A multi-cohort computational framework for detection and prognostic validation of conserved gene co-expression network dissolution in solid tumors

Abstract

Identifying disease-relevant disruptions in gene regulatory networks requires computational frameworks that move beyond differential expression analysis toward systematic modeling of interaction loss across heterogeneous multi-cohort datasets. We present a computational pipeline that systematically detects, filters, and validates co-expression interaction dissolution across four TCGA solid tumor cohorts (BRCA, LUAD, HNSC, and STAD), integrating multi-metric quality control, DEG-constrained network inference, double-threshold Pearson filtering benchmarked against GeneMANIA, and cross-cohort consensus ranking. We implement a four-step pipeline: 1) Network construction using a double-threshold filtering algorithm, optimized through benchmarking with GeneMANIA; 2) Multivariate stratification (molecular subtypes and anatomical regions) in four TCGA cohorts; 3) A hierarchical consensus intersection algorithm to identify conserved lost interactions; and 4) Development of a co-expression score based on z-score products for integration into Cox survival models. The pipeline identified a robust core of 18 conserved lost interactions across solid tumors. Survival analysis suggests that pairwise co-expression scores derived from dissolved regulatory links stratify overall survival with hazard ratios up to 2.01, providing prognostic value beyond what single-gene expression levels capture. The proposed framework is generalizable to any multi-cohort RNA-seq compendium and positions co-expression dissolution as a computationally tractable, clinically informative complement to standard differential expression pipelines.