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Identification and validation of the methylation biomarkers of non-small cell lung cancer (NSCLC)
BACKGROUND: DNA methylation was suggested as the promising biomarker for lung cancer diagnosis. However, it is a great challenge to search for the optimal combination of methylation biomarkers to obtain maximum diagnostic performance. RESULTS: In this study, we developed a panel of DNA methylation b...
Autores principales: | , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4318209/ https://www.ncbi.nlm.nih.gov/pubmed/25657825 http://dx.doi.org/10.1186/s13148-014-0035-3 |
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author | Guo, Shicheng Yan, Fengyang Xu, Jibin Bao, Yang Zhu, Ji Wang, Xiaotian Wu, Junjie Li, Yi Pu, Weilin Liu, Yan Jiang, Zhengwen Ma, Yanyun Chen, Xiaofeng Xiong, Momiao Jin, Li Wang, Jiucun |
author_facet | Guo, Shicheng Yan, Fengyang Xu, Jibin Bao, Yang Zhu, Ji Wang, Xiaotian Wu, Junjie Li, Yi Pu, Weilin Liu, Yan Jiang, Zhengwen Ma, Yanyun Chen, Xiaofeng Xiong, Momiao Jin, Li Wang, Jiucun |
author_sort | Guo, Shicheng |
collection | PubMed |
description | BACKGROUND: DNA methylation was suggested as the promising biomarker for lung cancer diagnosis. However, it is a great challenge to search for the optimal combination of methylation biomarkers to obtain maximum diagnostic performance. RESULTS: In this study, we developed a panel of DNA methylation biomarkers and validated their diagnostic efficiency for non-small cell lung cancer (NSCLC) in a large Chinese Han NSCLC retrospective cohort. Three high-throughput DNA methylation microarray datasets (458 samples) were collected in the discovery stage. After normalization, batch effect elimination and integration, significantly differentially methylated genes and the best combination of the biomarkers were determined by the leave-one-out SVM (support vector machine) feature selection procedure. Then, candidate promoters were examined by the methylation status determined single nucleotide primer extension technique (MSD-SNuPET) in an independent set of 150 pairwise NSCLC/normal tissues. Four statistical models with fivefold cross-validation were used to evaluate the performance of the discriminatory algorithms. The sensitivity, specificity and accuracy were 86.3%, 95.7% and 91%, respectively, in Bayes tree model. The logistic regression model incorporated five gene methylation signatures at AGTR1, GALR1, SLC5A8, ZMYND10 and NTSR1, adjusted for age, sex and smoking, showed robust performances in which the sensitivity, specificity, accuracy, and area under the curve (AUC) were 78%, 97%, 87%, and 0.91, respectively. CONCLUSIONS: In summary, a high-throughput DNA methylation microarray dataset followed by batch effect elimination can be a good strategy to discover optimal DNA methylation diagnostic panels. Methylation profiles of AGTR1, GALR1, SLC5A8, ZMYND10 and NTSR1, could be an effective methylation-based assay for NSCLC diagnosis. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13148-014-0035-3) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-4318209 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-43182092015-02-06 Identification and validation of the methylation biomarkers of non-small cell lung cancer (NSCLC) Guo, Shicheng Yan, Fengyang Xu, Jibin Bao, Yang Zhu, Ji Wang, Xiaotian Wu, Junjie Li, Yi Pu, Weilin Liu, Yan Jiang, Zhengwen Ma, Yanyun Chen, Xiaofeng Xiong, Momiao Jin, Li Wang, Jiucun Clin Epigenetics Research BACKGROUND: DNA methylation was suggested as the promising biomarker for lung cancer diagnosis. However, it is a great challenge to search for the optimal combination of methylation biomarkers to obtain maximum diagnostic performance. RESULTS: In this study, we developed a panel of DNA methylation biomarkers and validated their diagnostic efficiency for non-small cell lung cancer (NSCLC) in a large Chinese Han NSCLC retrospective cohort. Three high-throughput DNA methylation microarray datasets (458 samples) were collected in the discovery stage. After normalization, batch effect elimination and integration, significantly differentially methylated genes and the best combination of the biomarkers were determined by the leave-one-out SVM (support vector machine) feature selection procedure. Then, candidate promoters were examined by the methylation status determined single nucleotide primer extension technique (MSD-SNuPET) in an independent set of 150 pairwise NSCLC/normal tissues. Four statistical models with fivefold cross-validation were used to evaluate the performance of the discriminatory algorithms. The sensitivity, specificity and accuracy were 86.3%, 95.7% and 91%, respectively, in Bayes tree model. The logistic regression model incorporated five gene methylation signatures at AGTR1, GALR1, SLC5A8, ZMYND10 and NTSR1, adjusted for age, sex and smoking, showed robust performances in which the sensitivity, specificity, accuracy, and area under the curve (AUC) were 78%, 97%, 87%, and 0.91, respectively. CONCLUSIONS: In summary, a high-throughput DNA methylation microarray dataset followed by batch effect elimination can be a good strategy to discover optimal DNA methylation diagnostic panels. Methylation profiles of AGTR1, GALR1, SLC5A8, ZMYND10 and NTSR1, could be an effective methylation-based assay for NSCLC diagnosis. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13148-014-0035-3) contains supplementary material, which is available to authorized users. BioMed Central 2015-01-22 /pmc/articles/PMC4318209/ /pubmed/25657825 http://dx.doi.org/10.1186/s13148-014-0035-3 Text en © Guo et al.; licensee BioMed Central. 2015 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Guo, Shicheng Yan, Fengyang Xu, Jibin Bao, Yang Zhu, Ji Wang, Xiaotian Wu, Junjie Li, Yi Pu, Weilin Liu, Yan Jiang, Zhengwen Ma, Yanyun Chen, Xiaofeng Xiong, Momiao Jin, Li Wang, Jiucun Identification and validation of the methylation biomarkers of non-small cell lung cancer (NSCLC) |
title | Identification and validation of the methylation biomarkers of non-small cell lung cancer (NSCLC) |
title_full | Identification and validation of the methylation biomarkers of non-small cell lung cancer (NSCLC) |
title_fullStr | Identification and validation of the methylation biomarkers of non-small cell lung cancer (NSCLC) |
title_full_unstemmed | Identification and validation of the methylation biomarkers of non-small cell lung cancer (NSCLC) |
title_short | Identification and validation of the methylation biomarkers of non-small cell lung cancer (NSCLC) |
title_sort | identification and validation of the methylation biomarkers of non-small cell lung cancer (nsclc) |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4318209/ https://www.ncbi.nlm.nih.gov/pubmed/25657825 http://dx.doi.org/10.1186/s13148-014-0035-3 |
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