A Circulating miRNA Signature for Stratification of Breast Lesions among Women with Abnormal Screening Mammograms

Autor:	Chung Lie Oey, Ann Siew Gek Lee, Su Lin Jill Wong, Prabhakaran Munusamy, Jee Liang Thung, Geok Ling Koh, Choon Hua Thng, Sue Zann Lim, Mun Yew Patrick Chan, Sau Yeen Loke, Kong Wee Ong, Claire Hian Tzer Chan, Yirong Sim, Veronique Kiak Mien Tan, Bee Kiang Chong, Boon Kheng James Khoo, Ern Yu Tan, Wei Sean Yong, Teng Swan Juliana Ho, Preetha Madhukumar, Kiat Tee Benita Tan
Jazyk:	angličtina
Rok vydání:	2019
Předmět:	0301 basic medicine False discovery rate Oncology Cancer Research medicine.medical_specialty mammography detection Article 03 medical and health sciences Breast cancer screening liquid biopsies 0302 clinical medicine Breast cancer breast cancer stratification Internal medicine molecular diagnosis medicine Mammography skin and connective tissue diseases medicine.diagnostic_test Receiver operating characteristic business.industry Area under the curve Gold standard (test) medicine.disease blood-based test 030104 developmental biology 030220 oncology & carcinogenesis Multiple comparisons problem circulating microRNAs business
Zdroj:	Cancers Volume 11 Issue 12
ISSN:	2072-6694
DOI:	10.3390/cancers11121872
Popis:	Although mammography is the gold standard for breast cancer screening, the high rates of false-positive mammograms remain a concern. Thus, there is an unmet clinical need for a non-invasive and reliable test to differentiate between malignant and benign breast lesions in order to avoid subjecting patients with abnormal mammograms to unnecessary follow-up diagnostic procedures. Serum samples from 116 malignant breast lesions and 64 benign breast lesions were comprehensively profiled for 2,083 microRNAs (miRNAs) using next-generation sequencing. Of the 180 samples profiled, three outliers were removed based on the principal component analysis (PCA), and the remaining samples were divided into training (n = 125) and test (n = 52) sets at a 70:30 ratio for further analysis. In the training set, significantly differentially expressed miRNAs (adjusted p < 0.01) were identified after correcting for multiple testing using a false discovery rate. Subsequently, a predictive classification model using an eight-miRNA signature and a Bayesian logistic regression algorithm was developed. Based on the receiver operating characteristic (ROC) curve analysis in the test set, the model could achieve an area under the curve (AUC) of 0.9542. Together, this study demonstrates the potential use of circulating miRNAs as an adjunct test to stratify breast lesions in patients with abnormal screening mammograms.
Databáze:	OpenAIRE
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