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High-dimensional mediation analysis in survival models.

Chengwen Luo1,2, Botao Fa1,2, Yuting Yan2

  • 1Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.

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|April 18, 2020
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Summary
This summary is machine-generated.

We developed a new method for high-dimensional mediation analysis in survival models. This approach identifies novel epigenetic markers linking smoking to lung cancer survival, improving biomarker discovery.

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Area of Science:

  • Epigenetics
  • Biostatistics
  • Cancer Research

Background:

  • High-dimensional DNA methylation markers are crucial for understanding environmental exposures and health outcomes.
  • Existing high-dimensional mediation analysis methods do not adequately address time-to-event outcomes.
  • There is a need for robust statistical approaches to analyze complex epigenetic pathways in survival data.

Purpose of the Study:

  • To develop a novel high-dimensional mediation analysis procedure for survival models.
  • To identify epigenetic mediation pathways between environmental exposures and time-to-event outcomes.
  • To apply the method to uncover novel methylation markers linking smoking and lung cancer survival.

Main Methods:

  • Incorporation of sure independent screening and minimax concave penalty for variable selection.
  • Utilizing Sobel and joint methods for significance testing of indirect effects.
  • Application to a large dataset (TCGA lung cancer cohort) with 365,307 DNA methylation markers.

Main Results:

  • The proposed procedure demonstrates good performance in identifying correct biomarkers and controlling the false discovery rate.
  • Simulation studies confirm the method's effectiveness in minimizing estimation bias.
  • Identified three novel CpG sites (cg21926276, cg27042065, cg26387355) as potential epigenetic mediators linking smoking to lung cancer survival.

Conclusions:

  • The developed high-dimensional mediation analysis procedure is effective for mediator selection and indirect effect estimation in survival models.
  • Newly identified methylation markers offer potential for novel therapeutic targets and biomarkers in lung cancer.
  • This approach advances the field of epigenetic epidemiology and personalized medicine.