Development of Prediction Model Including MicroRNA Expression for Sentinel Lymph Node Metastasis in ER-Positive and HER2-Negative Breast Cancer
Development of Prediction Model Including MicroRNA Expression for Sentinel Lymph Node Metastasis in ER-Positive and HER2-Negative Breast Cancer
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DOI：
10.1245/s10434-020-08735-9
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发表时间：
2020-06-24
影响因子：
3.7
通讯作者：
Noguchi, Shinzaburo
中科院分区：
文献类型：
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作者：
Okuno, Jun;Miyake, Tomohiro;Noguchi, Shinzaburo
背景我们研究的目的是寻找与前哨淋巴结转移 (SLNM) 相关的 microRNA (miRNA)，并开发 ER 阳性和 HER2 阴性 (ER+/HER2-) 乳腺癌 SLNM 的预测模型。患者和方法 在本研究中，仅考虑 ER+/HER2- 原发性乳腺癌。 SLNM 相关 miRNA 的发现集包括 10 个有 SLNM 的肿瘤和 10 个没有 SLNM 的肿瘤。训练集和验证集均包含 100 个肿瘤。通过发现组中的 miRNA 微阵列以及训练组和验证组中的液滴数字 PCR 全面检查肿瘤中的 miRNA 表达。结果 在发现组中，发现 miR-98、miR-22 和 miR-223 与 SLNM 显着相关（P < 0.001，倍数变化 > 2.5）。在训练集中，我们使用 miR-98、肿瘤大小和淋巴血管侵袭（LVI）构建了高精度的 SLNM 预测模型（AUC，0.877）。该预测模型的准确性在验证集中得到了证实（AUC，0.883），并且优于传统的纪念斯隆凯特琳癌症中心列线图。原位杂交揭示了肿瘤细胞中 miR-98 表达的定位。结论 我们开发了一个由 miR-98、肿瘤大小和 LVI 组成的 SLNM 预测模型，在 ER+/HER2- 乳腺癌中具有高精度。该模型可能有助于确定该亚型的前哨淋巴结活检的适应症。
Background The aim of our study is to find microRNAs (miRNAs) associated with sentinel lymph node metastasis (SLNM) and to develop a prediction model for SLNM in ER-positive and HER2-negative (ER+/HER2-) breast cancer. Patients and Methods In the present study, only ER+/HER2- primary breast cancer was considered. The discovery set for SLNM-associated miRNAs included 10 tumors with and 10 tumors without SLNM. The training and validation sets both included 100 tumors. miRNA expression in tumors was examined comprehensively by miRNA microarray in the discovery set and by droplet digital PCR in the training and validation sets. Results In the discovery set, miR-98, miR-22, and miR-223 were found to be significantly (P < 0.001, fold-change > 2.5) associated with SLNM. In the training set, we constructed the prediction model for SLNM using miR-98, tumor size, and lymphovascular invasion (LVI) with high accuracy (AUC, 0.877). The accuracy of this prediction model was confirmed in the validation set (AUC, 0.883), and it outperformed the conventional Memorial Sloan Kettering Cancer Center nomogram. In situ hybridization revealed the localization of miR-98 expression in tumor cells. Conclusions We developed a prediction model consisting of miR-98, tumor size, and LVI for SLNM with high accuracy in ER+/HER2- breast cancer. This model might help decide the indication for SLN biopsy in this subtype.