ArticlePLoS computational biology2021
IUSMMT: Survival mediation analysis of gene expression with multiple DNA methylation exposures and its application to cancers of TCGA.
Article in PLoS computational biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- A multi-omics framework for survival mediation analysis of high-dimensional proteogenomic data.PLoS computational biology · 2026Article
- HILAMA: High-dimensional multi-omics mediation analysis with latent confounding.BMC medical research methodology · 2025Article
- How can childhood maltreatment affect post-traumatic stress disorder in adult: Results from a composite null hypothesis perspective of mediation analysis.Frontiers in psychiatry · 2023Article
- Detecting associated genes for complex traits shared across East Asian and European populations under the framework of composite null hypothesis testing.Journal of translational medicine · 2022Article
- Polygenic risk and causal inference of psychiatric comorbidity in inflammatory bowel disease among patients with European ancestry.Journal of translational medicine · 2022Article
- Efficient feature extraction from highly sparse binary genotype data for cancer prognosis prediction using an auto-encoder.Frontiers in oncology · 2022Article
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Authors and funding
6 authors.
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Abstract
Effective and powerful survival mediation models are currently lacking. To partly fill such knowledge gap, we particularly focus on the mediation analysis that includes multiple DNA methylations acting as exposures, one gene expression as the mediator and one survival time as the outcome. We proposed IUSMMT (intersection-union survival mixture-adjusted mediation test) to effectively examine the existence of mediation effect by fitting an empirical three-component mixture null distribution. With extensive simulation studies, we demonstrated the advantage of IUSMMT over existing methods. We applied IUSMMT to ten TCGA cancers and identified multiple genes that exhibited mediating effects. We further revealed that most of the identified regions, in which genes behaved as active mediators, were cancer type-specific and exhibited a full mediation from DNA methylation CpG sites to the survival risk of various types of cancers. Overall, IUSMMT represents an effective and powerful alternative for survival mediation analysis; our results also provide new insights into the functional role of DNA methylation and gene expression in cancer progression/prognosis and demonstrate potential therapeutic targets for future clinical practice.
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