Identification And Validation Of MicroRNA Signature For Breast Cancer Early Detection Based On Large Scale Profiling Data

As breast cancer is the most common invasive cancer in women worldwide,improved approaches for the early detection of breast cancer are urgently needed.This study focused on microRNAs(miRNAs),which possess critical regulatory ability and excellent stability in plasma,aiming to develop a method to identifying promising biomarkers for breast cancer early detection.In the present study,differential expression analysis was performed on a large scale early breast cancer miRNA expression profiles from The Cancer Genome Atlas database(miRNA-seq data of 2589 miRNAs of 409 early breast cancer patients and 87 healthy controls were included).20 miRNAs were selected as candidates under the criteria of data missing rate< 10%,p-value < 0.05,absolute fold-change > 3.5 and average expression level in healthy controls(mean_C)> 100.A novel diagnostic model was established and all combinations that can be defined from the 20 miRNA candidates were then evaluated using this model.Eventually,a signature of four miRNAs(miR-21-3p,miR-21-5p,miR-99a-5p and miR-10b-5p)was identified and further validated in independent serum samples from 113 early breast cancer patients and 47 healthy controls through RT-qPCR.Expression data of miR-10b-5p was not obtained due to primer issue.However,the signature consist of miR-21-5p,miR-21-3p,miR-99a-5p yielded an AUC of 0.895 in Receiver Operating Curve analysis,diagnostic sensitivity and specificity were 97.9% and 73.5%,respectively.Taken together,we established a novel and effective method to identify biomarkers for early breast cancer by utilizing high throughput miRNA expression data in the present study,which could be applied in any other cancer types for biomarker identification in the future.In addition,a prospective biomarker combination of three miRNAs for early breast cancer detection were identified through our method and successfully verified in Chinese clinical samples.