A multi-omics framework for survival mediation analysis of high-dimensional proteogenomic data

Seungjun Ahn, Weijia Fu, Maaike van Gerwen, Lei Liu and Zhigang Li

PLOS Computational Biology, 2026, vol. 22, issue 4, 1-17

Abstract: Survival analysis plays a crucial role in understanding time-to-event (survival) outcomes such as disease progression. Despite recent advancements in causal mediation frameworks for survival analysis, existing methods are typically based on Cox regression and primarily focus on a single exposure or individual omics layers, often overlooking multi-omics interplay. This limitation hinders the full potential of integrated biological insights. In this paper, we propose SMAHP, a novel method for survival mediation analysis that simultaneously handles high-dimensional exposures and mediators, integrates multi-omics data, and offers a robust statistical framework for identifying causal pathways on survival outcomes. This is one of the first attempts to introduce the accelerated failure time (AFT) model within a multi-omics causal mediation framework for survival outcomes. Through simulations across multiple scenarios, we demonstrate that SMAHP achieves high statistical power, while effectively controlling false discovery rate (FDR), compared with two other approaches. We further apply SMAHP to the largest head-and-neck carcinoma proteogenomic data, detecting a gene mediated by a protein that influences survival time. R package is freely available on CRAN repository and published under General Public License version 3.Author summary: In this study, we propose SMAHP, a novel multi-omics causal mediation framework that addresses the unique challenges of high-dimensional exposures, high-dimensional mediators, and survival outcomes. To our knowledge, this is the first methodological development specifically focused on survival causal mediation analysis in the context of multi-omics proteogenomic data. SMAHP incorporates a two-stage feature selection procedure combining penalization techniques and sure independence screening to efficiently identify relevant exposure and mediator candidates associated with survival outcomes. Through comprehensive simulation studies, we demonstrate the robustness of our approach. We further illustrate the practical utility of SMAHP by applying it to the largest proteogenomic dataset of head and neck cancer (CPTAC), uncovering a causal mediation pathway where a specific protein negatively mediates the effect of gene expression on survival time in patients with HPV-negative tumors. The proposed methodology is publicly available as an R package, SMAHP, on CRAN, accompanied by a detailed vignette to facilitate reproducibility and application.

Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1014217

DOI: 10.1371/journal.pcbi.1014217

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