Abstract

Background:

Cancer risk assessment of complex exposures, such as exposure to mixtures of polycyclic aromatic hydrocarbons (PAHs), is challenging due to the diverse biological activities of these compounds. With the help of text mining (TM), we have developed TM tools—the latest iteration of the Cancer Risk Assessment using Biomedical literature tool (CRAB3) and a Cancer Hallmarks Analytics Tool (CHAT)—that could be useful for automatic literature analyses in cancer risk assessment and research. Although CRAB3 analyses are based on carcinogenic modes of action (MOAs) and cover almost all the key characteristics of carcinogens, CHAT evaluates literature according to the hallmarks of cancer referring to the alterations in cellular behavior that characterize the cancer cell.

Objectives:

The objective was to evaluate the usefulness of these tools to support cancer risk assessment by performing a case study of 22 European Union and U.S. Environmental Protection Agency priority PAHs and diesel exhaust and a case study of PAH interactions with silica.

Methods:

We analyzed PubMed literature, comprising 57,498 references concerning priority PAHs and complex PAH mixtures, using CRAB3 and CHAT.

Results:

CRAB3 analyses correctly identified similarities and differences in genotoxic and nongenotoxic MOAs of the 22 priority PAHs and grouped them according to their known carcinogenic potential. CHAT had the same capacity and complemented the CRAB output when comparing, for example, benzo[a]pyrene and dibenzo[a,l]pyrene. Both CRAB3 and CHAT analyses highlighted potentially interacting mechanisms within and across complex PAH mixtures and mechanisms of possible importance for interactions with silica.

Conclusion:

These data suggest that our TM approach can be useful in the hazard identification of PAHs and mixtures including PAHs. The tools can assist in grouping chemicals and identifying similarities and differences in carcinogenic MOAs and their interactions. https://doi.org/10.1289/EHP6702

中文翻译：

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文本挖掘在化学品混合物风险评估中的应用：以多环芳烃（PAHs）为例

 抽象的

 背景：

由于这些化合物具有不同的生物活性，复杂暴露（例如多环芳烃（PAH）混合物的暴露）的癌症风险评估具有挑战性。在文本挖掘 (TM) 的帮助下，我们开发了 TM 工具——使用生物医学文献工具 (CRAB3) 和癌症标志分析工具 (CHAT) 的癌症风险评估的最新版本——可用于自动文献分析癌症风险评估和研究。尽管 CRAB3 分析是基于致癌作用模式 (MOA) 并涵盖了致癌物的几乎所有关键特征，但 CHAT 根据癌症标志（即癌细胞特征的细胞行为改变）来评估文献。

 目标：

目的是通过对 22 个欧盟和美国环境保护局优先考虑的 PAH 和柴油机尾气进行案例研究，以及 PAH 与二氧化硅相互作用的案例研究，评估这些工具在支持癌症风险评估方面的有用性。

 方法：

我们使用 CRAB3 和 CHAT 分析了 PubMed 文献，其中包括 57,498 篇有关优先 PAH 和复杂 PAH 混合物的参考文献。

 结果：

CRAB3 分析正确识别了 22 种优先 PAH 的基因毒性和非基因毒性 MOA 的相似性和差异，并根据其已知的致癌潜力对它们进行分组。例如，在比较苯并[ a ]芘和二苯并[ a,l ]芘时，CHAT 具有相同的容量并补充了 CRAB 输出。 CRAB3 和 CHAT 分析都强调了复杂 PAH 混合物内部和之间的潜在相互作用机制，以及与二氧化硅相互作用的可能重要机制。

 结论：

这些数据表明，我们的 TM 方法可用于 PAH 和包含 PAH 的混合物的危害识别。这些工具可以帮助对化学物质进行分组并识别致癌 MOA 及其相互作用的异同。 https://doi.org/10.1289/EHP6702