Validation of Reference Genes for Normalization of Relative qRT-PCR Studies in Papillary Thyroid Carcinoma

There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

Abstract

Quantitative reverse transcription polymerase chain reaction (qRT-PCR) in thyroid tumors require accurate data normalization, however, there are no sufficient studies addressing the suitable reference genes for gene expression analysis in malignant and normal thyroid tissue specimens. The purpose of this study was to identify valid internal control genes for normalization of relative qRT-PCR studies in human papillary thyroid carcinoma tissue samples. The expression characteristics of 12 candidate reference genes (GAPDH, ACTB, HPRT1, TBP, B2M, PPIA, 18SrRNA, HMBS, GUSB, PGK1, RPLP0, and PGM1) were assessed by qRT-PCR in 45 thyroid tissue samples (15 papillary thyroid carcinoma, 15 paired normal tissues and 15 multinodular goiters). These twelve candidate reference genes were selected by a systematic literature search. GeNorm, NormFinder, and BestKeeper statistical algorithms were applied to determine the most stable reference genes. The three algorithms were in agreement in identifying GUSB and HPRT1 as the most stably expressed genes in all thyroid tumors investigated. According to the NormFinder software, the pair of genes including ‘GUSB and HPRT1’ or ‘GUSB and HMBS’ or ‘GUSB and PGM1’ were the best combinations for selection of pair reference genes. The optimal number of genes required for reliable normalization of qPCR data in thyroid tissues would be three according to calculations made by GeNorm algorithm. These results suggest that GUSB and HPRT1 are promising reference genes for normalization of relative qRT-PCR studies in papillary thyroid carcinoma.

Related collections

Most cited references 49

Record: found
Abstract: found
Article: not found

Reference genes in real-time PCR

Bartłomiej Kozera, Marcin Rapacz (2013)

This paper aims to discuss various aspects of the use of reference genes in qPCR technique used in the thousands of present studies. Most frequently, these are housekeeping genes and they must meet several criteria so that they can lay claim to the name. Lots of papers report that in different conditions, for different organisms and even tissues the basic assumption—the constant level of the expression is not maintained for many genes that seem to be perfect candidates. Moreover, their transcription can not be affected by experimental factors. Sounds simple and clear but a great number of designed protocols and lack of consistency among them brings confusion on how to perform experiment properly. Since during selection of the most stable normalizing gene we can not use any reference gene, different ways and algorithms for their selection were developed. Such methods, including examples of best normalizing genes in some specific cases and possible mistakes are presented based on available sources. Numerous examples of reference genes applications, which are usually in too few numbers in relevant articles not allowing to make a solid fundament for a reader, will be shown along with instructive compilations to make an evidence for presented statements and an arrangement of future qPCR experiments. To include all the pitfalls and problems associated with the normalization methods there is no way not to begin from sample preparation and its storage going through candidate gene selection, primer design and statistical analysis. This is important because numerous short reviews available cover the topic only in lesser extent at the same time giving the reader false conviction of complete topic recognition.

0 comments Cited 324 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: not found

Gene expression studies in prostate cancer tissue: which reference gene should be selected for normalization?

Falk Ohl, Monika Jung, Chuanliang Xu … (2005)

Using quantitative reverse transcription-polymerase chain reaction (RT-PCR), reference genes are utilized as endogenous controls for relative quantification of target genes in gene profiling studies. The suitability of housekeeping genes for that purpose in prostate cancer tissue has not been sufficiently investigated so far. The objective of this study was to select from a panel of 16 potential candidate reference genes the most stable genes for gene normalization. Expression of mRNA encoding ACTB, ALAS1, ALB, B2M, G6PD, GAPD, HMBS, HPRT1, K-ALPHA-1, POLR2A, PPIA, RPL13A, SDHA, TBP, UBC, and YWHAZ was examined in matched, microdissected malignant and nonmalignant tissue specimens obtained from 17 nontreated prostate carcinomas after radical prostatectomy by real-time RT-PCR. The genes studied displayed a wide expression range with cycle threshold values between 16 and 37. The expression was not different between samples from pT2 and pT3 tumors or between samples with Gleason scores or=7 (P>0.05). ACTB, RPL13A, and HMBS showed significant differences (P<0.02 at least) in expressions between malignant and nonmalignant pairs. All other genes did not differ between the matched pairs, and the software programs geNorm and NormFinder were used to ascertain the most suitable reference genes from these candidates. HPRT1, ALAS1, and K-ALPHA-1 were calculated by both programs to be the most stable genes covering a broad range of expression. The expression of the target gene RECK normalized with HRPT1 alone and with the normalization factors generated by the combination of these three reference genes as well as with the unstable genes ACTB or RPL13A is given. That example shows the significance of using suitable reference genes to avoid erroneous normalizations in gene profiling studies for prostate cancer. The use of HPRT1 alone as a reference gene shown in our study was sufficient, but the normalization factors generated from two (HRPT1, ALAS1) or all three genes (HRPT1, ALAS1, K-ALPHA-1) should be considered for an improved reliability of normalization in gene profiling studies of prostate cancer.

0 comments Cited 80 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: not found

The clinical and economic burden of a sustained increase in thyroid cancer incidence.

Briseis Aschebrook-Kilfoy, Rebecca B Schechter, Ya-Chen Tina Shih … (2013)

Thyroid cancer incidence is increasing worldwide at an alarming rate, yet little is known of the impact this increase will have on society. We sought to determine the clinical and economic burden of a sustained increase in thyroid cancer incidence in the United States and to understand how these burdens correlate with the National Cancer Institute's (NCI) prioritization of thyroid cancer research funding. We used the NCI's SEER 13 database (1992-2009) and Joinpoint regression software to identify the current clinical burden of thyroid cancer and to project future incidence through 2019. We combined Medicare reimbursement rates with American Thyroid Association guidelines, and our clinical practice to create an economic model of thyroid cancer. We obtained research-funding data from the NCI's Office of Budget and Finance. RESULTS; By 2019, papillary thyroid cancer will double in incidence and become the third most common cancer in women of all ages at a cost of $18 to $21 billion dollars in the United States. Despite these substantial clinical and economic burdens, thyroid cancer research remains significantly underfunded by comparison, and in 2009 received only $14.7 million (ranked 30th) from the NCI. The impact of thyroid cancer on society has been significantly underappreciated, as is evidenced by its low priority in national research funding levels. Increased awareness in the medical community and the general public of the societal burden of thyroid cancer, and substantial increases in research on thyroid cancer etiology, prevention, and treatment are needed to offset these growing concerns.

0 comments Cited 71 times – based on 0 reviews      Review now

Bookmark

All references

Author and article information

Contributors

Mehdi Hedayati:

ORCID: http://orcid.org/0000-0001-5816-775X

hedayati47@gmail.com

Journal

Journal ID (nlm-ta): Sci Rep

Journal ID (iso-abbrev): Sci Rep

Title: Scientific Reports

Publisher: Nature Publishing Group UK (London )

ISSN (Electronic): 2045-2322

Publication date (Electronic): 23 October 2019

Publication date PMC-release: 23 October 2019

Publication date Collection: 2019

Volume: 9

Electronic Location Identifier: 15241

Affiliations

[1 ]GRID grid.411600.2, Cellular and Molecular Endocrine Research Center, Research Institute for Endocrine Sciences, , Shahid Beheshti University of Medical Sciences, ; Tehran, Iran

[2 ]Department of Research and Development (R&D), Saeed Pathobiology & Genetics Laboratory, Tehran, Iran

[3 ]ISNI 0000 0001 0706 2472, GRID grid.411463.5, Department of Biology, Faculty of Basic Sciences, Science and Research Branch, , Islamic Azad University, ; Tehran, Iran

[4 ]ISNI 0000 0004 4911 7066, GRID grid.411746.1, Cancer Control Research Center, Cancer Control Foundation, , Iran University of Medical Sciences, ; Tehran, Iran

[5 ]ISNI 0000 0001 0166 0922, GRID grid.411705.6, Department of Medical Genetics, School of Medicine, , Tehran University of Medical Sciences, ; Tehran, Iran

[6 ]ISNI 0000 0001 0166 0922, GRID grid.411705.6, Department of Surgery, Shariati Hospital, , School of Medicine, Tehran University of Medical Sciences, ; Tehran, Iran

[7 ]ISNI 0000 0001 0166 0922, GRID grid.411705.6, Department of Pathology, Shariati Hospital, School of Medicine, , Tehran University of Medical Sciences, ; Tehran, Iran

[8 ]GRID grid.411600.2, Endocrine Physiology Research Center, Research Institute for Endocrine Sciences, , Shahid Beheshti University of Medical Sciences, ; Tehran, Iran

Author information

Mehdi Hedayati http://orcid.org/0000-0001-5816-775X

Article

Publisher ID: 49247

DOI: 10.1038/s41598-019-49247-1

PMC ID: 6811563

SO-VID: 9968b33f-f079-489f-b347-22082e9949a1

License:

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

History

Date received : 16 January 2019

Date accepted : 19 August 2019

Funding

Funded by: FundRef https://doi.org/10.13039/501100007427, SBUMS | Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences (Research Institute for Endocrine Sciences);

Custom metadata

ScienceOpen disciplines: Uncategorized

Keywords: thyroid cancer,gene expression

Data availability:

ScienceOpen disciplines: Uncategorized

Keywords: thyroid cancer, gene expression

Comments

Comment on this article

scite_

Cited by 14

See all cited by

Most referenced authors 835

See all reference authors

Validation of Reference Genes for Normalization of Relative qRT-PCR Studies in Papillary Thyroid Carcinoma

Read this article at

Abstract

Related collections

Measurement of Glucocorticoid Receptor Signaling in Major Depression

Most cited references 49

Reference genes in real-time PCR

Gene expression studies in prostate cancer tissue: which reference gene should be selected for normalization?

The clinical and economic burden of a sustained increase in thyroid cancer incidence.

Author and article information

Contributors

Journal

Affiliations

Author information

Article

History

Funding

Categories

Custom metadata

Comments

Comment on this article

Similar content 337

Cited by 14

Most referenced authors 835