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<p>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 <i>z</i>-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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年8月4日

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  • Marc Ríos-Cadenas,
  • Iván Segura-Carmona,
  • Aurelio López-Fernández,
  • Francisco A. Gómez-Vela

摘要

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.