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Identification of prostate cancer specific methylation biomarkers from a multi-cancer analysis

BACKGROUND: Detecting prostate cancer at a non-aggressive stage is the main goal of prostate cancer screening. DNA methylation has been widely used as biomarkers for cancer diagnosis and prognosis, however, with low clinical translation rate. By taking advantage of multi-cancer data from The Cancer...

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Autores principales: Pu, Yiyi, Li, Chao, Yuan, Haining, Wang, Xiaoju
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8507340/
https://www.ncbi.nlm.nih.gov/pubmed/34641790
http://dx.doi.org/10.1186/s12859-021-04416-w
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author Pu, Yiyi
Li, Chao
Yuan, Haining
Wang, Xiaoju
author_facet Pu, Yiyi
Li, Chao
Yuan, Haining
Wang, Xiaoju
author_sort Pu, Yiyi
collection PubMed
description BACKGROUND: Detecting prostate cancer at a non-aggressive stage is the main goal of prostate cancer screening. DNA methylation has been widely used as biomarkers for cancer diagnosis and prognosis, however, with low clinical translation rate. By taking advantage of multi-cancer data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), we aimed to identify prostate cancer specific biomarkers which can separate between non-aggressive and aggressive prostate cancer based on DNA methylation patterns. RESULTS: We performed a comparison analysis of DNA methylation status between normal prostate tissues and prostate adenocarcinoma (PRAD) samples at different Gleason stages. The candidate biomarkers were selected by excluding the biomarkers existing in multiple cancers (pan-cancer) and requiring significant difference between PRAD and other urinary samples. By least absolute shrinkage and selection operator (LASSO) selection, 8 biomarkers (cg04633600, cg05219445, cg05796128, cg10834205, cg16736826, cg23523811, cg23881697, cg24755931) were identified and in-silico validated by model constructions. First, all 8 biomarkers could separate PRAD at different stages (Gleason 6 vs. Gleason 3 + 4: AUC = 0.63; Gleason 6 vs. Gleason 4 + 3 and 8–10: AUC = 0.87). Second, 5 biomarkers (cg04633600, cg05796128, cg23523811, cg23881697, cg24755931) effectively detected PRAD from normal prostate tissues (AUC ranged from 0.88 to 0.92). Last, 6 biomarkers (cg04633600, cg05219445, cg05796128, cg23523811, cg23881697, cg24755931) completely distinguished PRAD with other urinary samples (AUC = 1). CONCLUSIONS: Our study identified and in-silico validated a panel of prostate cancer specific DNA methylation biomarkers with diagnosis value. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-021-04416-w.
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spelling pubmed-85073402021-10-20 Identification of prostate cancer specific methylation biomarkers from a multi-cancer analysis Pu, Yiyi Li, Chao Yuan, Haining Wang, Xiaoju BMC Bioinformatics Research BACKGROUND: Detecting prostate cancer at a non-aggressive stage is the main goal of prostate cancer screening. DNA methylation has been widely used as biomarkers for cancer diagnosis and prognosis, however, with low clinical translation rate. By taking advantage of multi-cancer data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), we aimed to identify prostate cancer specific biomarkers which can separate between non-aggressive and aggressive prostate cancer based on DNA methylation patterns. RESULTS: We performed a comparison analysis of DNA methylation status between normal prostate tissues and prostate adenocarcinoma (PRAD) samples at different Gleason stages. The candidate biomarkers were selected by excluding the biomarkers existing in multiple cancers (pan-cancer) and requiring significant difference between PRAD and other urinary samples. By least absolute shrinkage and selection operator (LASSO) selection, 8 biomarkers (cg04633600, cg05219445, cg05796128, cg10834205, cg16736826, cg23523811, cg23881697, cg24755931) were identified and in-silico validated by model constructions. First, all 8 biomarkers could separate PRAD at different stages (Gleason 6 vs. Gleason 3 + 4: AUC = 0.63; Gleason 6 vs. Gleason 4 + 3 and 8–10: AUC = 0.87). Second, 5 biomarkers (cg04633600, cg05796128, cg23523811, cg23881697, cg24755931) effectively detected PRAD from normal prostate tissues (AUC ranged from 0.88 to 0.92). Last, 6 biomarkers (cg04633600, cg05219445, cg05796128, cg23523811, cg23881697, cg24755931) completely distinguished PRAD with other urinary samples (AUC = 1). CONCLUSIONS: Our study identified and in-silico validated a panel of prostate cancer specific DNA methylation biomarkers with diagnosis value. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-021-04416-w. BioMed Central 2021-10-12 /pmc/articles/PMC8507340/ /pubmed/34641790 http://dx.doi.org/10.1186/s12859-021-04416-w Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/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/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://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
Pu, Yiyi
Li, Chao
Yuan, Haining
Wang, Xiaoju
Identification of prostate cancer specific methylation biomarkers from a multi-cancer analysis
title Identification of prostate cancer specific methylation biomarkers from a multi-cancer analysis
title_full Identification of prostate cancer specific methylation biomarkers from a multi-cancer analysis
title_fullStr Identification of prostate cancer specific methylation biomarkers from a multi-cancer analysis
title_full_unstemmed Identification of prostate cancer specific methylation biomarkers from a multi-cancer analysis
title_short Identification of prostate cancer specific methylation biomarkers from a multi-cancer analysis
title_sort identification of prostate cancer specific methylation biomarkers from a multi-cancer analysis
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8507340/
https://www.ncbi.nlm.nih.gov/pubmed/34641790
http://dx.doi.org/10.1186/s12859-021-04416-w
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