Department of General Surgery, The First Affiliated Hospital of SooChow University, Suzhou, Jiangsu 215006, P.R. China.
Mol Med Rep. 2018 Feb;17(2):3152-3157. doi: 10.3892/mmr.2017.8234. Epub 2017 Dec 8.
Tamoxifen is the most commonly used drug to treat estrogen receptor positive (ER+) breast cancer. However, many patients with ER+ breast cancer have experienced resistance and other adverse side effects following treatment with tamoxifen. Furthermore, clinical and pathological parameters have thus far failed to predict the efficiency of tamoxifen administration. Therefore, gene signature based models for the prediction of survival time of such patients are urgently needed. In the current study, gene expression levels and follow‑up information of samples from GSE17705 and GSE22219 databases were used to construct a risk score model based on Cox multivariate regression. The expression levels of 10 genes were included in the model: CCNB2, CCNA2, FOXD1, WSB2, RBPMS, CTDSP1, BIN3, SLBP, EPRS, FTO. The samples in the high‑risk group had a relative early distant relapse time period (median survival time of 3.75 years) compared with the patients in the low risk group (median survival time of 6.5 years, P<0.01). For further validation, a further two independent datasets (GSE26971, GSE58644) were assessed. The overall survival time period of patients with high‑risk scores in these datasets was significantly longer than those with low‑risk scores (P<0.01). Furthermore, the associations between clinical parameters and risk score were investigated, and it was revealed that the risk score was significantly correlated with tumor age, tumor stage and grade. In addition, a 5‑year survival nomogram was plotted in order to facilitate the utilization of risk score along with other clinical data. In summary, using the transcriptomic profile, a multi‑gene expression based risk score was developed and was revealed as being able to successfully predict the outcome of patients with ER+ breast cancer treated with tamoxifen for 5 years.
他莫昔芬是治疗雌激素受体阳性(ER+)乳腺癌最常用的药物。然而,许多接受他莫昔芬治疗的 ER+乳腺癌患者经历了耐药和其他不良反应。此外,迄今为止,临床和病理参数未能预测他莫昔芬给药的效率。因此,迫切需要基于基因特征的模型来预测此类患者的生存时间。在本研究中,使用 GSE17705 和 GSE22219 数据库中的基因表达水平和随访信息,构建了基于 Cox 多变量回归的风险评分模型。该模型包含 10 个基因的表达水平:CCNB2、CCNA2、FOXD1、WSB2、RBPMS、CTDSP1、BIN3、SLBP、EPRS、FTO。高风险组的样本具有相对较早的远处复发时间(中位生存时间为 3.75 年),而低风险组的患者中位生存时间为 6.5 年(P<0.01)。为了进一步验证,进一步评估了另外两个独立数据集(GSE26971、GSE58644)。这些数据集高风险评分患者的总生存时间明显长于低风险评分患者(P<0.01)。此外,还研究了临床参数与风险评分之间的关系,结果表明风险评分与肿瘤年龄、肿瘤分期和分级显著相关。此外,还绘制了一个 5 年生存诺模图,以便于将风险评分与其他临床数据一起使用。总之,使用转录组谱,开发了一个基于多基因表达的风险评分,并成功预测了接受他莫昔芬治疗的 ER+乳腺癌患者 5 年的预后。