Protein sequence-based risk classification for human papillomaviruses

Investigation of the relationship between the changes in vaginal microecological enzymes and human papillomavirus (HPV) infection

This study aims to investigate the relationship between the human papillomavirus (HPV) infection and the altered vaginal microecological environment of patients. Initially, HPV genotyping and microecological detection were performed in 1281 subjects in the Department of Obstetrics and Gynecology of The First Hospital of Qinhuangdao (Qinhuangdao, China). The relationship between the enzymes of vaginal microecology, that is, proline aminopeptidase and acetylglucosaminidase, and vaginal inflammatory diseases, as well as the prognosis of HPV infection, was analyzed. The experimental findings indicated a close relationship between the expression of positive prolyl aminopeptidase and trichomonas vaginitis, as well as bacterial vaginitis. In addition, the expression of acetylglucosaminidase is closely associated with trichomonas vaginitis and vulvovaginal candidiasis. Furthermore, the observations indicated that positive prolyl aminopeptidase and acetylglucosaminidase could increase the risk of various subtypes of HPV infection in patients. The receiver operating characteristic curve analysis presented that the expression of prolyl aminopeptidase and acetylglucosaminidase could offer exceptional diagnostic efficacy, indicating their association with persistent HPV infection. In summary, our results highlighted that the expression of positive prolyl aminopeptidase and acetylglucosaminidase in the vaginal microecology could be substantially correlated to the occurrence and the development of vaginal inflammatory diseases, as well as the outcome and the risk of persistent HPV infection.

Mathematical Modeling and Computational Prediction of High-Risk Types of Human Papillomaviruses

Cervical cancer is one of the main causes of cancer death all over the world. Most diseases such as cervical epithelial atypical hyperplasia and invasive cervical cancer are closely related to the continuous infection of high-risk types of human papillomavirus. Therefore, the high-risk types of human papillomavirus are the key to the prevention and treatment of cervical cancer. With the accumulation of high-throughput and clinical data, the use of systematic and quantitative methods for mathematical modeling and computational prediction has become more and more important. This paper summarizes the mathematical models and prediction methods of the risk types of human papillomavirus, especially around the key steps such as feature extraction, feature selection, and prediction algorithms. We summarized and discussed the advantages and disadvantages of existing algorithms, which provides a theoretical basis for follow-up research.

Assessment of HPV Risk Type in H&E-stained Biopsy Specimens of the Cervix by Microscopy Image Analysis

OBJECTIVES

The objective of this study was (a) to identify, by computer processing of digitized images of hematoxylin and eosin (H&E)-stained biopsy material of the cervix, differences in the structure of nuclei between high-risk (HR) and low-risk (LR) human papillomavirus virus (HPV) types and (b) to assess the HPV risk type by designing a decision-support system (DSS).

MATERIALS AND METHODS

Clinical material comprised H&E-stained biopsies from squamous intraepithelial lesions of 55 patients with polymerase chain reaction-verified HR-HPV (26 patients) or LR-HPV (29 patients) infection. From each patient's biopsy specimen, we digitized 1 region of interest, guided by the expert physician. After the segmentation of nuclei, we quantified from each nucleus 77 textural and morphologic features. We represented each patient by a 77-feature vector, the feature means of all nuclei, and we created 2 classes for HR-HPV and LR-HPV types. We carried out (a) a statistical analysis to determine features with statistically significant differences between the 2 classes and (b) a discriminant analysis, by designing a DSS, to estimate the HPV risk type.

RESULTS

Statistical analysis revealed 40 features with between-classes statistically significant differences and discriminant analysis showed that the best DSS design achieved a high accuracy of about 93% in identifying the HPV risk type on data not used in the design of the DSS.

CONCLUSIONS

Nuclei of HR-HPV types were of higher intensity, contained larger structures, had higher edges, were coarser, rougher, had higher contrast, were larger, and attained more irregular shapes. The proposed DSS indicates that discrimination of HPV risk type from images of H&E-stained biopsy material of the cervix is promising.

Prediction of High-Risk Types of Human Papillomaviruses Using Reduced Amino Acid Modes

A human papillomavirus type plays an important role in the early diagnosis of cervical cancer. Most of the prediction methods use protein sequence and structure information, but the reduced amino acid modes have not been used until now. In this paper, we introduced the modes of reduced amino acids to predict high-risk HPV. We first reduced 20 amino acids into several nonoverlapping groups and calculated their structure and physicochemical modes for high-risk HPV prediction, which was tested and compared with the existing methods on 68 samples of known HPV types. The experiment result indicates that the proposed method achieved better performance with an accuracy of 96.49%, indicating that the reduced amino acid modes might be used to improve the prediction of high-risk HPV types.

Multiple sexual partners and vaginal microecological disorder are associated with HPV infection and cervical carcinoma development

There is an indirect link between multiple sexual partners (MSP) and cervical intraepithelial neoplasia (CIN) or even cervical cancer (CC). MSP may also lead to bacterial vaginosis (BV). The relationship among MSP, BV, human papillomavirus (HPV) infection and CIN/CC development in Chinese women remains unclear. The present study was designed to clarify their association. The study retrospectively analyzed 549 female patients who had visited a physical examination center. The MSP information was acquired, and vaginal microecology, HPV and cervical conization pathology (CCP) tests were performed when necessary. MSP status was distinct among patients with different levels of BV severity. In addition, as the severity of BV progressed, the HPV-positive ratio increased. Meanwhile, MSP was significantly associated with a positive HPV outcome, including HPV 16, HPV 18 and other high-risk HPV infections. The MSP group had a significantly higher percentage of positive CCP outcomes (particularly cases with CIN-II and CIN-III). Similarly, higher BV severity meant more severe CIN/CC progression. A logistic regression model based on age, MSP status and the Nugent score level was used in order to predict the CCP outcome. Furthermore, a receiver operating characteristic curve analysis resulted in an area under the curve of 0.834. In conclusion, the combination of MSP information and BV examination may provide a rapid, economic and accurate prediction of CIN/CC. Health education on sexual behavior and timely detection/treatment of BV should be conducted to reduce the risk of CC.

Recent Advances on Prediction of Human Papillomaviruses Risk Types

BACKGROUND

Some studies have shown that Human Papillomavirus (HPV) is strongly associated with cervical cancer. As we all know, cervical cancer still remains the fourth most common cancer, affecting women worldwide. Thus, it is both challenging and essential to detect risk types of human papillomaviruses.

METHODS

In order to discriminate whether HPV type is highly risky or not, many epidemiological and experimental methods have been proposed recently. For HPV risk type prediction, there also have been a few computational studies which are all based on Machine Learning (ML) techniques, but adopt different feature extraction methods. Therefore, we conclude and discuss several classical approaches which have got a better result for the risk type prediction of HPV.

RESULTS

This review summarizes the common methods to detect human papillomavirus. The main methods are sequence- derived features, text-based classification, gap-kernel method, ensemble SVM, Word statistical model, position- specific statistical model and mismatch kernel method (SVM). Among these methods, position-specific statistical model get a relatively high accuracy rate (accuracy=97.18%). Word statistical model is also a novel approach, which extracted the information of HPV from the protein "sequence space" with word statistical model to predict high-risk types of HPVs (accuracy=95.59%). These methods could potentially be used to improve prediction of highrisk types of HPVs.

CONCLUSION

From the prediction accuracy, we get that the classification results are more accurate by establishing mathematical models. Thus, adopting mathematical methods to predict risk type of HPV will be the main goal of research in the future.

Prediction of high-risk types of human papillomaviruses using statistical model of protein "sequence space"

Discrimination of high-risk types of human papillomaviruses plays an important role in the diagnosis and remedy of cervical cancer. Recently, several computational methods have been proposed based on protein sequence-based and structure-based information, but the information of their related proteins has not been used until now. In this paper, we proposed using protein “sequence space” to explore this information and used it to predict high-risk types of HPVs. The proposed method was tested on 68 samples with known HPV types and 4 samples without HPV types and further compared with the available approaches. The results show that the proposed method achieved the best performance among all the evaluated methods with accuracy 95.59% and F1-score 90.91%, which indicates that protein “sequence space” could potentially be used to improve prediction of high-risk types of HPVs.

Ensembled support vector machines for human papillomavirus risk type prediction from protein secondary structures

Infection by the human papillomavirus (HPV) is regarded as the major risk factor in the development of cervical cancer. Detection of high-risk HPV is important for understanding its oncogenic mechanisms and for developing novel clinical tools for its diagnosis, treatment, and prevention. Several methods are available to predict the risk types for HPV protein sequences. Nevertheless, no tools can achieve a universally good performance for all domains, including HPV and nor do they provide confidence levels for their decisions. Here, we describe ensembled support vector machines (SVMs) to classify HPV risk types, which assign given proteins into high-, possibly high-, or low-risk type based on their confidence level. Our approach uses protein secondary structures to obtain the differential contribution of subsequences for the risk type, and SVM classifiers are combined with a simple but efficient string kernel to handle HPV protein sequences. In the experiments, we compare our approach with previous methods in accuracy and F1-score, and present the predictions for unknown HPV types, which provides promising results.