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Validation of Tuba1a as appropriate internal control for normalization of gene expression analysis during mouse lung development.

ABSTRACT: The expression ratio between the analysed gene and an internal control gene is the most widely used normalization method for quantitative RT-PCR (qRT-PCR) expression analysis. The ideal reference gene for a specific experiment is the one whose expression is not affected by the different experimental conditions tested. In this study, we validate the applicability of five commonly used reference genes during different stages of mouse lung development. The stability of expression of five different reference genes (Tuba1a, Actb Gapdh, Rn18S and Hist4h4) was calculated within five experimental groups using the statistical algorithm of geNorm software. Overall, Tuba1a showed the least variability in expression among the different stages of lung development, while Hist4h4 and Rn18S showed the maximum variability in their expression. Expression analysis of two lung specific markers, surfactant protein C (SftpC) and Clara cell-specific 10 kDA protein (Scgb1a1), normalized to each of the five reference genes tested here, confirmed our results and showed that incorrect reference gene choice can lead to artefacts. Moreover, a combination of two internal controls for normalization of expression analysis during lung development will increase the accuracy and reliability of results.

SUBMITTER: Mehta A

PROVIDER: S-EPMC4394432 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Validation of Tuba1a as appropriate internal control for normalization of gene expression analysis during mouse lung development.

Mehta Aditi A Dobersch Stephanie S Dammann Reinhard H RH Bellusci Saverio S Ilinskaya Olga N ON Braun Thomas T Barreto Guillermo G

International journal of molecular sciences 20150225 3

The expression ratio between the analysed gene and an internal control gene is the most widely used normalization method for quantitative RT-PCR (qRT-PCR) expression analysis. The ideal reference gene for a specific experiment is the one whose expression is not affected by the different experimental conditions tested. In this study, we validate the applicability of five commonly used reference genes during different stages of mouse lung development. The stability of expression of five different ...[more]

PMID: 25723738

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Project description:BackgroundLow-dose computed tomography screening has been proved to reduce lung cancer mortality, however, the issues of high false-positive rate and overdiagnosis remain unsolved. Risk prediction models for lung cancer that could accurately identify high-risk populations may help to increase efficiency. We thus sought to develop a risk prediction model for lung cancer incorporating epidemiological and metabolic markers in a Chinese population.MethodsDuring 2006 and 2015, a total of 122 497 people were observed prospectively for lung cancer incidence with the total person-years of 976 663. Stepwise multivariable-adjusted logistic regressions with Pentry = .15 and Pstay = .20 were conducted to select the candidate variables including demographics and metabolic markers such as high-sensitivity C-reactive protein (hsCRP) and low-density lipoprotein cholesterol (LDL-C) into the prediction model. We used the C-statistic to evaluate discrimination, and Hosmer-Lemeshow tests for calibration. Tenfold cross-validation was conducted for internal validation to assess the model's stability.ResultsA total of 984 lung cancer cases were identified during the follow-up. The epidemiological model including age, gender, smoking status, alcohol intake status, coal dust exposure status, and body mass index generated a C-statistic of 0.731. The full model additionally included hsCRP and LDL-C showed significantly better discrimination (C-statistic = 0.735, P = .033). In stratified analysis, the full model showed better predictive power in terms of C-statistic in younger participants (<50 years, 0.709), females (0.726), and former or current smokers (0.742). The model calibrated well across the deciles of predicted risk in both the overall population (PHL = .689) and all subgroups.ConclusionsWe developed and internally validated an easy-to-use risk prediction model for lung cancer among the Chinese population that could provide guidance for screening and surveillance.

Dataset Information

Validation of Tuba1a as appropriate internal control for normalization of gene expression analysis during mouse lung development.

Publications

Validation of Tuba1a as appropriate internal control for normalization of gene expression analysis during mouse lung development.

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