A Network-Guided Penalized Regression with Application to Proteomics Data

TL;DR

This paper introduces a network-guided penalized regression method that leverages protein interaction networks to identify prognostic biomarkers in high-dimensional proteomics data, improving variable selection and biomarker discovery.

Contribution

The study develops a novel network-guided penalized regression approach that integrates network structure with clinical data for better biomarker identification in proteomics.

Findings

Method outperforms existing approaches in simulations.

Identified potential prognostic biomarkers in CPTAC data.

Demonstrated variable selection consistency and asymptotic normality.

Abstract

Network theory has proven invaluable in unraveling complex protein interactions. Previous studies have employed statistical methods rooted in network theory, including the Gaussian graphical model, to infer networks among proteins, identifying hub proteins based on key structural properties of networks such as degree centrality. However, there has been limited research examining a prognostic role of hub proteins on outcomes, while adjusting for clinical covariates in the context of high-dimensional data. To address this gap, we propose a network-guided penalized regression method. First, we construct a network using the Gaussian graphical model to identify hub proteins. Next, we preserve these identified hub proteins along with clinically relevant factors, while applying adaptive Lasso to non-hub proteins for variable selection. Our network-guided estimators are shown to have variable selection consistency and asymptotic normality. Simulation results suggest that our method produces better results compared to existing methods and demonstrates promise for advancing biomarker identification in proteomics research. Lastly, we apply our method to the Clinical Proteomic Tumor Analysis Consortium (CPTAC) data and identified hub proteins that may serve as prognostic biomarkers for various diseases, including rare genetic disorders and immune checkpoint for cancer immunotherapy.