Wang Yutao, Lin Jiaxing, Yan Kexin, Wang Jianfeng
Department of Urology, The First Hospital of China Medical University, Shenyang, Liaoning, China.
Department of Dermatology, The First Hospital of China Medical University, Shenyang, Liaoning, China.
Int J Genomics. 2020 May 27;2020:1097602. doi: 10.1155/2020/1097602. eCollection 2020.
In this paper, we aimed to develop and validate a risk prediction method using independent prognosis genes selected robustly in prostate cancer.
We considered 723 samples obtained from TCGA (the Cancer Genome Atlas), GSE46602, and GSE21032. Prostate cancer prognosis-related genes with < 0.05 were selected using Univariable Cox regression analysis. We then built the lowest AIC (Akaike information criterion score) optimal gene model using the "Rbsurv" package in TCGA train set. The coefficients were obtained by Multivariable Cox regression analysis. We named the new prognosis method CMU5. The CMU5 risk score was verified in TCGA test set, GSE46602, and GSE21032.
, , , , and were identified as independent prognosis factors in prostate cancer patients. We built the computing model as follows: CMU5 risk score = 1.158∗ + 1.737∗ - 0.737∗ - 0.651∗ - 0.793∗. The AUC of DFS was 0.809 in the train set (274 samples), 0.710 in the test set (273 samples), and 0.768 in the complete set (547 samples). The benign prediction capacity of CMU5 was verified by GSE46602 (36 samples; AUC = 0.6039) and GSE21032 GPL5188 (140 samples; AUC = 0.7083). Using the cut-off point of 2.056, a significant difference was shown between high- and low-risk groups.
A prognosis-related risk score formula named CMU5 was built and verified, providing reliable prediction of prostate cancer outcome. This signature might provide a basis for individualized treatment of prostate cancer.
在本文中，我们旨在开发并验证一种使用在前列腺癌中稳健选择的独立预后基因的风险预测方法。
我们考虑了从TCGA（癌症基因组图谱）、GSE46602和GSE21032获取的723个样本。使用单变量Cox回归分析选择P值<0.05的前列腺癌预后相关基因。然后我们在TCGA训练集中使用“Rbsurv”软件包构建最低AIC（赤池信息准则得分）最优基因模型。通过多变量Cox回归分析获得系数。我们将新的预后方法命名为CMU5。CMU5风险评分在TCGA测试集、GSE46602和GSE21032中进行了验证。
[此处原文缺失具体基因名称]被确定为前列腺癌患者的独立预后因素。我们构建了如下计算模型：CMU5风险评分 = 1.158×[此处原文缺失具体基因名称1] + 1.737×[此处原文缺失具体基因名称2] - 0.737×[此处原文缺失具体基因名称3] - 0.651×[此处原文缺失具体基因名称4] - 0.793×[此处原文缺失具体基因名称5]。训练集（274个样本）中DFS的AUC为0.809，测试集（273个样本）中为0.710，完整集（547个样本）中为0.768。CMU5的良性预测能力在GSE46602（36个样本；AUC = 0.6039）和GSE21032 GPL5188（140个样本；AUC = 0.7083）中得到验证。使用2.056的截断点，高风险组和低风险组之间显示出显著差异。
构建并验证了一个名为CMU5的预后相关风险评分公式，为前列腺癌预后提供了可靠预测。该特征可能为前列腺癌的个体化治疗提供依据。
