorphisms were amplified using specific primers. Genotyping was performed with restriction fragment length polymorphisms. The results revealed significant association of TP53 p.Arg72Pro polymorphism in both heterozygous and mutant homozygous genotypes with incidence of NHLs, while both TP53 p.Pro47Ser and r.13494 G>A polymorphisms had no such association. These results strongly indicate the importance of proline allele of TP53 p.Arg72Pro as a predisposing factor for NHLs.


Article
In Silico Model for Lung Cancer Prediction Based on TP53 mutations Using Neural Network

Authors: Ban Nadeem Dhannoon --- Zahraa Naser Shahweli
Journal: Al-Nahrain Journal of Science مجلة النهرين للعلوم ISSN: (print)26635453,(online)26635461 Year: 2018 Volume: 00 Issue: 1 Pages: 196-201
Publisher: Al-Nahrain University جامعة النهرين

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Abstract

In silico models have become well known in the current decade because they assist researchers and specialists in organizing and analyzing big data. To complete their work, these models require powerful techniques and algorithms, the most important of which are machine learning algorithms. This work utilizes the Relief F algorithm for feature selection and trains the back propagation neural network (BPNN) algorithm on the UMD TP53 all-2012-R1-US database for lung cancer. Lung cancer is the most commonly diagnosed cancer among women and men, and can be predicted from mutations that occur in the TP53 tumor suppressor gene. Five measures are used to estimate performance: sensitivity and specificity are important dimensions utilized to obtain the receiver operating characteristic (ROC) curve; accuracy and F measure are necessary to determine algorithm precision; and Matthews correlation coefficient (MCC), which is the most important measure, provides the right criterion for classification algorithms. The Relief F and BPNN algorithms achieve satisfactory results that reach 99.41 for sensitivity, 95.39 for specificity, 99.04 for accuracy, 99.47 for F measure, and 0.93 for MCC.

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