A risk evaluation model of cervical cancer based on etiology and human leukocyte antigen allele susceptibility

Abstract

There are no reliable risk factors to accurately predict progression to cervical cancer in patients with chronic cervicitis infected with human papillomavirus (HPV). The aim of this study was to create a validated predictive model based on the risk factors for cervical cancer. A model to estimate the risk of cervical cancer may help select patients for intervention therapy in order to reduce the occurrence of cervical cancer after HPV infection. This retrospective analysis included 68 patients with cervical cancer and 202 healthy female controls. HPV infection and human leukocyte antigen (HLA) class II alleles in HLA-DRB1, 3-7, and 9 were detected. Other information was collected, including level of education and age at first parturition. Multiple regression analysis and an artificial neural network (ANN) were performed to identify the independent risk factors for cervical cancer, and based on these, an evaluation model for the prediction of the incidence of cervical cancer was formed. This model showed HPV to be a pivotal player in cervical cancer that increased the risk by 7.6-fold. The presence of the HLA-DRB1*13-2 and HLA-DRB1*3(17) alleles was associated with an increased risk of developing cervical cancer. Conversely, the HLA-DRB1*09012 and HLA-DRB1*1201 alleles were found to be associated with a reduced cervical cancer risk. In addition, other factors, such as age at first parturition and education level, had significant effects on cervical cancer risk. The model was applied to conduct a risk assessment of women in the mountain area of Wufeng County, Hubei Province in China. The sensitivity and specificity of our model both exceeded 95%. This model, based on etiology and HLA allele susceptibility, can estimate the risk of cervical cancer in chronic cervicitis patients after HPV infection. It combines genetic and environmental factors and significantly enhances the accuracy of risk evaluation for cervical cancer. This model could be used to select patients for intervention therapy and to guide patient classification management.