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一种用于术前预测食管鳞状细胞癌淋巴结转移的新型血清外泌体微小RNA列线图的开发

Development of a Novel Serum Exosomal MicroRNA Nomogram for the Preoperative Prediction of Lymph Node Metastasis in Esophageal Squamous Cell Carcinoma.

作者信息

Liu Tong, Du Lu-Tao, Wang Yun-Shan, Gao Shan-Yu, Li Juan, Li Pei-Long, Sun Zhao-Wei, Binang Helen, Wang Chuan-Xin

机构信息

Department of Clinical Laboratory, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China.

Shandong Engineering & Technology Research Center for Tumor Marker Detection, Jinan, China.

出版信息

Front Oncol. 2020 Oct 6;10:573501. doi: 10.3389/fonc.2020.573501. eCollection 2020.

Abstract

Preoperative prediction of lymph node (LN) metastasis is accepted as a crucial independent risk factor for treatment decision-making for esophageal squamous cell carcinoma (ESCC) patients. Our study aimed to establish a non-invasive nomogram to identify LN metastasis preoperatively in ESCC patients. Construction of the nomogram involved three sequential phases with independent patient cohorts. In the discovery phase ( = 20), LN metastasis-associated microRNAs (miRNAs) were selected from next-generation sequencing (NGS) assay of human ESCC serum exosome samples. In the training phase ( = 178), a nomogram that incorporated exosomal miRNA model and clinicopathologic was developed by multivariate logistic regression analysis to preoperatively predict LN status. In the validation phase ( = 188), we validated the predicted nomogram's calibration, discrimination, and clinical usefulness. Four differently expressed miRNAs (chr 8-23234-3p, chr 1-17695-5p, chr 8-2743-5p, and miR-432-5p) were tested and selected in the serum exosome samples from ESCC patients who have or do not have LN metastasis. Subsequently, an optimized four-exosomal miRNA model was constructed and validated in the clinical samples, which could effectively identify ESCC patients with LN metastasis, and was significantly superior to preoperative computed tomography (CT) report. In addition, a clinical nomogram consisting of the four-exosomal miRNA model and CT report was established in training cohort, which showed high predictive value in both training and validation cohorts [area under the receiver operating characteristic curve (AUC): 0.880 and 0.869, respectively]. The Hosmer-Lemeshow test and decision curve analysis implied the nomogram's clinical applicability. Our novel non-invasive nomogram is a robust prediction tool with promising clinical potential for preoperative LN metastasis prediction of ESCC patients, especially in T1 stage.

摘要

术前预测淋巴结(LN)转移被认为是食管鳞状细胞癌(ESCC)患者治疗决策的关键独立危险因素。我们的研究旨在建立一种非侵入性列线图,以术前识别ESCC患者的LN转移。列线图的构建包括三个连续阶段,使用独立的患者队列。在发现阶段(n = 20),从人ESCC血清外泌体样本的下一代测序(NGS)分析中选择与LN转移相关的微小RNA(miRNA)。在训练阶段(n = 178),通过多变量逻辑回归分析开发了一种纳入外泌体miRNA模型和临床病理特征的列线图,以术前预测LN状态。在验证阶段(n = 188),我们验证了预测列线图的校准、区分度和临床实用性。在有或无LN转移的ESCC患者的血清外泌体样本中检测并选择了四种差异表达的miRNA(chr 8-23234-3p、chr 1-17695-5p、chr 8-2743-5p和miR-432-5p)。随后,构建了优化的四外泌体miRNA模型并在临床样本中进行验证,该模型可有效识别有LN转移的ESCC患者,且显著优于术前计算机断层扫描(CT)报告。此外,在训练队列中建立了由四外泌体miRNA模型和CT报告组成的临床列线图,其在训练队列和验证队列中均显示出较高的预测价值[受试者操作特征曲线(AUC)下面积分别为0.880和0.869]。Hosmer-Lemeshow检验和决策曲线分析表明了列线图的临床适用性。我们新的非侵入性列线图是一种强大的预测工具,在术前预测ESCC患者LN转移方面具有良好的临床潜力,尤其是在T1期。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/336f/7573187/0ae8959923ef/fonc-10-573501-g0001.jpg

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