Prediction of the combined effects of multiple estrogenic chemicals on MCF- 7 human breast cancer cells and a preliminary molecular exploration of the estrogenic proliferative effects and related gene expression

摘要

The environmental risks of environmental estrogens (EEs) are often assessed via the same mode of action in the concentration addition (CA) model, neglecting the complex combined mechanisms at the genetic level. In this study, the cell proliferation effects of estrone, 17 alpha-ethinylestradiol, 17 beta-estradiol, estriol, diethylstilbestrol, estradiol valerate, bisphenol A, 4-tert-octylphenol and 4-nonylphenol were determined individually using the CCK-8 method, and the proliferation effects of a multicomponent mixture of estrogenic chemicals mixed at equipotent concentrations using a fixed-ratio design were studied using estrogen-sensitive MCF-7 cells. Furthermore, transcription factors related to cell proliferation were analyzed using RT-PCR assays to explore the potential molecular mechanisms related to the estrogenic proliferative effects. The results showed that the estrogenic chemicals act together in an additive mode, and the combined proliferative effects could be predicted more accurately by the response addition model than the CA model with regard to their adverse outcomes. Furthermore, different signaling pathways were involved depending on the different mixtures. The RT-PCR analyses showed that different estrogens have distinct avidities and preferences for different estrogen receptors at the gene level. Furthermore, the results indicated that estrogenic mixtures increased ER alpha, PIK3CA, GPER, and PTEN levels and reduced Akt1 level to display combined estrogenicity. These findings indicated that the potential combined environmental risks were greater than those found in some specific assessment procedures based on a similar mode of action due to the diversity of environmental pollutions and their multiple unknown modes of action. Thus, more efforts are needed for mode-of-action-driven analyses at the molecular level. Furthermore, to more accurately predict and assess the individual responses in vivo from the cellular effects in vitro, more parameters and correction factors should be taken into consideration in the addition model.