J Breast Cancer.  2018 Dec;21(4):363-370. 10.4048/jbc.2018.21.e56.

Identification and Validation of Circulating MicroRNA Signatures for Breast Cancer Early Detection Based on Large Scale Tissue-Derived Data

• Yu X
• Liang J
• Xu J
• Li X
• Xing S
• Li H
• Liu W
• Liu D
• Xu J
• Huang L
• Du H

• KMID: 2429814
• DOI: http://doi.org/10.4048/jbc.2018.21.e56

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.

Keyword

Breast neoplasms; Data mining; Early detection of cancer; MicroRNAs; Tumor biomarkers

MeSH Terms

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
Biomarkers
Biomarkers, Tumor
MicroRNAs

Figure

• 

  Figure 1 Flow chart of the analysis design in the present study. The expression change-based method pipeline was described on the left and the random forest algorithm-based method on the right. Tissue profiles were used in discovery stage while independent serum profiles were used in validation stage. Intermediate results of the expression change-based method were compared with those of the random forest algorithm-based method [11] for evaluation purpose.miRNA=microRNA; qPCR=quantitative real-time polymerase chain reaction.

• 

  Figure 2 Comparison between three threshold defining methods. Signatures from the 20 microRNA (miRNA) candidates in the expression change-based method were grouped by the number of miRNAs, and the mean sensitivity and specificity were calculated respectively.

• 

  Figure 3 Expression levels of the three final microRNAs (miRNAs) in serum samples. The relative expression level of miRNAs was normalized to 2−ΔΔCq value and two-sided Student t-test was used to compare miRNA expression level.*p-value < 0.01.

• 

  Figure 4 Receiver operating characteristic curve of the final signature based on tissue data and independent serum data. The number of normal expressed microRNAs in signature was used as diagnostic index in this analysis.AUC=area under curve.

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