Predicting risk factors for Epstein-Barr virus reactivation using Bayesian network analysis: a population-based study of high-risk areas for nasopharyngeal cancer
Background and objectiveNasopharyngeal 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...
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| Format: | Article |
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Oncology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2024.1369765/full |
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| author | Zhiwen Zeng Kena Lin Xueqi Li Xueqi Li Tong Li Xiaoman Li Jiayi Li Zule Ning Qinxian Liu Shanghang Xie Sumei Cao Sumei Cao Jinlin Du |
| author_facet | Zhiwen Zeng Kena Lin Xueqi Li Xueqi Li Tong Li Xiaoman Li Jiayi Li Zule Ning Qinxian Liu Shanghang Xie Sumei Cao Sumei Cao Jinlin Du |
| author_sort | Zhiwen Zeng |
| collection | DOAJ |
| description | Background and objectiveNasopharyngeal 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.MethodsThe 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.ResultsA 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.ConclusionsBN 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. |
| format | Article |
| id | doaj-art-37ca9490518344c282786adba58470d5 |
| institution | Kabale University |
| issn | 2234-943X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Oncology |
| spelling | doaj-art-37ca9490518344c282786adba58470d52025-01-21T05:43:43ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2025-01-011410.3389/fonc.2024.13697651369765Predicting risk factors for Epstein-Barr virus reactivation using Bayesian network analysis: a population-based study of high-risk areas for nasopharyngeal cancerZhiwen Zeng0Kena Lin1Xueqi Li2Xueqi Li3Tong Li4Xiaoman Li5Jiayi Li6Zule Ning7Qinxian Liu8Shanghang Xie9Sumei Cao10Sumei Cao11Jinlin Du12School of Public Health, Guangdong Medical University, Dongguan, Guangdong, ChinaSchool of Public Health, Guangdong Medical University, Dongguan, Guangdong, ChinaDepartment of Cancer Prevention, Sun Yat-sen University Cancer Center, Guangzhou, ChinaSchool of Public Health, Sun Yat-sen University, Guangzhou, ChinaDepartment of Cancer Prevention, Sun Yat-sen University Cancer Center, Guangzhou, ChinaSchool of Public Health, Guangdong Medical University, Dongguan, Guangdong, ChinaSchool of Public Health, Guangdong Medical University, Dongguan, Guangdong, ChinaSchool of Public Health, Guangdong Medical University, Dongguan, Guangdong, ChinaSchool of Public Health, Guangdong Medical University, Dongguan, Guangdong, ChinaDepartment of Cancer Prevention, Sun Yat-sen University Cancer Center, Guangzhou, ChinaDepartment of Cancer Prevention, Sun Yat-sen University Cancer Center, Guangzhou, ChinaState Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, and Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-Sen University Cancer Center, Guangzhou, ChinaSchool of Public Health, Guangdong Medical University, Dongguan, Guangdong, ChinaBackground and objectiveNasopharyngeal 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.MethodsThe 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.ResultsA 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.ConclusionsBN 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.https://www.frontiersin.org/articles/10.3389/fonc.2024.1369765/fullBayesian networkEBV reactivationmodel constructionnasopharyngeal carcinomalogistic regression |
| spellingShingle | Zhiwen Zeng Kena Lin Xueqi Li Xueqi Li Tong Li Xiaoman Li Jiayi Li Zule Ning Qinxian Liu Shanghang Xie Sumei Cao Sumei Cao Jinlin Du Predicting risk factors for Epstein-Barr virus reactivation using Bayesian network analysis: a population-based study of high-risk areas for nasopharyngeal cancer Frontiers in Oncology Bayesian network EBV reactivation model construction nasopharyngeal carcinoma logistic regression |
| title | Predicting risk factors for Epstein-Barr virus reactivation using Bayesian network analysis: a population-based study of high-risk areas for nasopharyngeal cancer |
| title_full | Predicting risk factors for Epstein-Barr virus reactivation using Bayesian network analysis: a population-based study of high-risk areas for nasopharyngeal cancer |
| title_fullStr | Predicting risk factors for Epstein-Barr virus reactivation using Bayesian network analysis: a population-based study of high-risk areas for nasopharyngeal cancer |
| title_full_unstemmed | Predicting risk factors for Epstein-Barr virus reactivation using Bayesian network analysis: a population-based study of high-risk areas for nasopharyngeal cancer |
| title_short | Predicting risk factors for Epstein-Barr virus reactivation using Bayesian network analysis: a population-based study of high-risk areas for nasopharyngeal cancer |
| title_sort | predicting risk factors for epstein barr virus reactivation using bayesian network analysis a population based study of high risk areas for nasopharyngeal cancer |
| topic | Bayesian network EBV reactivation model construction nasopharyngeal carcinoma logistic regression |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2024.1369765/full |
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