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DNA hypermethylation analysis in sputum for the diagnosis of lung cancer: training validation set approach

BACKGROUND: Lung cancer has the highest mortality of all cancers. The aim of this study was to examine DNA hypermethylation in sputum and validate its diagnostic accuracy for lung cancer. METHODS: DNA hypermethylation of RASSF1A, APC, cytoglobin, 3OST2, PRDM14, FAM19A4 and PHACTR3 was analysed in sp...

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Autores principales: Hubers, A J, Heideman, D A M, Burgers, S A, Herder, G J M, Sterk, P J, Rhodius, R J, Smit, H J, Krouwels, F, Welling, A, Witte, B I, Duin, S, Koning, R, Comans, E F I, Steenbergen, R D M, Postmus, P E, Meijer, G A, Snijders, P J F, Smit, E F, Thunnissen, E
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4366885/
https://www.ncbi.nlm.nih.gov/pubmed/25719833
http://dx.doi.org/10.1038/bjc.2014.636
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author Hubers, A J
Heideman, D A M
Burgers, S A
Herder, G J M
Sterk, P J
Rhodius, R J
Smit, H J
Krouwels, F
Welling, A
Witte, B I
Duin, S
Koning, R
Comans, E F I
Steenbergen, R D M
Postmus, P E
Meijer, G A
Snijders, P J F
Smit, E F
Thunnissen, E
author_facet Hubers, A J
Heideman, D A M
Burgers, S A
Herder, G J M
Sterk, P J
Rhodius, R J
Smit, H J
Krouwels, F
Welling, A
Witte, B I
Duin, S
Koning, R
Comans, E F I
Steenbergen, R D M
Postmus, P E
Meijer, G A
Snijders, P J F
Smit, E F
Thunnissen, E
author_sort Hubers, A J
collection PubMed
description BACKGROUND: Lung cancer has the highest mortality of all cancers. The aim of this study was to examine DNA hypermethylation in sputum and validate its diagnostic accuracy for lung cancer. METHODS: DNA hypermethylation of RASSF1A, APC, cytoglobin, 3OST2, PRDM14, FAM19A4 and PHACTR3 was analysed in sputum samples from symptomatic lung cancer patients and controls (learning set: 73 cases, 86 controls; validation set: 159 cases, 154 controls) by quantitative methylation-specific PCR. Three statistical models were used: (i) cutoff based on Youden's J index, (ii) cutoff based on fixed specificity per marker of 96% and (iii) risk classification of post-test probabilities. RESULTS: In the learning set, approach (i) showed that RASSF1A was best able to distinguish cases from controls (sensitivity 42.5%, specificity 96.5%). RASSF1A, 3OST2 and PRDM14 combined demonstrated a sensitivity of 82.2% with a specificity of 66.3%. Approach (ii) yielded a combination rule of RASSF1A, 3OST2 and PHACTR3 (sensitivity 67.1%, specificity 89.5%). The risk model (approach iii) distributed the cases over all risk categories. All methods displayed similar and consistent results in the validation set. CONCLUSIONS: Our findings underscore the impact of DNA methylation markers in symptomatic lung cancer diagnosis. RASSF1A is validated as diagnostic marker in lung cancer.
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spelling pubmed-43668852016-03-17 DNA hypermethylation analysis in sputum for the diagnosis of lung cancer: training validation set approach Hubers, A J Heideman, D A M Burgers, S A Herder, G J M Sterk, P J Rhodius, R J Smit, H J Krouwels, F Welling, A Witte, B I Duin, S Koning, R Comans, E F I Steenbergen, R D M Postmus, P E Meijer, G A Snijders, P J F Smit, E F Thunnissen, E Br J Cancer Molecular Diagnostics BACKGROUND: Lung cancer has the highest mortality of all cancers. The aim of this study was to examine DNA hypermethylation in sputum and validate its diagnostic accuracy for lung cancer. METHODS: DNA hypermethylation of RASSF1A, APC, cytoglobin, 3OST2, PRDM14, FAM19A4 and PHACTR3 was analysed in sputum samples from symptomatic lung cancer patients and controls (learning set: 73 cases, 86 controls; validation set: 159 cases, 154 controls) by quantitative methylation-specific PCR. Three statistical models were used: (i) cutoff based on Youden's J index, (ii) cutoff based on fixed specificity per marker of 96% and (iii) risk classification of post-test probabilities. RESULTS: In the learning set, approach (i) showed that RASSF1A was best able to distinguish cases from controls (sensitivity 42.5%, specificity 96.5%). RASSF1A, 3OST2 and PRDM14 combined demonstrated a sensitivity of 82.2% with a specificity of 66.3%. Approach (ii) yielded a combination rule of RASSF1A, 3OST2 and PHACTR3 (sensitivity 67.1%, specificity 89.5%). The risk model (approach iii) distributed the cases over all risk categories. All methods displayed similar and consistent results in the validation set. CONCLUSIONS: Our findings underscore the impact of DNA methylation markers in symptomatic lung cancer diagnosis. RASSF1A is validated as diagnostic marker in lung cancer. Nature Publishing Group 2015-03-17 2015-02-26 /pmc/articles/PMC4366885/ /pubmed/25719833 http://dx.doi.org/10.1038/bjc.2014.636 Text en Copyright © 2015 Cancer Research UK http://creativecommons.org/licenses/by-nc-sa/4.0/ From twelve months after its original publication, this work is licensed under the Creative Commons Attribution-NonCommercial-Share Alike 4.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-sa/4.0/
spellingShingle Molecular Diagnostics
Hubers, A J
Heideman, D A M
Burgers, S A
Herder, G J M
Sterk, P J
Rhodius, R J
Smit, H J
Krouwels, F
Welling, A
Witte, B I
Duin, S
Koning, R
Comans, E F I
Steenbergen, R D M
Postmus, P E
Meijer, G A
Snijders, P J F
Smit, E F
Thunnissen, E
DNA hypermethylation analysis in sputum for the diagnosis of lung cancer: training validation set approach
title DNA hypermethylation analysis in sputum for the diagnosis of lung cancer: training validation set approach
title_full DNA hypermethylation analysis in sputum for the diagnosis of lung cancer: training validation set approach
title_fullStr DNA hypermethylation analysis in sputum for the diagnosis of lung cancer: training validation set approach
title_full_unstemmed DNA hypermethylation analysis in sputum for the diagnosis of lung cancer: training validation set approach
title_short DNA hypermethylation analysis in sputum for the diagnosis of lung cancer: training validation set approach
title_sort dna hypermethylation analysis in sputum for the diagnosis of lung cancer: training validation set approach
topic Molecular Diagnostics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4366885/
https://www.ncbi.nlm.nih.gov/pubmed/25719833
http://dx.doi.org/10.1038/bjc.2014.636
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