RT Journal Article
T1 A multi-cohort computational framework for detection and prognostic validation of conserved gene co-expression network dissolution in solid tumors
A1 Rios Cadenas, Marc
A1 Segura Carmona, Iván
A1 López Fernández, Aurelio
A1 Gómez-Vela, Francisco Antonio
K1 Gene co-expression network
K1 Computational pipeline
K1 Differential co-expression analysis
K1 Network biomarkers
K1 Multi-cohort integration
K1 Pan-cancer transcriptomics
K1 Survival analysis
AB 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.
PB Springer
YR 2026
FD 2026-08-04
LK https://hdl.handle.net/10433/27428
UL https://hdl.handle.net/10433/27428
LA en
NO Netw Model Anal Health Inform Bioinforma 15, 170 (2026).
NO Universidad Pablo de Olavide de Sevilla, Departamento de deporte e informática
DS RIO
RD Sep 23, 2026