Pan-Cancer Analysis of TCGA Data Revealed Promising Reference Genes for qPCR Normalization

Abstract

Quantitative PCR (qPCR) remains the most widely used technique for gene expression evaluation. Obtaining reliable data using this method requires reference genes (RGs) with stable mRNA level under experimental conditions. This issue is especially crucial in cancer studies because each tumor has a unique molecular portrait. The Cancer Genome Atlas (TCGA) project provides RNA-Seq data for thousands of samples corresponding to dozens of cancers and presents the basis for assessment of the suitability of genes as reference ones for qPCR data normalization. Using TCGA RNA-Seq data and previously developed CrossHub tool, we evaluated mRNA level of 32 traditionally used RGs in 12 cancer types, including those of lung, breast, prostate, kidney, and colon. We developed an 11-component scoring system for the assessment of gene expression stability. Among the 32 genes, PUM1 was one of the most stably expressed in the majority of examined cancers, whereas GAPDH , which is widely used as a RG, showed significant mRNA level alterations in more than a half of cases. For each of 12 cancer types, we suggested a pair of genes that are the most suitable for use as reference ones. These genes are characterized by high expression stability and absence of correlation between their mRNA levels. Next, the scoring system was expanded with several features of a gene: mutation rate, number of transcript isoforms and pseudogenes, participation in cancer-related processes on the basis of Gene Ontology, and mentions in PubMed-indexed articles. All the genes covered by RNA-Seq data in TCGA were analyzed using the expanded scoring system that allowed us to reveal novel promising RGs for each examined cancer type and identify several “universal” pan-cancer RG candidates, including SF3A1, CIAO1 , and SFRS4 . The choice of RGs is the basis for precise gene expression evaluation by qPCR. Here, we suggested optimal pairs of traditionally used RGs for 12 cancer types and identified novel promising RGs that demonstrate high expression stability and other features of reliable and convenient RGs (high expression level, low mutation rate, non-involvement in cancer-related processes, single transcript isoform, and absence of pseudogenes).

Stable gene expression for normalisation and single-sample scoring

“…Our list outperforms other lists in the blood data generated by Schmiedel et al (39). Interestingly, control genes used in the NanoString nCounter® PanCancer Immune panel tend to be less stable in blood data than our stable genes and those proposed by Krasnov et al (20). Finally, the list by Lin et al (21) is the most stable in the label-free quantification proteomic dataset from the CPTAC project.…”

“…Alternatively, housekeeping genes that are assumed to be invariant across tissues due to their involvement in core cellular processes can be used as endogenous controls (19), however they have also been shown to vary across different tissues (9,15,20,21).…”

“…As such, using multiple datasets to identify stable genes would produce a more robust prioritisation. Additionally, Krasnov et al (20) proposed a cancer-specific set of stable genes but only used data from tumours to define their list. Most cancer-specific analyses are initially performed on cell lines and later translated to patients or patient derived xenografts.…”

“…As such, the list of stable genes we propose is relatively novel. Additionally, the set of reference genes commonly used across many gene expression panels (gathered by Krasnov et al (20)) have the little overlap with genes identified from data-centric approaches.…”

Development of a Bioinformatics Framework for Identification and Validation of Genomic Biomarkers and Key Immunopathology Processes and Controllers in Infectious and Non-infectious Severe Inflammatory Response Syndrome

“…A total of 11,846 probe sets were found with SRE > 1.0 (5,445 probe sets) and SRE <-1.0 (6,401 probe sets S4), demonstrating that overall these genes had higher expression in these two groups compared with any other group in the study. candidate housekeeping genes, including several commonly used entities e.g., GAPDH (52)(53)(54)(55)(56), were investigated in this study. The stability of these genes was ranked according to predictive power of the genes in stratifying adult sepsis from adult SIRS and their pvalues.…”