Department of Toxicology of School of Public Health and Department of Gynecologic Oncology of Women's Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China.
The State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China.
Ecotoxicol Environ Saf. 2023 Jan 15;250:114466. doi: 10.1016/j.ecoenv.2022.114466. Epub 2022 Dec 30.
Given the increasing exposure of humans to environmental chemicals and the limitations of conventional toxicity test, there is an urgent need to develop next-generation risk assessment methods.
This study aims to establish a novel computational system named Toxicogenomics Scoring System (TGSS) to predict the carcinogenicity of chemicals coupling chemical-gene interactions with multiple cancer transcriptomic datasets.
Chemical-related gene signatures were derived from chemical-gene interaction data from the Comparative Toxicogenomics Database (CTD). For each cancer type in TCGA, genes were ranked by their effects on tumorigenesis, which is based on the differential expression between tumor and normal samples. Next, we developed carcinogenicity scores (C-scores) using pre-ranked GSEA to quantify the correlation between chemical-related gene signatures and ranked gene lists. Then we established TGSS by systematically evaluating the C-scores in multiple chemical-tumor pairs. Furthermore, we examined the performance of our approach by ROC curves or prognostic analyses in TCGA and multiple independent cancer cohorts.
Forty-six environmental chemicals were finally included in the study. C-score was calculated for each chemical-tumor pair. The C-scores of IARC Group 3 chemicals were significantly lower than those of chemicals in Group 1 (P-value = 0.02) and Group 2 (P-values = 7.49 ×10). ROC curves analysis indicated that C-score could distinguish "high-risk chemicals" from the other compounds (AUC = 0.67) with a specificity and sensitivity of 0.86 and 0.57. The results of survival analysis were also in line with the assessed carcinogenicity in TGSS for the chemicals in Group 1. Finally, consistent results were further validated in independent cancer cohorts.
TGSS highlighted the great potential of integrating chemical-gene interactions with gene-cancer relationships to predict the carcinogenic risk of chemicals, which would be valuable for systems toxicology.
鉴于人类接触环境化学物质的不断增加以及传统毒性测试的局限性，迫切需要开发下一代风险评估方法。
本研究旨在建立一种新的计算系统，命名为毒代动力学基因组评分系统（TGSS），通过结合化学-基因相互作用和多个癌症转录组数据集，预测化学物质的致癌性。
从比较毒理学基因组数据库（CTD）中的化学-基因相互作用数据中提取化学相关基因特征。对于 TCGA 中的每种癌症类型，根据肿瘤和正常样本之间的差异表达，对影响肿瘤发生的基因进行排序。接下来，我们使用预先排序的 GSEA 计算致癌性评分（C-score），以量化化学相关基因特征与排序基因列表之间的相关性。然后，我们通过系统地评估多个化学-肿瘤对的 C-score 来建立 TGSS。此外，我们通过在 TCGA 和多个独立的癌症队列中进行 ROC 曲线或预后分析来检验我们方法的性能。
最终有 46 种环境化学物质被纳入研究。为每个化学-肿瘤对计算 C-score。IARC 第 3 组化学物质的 C-score 显著低于第 1 组（P 值=0.02）和第 2 组（P 值=7.49×10）化学物质的 C-score。ROC 曲线分析表明，C-score 可以区分“高风险化学物质”和其他化合物（AUC=0.67），特异性和灵敏度分别为 0.86 和 0.57。生存分析的结果也与 TGSS 对第 1 组化学物质的评估致癌性一致。最后，在独立的癌症队列中进一步验证了一致的结果。
TGSS 强调了整合化学-基因相互作用与基因-癌症关系来预测化学物质致癌风险的巨大潜力，这对于系统毒理学将是有价值的。
Zotero 插件