A Multi-Omics Framework for Survival Mediation Analysis of High-Dimensional Proteogenomic Data

Area of Science:

• Biostatistics
• Genomics
• Proteomics

Background:

• Survival analysis is vital for time-to-event outcomes like disease progression.
• Current causal mediation methods for survival analysis often use Cox regression, limiting integration of multi-omics data and overlooking interplay.
• This restricts leveraging comprehensive biological insights from integrated data.

Purpose of the Study:

• Propose SMAHP, a novel method for survival mediation analysis.
• Simultaneously handle high-dimensional exposures and mediators.
• Integrate multi-omics data within a robust statistical framework to identify causal pathways on survival outcomes.

Main Methods:

• Introduce the accelerated failure time (AFT) model within a multi-omics causal mediation framework.
• Develop SMAHP for simultaneous analysis of high-dimensional exposures and mediators.
• Validate through simulations and application to real-world proteogenomic data.

Main Results:

• SMAHP demonstrates high statistical power and effective false discovery rate (FDR) control in simulations.
• The method successfully identifies a gene-protein mediation pathway influencing survival in head-and-neck carcinoma.
• This highlights the utility of SMAHP in analyzing complex multi-omics survival data.

Conclusions:

• SMAHP provides a robust statistical framework for multi-omics survival mediation analysis.
• The method effectively integrates high-dimensional data and identifies causal pathways.
• SMAHP advances the analysis of time-to-event outcomes by incorporating multi-omics interplay.