Guan Qingzhou, Song Xuekun, Zhang Zhenzhen, Zhang Yizhi, Chen Yating, Li Jing
Co-construction Collaborative Innovation Center for Chinese Medicine and Respiratory Diseases by Henan & Education Ministry of P.R. China, Academy of Chinese Medical Sciences, Henan University of Chinese Medicine, Zhengzhou, China.
College of Information Technology, Henan University of Chinese Medicine, Zhengzhou, China.
Front Mol Biosci. 2020 Dec 4;7:564005. doi: 10.3389/fmolb.2020.564005. eCollection 2020.
Breast cancer cell lines are frequently used to elucidate the molecular mechanisms of the disease. However, a large proportion of cell lines are affected by problems such as mislabeling and cross-contamination. Therefore, it is of great clinical significance to select optimal breast cancer cell lines models. Using tamoxifen survival-related genes from breast cancer tissues as the gold standard, we selected the optimal cell line model to represent the characteristics of clinical tissue samples. Moreover, using relative expression orderings of gene pairs, we developed a gene pair signature that could predict tamoxifen therapy outcomes. Based on 235 consistently identified survival-related genes from datasets GSE17705 and GSE6532, we found that only the differentially expressed genes (DEGs) from the cell line dataset GSE26459 were significantly reproducible in tissue samples (binomial test, = 2.13E-07). Finally, using the consistent DEGs from cell line dataset GSE26459 and tissue samples, we used the transcriptional qualitative feature to develop a two-gene pair (, ; , ) for predicting clinical tamoxifen resistance in the training data (logrank = 1.98E-07); this signature was verified using an independent dataset (logrank = 0.009909). Our results indicate that the cell line model from dataset GSE26459 provides a good representation of the characteristics of clinical tissue samples; thus, it will be a good choice for the selection of drug-resistant and drug-sensitive breast cancer cell lines in the future. Moreover, our signature could predict tamoxifen treatment outcomes in breast cancer patients.
乳腺癌细胞系常用于阐明该疾病的分子机制。然而，很大一部分细胞系受到诸如错误标记和交叉污染等问题的影响。因此，选择最佳的乳腺癌细胞系模型具有重要的临床意义。以乳腺癌组织中与他莫昔芬生存相关的基因作为金标准，我们选择了最佳的细胞系模型来代表临床组织样本的特征。此外，利用基因对的相对表达顺序，我们开发了一种可以预测他莫昔芬治疗结果的基因对特征。基于数据集GSE17705和GSE6532中一致鉴定出的235个生存相关基因，我们发现只有细胞系数据集GSE26459中的差异表达基因（DEGs）在组织样本中具有显著的可重复性（二项式检验，=2.13E-07）。最后，利用细胞系数据集GSE26459和组织样本中一致的DEGs，我们利用转录定性特征开发了一个双基因对（，；，）用于预测训练数据中临床他莫昔芬耐药性（对数秩=1.98E-07）；该特征在一个独立数据集中得到验证（对数秩=0.009909）。我们的结果表明，数据集GSE26459的细胞系模型能够很好地代表临床组织样本的特征；因此，它将是未来选择耐药和敏感乳腺癌细胞系的一个不错选择。此外，我们的特征可以预测乳腺癌患者的他莫昔芬治疗结果。
