ArticleFrontiers in oncology2024
Predicting risk factors for Epstein-Barr virus reactivation using Bayesian network analysis: a population-based study of high-risk areas for nasopharyngeal cancer.
Article in Frontiers in oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed.
- Repurposing statins for choriocarcinoma: targeting the mevalonate pathway to disrupt mechanosignaling and overcome therapeutic resistance.Clinical & experimental metastasis · 2026Review
- Epstein-Barr virus-associated multiple sclerosis: recent mechanistic advances and clinical therapeutic perspectives.Frontiers in immunology · 2026Review
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11 authors.
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Abstract
Background and objective: Nasopharyngeal carcinoma (NPC) is a rare disease in most parts of the world, but it is highly prevalent in South China. Epstein-Barr virus (EBV) is one of the major risk factors for NPC. Hence, understanding the factors associated with the reactivation of EBV from the latent stage is crucial for preventing NPC. This study aimed to investigate the risk factors for EBV reactivation associated with NPC in high-prevalence areas in China using a Bayesian network (BN) model combined with structural equation modeling tools. Methods: The baseline information for this study was derived from NPC screening data from a population-based prospective cohort in Sihui City, Guangdong Province, China. We divided the data into a training dataset and a test dataset. We then constructed an interaction networktionba BN prediction model to explore the risk factors for EBV reactivation, which was compared with a conventional logistic regression model. Results: A total of 12,579 participants were included in the analyses, with 1596 participant pairs finally included after the use of a nested case-control study. The results of multivariable logistic regression showed that only being older than 60 years (OR = 1.718, 95% CI = 1.273,2.322) and being a current smoker (OR = 1.477, 95% CI = 1.167 - 1.872) were the risk factors for EBV reactivation. The results of the model constructed using BN showed that age and smoking were directly associated with EBV reactivation. In contrast, sex, education level, tea drinking, cooking, and family history of cancer were indirectly associated with EBV reactivation. Further, we predicted the risk of EBV reactivation using Bayesian inference and visualized the BN inference. Model prediction performance was evaluated using the test dataset. The results showed that the BN model slightly outperformed the traditional logistic regression model in all metrics. Conclusions: BN not only reflects the complex interaction between factors but also visualizes the prediction results. It has a promising application potential in the risk prediction of EBV reactivation associated with NPC.
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