Epithelial-mesenchymal transition biomarkers and support vector machine guided model in preoperatively predicting regional lymph node metastasis for rectal cancer.
Epithelial-mesenchymal transition biomarkers and support vector machine guided model in preoperatively predicting regional lymph node metastasis for rectal cancer.
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DOI：
10.1038/bjc.2012.82
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发表时间：
2012-05-22
影响因子：
8.8
通讯作者：
Wang, J-P
中科院分区：
文献类型：
--
作者：
Fan, X-J;Wan, X-B;Huang, Y.;Cai, H-M;Fu, X-H;Yang, Z-L;Chen, D-K;Song, S-X;Wu, P-H;Liu, Q.;Wang, L.;Wang, J-P
目前的影像学方法不足以在术前预测直肠癌（RC）的区域淋巴结转移（RLNM）状态。在这里，我们设计了支持向量机（SVM）模型来解决这个问题，整合上皮间质转化（EMT）相关的生物标志物沿着与临床病理变量。使用组织芯片和免疫组化，EMT相关的生物标志物的表达在193例RC患者进行了测量。其中，74例患者被分配到训练集，以选择用于设计SVM模型的稳健变量。SVM模型预测值在测试集（119例患者）中得到验证。在训练集中，选择了8个变量，包括6个EMT相关的生物标志物和2个临床病理变量，设计SVM模型。在测试集中，我们确定了63例RLNM高风险患者和56例低风险患者。SVM预测RLNM的敏感性、特异性和总准确性分别为68.3%、81.1%和72.3%。重要的是，多变量logistic回归分析显示SVM模型确实是RLNM状态的独立预测因子（比值比，11.536; 95%置信区间，4.113-32.361; P<0.0001）。我们的基于SVM的模型在定义RC患者的RLNM状态方面显示出中等强的预测能力，为选择RLNM高危亚组进行新辅助放化疗提供了重要方法。
Current imaging modalities are inadequate in preoperatively predicting regional lymph node metastasis (RLNM) status in rectal cancer (RC). Here, we designed support vector machine (SVM) model to address this issue by integrating epithelial–mesenchymal-transition (EMT)-related biomarkers along with clinicopathological variables. Using tissue microarrays and immunohistochemistry, the EMT-related biomarkers expression was measured in 193 RC patients. Of which, 74 patients were assigned to the training set to select the robust variables for designing SVM model. The SVM model predictive value was validated in the testing set (119 patients). In training set, eight variables, including six EMT-related biomarkers and two clinicopathological variables, were selected to devise SVM model. In testing set, we identified 63 patients with high risk to RLNM and 56 patients with low risk. The sensitivity, specificity and overall accuracy of SVM in predicting RLNM were 68.3%, 81.1% and 72.3%, respectively. Importantly, multivariate logistic regression analysis showed that SVM model was indeed an independent predictor of RLNM status (odds ratio, 11.536; 95% confidence interval, 4.113–32.361; P<0.0001). Our SVM-based model displayed moderately strong predictive power in defining the RLNM status in RC patients, providing an important approach to select RLNM high-risk subgroup for neoadjuvant chemoradiotherapy.
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