Department of Anatomic Pathology, H. Lee Moffitt Cancer Center and Research Institute, 12902 Magnolia Dr., Tampa, FL, 33612, USA.
Department of Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA.
Dig Dis Sci. 2022 Feb;67(2):516-523. doi: 10.1007/s10620-021-06863-0. Epub 2021 Mar 13.
Progression of Barrett esophagus (BE) to esophageal adenocarcinoma occurs among a minority of BE patients. To date, BE behavior cannot be predicted on the basis of histologic features.
We compared BE samples that did not develop dysplasia or carcinoma upon follow-up of ≥ 7 years (BE nonprogressed [BEN]) with BE samples that developed carcinoma upon follow-up of 3 to 4 years (BE progressed [BEP]).
The NanoString nCounter miRNA assay was used to profile 24 biopsy samples of BE, including 13 BENs and 11 BEPs. Fifteen samples were randomly selected for miRNA prediction model training; nine were randomly selected for miRNA validation.
Unpaired t tests with Welch's correction were performed on 800 measured miRNAs to identify the most differentially expressed miRNAs for cases of BEN and BEP. The top 12 miRNAs (P < .003) were selected for principal component analyses: miR-1278, miR-1301, miR-1304-5p, miR-517b-3p, miR-584-5p, miR-599, miR-103a-3p, miR-1197, miR-1256, miR-509-3-5p, miR-544b, miR-802. The 12-miRNA signature was first self-validated on the training dataset, resulting in 7 out of the 7 BEP samples being classified as BEP (100% sensitivity) and 7 out of the 8 BEN samples being classified as BEN (87.5% specificity). Upon validation, 4 out of the 4 BEP samples were classified as BEP (100% sensitivity) and 4 out of the 5 BEN samples were classified as BEN (80% specificity). Twenty-four samples were evaluated, and 22 cases were correctly classified. Overall accuracy was 91.67%.
Using miRNA profiling, we have identified a 12-miRNA signature able to reliably differentiate cases of BEN from BEP.
巴雷特食管（BE）进展为食管腺癌仅发生在少数 BE 患者中。迄今为止，基于组织学特征无法预测 BE 的行为。
我们比较了随访时间≥7 年未发生异型增生或癌的 BE 样本（BE 未进展 [BEN]）与随访时间 3-4 年发生癌的 BE 样本（BE 进展 [BEP]）。
使用 NanoString nCounter miRNA 分析对 24 例 BE 活检样本进行分析，包括 13 例 BEN 和 11 例 BEP。随机选择 15 个样本进行 miRNA 预测模型训练；随机选择 9 个样本进行 miRNA 验证。
使用配对 t 检验（Welch 校正）对 800 个测量的 miRNA 进行分析，以确定 BEN 和 BEP 病例中差异表达最明显的 miRNA。选择前 12 个 miRNA（P<.003）进行主成分分析：miR-1278、miR-1301、miR-1304-5p、miR-517b-3p、miR-584-5p、miR-599、miR-103a-3p、miR-1197、miR-1256、miR-509-3-5p、miR-544b、miR-802。该 12-miRNA 特征首先在训练数据集中进行自我验证，结果 7 例 BEP 样本均被归类为 BEP（100%灵敏度），8 例 BEN 样本均被归类为 BEN（87.5%特异性）。验证时，4 例 BEP 样本均被归类为 BEP（100%灵敏度），5 例 BEN 样本均被归类为 BEN（80%特异性）。共评估了 24 个样本，22 个样本被正确分类。总体准确率为 91.67%。
使用 miRNA 分析，我们确定了一种能够可靠地区分 BEN 和 BEP 的 12-miRNA 特征。
