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Identification of differentially methylated genes as diagnostic and prognostic biomarkers of breast cancer

BACKGROUND: Aberrant DNA methylation is significantly associated with breast cancer. METHODS: In this study, we aimed to determine novel methylation biomarkers using a bioinformatics analysis approach that could have clinical value for breast cancer diagnosis and prognosis. Firstly, differentially m...

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Autores principales: Mao, Xiao-hong, Ye, Qiang, Zhang, Guo-bing, Jiang, Jin-ying, Zhao, Hong-ying, Shao, Yan-fei, Ye, Zi-qi, Xuan, Zi-xue, Huang, Ping
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7839189/
https://www.ncbi.nlm.nih.gov/pubmed/33499882
http://dx.doi.org/10.1186/s12957-021-02124-6
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author Mao, Xiao-hong
Ye, Qiang
Zhang, Guo-bing
Jiang, Jin-ying
Zhao, Hong-ying
Shao, Yan-fei
Ye, Zi-qi
Xuan, Zi-xue
Huang, Ping
author_facet Mao, Xiao-hong
Ye, Qiang
Zhang, Guo-bing
Jiang, Jin-ying
Zhao, Hong-ying
Shao, Yan-fei
Ye, Zi-qi
Xuan, Zi-xue
Huang, Ping
author_sort Mao, Xiao-hong
collection PubMed
description BACKGROUND: Aberrant DNA methylation is significantly associated with breast cancer. METHODS: In this study, we aimed to determine novel methylation biomarkers using a bioinformatics analysis approach that could have clinical value for breast cancer diagnosis and prognosis. Firstly, differentially methylated DNA patterns were detected in breast cancer samples by comparing publicly available datasets (GSE72245 and GSE88883). Methylation levels in 7 selected methylation biomarkers were also estimated using the online tool UALCAN. Next, we evaluated the diagnostic value of these selected biomarkers in two independent cohorts, as well as in two mixed cohorts, through ROC curve analysis. Finally, prognostic value of the selected methylation biomarkers was evaluated breast cancer by the Kaplan-Meier plot analysis. RESULTS: In this study, a total of 23 significant differentially methylated sites, corresponding to 9 different genes, were identified in breast cancer datasets. Among the 9 identified genes, ADCY4, CPXM1, DNM3, GNG4, MAST1, mir129-2, PRDM14, and ZNF177 were hypermethylated. Importantly, individual value of each selected methylation gene was greater than 0.9, whereas predictive value for all genes combined was 0.9998. We also found the AUC for the combined signature of 7 genes (ADCY4, CPXM1, DNM3, GNG4, MAST1, PRDM14, ZNF177) was 0.9998 [95% CI 0.9994–1], and the AUC for the combined signature of 3 genes (MAST1, PRDM14, and ZNF177) was 0.9991 [95% CI 0.9976–1]. Results from additional validation analyses showed that MAST1, PRDM14, and ZNF177 had high sensitivity, specificity, and accuracy for breast cancer diagnosis. Lastly, patient survival analysis revealed that high expression of ADCY4, CPXM1, DNM3, PRDM14, PRKCB, and ZNF177 were significantly associated with better overall survival. CONCLUSIONS: Methylation pattern of MAST1, PRDM14, and ZNF177 may represent new diagnostic biomarkers for breast cancer, while methylation of ADCY4, CPXM1, DNM3, PRDM14, PRKCB, and ZNF177 may hold prognostic potential for breast cancer. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12957-021-02124-6.
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spelling pubmed-78391892021-01-27 Identification of differentially methylated genes as diagnostic and prognostic biomarkers of breast cancer Mao, Xiao-hong Ye, Qiang Zhang, Guo-bing Jiang, Jin-ying Zhao, Hong-ying Shao, Yan-fei Ye, Zi-qi Xuan, Zi-xue Huang, Ping World J Surg Oncol Research BACKGROUND: Aberrant DNA methylation is significantly associated with breast cancer. METHODS: In this study, we aimed to determine novel methylation biomarkers using a bioinformatics analysis approach that could have clinical value for breast cancer diagnosis and prognosis. Firstly, differentially methylated DNA patterns were detected in breast cancer samples by comparing publicly available datasets (GSE72245 and GSE88883). Methylation levels in 7 selected methylation biomarkers were also estimated using the online tool UALCAN. Next, we evaluated the diagnostic value of these selected biomarkers in two independent cohorts, as well as in two mixed cohorts, through ROC curve analysis. Finally, prognostic value of the selected methylation biomarkers was evaluated breast cancer by the Kaplan-Meier plot analysis. RESULTS: In this study, a total of 23 significant differentially methylated sites, corresponding to 9 different genes, were identified in breast cancer datasets. Among the 9 identified genes, ADCY4, CPXM1, DNM3, GNG4, MAST1, mir129-2, PRDM14, and ZNF177 were hypermethylated. Importantly, individual value of each selected methylation gene was greater than 0.9, whereas predictive value for all genes combined was 0.9998. We also found the AUC for the combined signature of 7 genes (ADCY4, CPXM1, DNM3, GNG4, MAST1, PRDM14, ZNF177) was 0.9998 [95% CI 0.9994–1], and the AUC for the combined signature of 3 genes (MAST1, PRDM14, and ZNF177) was 0.9991 [95% CI 0.9976–1]. Results from additional validation analyses showed that MAST1, PRDM14, and ZNF177 had high sensitivity, specificity, and accuracy for breast cancer diagnosis. Lastly, patient survival analysis revealed that high expression of ADCY4, CPXM1, DNM3, PRDM14, PRKCB, and ZNF177 were significantly associated with better overall survival. CONCLUSIONS: Methylation pattern of MAST1, PRDM14, and ZNF177 may represent new diagnostic biomarkers for breast cancer, while methylation of ADCY4, CPXM1, DNM3, PRDM14, PRKCB, and ZNF177 may hold prognostic potential for breast cancer. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12957-021-02124-6. BioMed Central 2021-01-26 /pmc/articles/PMC7839189/ /pubmed/33499882 http://dx.doi.org/10.1186/s12957-021-02124-6 Text en © The Author(s) 2021 Open AccessThis 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/. 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 in a credit line to the data.
spellingShingle Research
Mao, Xiao-hong
Ye, Qiang
Zhang, Guo-bing
Jiang, Jin-ying
Zhao, Hong-ying
Shao, Yan-fei
Ye, Zi-qi
Xuan, Zi-xue
Huang, Ping
Identification of differentially methylated genes as diagnostic and prognostic biomarkers of breast cancer
title Identification of differentially methylated genes as diagnostic and prognostic biomarkers of breast cancer
title_full Identification of differentially methylated genes as diagnostic and prognostic biomarkers of breast cancer
title_fullStr Identification of differentially methylated genes as diagnostic and prognostic biomarkers of breast cancer
title_full_unstemmed Identification of differentially methylated genes as diagnostic and prognostic biomarkers of breast cancer
title_short Identification of differentially methylated genes as diagnostic and prognostic biomarkers of breast cancer
title_sort identification of differentially methylated genes as diagnostic and prognostic biomarkers of breast cancer
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7839189/
https://www.ncbi.nlm.nih.gov/pubmed/33499882
http://dx.doi.org/10.1186/s12957-021-02124-6
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