Identification and Validation of Circulating MicroRNA Signatures for Breast Cancer Early Detection Based on Large Scale Tissue-Derived Data.
- Author:
Xiaokang YU
1
;
Jinsheng LIANG
;
Jiarui XU
;
Xingsong LI
;
Shan XING
;
Huilan LI
;
Wanli LIU
;
Dongdong LIU
;
Jianhua XU
;
Lizhen HUANG
;
Hongli DU
Author Information
- Publication Type:Original Article
- Keywords: Breast neoplasms; Data mining; Early detection of cancer; MicroRNAs; Tumor biomarkers
- MeSH: Area Under Curve; Biomarkers; Biomarkers, Tumor; Breast Neoplasms*; Breast*; Carcinogenesis; Data Mining; Early Detection of Cancer; Female; Genome; Humans; Methods; MicroRNAs*; Plasma; Prospective Studies; Real-Time Polymerase Chain Reaction; Reverse Transcription; Sensitivity and Specificity
- From:Journal of Breast Cancer 2018;21(4):363-370
- CountryRepublic of Korea
- Language:English
- Abstract: PURPOSE: Breast cancer is the most commonly occurring cancer among women worldwide, and therefore, improved approaches for its early detection are urgently needed. As microRNAs (miRNAs) are increasingly recognized as critical regulators in tumorigenesis and possess excellent stability in plasma, this study focused on using miRNAs to develop a method for identifying noninvasive biomarkers. METHODS: To discover critical candidates, differential expression analysis was performed on tissue-originated miRNA profiles of 409 early breast cancer patients and 87 healthy controls from The Cancer Genome Atlas database. We selected candidates from the differentially expressed miRNAs and then evaluated every possible molecular signature formed by the candidates. The best signature was validated in independent serum samples from 113 early breast cancer patients and 47 healthy controls using reverse transcription quantitative real-time polymerase chain reaction. RESULTS: The miRNA candidates in our method were revealed to be associated with breast cancer according to previous studies and showed potential as useful biomarkers. When validated in independent serum samples, the area under curve of the final miRNA signature (miR-21-3p, miR-21-5p, and miR-99a-5p) was 0.895. Diagnostic sensitivity and specificity were 97.9% and 73.5%, respectively. CONCLUSION: The present study established a novel and effective method to identify biomarkers for early breast cancer. And the method, is also suitable for other cancer types. Furthermore, a combination of three miRNAs was identified as a prospective biomarker for breast cancer early detection.
