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... HPV life-cycle is controlled by relatively few viral proteins and the virus must utilize host cell factors to regulate viral transcription and replication. Of particular interest are the HPV E6, E7 and E2 proteins. The E6 and E7 proteins are coded for in a region referred to as the long control region (LCR). These two proteins are produced early in the viral cycle and are used to deregulate the host cell growth cycle. They do this by binding with certain tumour suppressor proteins, cell cyclins, and cyclin-dependant kinases, effectively inactivating them (43) (Figure 5). Two of these inactivated proteins – the tumour suppressor protein p53 and the retinoblastoma gene product pRB – are major players in cell growth. HPV E6 proteins bind to p53 and ensure its degradation through ubiquitination, whereas HPV E7 proteins bind to hypophosphorylated pRB forms and abrogates their ability to form a complex with the cellular transcription factor E2F-1. Once liberated, ...
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[This corrects the article DOI: 10.3892/ol.2015.3143.].
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... Cervical cancer also known as cancer of the cervix (the lowermost part of the uterus), is a malignant tumor that occurs when tissue cells covering the cervix begin to grow and reproduce uncontrollably without following proper mechanism for cell division [3]. As per the statistics issued by WHO, every year more than 270,000 women die from cervical cancer and more than 85% of these deaths are in developing countries with estimated annual new cases of 444,500 annually [4,5]. ...
Cervical cancer is one of the leading causes of premature mortality among women worldwide and more than 85% of these deaths are in developing countries. There are several risk factors associated with cervical cancer. In this paper, we developed a predictive model for predicting the outcome of patients with cervical cancer, given risk patterns from individual medical records and preliminary screening. This work presents a decision tree (DT) classification algorithm to analyze the risk factors of cervical cancer. Recursive feature elimination (RFE) and least absolute shrinkage and selection operator (LASSO) feature selection techniques were fully explored to determine the most important attributes for cervical cancer prediction. The dataset employed here contains missing values and is highly imbalanced. Therefore, a combination of under and oversampling techniques called SMOTETomek was employed. A comparative analysis of the proposed model has been performed to show the effectiveness of feature selection and class imbalance based on the classifier’s accuracy, sensitivity, and specificity. The DT with the selected features from RFE and SMOTETomek has better results with an accuracy of 98.72% and sensitivity of 100%. DT classifier is shown to have better performance in handling classification problems when the features are reduced, and the problem of high class imbalance is addressed.
