College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150086, China.
Department of Breast Surgery, Harbin Medical University Cancer Hospital, Harbin, Heilongjiang 150081, China.
Exp Biol Med (Maywood). 2021 Jul;246(14):1626-1642. doi: 10.1177/15353702211007766. Epub 2021 Apr 28.
Since genetic alteration only accounts for 20%-30% in the drug effect-related factors, the role of epigenetic regulation mechanisms in drug response is gradually being valued. However, how epigenetic changes and abnormal gene expression affect the chemotherapy response remains unclear. Therefore, we constructed a variety of mathematical models based on the integrated DNA methylation, gene expression, and anticancer drug response data of cancer cell lines from pan-cancer levels to identify genes whose DNA methylation is associated with drug response and then to assess the impact of epigenetic regulation of gene expression on the sensitivity of anticancer drugs. The innovation of the mathematical models lies in: Linear regression model is followed by logistic regression model, which greatly shortens the calculation time and ensures the reliability of results by considering the covariates. Second, reconstruction of prediction models based on multiple dataset partition methods not only evaluates the model stability but also optimizes the drug-gene pairs. For 368,520 drug-gene pairs with < 0.05 in linear models, 999 candidate pairs with both AUC ≥ 0.8 and < 0.05 were obtained by logistic regression models between drug response and DNA methylation. Then 931 drug-gene pairs with 45 drugs and 491 genes were optimized by model stability assessment. Integrating both DNA methylation and gene expression markedly increased predictive power for 732 drug-gene pairs where 598 drug-gene pairs including 44 drugs and 359 genes were prioritized. Several drug target genes were enriched in the modules of the drug-gene-weighted interaction network. Besides, for cancer driver genes such as , , and , synergistic effects of DNA methylation and gene expression can predict certain anticancer drugs' responses. In summary, we identified potential drug sensitivity-related markers from pan-cancer levels and concluded that synergistic regulation of DNA methylation and gene expression affect anticancer drug response.
由于遗传改变仅占药物效应相关因素的 20%-30%，因此，表观遗传调控机制在药物反应中的作用逐渐受到重视。然而，表观遗传变化和异常基因表达如何影响化疗反应尚不清楚。因此，我们基于癌症细胞系的综合 DNA 甲基化、基因表达和抗癌药物反应数据，从泛癌水平构建了多种数学模型，以鉴定与药物反应相关的 DNA 甲基化的基因，然后评估基因表达的表观遗传调控对抗癌药物敏感性的影响。数学模型的创新之处在于：线性回归模型后接逻辑回归模型，通过考虑协变量，大大缩短了计算时间并保证了结果的可靠性。其次，基于多种数据集划分方法的预测模型重建不仅评估了模型的稳定性，而且优化了药物-基因对。对于线性模型中 < 0.05 的 368520 个药物-基因对，通过药物反应与 DNA 甲基化之间的逻辑回归模型得到了 999 对候选对，AUC ≥ 0.8 和 < 0.05。然后通过模型稳定性评估优化了 931 对药物-基因对，其中 45 种药物和 491 个基因。整合 DNA 甲基化和基因表达显著提高了 732 对药物-基因对的预测能力，其中包括 44 种药物和 359 个基因的 598 对药物-基因对被优先考虑。药物-基因加权互作网络的模块中富集了几个药物靶基因。此外，对于癌症驱动基因，如 、 、 ，DNA 甲基化和基因表达的协同调节可以预测某些抗癌药物的反应。总之，我们从泛癌水平鉴定了潜在的药物敏感性相关标记物，并得出结论，DNA 甲基化和基因表达的协同调节影响抗癌药物反应。
