RNA biomarkers in colorectal cancer

Bustin, Stephen A.; Murphy, Jamie

doi:10.1016/j.ymeth.2012.10.003

Cited by 24 publications

(21 citation statements)

References 151 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…These results demonstrate that a majority of researchers, and also reviewers and editors, ignore the MIQE guidelines and the reports highlighting the importance of the use of proper reference genes for normalization for the HepaRG cell line [e.g., Ceelen et al (2011) (6 )] and apparently are still not aware of the importance of an adequate normalization approach. Although highly worrisome, similar situations have been observed in other research fields (7,16,17 ).…”

Section: Literature Review and Discussionsupporting

confidence: 76%

“…One of the major issues in RT-qPCR is the choice of a proper normalization strategy, because RT-qPCR involves multiple processing steps that can induce technical variations (7 ). The use of internal reference genes for normalization is well established.…”

confidence: 99%

“…However, the expression profiles of commonly used reference genes are known to vary across different cell types, and also within one cell type when subjected to different experimental treatments (5,8,9,10,11 ). Therefore, it is important to validate reference genes used for normalization, documenting that their expression will remain constant under the experimental conditions designed for the target gene (4,5,7,8,9,11,12,13 ). In addition, as mentioned by Vandesompele et al (2002) (4 ), mRNA expression data are much more reliable and accurate when they are normalized by use of the geometric mean of a set of stably expressed reference genes.…”

confidence: 99%

See 2 more Smart Citations

Evaluation of Normalization Strategies Used in Real-time Quantitative PCR Experiments in HepaRG Cell Line Studies

Ceelen¹,

Craene

Spiegelaere

2014

Clinical Chemistry

View full text Add to dashboard Cite

BACKGROUND The HepaRG cell line is widely used as an alternative for primary human hepatocytes for numerous applications, including drug screening, and is progressively gaining importance as a human-relevant cell source. Consequently, increasing numbers of experiments are being performed with this cell line, including real-time quantitative PCR (RT-qPCR) experiments for gene expression studies. CONTENT When RT-qPCR experiments are performed, results are reliable only when attention is paid to several critical aspects, including a proper normalization strategy. Therefore, in 2011 we determined the most optimal reference genes for gene expression studies in the HepaRG cell system, according to the MIQE (Minimum Information for Publication of Quantitative Real-Time PCR Experiments) guidelines. This study additionally provided clear evidence that the use of a single reference gene [glyceraldehyde-3-phosphate dehydrogenase (GAPDH), ribosomal protein S18 (RPS18), or actin, beta (ACTB)] was insufficient for normalization in HepaRG cells. Our screening of relevant studies published after our study suggested that the findings of our study were completely ignored. SUMMARY In none of the 24 reviewed studies was a proper normalization method used. Only 1 reference gene was included for normalization in 21 out of the 24 reported studies we screened, with RPS18 and GAPDH used most frequently, followed by hypoxanthine phosphoribosyltransferase 1 (HPRT1), glutathione synthetase (GSS) (hGus), β-2 microglobin (B2M), and acidic ribosomal phosphoprotein P0 (36B4). For 2 studies the use of multiple reference genes (2 and 3) was reported, but these had not been prevalidated for expression stability in HepaRG cells. In 1 study, there was no evidence that any reference gene had been used. Current RT-qPCR gene expression studies in HepaRG cells are being performed without adequate consideration or evaluation of reference genes. Such studies can yield erroneous and biologically irrelevant results.

show abstract

Section: Literature Review and Discussionsupporting

confidence: 76%

confidence: 99%

See 1 more Smart Citation

Evaluation of Normalization Strategies Used in Real-time Quantitative PCR Experiments in HepaRG Cell Line Studies

Ceelen¹,

Craene

Spiegelaere

2014

Clinical Chemistry

View full text Add to dashboard Cite

show abstract

“…One of its most important uses involves the identification of RNA-based biomarkers, for example, those associated with environmentally induced diseases (1 ) or prognostic stratification and adjuvant therapy selection in cancer (2 ). Despite this central role, relatively few publications have explored the reliability and reproducibility of results and conclusions on the basis of the use of RT.…”

confidence: 99%

Variability of the Reverse Transcription Step: Practical Implications

et al. 2015

View full text Add to dashboard Cite

show abstract

“…More than 95% of people with colorectal cancer are estimated to benefit from curative surgery if diagnosed early (2), and screening programs have been implemented in many nations (3)(4)(5). Despite its proven effectiveness, colorectal cancer screening may have its downsides, e.g., due to investigation side effects, overdiagnosis, falsepositives, and psychologic distress (6).…”

Section: Introductionmentioning

confidence: 99%

Development and Validation of a Risk Score Predicting Risk of Colorectal Cancer

Steffen

MacInnis

Joshy

et al. 2014

Cancer Epidemiology, Biomarkers &Amp; Prevention

View full text Add to dashboard Cite

Background: Quantifying the risk of colorectal cancer for individuals is likely to be useful for health service provision. Our aim was to develop and externally validate a prediction model to predict 5-year colorectal cancer risk.Methods: We used proportional hazards regression to develop the model based on established personal and lifestyle colorectal cancer risk factors using data from 197,874 individuals from the 45 and Up Study, Australia. We subsequently validated the model using 24,233 participants from the Melbourne Collaborative Cohort Study (MCCS).Results: A total of 1,103 and 224 cases of colorectal cancer were diagnosed in the development and validation sample, respectively. Our model, which includes age, sex, BMI, prevalent diabetes, ever having undergone colorectal cancer screening, smoking, and alcohol intake, exhibited a discriminatory accuracy of 0.73 [95% confidence interval (CI), 0.72-0.75] and 0.70 (95% CI, 0.66-0.73) using the development and validation sample, respectively. Calibration was good for both study samples. Stratified models according to colorectal cancer screening history, that additionally included family history, showed discriminatory accuracies of 0.75 (0.73-0.76) and 0.70 (0.67-0.72) for unscreened and screened individuals of the development sample, respectively. In the validation sample, discrimination was 0.68 (0.64-0.73) and 0.72 (0.67-0.76), respectively. Conclusion: Our model exhibited adequate predictive performance that was maintained in the external population.Impact: The model may be useful to design more powerful cancer prevention trials. In the group of unscreened individuals, the model may be useful as a preselection tool for population-based screening programs. Cancer Epidemiol Biomarkers Prev; 23(11); 2543-52. Ó2014 AACR.

show abstract

RNA biomarkers in colorectal cancer

Cited by 24 publications

References 151 publications

Evaluation of Normalization Strategies Used in Real-time Quantitative PCR Experiments in HepaRG Cell Line Studies

Evaluation of Normalization Strategies Used in Real-time Quantitative PCR Experiments in HepaRG Cell Line Studies

Variability of the Reverse Transcription Step: Practical Implications

Development and Validation of a Risk Score Predicting Risk of Colorectal Cancer

Contact Info

Product

Resources

About