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Quantification of distinct let‑7 microRNA family members by a modified stem‑loop RT‑qPCR.

Authors :
Wang, Yilin
Zhou, JianwEN
ChEN, Yanlian
Wang, Chunhua
Wu, Enyin
Fu, Liang
Xie, ChEN
Source :
Molecular Medicine Reports. Mar2018, Vol. 17 Issue 3, p3690-3696. 7p. 1 Diagram, 1 Chart, 4 Graphs.
Publication Year :
2018

Abstract

Lethal‑7 (let‑7) microRNA (miRNA) serves a pivotal role in a number of physiological processes and is associated with the occurrence and development of multiple disorders such as cancer. The present study aimed to use a newly developed stem‑loop strategy for reverse transcription‑quantitative polymerase chain reaction (RT‑qPCR) to distinguish let‑7 miRNA family members that differ by as little as a single nucleotide. For the miRNAs comprising 16 identical nucleotides at the 5'‑end, different stem‑loop RT primers were designed and used in RT‑qPCR to assess the expression profiles of a panel of let‑7 family member miRNAs in human glioblastoma U87 cells. Amplification efficiency was evaluated through correlation analysis between total RNA input and the quantification threshold values. Melting curve profiles were measured to estimate the amplification specificity of the improved stem‑loop RT‑qPCR compared with those of the poly(A)‑tailing method. In addition, the discrimination ability of the modified stem‑loop method was examined. Compared with poly(A) tailing, the modified stem‑loop RT method was able to specifically reverse transcribe the diverse let‑7 miRNA family members followed by accurate quantification, with a theoretical amplification efficiency of ~100%. This modified stem‑loop method was able to distinguish miRNAs with a single base difference. This innovative method may be used in the clinical detection of let‑7 expression levels in a variety of tumour samples, and may provide valuable data for disease diagnosis and prognostic evaluation. In addition, this method may offer a new avenue for developing particular stem‑loop approaches in measuring other miRNAs with little discrepancy. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
17912997
Volume :
17
Issue :
3
Database :
Academic Search Index
Journal :
Molecular Medicine Reports
Publication Type :
Academic Journal
Accession number :
127894553
Full Text :
https://doi.org/10.3892/mmr.2017.8297