ABSTRACT
Routine tissue-specific reference genes are often used in expression studies, but target genes are not taken into account. Using the relative RT-qPCR approach, we evaluated the expression of three target genes. At the same time, meta-analyses were conducted in various ethnic groups, genders, and thyroid cancer subtypes. When eight common reference genes were examined, it was discovered that some of them not only lacked consistent expression but also had considerable expression variance. It is worth noting that while choosing a reference gene, the mean gene expression and its standard deviation should be carefully addressed. An equation was developed based on this, and it was used to perform statistical analysis on over 25,000 genes. According to the subtype of thyroid cancer and, of course, the target genes in this investigation, appropriate reference genes were proposed. The intuitive choice of GAPDH as a common reference gene caused a major shift in the quantitative expression data of target genes, inverting the relative expression values. As a result, choosing the appropriate reference gene(s) for quantification of transcription data, and especially for relative studies of the expression of target gene(s), is critical and should be carefully considered during the study design.
Subject(s)
Selection, Genetic , Thyroid Neoplasms , Female , Humans , Male , Real-Time Polymerase Chain Reaction , Reference Standards , Thyroid Neoplasms/geneticsABSTRACT
In RT-qPCR, accuracy requires multiple levels of standardization, but results could be obfuscated by human errors and technical limitations. Data normalization against suitable reference genes is critical, yet their observed expression can be confounded by pseudogenes. Eight reference genes were selected based on literature review and analysis of papillary thyroid carcinoma (PTC) microarray data. RNA extraction and cDNA synthesis were followed by RT-qPCR amplification in triplicate with exon-junction or intron-spanning primers. Several statistical analyses were applied using Microsoft Excel, NormFinder, and BestKeeper. In normal tissues, the least correlation of variation (CqCV%) and the lowest maximum fold change (MFC) were respectively recorded for PYCR1 and SYMPK. In PTC tissues, SYMPK had the lowest CqCV% (5.16%) and MFC (1.17). According to NormFinder, the best reference combination was SYMPK and ACTB (stability value = 0.209). BestKeeper suggested SYMPK as the best reference in both normal (r = 0.969) and PTC tissues (r = 0.958). SYMPK is suggested as the best reference gene for overcoming the pseudogene problem in RT-qPCR data normalization, with a stability value of 0.319.
