N-Gene and T-Gene deregulation networks: a data-driven causal framework for the analysis of gene interventions in cancer.

Background: Current gene regulatory networks are limited by incomplete functional annotation and the difficulty of inferring causal relationships from expression data. Here we introduce Gene Deregulation Networks (GDNs), a new structure in which a directed link from gene C to gene E indicates that a deregulation of C makes a deregulation of E more probable. GDNs are inferred from expression data using a probabilistic theory of causation, without requiring prior biological knowledge. Methods: Using previously defined N- and T-genes with exclusive expression intervals for normal tissue and tumors, respectively, we construct separate GDNs for normal and for tumor tissues. Data are TCGA RNA-Seq Results: The GDNs are represented by sparse, directed acyclic graphs. Genes with low deregulation frequency have high out-degrees, suggesting they act as upstream regulators. High-frequency genes have high in-degrees, indicating they are convergence points of cascades. Projecting samples onto the T-GDN reveals that early tumors rely mostly on spontaneous T-gene activations, whereas advanced tumors show wide, branching cascades. The N-GDNs show consistent size and structure across distinct tissues revealing similar protective machinery against tumor formation. In contrast, the T-GDNs quantitatively differ from tissue to tissue indicating different levels of transcriptional reprogramming. Simulated knockdown of Conclusion: GDNs provide a robust, scalable, and annotation-free framework to understand cancer onset and progression. The separation into N- and T-GDNs, connected by NT-genes, offers a systematic basis for studying carcinogenesis and designing targeted therapies.