Wen Jing, Luo Kongjia, Liu Hui, Liu Shiliang, Lin Guangrong, Hu Yi, Zhang Xu, Wang Geng, Chen Yuping, Chen Zhijian, Li Yi, Lin Ting, Xie Xiuying, Liu Mengzhong, Wang Huiyun, Yang Hong, Fu Jianhua
*State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China †Guangdong Esophageal Cancer Institute Guangzhou, China ‡Department of Thoracic Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China §Department of Radiotherapy, Sun Yat-sen University Cancer Center, Guangzhou, China ¶Guangzhou Haige Communications Group Incorporated Company, Guangzhou, China ||School of Electronic & Information Engineering, South China University of Technology, Guangzhou, China **Department of Thoracic Surgery, Cancer Hospital of Shantou University Medical College, Shantou, China ††Department of Radiotherapy, Cancer Hospital of Shantou University Medical College, Shantou, China ‡‡Department of Anesthesiology, Sun Yat-sen University Cancer Center, Guangzhou, China.
Ann Surg. 2016 May;263(5):942-8. doi: 10.1097/SLA.0000000000001489.
To identify miRNA markers useful for esophageal squamous cell carcinoma (ESCC) neoadjuvant chemoradiotherapy (neo-CRT) response prediction.
Neo-CRT followed by surgery improves ESCC patients' survival compared with surgery alone. However, CRT outcomes are heterogeneous, and no current methods can predict CRT responses.
Differentially expressed miRNAs between ESCC pathological responders and nonresponders after neo-CRT were identified by miRNA profiling and verified by real-time quantitative polymerase chain reaction (qPCR) of 27 ESCCs in the training set. Several class prediction algorithms were used to build the response-classifying models with the qPCR data. Predictive powers of the models were further assessed with a second set of 79 ESCCs.
Ten miRNAs with greater than a 1.5-fold change between pathological responders and nonresponders were identified and verified, respectively. A support vector machine (SVM) prediction model, composed of 4 miRNAs (miR-145-5p, miR-152, miR-193b-3p, and miR-376a-3p), were developed. It provided overall accuracies of 100% and 87.3% for discriminating pathological responders and nonresponders in the training and external validation sets, respectively. In multivariate analysis, the subgroup determined by the SVM model was the only independent factor significantly associated with neo-CRT response in the external validation sets.
Combined qPCR of the 4 miRNAs provides the possibility of ESCC neo-CRT response prediction, which may facilitate individualized ESCC treatment. Further prospective validation in larger independent cohorts is necessary to fully assess its predictive power.
确定可用于预测食管鳞状细胞癌（ESCC）新辅助放化疗（neo-CRT）疗效的微小RNA（miRNA）标志物。
与单纯手术相比，neo-CRT后再行手术可提高ESCC患者的生存率。然而，CRT的疗效存在异质性，目前尚无方法可预测CRT疗效。
通过miRNA谱分析鉴定neo-CRT后ESCC病理反应者和无反应者之间差异表达的miRNA，并在训练集中对27例ESCC进行实时定量聚合酶链反应（qPCR）验证。使用几种分类预测算法，根据qPCR数据建立反应分类模型。用另一组79例ESCC进一步评估模型的预测能力。
分别鉴定并验证了病理反应者和无反应者之间变化大于1.5倍的10种miRNA。开发了一种由4种miRNA（miR-145-5p、miR-152、miR-193b-3p和miR-376a-3p）组成的支持向量机（SVM）预测模型。在训练集和外部验证集中，该模型区分病理反应者和无反应者的总体准确率分别为100%和87.3%。在多变量分析中，SVM模型确定的亚组是外部验证集中与neo-CRT疗效显著相关的唯一独立因素。
联合检测这4种miRNA的qPCR为预测ESCC的neo-CRT疗效提供了可能，这可能有助于ESCC的个体化治疗。需要在更大的独立队列中进行进一步的前瞻性验证，以充分评估其预测能力。
