High-dimensional mediation analysis in survival models.

Mediation analysis with high-dimensional DNA methylation markers is important in identifying epigenetic pathways between environmental exposures and health outcomes. There have been some methodology developments of mediation analysis with high-dimensional mediators. However, high-dimensional mediati...

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Main Authors: Chengwen Luo, Botao Fa, Yuting Yan, Yang Wang, Yiwang Zhou, Yue Zhang, Zhangsheng Yu
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2020-04-01
Series:PLoS Computational Biology
Online Access:https://doi.org/10.1371/journal.pcbi.1007768
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spelling doaj-b9c6225c4fc748e7acf9dc46689d0a302021-04-21T15:15:14ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582020-04-01164e100776810.1371/journal.pcbi.1007768High-dimensional mediation analysis in survival models.Chengwen LuoBotao FaYuting YanYang WangYiwang ZhouYue ZhangZhangsheng YuMediation analysis with high-dimensional DNA methylation markers is important in identifying epigenetic pathways between environmental exposures and health outcomes. There have been some methodology developments of mediation analysis with high-dimensional mediators. However, high-dimensional mediation analysis methods for time-to-event outcome data are still yet to be developed. To address these challenges, we propose a new high-dimensional mediation analysis procedure for survival models by incorporating sure independent screening and minimax concave penalty techniques for variable selection, with the Sobel and the joint method for significance test of indirect effect. The simulation studies show good performance in identifying correct biomarkers, false discovery rate control, and minimum estimation bias of the proposed procedure. We also apply this approach to study the causal pathway from smoking to overall survival among lung cancer patients potentially mediated by 365,307 DNA methylations in the TCGA lung cancer cohort. Mediation analysis using a Cox proportional hazards model estimates that patients who have serious smoking history increase the risk of lung cancer through methylation markers including cg21926276, cg27042065, and cg26387355 with significant hazard ratios of 1.2497(95%CI: 1.1121, 1.4045), 1.0920(95%CI: 1.0170, 1.1726), and 1.1489(95%CI: 1.0518, 1.2550), respectively. The three methylation sites locate in the three genes which have been showed to be associated with lung cancer event or overall survival. However, the three CpG sites (cg21926276, cg27042065 and cg26387355) have not been reported, which are newly identified as the potential novel epigenetic markers linking smoking and survival of lung cancer patients. Collectively, the proposed high-dimensional mediation analysis procedure has good performance in mediator selection and indirect effect estimation.https://doi.org/10.1371/journal.pcbi.1007768
collection DOAJ
language English
format Article
sources DOAJ
author Chengwen Luo
Botao Fa
Yuting Yan
Yang Wang
Yiwang Zhou
Yue Zhang
Zhangsheng Yu
spellingShingle Chengwen Luo
Botao Fa
Yuting Yan
Yang Wang
Yiwang Zhou
Yue Zhang
Zhangsheng Yu
High-dimensional mediation analysis in survival models.
PLoS Computational Biology
author_facet Chengwen Luo
Botao Fa
Yuting Yan
Yang Wang
Yiwang Zhou
Yue Zhang
Zhangsheng Yu
author_sort Chengwen Luo
title High-dimensional mediation analysis in survival models.
title_short High-dimensional mediation analysis in survival models.
title_full High-dimensional mediation analysis in survival models.
title_fullStr High-dimensional mediation analysis in survival models.
title_full_unstemmed High-dimensional mediation analysis in survival models.
title_sort high-dimensional mediation analysis in survival models.
publisher Public Library of Science (PLoS)
series PLoS Computational Biology
issn 1553-734X
1553-7358
publishDate 2020-04-01
description Mediation analysis with high-dimensional DNA methylation markers is important in identifying epigenetic pathways between environmental exposures and health outcomes. There have been some methodology developments of mediation analysis with high-dimensional mediators. However, high-dimensional mediation analysis methods for time-to-event outcome data are still yet to be developed. To address these challenges, we propose a new high-dimensional mediation analysis procedure for survival models by incorporating sure independent screening and minimax concave penalty techniques for variable selection, with the Sobel and the joint method for significance test of indirect effect. The simulation studies show good performance in identifying correct biomarkers, false discovery rate control, and minimum estimation bias of the proposed procedure. We also apply this approach to study the causal pathway from smoking to overall survival among lung cancer patients potentially mediated by 365,307 DNA methylations in the TCGA lung cancer cohort. Mediation analysis using a Cox proportional hazards model estimates that patients who have serious smoking history increase the risk of lung cancer through methylation markers including cg21926276, cg27042065, and cg26387355 with significant hazard ratios of 1.2497(95%CI: 1.1121, 1.4045), 1.0920(95%CI: 1.0170, 1.1726), and 1.1489(95%CI: 1.0518, 1.2550), respectively. The three methylation sites locate in the three genes which have been showed to be associated with lung cancer event or overall survival. However, the three CpG sites (cg21926276, cg27042065 and cg26387355) have not been reported, which are newly identified as the potential novel epigenetic markers linking smoking and survival of lung cancer patients. Collectively, the proposed high-dimensional mediation analysis procedure has good performance in mediator selection and indirect effect estimation.
url https://doi.org/10.1371/journal.pcbi.1007768
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