Prostate cancer early diagnosis: circulating microRNA pairs potentially beyond single microRNAs upon 1231 serum samples

Author:

Liu Hua-Ping1,Lai Hung-Ming2,Guo Zheng2ORCID

Affiliation:

1. Bioinformatics at Harbin Medical University

2. King's College London

Abstract

Abstract The accuracy of prostate-specific antigen or clinical examination in prostate cancer (PCa) screening is in question, and circulating microRNAs (miRNAs) can be alternatives to PCa diagnosis. However, recent circulating miRNA biomarkers either are identified upon small sample sizes or cannot have robust diagnostic performance in every aspect of performance indicators. These may decrease applicability of potential biomarkers for the early detection of PCa. We reviewed recent studies on blood-derived miRNAs for prostate cancer diagnosis and carried out a large case study to understand whether circulating miRNA pairs, rather than single circulating miRNAs, could contribute to a more robust diagnostic model to significantly improve PCa diagnosis. We used 1231 high-throughput miRNA-profiled serum samples from two cohorts to design and verify a model based on class separability miRNA pairs (cs-miRPs). The pairwise model was composed of five circulating miRNAs coupled to miR-5100 and miR-1290 (i.e. five miRNA pairs, 5-cs-miRPs), reaching approximately 99% diagnostic performance in almost all indicators (sensitivity = 98.96%, specificity = 100%, accuracy = 99.17%, PPV = 100%, NPV = 96.15%) shown by a test set (n = 484: PCa = 384, negative prostate biopsies = 100). The nearly 99% diagnostic performance was also verified by an additional validation set (n = 140: PCa = 40, healthy controls = 100). Overall, the 5-cs-miRP model had 1 false positive and 7 false negatives among the 1231 serum samples and was superior to a recent 2-miRNA model (so far the best for PCa diagnosis) with 18 false positives and 80 false negatives. The present large case study demonstrated that circulating miRNA pairs could potentially bring more benefits to PCa early diagnosis for clinical practice.

Funder

National Natural Science Foundation of China

Publisher

Oxford University Press (OUP)

Subject

Molecular Biology,Information Systems

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