In Ecotoxicology and environmental safety ; h5-index 67.0
Benzene is a common industrial chemical and environmental pollutant. However, the mechanism of hematotoxicity caused by exposure to low doses of benzene is unknown. Let-7e-5p pathway regulatory networks were constructed by bioinformatics analysis using a benzene-induced aplastic anemia (BIAA) mouse model. The MTT assay, EdU staining, flow cytometric analysis, dual luciferase reporter gene assay, and RIP assay were utilized to evaluate the effects of benzoquinone (1,4-BQ) on let-7e-5p pathway. This study consisted of 159 workers with a history of low-level benzene exposure and 159 workers with no history of benzene exposure. After the confounding factors were identified, the associations between let-7e-5p expression and hematotoxicity were assessed by multiple linear regression. Furthermore, we used four machine learning algorithms (decision trees, neural network, Bayesian network, and support vector machines) to construct a predictive model for detecting benzene-causing hematotoxicity in workers. In this study, compared with respective controls, let-7e-5p expression was decreased in BIAA mice and benzene-exposed workers. After 1,4-BQ exposure, let-7e-5p overexpression negatively regulated caspase-3 and p21 expression, protected cells from apoptosis, and facilitated cell proliferation. RIP assays, and dual luciferase reporter gene assays confirmed that let-7e-5p could target p21 and caspase-3 and regulate the cell cycle and apoptosis. The support vector machines classifier achieved the best prediction of benzene-induced hematotoxicity (prediction accuracy = 88.27, AUC = 0.83) by statistically characterizing the internal dose of benzene exposure and the oxidative stress index, as well as the expression levels of let-7e-5p pathway-related genes in benzene-exposed workers. Let-7e-5p may be a potential therapeutic target of benzene-induced hematotoxicity, provide a basis for evaluating the health hazards of long-term and low-dose benzene exposure in workers, and supply a reference for revising occupational health standards.
Wang Boshen, Xu Shouxiang, Sun Qianyu, Li Xiaoqin, Wang Tong, Xu Kai, Yin Lihong, Sun Rongli, Pu Yuepu, Zhang Juan
Benzene, Hematotoxicity, Let-7e-5p, Machine learning