Network-based integration of gene expression and DNA methylation identifies prognostic biomarkers for early-stage pancreatic cancer

Abstract

Pancreatic ductal adenocarcinoma remains one of the most lethal malignancies, largely due to the absence of reliable early-stage biomarkers. Here, we present a network-based multi-omics framework that integrates gene expression and DNA methylation data through partial correlation analysis to uncover prognostic markers. Four distinct networks were constructed: gene expression co-expression, methylation-only, multiplex (inter-layer connections linking the same genes across omics layers), and monoplex (fused multi-omics). Weighted gene co-expression network analysis (WGCNA) was applied to each network to select non-redundant, topologically representative hub genes as features for machine learning classification. Models trained on cross-layer (multiplex) features achieved an ROC of 82%, compared with 50-60% using single-omics features alone. The most strongly associated genes with poor prognosis include TFCP2L1, DHX32, and NCK1.