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Development of Prediction Model Including MicroRNA Expression for Sentinel Lymph Node Metastasis in ER-Positive and HER2-Negative Breast Cancer.
Ann Surg Oncol. 2020 Jun 24 [Online ahead of print]AS

Abstract

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.

Authors+Show Affiliations

Department of Breast and Endocrine Surgery, Osaka University Graduate School of Medicine, Suita City, Osaka, Japan.Department of Breast and Endocrine Surgery, Osaka University Graduate School of Medicine, Suita City, Osaka, Japan. t_miyake@onsurg.med.osaka-u.ac.jp.Department of Breast and Endocrine Surgery, Osaka University Graduate School of Medicine, Suita City, Osaka, Japan.Department of Breast and Endocrine Surgery, Osaka University Graduate School of Medicine, Suita City, Osaka, Japan.Department of Breast and Endocrine Surgery, Osaka University Graduate School of Medicine, Suita City, Osaka, Japan.Department of Breast and Endocrine Surgery, Osaka University Graduate School of Medicine, Suita City, Osaka, Japan.Department of Breast and Endocrine Surgery, Osaka University Graduate School of Medicine, Suita City, Osaka, Japan.Department of Breast and Endocrine Surgery, Osaka University Graduate School of Medicine, Suita City, Osaka, Japan.Department of Breast and Endocrine Surgery, Osaka University Graduate School of Medicine, Suita City, Osaka, Japan.Department of Breast and Endocrine Surgery, Osaka University Graduate School of Medicine, Suita City, Osaka, Japan.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

32583195

Citation

Okuno, Jun, et al. "Development of Prediction Model Including MicroRNA Expression for Sentinel Lymph Node Metastasis in ER-Positive and HER2-Negative Breast Cancer." Annals of Surgical Oncology, 2020.
Okuno J, Miyake T, Sota Y, et al. Development of Prediction Model Including MicroRNA Expression for Sentinel Lymph Node Metastasis in ER-Positive and HER2-Negative Breast Cancer. Ann Surg Oncol. 2020.
Okuno, J., Miyake, T., Sota, Y., Tanei, T., Kagara, N., Naoi, Y., Shimoda, M., Shimazu, K., Kim, S. J., & Noguchi, S. (2020). Development of Prediction Model Including MicroRNA Expression for Sentinel Lymph Node Metastasis in ER-Positive and HER2-Negative Breast Cancer. Annals of Surgical Oncology. https://doi.org/10.1245/s10434-020-08735-9
Okuno J, et al. Development of Prediction Model Including MicroRNA Expression for Sentinel Lymph Node Metastasis in ER-Positive and HER2-Negative Breast Cancer. Ann Surg Oncol. 2020 Jun 24; PubMed PMID: 32583195.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Development of Prediction Model Including MicroRNA Expression for Sentinel Lymph Node Metastasis in ER-Positive and HER2-Negative Breast Cancer. AU - Okuno,Jun, AU - Miyake,Tomohiro, AU - Sota,Yoshiaki, AU - Tanei,Tomonori, AU - Kagara,Naofumi, AU - Naoi,Yasuto, AU - Shimoda,Masafumi, AU - Shimazu,Kenzo, AU - Kim,Seung Jin, AU - Noguchi,Shinzaburo, Y1 - 2020/06/24/ PY - 2019/12/12/received PY - 2020/6/26/entrez JF - Annals of surgical oncology JO - Ann. Surg. Oncol. N2 - 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. SN - 1534-4681 UR - https://www.unboundmedicine.com/medline/citation/32583195/Development_of_Prediction_Model_Including_MicroRNA_Expression_for_Sentinel_Lymph_Node_Metastasis_in_ER-Positive_and_HER2-Negative_Breast_Cancer L2 - https://dx.doi.org/10.1245/s10434-020-08735-9 DB - PRIME DP - Unbound Medicine ER -
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