Deciphering a TB-related DNA methylation biomarker and constructing a TB diagnostic classifier

We systemically identified tuberculosis (TB)-related DNA methylation biomarkers and further constructed classifiers for TB diagnosis. TB-related DNA methylation datasets were searched through October 3, 2020. Limma and DMRcate were employed to identify differentially methylated probes (DMPs) and reg...

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Autores principales:	Mengyuan Lyu, Jian Zhou, Lin Jiao, Yili Wang, Yanbing Zhou, Hongli Lai, Wei Xu, Binwu Ying
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2022
Materias:	tuberculosis DNA methylation biomarker classifier molecular diagnosis Therapeutics. Pharmacology RM1-950
Acceso en línea:	https://doaj.org/article/adb862e2aa24495b8a2c24d3a216a2be
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spelling	oai:doaj.org-article:adb862e2aa24495b8a2c24d3a216a2be2021-12-02T05:00:27ZDeciphering a TB-related DNA methylation biomarker and constructing a TB diagnostic classifier2162-253110.1016/j.omtn.2021.11.014https://doaj.org/article/adb862e2aa24495b8a2c24d3a216a2be2022-03-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2162253121002894https://doaj.org/toc/2162-2531We systemically identified tuberculosis (TB)-related DNA methylation biomarkers and further constructed classifiers for TB diagnosis. TB-related DNA methylation datasets were searched through October 3, 2020. Limma and DMRcate were employed to identify differentially methylated probes (DMPs) and regions (DMRs). Machine learning methods were used to construct classifiers. The performance of the classifiers was evaluated in discovery datasets and a prospective independent cohort. Eighty-nine DMPs and 24 DMRs were identified based on 67 TB patients and 45 healthy controls from 4 datasets. Nine and three DMRs were selected by elastic net regression and logistic regression, respectively. Among the selected DMRs, two regions (chr3: 195635643–195636243 and chr6: 29691631–29692475) were differentially methylated in the independent cohort (p = 4.19 × 10−5 and 0.024, respectively). Among the ten classifiers, the 3-DMR logistic regression classifier exhibited the strongest performance. The sensitivity, specificity, and area under the curve were, respectively, 79.1%, 84.4%, and 0.888 in the discovery datasets and 64.5%, 90.3%, and 0.838 in the independent cohort. The differential diagnostic ability of this classifier was also assessed. Collectively, these data showed that DNA methylation might be a promising TB diagnostic biomarker. The 3-DMR logistic regression classifier is a potential clinical tool for TB diagnosis, and further validation is needed.Mengyuan LyuJian ZhouLin JiaoYili WangYanbing ZhouHongli LaiWei XuBinwu YingElsevierarticletuberculosisDNA methylationbiomarkerclassifiermolecular diagnosisTherapeutics. PharmacologyRM1-950ENMolecular Therapy: Nucleic Acids, Vol 27, Iss , Pp 37-49 (2022)
institution	DOAJ
collection	DOAJ
language	EN
topic	tuberculosis DNA methylation biomarker classifier molecular diagnosis Therapeutics. Pharmacology RM1-950
spellingShingle	tuberculosis DNA methylation biomarker classifier molecular diagnosis Therapeutics. Pharmacology RM1-950 Mengyuan Lyu Jian Zhou Lin Jiao Yili Wang Yanbing Zhou Hongli Lai Wei Xu Binwu Ying Deciphering a TB-related DNA methylation biomarker and constructing a TB diagnostic classifier
description	We systemically identified tuberculosis (TB)-related DNA methylation biomarkers and further constructed classifiers for TB diagnosis. TB-related DNA methylation datasets were searched through October 3, 2020. Limma and DMRcate were employed to identify differentially methylated probes (DMPs) and regions (DMRs). Machine learning methods were used to construct classifiers. The performance of the classifiers was evaluated in discovery datasets and a prospective independent cohort. Eighty-nine DMPs and 24 DMRs were identified based on 67 TB patients and 45 healthy controls from 4 datasets. Nine and three DMRs were selected by elastic net regression and logistic regression, respectively. Among the selected DMRs, two regions (chr3: 195635643–195636243 and chr6: 29691631–29692475) were differentially methylated in the independent cohort (p = 4.19 × 10−5 and 0.024, respectively). Among the ten classifiers, the 3-DMR logistic regression classifier exhibited the strongest performance. The sensitivity, specificity, and area under the curve were, respectively, 79.1%, 84.4%, and 0.888 in the discovery datasets and 64.5%, 90.3%, and 0.838 in the independent cohort. The differential diagnostic ability of this classifier was also assessed. Collectively, these data showed that DNA methylation might be a promising TB diagnostic biomarker. The 3-DMR logistic regression classifier is a potential clinical tool for TB diagnosis, and further validation is needed.
format	article
author	Mengyuan Lyu Jian Zhou Lin Jiao Yili Wang Yanbing Zhou Hongli Lai Wei Xu Binwu Ying
author_facet	Mengyuan Lyu Jian Zhou Lin Jiao Yili Wang Yanbing Zhou Hongli Lai Wei Xu Binwu Ying
author_sort	Mengyuan Lyu
title	Deciphering a TB-related DNA methylation biomarker and constructing a TB diagnostic classifier
title_short	Deciphering a TB-related DNA methylation biomarker and constructing a TB diagnostic classifier
title_full	Deciphering a TB-related DNA methylation biomarker and constructing a TB diagnostic classifier
title_fullStr	Deciphering a TB-related DNA methylation biomarker and constructing a TB diagnostic classifier
title_full_unstemmed	Deciphering a TB-related DNA methylation biomarker and constructing a TB diagnostic classifier
title_sort	deciphering a tb-related dna methylation biomarker and constructing a tb diagnostic classifier
publisher	Elsevier
publishDate	2022
url	https://doaj.org/article/adb862e2aa24495b8a2c24d3a216a2be
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Deciphering a TB-related DNA methylation biomarker and constructing a TB diagnostic classifier

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