Journal of Cancer Research and Clinical Oncology

, Volume 140, Issue 1, pp 145–150

Digital PCR quantification of miRNAs in sputum for diagnosis of lung cancer

  • Ning Li
  • Jie Ma
  • Maria A. Guarnera
  • HongBin Fang
  • Ling Cai
  • Feng Jiang
Original Paper

DOI: 10.1007/s00432-013-1555-5

Cite this article as:
Li, N., Ma, J., Guarnera, M.A. et al. J Cancer Res Clin Oncol (2014) 140: 145. doi:10.1007/s00432-013-1555-5

Abstract

Purpose

MicroRNAs (miRNAs) play important roles in the initiation and progression of lung cancer. Measuring miRNA expression levels in sputum could provide a potential approach for the diagnosis of lung cancer. The emerging digital PCR is a straightforward technique for precise, direct, and absolute quantification of nucleic acids. The objective of the study was to investigate whether digital PCR could be used to quantify miRNAs in sputum for lung cancer diagnosis.

Methods

We first determined and compared dynamic ranges of digital PCR and conventional quantitative reverse transcriptase PCR (qRT-PCR) for miRNA quantification using RNA isolated from sputum of five healthy individuals. We then used digital PCR to quantify copy number of two lung cancer-associated miRNAs (miR-31 and miR-210) in 35 lung cancer patients and 40 cancer-free controls.

Results

Copy number of the miRNAs measured by digital PCR displayed a linear response to input cDNA amount in a twofold dilution series over seven orders of magnitude. miRNA quantification determined by digital PCR assay was in good agreement with that obtained from qRT-PCR analysis in sputum. Furthermore, combined quantification of miR-31 and miR-210 copy number by using digital PCR in sputum of the cases and controls provided 65.71 % sensitivity and 85.00 % specificity for lung cancer diagnosis.

Conclusion

As digital PCR becomes more established, it would be a robust tool for quantitative assessment of miRNA copy number in sputum for lung cancer diagnosis.

Keywords

Digital PCR miRNAs Sputum Diagnosis Lung cancer 

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Ning Li
    • 1
    • 3
  • Jie Ma
    • 1
  • Maria A. Guarnera
    • 1
  • HongBin Fang
    • 2
  • Ling Cai
    • 2
  • Feng Jiang
    • 1
  1. 1.Department of Pathology, The University of Maryland Greenebaum Cancer CenterUniversity of Maryland School of MedicineBaltimoreUSA
  2. 2.Division of Biostatistics, The University of Maryland Greenebaum Cancer CenterUniversity of Maryland School of MedicineBaltimoreUSA
  3. 3.School of Traditional Chinese Materia Medica, Key Laboratory of Structure-Based Drug Design and Discovery (Shenyang Pharmaceutical University), Ministry of EducationShenyangChina