Identification of Reference Genes across Physiological States for qRT-PCR through Microarray Meta-Analysis

Abstract

BackgroundThe accuracy of quantitative real-time PCR (qRT-PCR) is highly dependent on reliable reference gene(s). Some housekeeping genes which are commonly used for normalization are widely recognized as inappropriate in many experimental conditions. This study aimed to identify reference genes for clinical studies through microarray meta-analysis of human clinical samples.Methodology/Principal FindingsAfter uniform data preprocessing and data quality control, 4,804 Affymetrix HU-133A arrays performed by clinical samples were classified into four physiological states with 13 organ/tissue types. We identified a list of reference genes for each organ/tissue types which exhibited stable expression across physiological states. Furthermore, 102 genes identified as reference gene candidates in multiple organ/tissue types were selected for further analysis. These genes have been frequently identified as housekeeping genes in previous studies, and approximately 71% of them fall into Gene Expression (GO:0010467) category in Gene Ontology.Conclusions/SignificanceBased on microarray meta-analysis of human clinical sample arrays, we identified sets of reference gene candidates for various organ/tissue types and then examined the functions of these genes. Additionally, we found that many of the reference genes are functionally related to transcription, RNA processing and translation. According to our results, researchers could select single or multiple reference gene(s) for normalization of qRT-PCR in clinical studies.

Identification of reference genes for qRT-PCR in human lung squamous-cell carcinoma by RNA-Seq

“…Three of our six successfully screened genes belong to this family, and many genes in this set have been verified in other studies to be suitable reference genes [23][24][25]. We also found that the expression levels of certain ribosome protein-encoding genes, such as ribosomal protein S7 (RPS7), ribosomal protein S10 (RPS10), RPLP0, and ribosomal protein L21 (RPL21) varied widely, ranging from single digits to tens of thousands of RPKM value.…”

“…Moreover, the expression of GAPDH has been reported to be upregulated in many other disorders such as inflammation, diabetes, hypoxia, and some respiratory diseases, suggesting that it should only be cautiously used as a reference gene in studies of these diseases [36 -40]. Indeed, in a large-scale microarray metaanalysis, GAPDH was not recommended as a reference gene, except in heart or muscle [23].…”

“…Like RNA-Seq, microarray is capable of assaying genome-wide transcript expression levels in a single sample, and is frequently used in the screening of reference genes [23,46,47]. However, since microarray-based gene expression measurements are derived from nucleotide hybridization, which is prone to false signals arising from cross-hybridization, such data often require validation [48,49].…”

Quality Control of Expression Profiling Data

“…A common shortcoming of many previous attempts is that tissue specificity of the genes was reported [102][103][104][105][106][107][108][109][110][111][112], or avoided [113][114][115]. However, no attempts were made to actually use such data for quality control or evaluation of the expression data, or if they were, it was for cancer analysis within one tissue [116][117][118] or to study the expression of synonymous codons in plants [119].…”

Microarray Meta-Analysis: From Data to Expression to Biological Relationships

“…An integrative data analysis, a so-called meta-analysis, can serve as a remedy by combining information from independent but related studies in order to enhance the statistical power, reliability, and generalizability of results (Normand 1999;Ramasamy et al 2008). In addition to refining and validating hypotheses between analogous studies (Arasappan et al 2011;Griffith et al 2006;Grutzmann et al 2005;LaCroix-Fralish et al 2011;Parmigiani et al 2004;Rhodes et al 2002;Shen et al 2004;Smith et al 2008;Vierlinger et al 2011;Wang et al 2004), metaanalyses can be used to identify a meta-signature across related studies (Anders et al 2011;Daves et al 2011;Pihur et al 2008;Rhodes et al 2004); to address novel questions Cheng et al 2011;Wennmalm et al 2005); and/ or to infer co-expression patterns and gene function (Lee et al 2004;Stuart et al 2003;Wren 2009;Zhou et al 2005). Ultimately, metaanalyses can provide the opportunity to maximize the use of available data to help to uncover underlying biological mechanisms.…”