Validation of Housekeeping Genes for Gene Expression Analysis in Glioblastoma Using Quantitative Real-Time Polymerase Chain Reaction.
10.14791/btrt.2015.3.1.24
- Author:
Madhuri G S AITHAL
1
;
Narayanappa RAJESWARI
Author Information
1. Department of Biotechnology, Dayananda Sagar College of Engineering, Bangalore, India. nraja7@gmail.com
- Publication Type:Original Article
- Keywords:
Gene expression;
Glioblastoma;
Housekeeping genes;
Quantitative real-time PCR
- MeSH:
Brain;
Formaldehyde;
Gene Expression Profiling;
Gene Expression*;
Genes, Essential*;
Glioblastoma*;
Humans;
Hypoxanthine Phosphoribosyltransferase;
Paraffin;
Real-Time Polymerase Chain Reaction*;
RNA
- From:Brain Tumor Research and Treatment
2015;3(1):24-29
- CountryRepublic of Korea
- Language:English
-
Abstract:
BACKGROUND: Quantitative real-time polymerase chain reaction (qPCR) is the most reliable tool for gene expression studies. Selection of housekeeping genes (HKGs) that are having most stable expression is critical to carry out accurate gene expression profiling. There is no 'universal' HKG having stable expression in all kinds of tissues under all experimental conditions. METHODS: The present study aims to identify most appropriate HKGs for gene expression analysis in glioblastoma (GBM) samples. Based on literature survey, six most commonly used HKGs that are invariant in GBM were chosen. We performed qPCR using RNA from formalin fixed paraffin embedded GBM samples and normal brain samples to investigate the expression pattern of HPRT, GAPDH, TBP, B2M, B2M, RPL13A, and RN18S1 with different abundance. A simple Deltacycle threshold approach was employed to calculate the fold change. RESULTS: Our study shows that the expression of RPL13A and TBP were found to be most stable across all the samples and are thus suitable for gene expression analysis in human GBM. Except for TBP, none of the other conventionally used HKGs in GBM studies e.g., HPRT and GAPDH were found to be suitable as they showed variation in RNA expression. CONCLUSION: Validation of HKGs is therefore immensely specific for a particular experimental setup and is crucial in assessing any new setup.
