Dataset Information

Unbiased RNA-Seq-driven identification and validation of reference genes for quantitative RT-PCR analyses of pooled cancer exosomes.

ABSTRACT:

Background

Exosomes are extracellular vesicles (EVs) derived from endocytic compartments of eukaryotic cells which contain various biomolecules like mRNAs or miRNAs. Exosomes influence the biologic behaviour and progression of malignancies and are promising candidates as non-invasive diagnostic biomarkers or as targets for therapeutic interventions. Usually, quantitative real-time polymerase chain reaction (qRT-PCR) is used to assess gene expression in cancer exosomes, however, the ideal reference genes for normalization yet remain to be identified.

Results

In this study, we performed an unbiased analysis of high-throughput mRNA and miRNA-sequencing data from exosomes of patients with various cancer types and identify candidate reference genes and miRNAs in cancer exosomes. The expression stability of these candidate reference genes was evaluated by the coefficient of variation "CV" and the average expression stability value "M". We subsequently validated these candidate reference genes in exosomes from an independent cohort of ovarian cancer patients and healthy control individuals by qRT-PCR.

Conclusions

Our study identifies OAZ1 and hsa-miR-6835-3p as the most reliable individual reference genes for mRNA and miRNA quantification, respectively. For superior accuracy, we recommend the use of a combination of reference genes - OAZ1/SERF2/MPP1 for mRNA and hsa-miR-6835-3p/hsa-miR-4468-3p for miRNA analyses.

SUBMITTER: Dai Y

PROVIDER: S-EPMC7789813 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

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Publications

Unbiased RNA-Seq-driven identification and validation of reference genes for quantitative RT-PCR analyses of pooled cancer exosomes.

Dai Yao Y Cao Yumeng Y Köhler Jens J Lu Aiping A Xu Shaohua S Wang Haiyun H

BMC genomics 20210106 1

<h4>Background</h4>Exosomes are extracellular vesicles (EVs) derived from endocytic compartments of eukaryotic cells which contain various biomolecules like mRNAs or miRNAs. Exosomes influence the biologic behaviour and progression of malignancies and are promising candidates as non-invasive diagnostic biomarkers or as targets for therapeutic interventions. Usually, quantitative real-time polymerase chain reaction (qRT-PCR) is used to assess gene expression in cancer exosomes, however, the ideal ...[more]

PMID: 33407103

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Project description:Studies on the transcriptional control of gene expression are crucial to understand changes in organism's physiological or cellular conditions. To obtain reliable data on mRNA amounts and the estimation of gene expression levels, it is crucial to normalize the target gene with one or more internal reference gene(s). However, the use of constitutive genes as reference genes is controversial, as their expression patterns are sometimes more complex than previously thought. In various arthropod vectors, including ticks, several constitutive genes have been identified by studying gene expression in different tissues and life stages. The cattle tick Rhipicephalus microplus is a major vector for several pathogens and is widely distributed in tropical and subtropical regions globally. Tick developmental physiology is an essential aspect of research, particularly embryogenesis, where many important developmental events occur, thus the identification of stable reference genes is essential for the interpretation of reliable gene expression data. This study aimed to identify and select R. microplus housekeeping genes and evaluate their stability during embryogenesis. Reference genes used as internal control in molecular assays were selected based on previous studies. These genes were screened by quantitative PCR (qPCR) and tested for gene expression stability during embryogenesis. Results demonstrated that the relative stability of reference genes varied at different time points during the embryogenesis. The GeNorm tool showed that elongation factor 1α (Elf1a) and ribosomal protein L4 (Rpl4) were the most stable genes, while H3 histone family 3A (Hist3A) and ribosomal protein S18 (RpS18) were the least stable. The NormFinder tool showed that Rpl4 was the most stable gene, while the ranking of Elf1a was intermediate in all tested conditions. The BestKeeper tool showed that Rpl4 and cyclophilin A (CycA) were the more and less stable genes, respectively. These data collectively demonstrate that Rpl4, Elf1a, and GAPDH are suitable internal controls for normalizing qPCR during R. microplus embryogenesis. These genes were consistently identified as the most stable in various analysis methods employed in this study. Thus, findings presented in this study offer valuable information for the study of gene expression during embryogenesis in R. microplus.

Project description:BackgroundNormalization in real-time qRT-PCR is necessary to compensate for experimental variation. A popular normalization strategy employs reference gene(s), which may introduce additional variability into normalized expression levels due to innate variation (between tissues, individuals, etc). To minimize this innate variability, multiple reference genes are used. Current methods of selecting reference genes make an assumption of independence in their innate variation. This assumption is not always justified, which may lead to selecting a suboptimal set of reference genes.ResultsWe propose a robust approach for selecting optimal subset(s) of reference genes with the smallest variance of the corresponding normalizing factors. The normalizing factor variance estimates are based on the estimated unstructured covariance matrix of all available candidate reference genes, adjusting for all possible correlations. Robustness is achieved through bootstrapping all candidate reference gene data and obtaining the bootstrap upper confidence limits for the variances of the log-transformed normalizing factors. The selection of the reference gene subset is optimized with respect to one of the following criteria: (A) to minimize the variability of the normalizing factor; (B) to minimize the number of reference genes with acceptable upper limit on variability of the normalizing factor, (C) to minimize the average rank of the variance of the normalizing factor. The proposed approach evaluates all gene subsets of various sizes rather than ranking individual reference genes by their stability, as in the previous work. In two publicly available data sets and one new data set, our approach identified subset(s) of reference genes with smaller empirical variance of the normalizing factor than in subsets identified using previously published methods. A small simulation study indicated an advantage of the proposed approach in terms of sensitivity to identify the true optimal reference subset in the presence of even modest, especially negative correlation among the candidate reference genes.ConclusionsThe proposed approach performs comprehensive and robust evaluation of the variability of normalizing factors based on all possible subsets of candidate reference genes. The results of this evaluation provide flexibility to choose from important criteria for selecting the optimal subset(s) of reference genes, unless one subset meets all the criteria. This approach identifies gene subset(s) with smaller variability of normalizing factors than current standard approaches, particularly if there is some nontrivial innate correlation among the candidate genes.

Project description:BACKGROUND: Usually the reference genes used in gene expression analysis have been chosen for their known or suspected housekeeping roles, however the variation observed in most of them hinders their effective use. The assessed lack of validated reference genes emphasizes the importance of a systematic study for their identification. For selecting candidate reference genes we have developed a simple in silico method based on the data publicly available in the wheat databases Unigene and TIGR. RESULTS: The expression stability of 32 genes was assessed by qRT-PCR using a set of cDNAs from 24 different plant samples, which included different tissues, developmental stages and temperature stresses. The selected sequences included 12 well-known HKGs representing different functional classes and 20 genes novel with reference to the normalization issue. The expression stability of the 32 candidate genes was tested by the computer programs geNorm and NormFinder using five different data-sets. Some discrepancies were detected in the ranking of the candidate reference genes, but there was substantial agreement between the groups of genes with the most and least stable expression. Three new identified reference genes appear more effective than the well-known and frequently used HKGs to normalize gene expression in wheat. Finally, the expression study of a gene encoding a PDI-like protein showed that its correct evaluation relies on the adoption of suitable normalization genes and can be negatively affected by the use of traditional HKGs with unstable expression, such as actin and alpha-tubulin. CONCLUSION: The present research represents the first wide screening aimed to the identification of reference genes and of the corresponding primer pairs specifically designed for gene expression studies in wheat, in particular for qRT-PCR analyses. Several of the new identified reference genes outperformed the traditional HKGs in terms of expression stability under all the tested conditions. The new reference genes will enable more accurate normalization and quantification of gene expression in wheat and will be helpful for designing primer pairs targeting orthologous genes in other plant species.

Dataset Information

Unbiased RNA-Seq-driven identification and validation of reference genes for quantitative RT-PCR analyses of pooled cancer exosomes.

Background

Results

Conclusions

Publications

Unbiased RNA-Seq-driven identification and validation of reference genes for quantitative RT-PCR analyses of pooled cancer exosomes.

Similar Datasets

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