Jeong Hyeri, Kim Jongwoon, Kim Youngjun
Environmental Safety Group, Korea Institute of Science and Technology (KIST) Europe, Campus E 7.1, Saarbruecken D-66123, Germany.
Division of Energy and Environment Technology, KIST School, University of Science and Technology, Hwarang-ro 14-gil 5, Seoul 02792, Korea.
Int J Environ Res Public Health. 2017 Sep 30;14(10):1158. doi: 10.3390/ijerph14101158.
Approximately 1000 chemicals have been reported to possibly have endocrine disrupting effects, some of which are used in consumer products, such as personal care products (PCPs) and cosmetics. We conducted data integration combined with gene network analysis to: (i) identify causal molecular mechanisms between endocrine disrupting chemicals (EDCs) used in PCPs and breast cancer; and (ii) screen candidate EDCs associated with breast cancer. Among EDCs used in PCPs, four EDCs having correlation with breast cancer were selected, and we curated 27 common interacting genes between those EDCs and breast cancer to perform the gene network analysis. Based on the gene network analysis, ESR1, TP53, NCOA1, AKT1, and BCL6 were found to be key genes to demonstrate the molecular mechanisms of EDCs in the development of breast cancer. Using GeneMANIA, we additionally predicted 20 genes which could interact with the 27 common genes. In total, 47 genes combining the common and predicted genes were functionally grouped with the gene ontology and KEGG pathway terms. With those genes, we finally screened candidate EDCs for their potential to increase breast cancer risk. This study highlights that our approach can provide insights to understand mechanisms of breast cancer and identify potential EDCs which are in association with breast cancer.
据报道，约有1000种化学物质可能具有内分泌干扰作用，其中一些用于消费品，如个人护理产品（PCP）和化妆品。我们进行了数据整合并结合基因网络分析，以：（i）确定PCP中使用的内分泌干扰化学物质（EDC）与乳腺癌之间的因果分子机制；以及（ii）筛选与乳腺癌相关的候选EDC。在PCP中使用的EDC中，选择了四种与乳腺癌相关的EDC，我们整理了这些EDC与乳腺癌之间的27个常见相互作用基因，以进行基因网络分析。基于基因网络分析，发现ESR1、TP53、NCOA1、AKT1和BCL6是证明EDC在乳腺癌发生发展中分子机制的关键基因。使用GeneMANIA，我们还预测了20个可能与这27个常见基因相互作用的基因。总共47个将常见基因和预测基因结合在一起的基因通过基因本体论和KEGG通路术语进行了功能分组。利用这些基因，我们最终筛选了候选EDC增加乳腺癌风险的潜力。这项研究强调，我们的方法可以为理解乳腺癌机制和识别与乳腺癌相关的潜在EDC提供见解。
