A Direct Plasma miRNA Assay for Early Detection and Histological Classification of Lung Cancer
Name:
Publisher version
View Source
Access full-text PDFOpen Access
View Source
Check access options
Check access options
Date
2018Journal
Translational OncologyPublisher
Neoplasia Press, Inc.Type
Article
Metadata
Show full item recordAbstract
Cell-free microRNAs in plasma provide circulating biomarkers for lung cancer. Most techniques for analysis of miRNAs require a large plasma volume to purify a sufficient RNA yield followed by complicated downstream processing. Small differences in the multiple procedures often cause large analytical variations and poor diagnostic values of the plasma biomarkers. Here we investigate whether directly quantifying plasma miRNAs without RNA purification could diagnose lung cancer. FirePlex assay was directly applied to 20 ?l plasma of 56 lung cancer patients and 28 cancer free controls for quantifying 11 lung tumor-associated miRNAs. FirePlex assay is easier, less expensive and time-consuming for quantification of plasma miRNAs compared with conventional reverse transcription PCR with an equivalent analytic performance. From the lung tumor-associated miRNAs, a prediction model based on two miRNAs (miRs-205-5p and -210-3p) was developed, producing 78.6% sensitivity and 89.3% specificity for identifying lung cancer. The diagnostic value was independent of stage of lung tumor, and patients' age and sex (all P > 0.05). Furthermore, based on the same two miRNAs, additional prediction models were developed with 75.0% sensitivity and 89.3% specificity for diagnosis of lung squamous cell carcinoma, and 82.2% sensitivity and 89.3% specificity for lung adenocarcinoma. The direct plasma assay can improve the efficacy of miRNA assessment in a small plasma volume by reducing multiple procedure-associated analytical variables. The developed plasma miRNA biomarkers might be useful for the early detection and histological classification of lung cancer. Copyright 2018Sponsors
Grant support: This work was supported in part by NCI R21CA205746, VA Merit Award I01 CX000512, Award from the Geaton and JoAnn DeCesaris Family Foundation, UMD-UMB Research and Innovation Seed Grant, DoD-Idea Development Award, and Maryland Innovation Initiative (MII) Commercialization Program- Phase 1/2 Grant (F.J.)Identifier to cite or link to this item
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85047098017&doi=10.1016%2fj.tranon.2018.05.001&partnerID=40&md5=42b8d698a81dbab870f46c6d71f110bb; http://hdl.handle.net/10713/8882ae974a485f413a2113503eed53cd6c53
10.1016/j.tranon.2018.05.001