Over-expression of glyceraldehyde-3-phosphate dehydrogenase in human endometrial cancer.

Gynecologic cancers including cancers of the endometrium are a clinical problem (1-4). We mined published microarray data (5, 6) to discover genes associated with endometrial cancers by comparing transcriptomes of the normal endometrium and endometrial tumors from humans. We identified glyceraldehyde-3-phosphate dehydrogenase, encoded by GAPDH, as among the most differentially expressed genes, transcriptome-wide, in cancers of the endometrium. GAPDH was expressed at significantly higher levels in endometrial tumor tissues as compared to the endometrium. Importantly, in human endometrial cancer, primary tumor expression of GAPDH was correlated with recurrence-free survival in white patients with low mutational burden. GAPDH may be a molecule of interest in understanding the etiology or progression of human endometrial cancer. These data argue that GAPDH should not be utilized as an internal reference or normalization gene for quantitative real-time polymerase chain reaction measurement of target depletion in loss-of-function genetic analyses in human endometrial cancer.

Can Peripheral Blood-Derived Gene Expressions Characterize Individuals at Ultra-high Risk for Psychosis?

The ultra-high risk (UHR) state was originally conceived to identify individuals at imminent risk of developing psychosis. Although recent studies have suggested that most individuals designated UHR do not, they constitute a distinctive group, exhibiting cognitive and functional impairments alongside multiple psychiatric morbidities. UHR characterization using molecular markers may improve understanding, provide novel insight into pathophysiology, and perhaps improve psychosis prediction reliability. Whole-blood gene expressions from 56 UHR subjects and 28 healthy controls are checked for existence of a consistent gene expression profile (signature) underlying UHR, across a variety of normalization and heterogeneity-removal techniques, including simple log-conversion, quantile normalization, gene fuzzy scoring (GFS), and surrogate variable analysis. During functional analysis, consistent and reproducible identification of important genes depends largely on how data are normalized. Normalization techniques that address sample heterogeneity are superior. The best performer, the unsupervised GFS, produced a strong and concise 12-gene signature, enriched for psychosis-associated genes. Importantly, when applied on random subsets of data, classifiers built with GFS are “meaningful” in the sense that the classifier models built using genes selected after other forms of normalization do not outperform random ones, but GFS-derived classifiers do. Data normalization can present highly disparate interpretations on biological data. Comparative analysis has shown that GFS is efficient at preserving signals while eliminating noise. Using this, we demonstrate confidently that the UHR designation is well correlated with a distinct blood-based gene signature.

Validation of Reference Genes for Normalization Gene Expression in Reverse Transcription Quantitative PCR in Human Normal Thyroid and Goiter Tissue

Reverse transcription quantitative polymerase chain reaction (RT-qPCR) has been recognized as the most accurate method for quantifying mRNA transcripts, but normalization of samples is a prerequisite for correct data interpretation. So, this study aimed to evaluate the most stable reference gene for RT-qPCR in human normal thyroid and goiter tissues. Beta-actin (ACTB); glyceraldehyde-3-phosphate dehydrogenase (GAPDH); succinate dehydrogenase, subunit A, flavoprotein (Fp) (SDHA); hypoxanthine phosphoribosyltransferase I (HPRTI); tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein, zeta polypeptide (YWHAZ); and beta-2-microglobulin (B2M) were evaluated in 14 thyroid tissue samples (7 normal and 7 goiter tissues) by RT-qPCR. The mean Cq and the maximum fold change (MFC) and NormFinder software were used to assess the stability of the genes. As a result, ACTB gene was more stable than GAPDH, SDHA, HPRTI, YWHAZ, and B2M. In conclusion, ACTB could be used to normalize RT-qPCR data in normal thyroid and goiter tissues.

Selection of reference genes for quantitative real-time PCR evaluation of chronic erythropoietin treatment effect on the SH-SY5Y and PC12 cells

Abstract The quantitative real-time polymerase chain reaction (qPCR) is a sensitive technique for examining the influence of erythropoietin (Epo) on gene expression. A critical and fundamental step for data analysis is the selection of and normalization to the optimal reference gene(s). We identified appropriate reference gene(s) among 32 genes during chronic recombinant human Epo (rHuEpo) treatment of SH-SY5Y cells using TaqMan human Express Endogenous Control Plate. Expression stability of the selected reference gene (RPLP) was retested with qPCR, together with two commonly used reference genes (GAPDH, ACTB) and six genes of interest (EPOR, EPO, STAT5B, STAT5A, JUN, AKT). In PC12 cells, three commonly used reference genes (Gapdh, CycA and Ywhaz) and seven genes of interest (EpoR, Epo, Stat5b, Stat5a, Jun, Akt, Fos) were evaluated. For the evaluation of expression stability, geNorm, NormFinder and BestKeeper software were used. All three gave similar results. We demonstrated that among the housekeeping genes, RPLP in SH-SY5Y and CycA and Ywhaz in PC12 are the most stable genes. Additionally, we showed that normalization with GAPDH gave misleading results compared to normalization with geNorm. In conclusion, selection of the appropriate normalization gene(s) is crucial for correct interpretation of rHuEpo treatment results. Graphical abstract