Bidirectional network hubs: NT-genes as candidate targets for partial cancer reversal.

Background: Reversing the tumor phenotype is a long-standing challenge in cancer biology. Although GRNs comprise thousands of genes, normal and tumor tissues occupy distinct, low-dimensional attractors in expression space, raising the possibility that targeting a few key genes could induce widespread transcriptional changes. We build on two previously developed concepts: 1) N- and T-markers (genes with exclusive expression intervals in normal or tumor samples, respectively), and 2) Gene Deregulation Networks (GDNs) - directed acyclic graphs inferred from expression data, in which a link from C to E indicates that a deregulation at C increases the probability of a deregulation at E. A subset of N and T-genes, namely, NT-markers, appear in both networks and may act as bridges for phenotype reversal. Methods: Using TCGA bulk RNA-Seq data from five cancer types we identified N-, T- and NT-genes based on statistically significant exclusive expression intervals. Discretized expression states (N-active, T-active, inactive) are then associated with normal-exclusive, tumor-exclusive and non-exclusive expression intervals. GDNs were constructed with the CChains algorithm using the Loevinger coefficient, followed by Reichenbach (common cause) and Mokken (transitivity) pruning. We introduced a quantitative model to predict intervention outcomes in a tumor using gene activation frequencies and two topological metrics: the composed coverage and the fraction of the T-network unreached by the reverse deactivation cascade (U). We also use a Glauber-like dynamical model to simulate interventions. Results: We predict that pure T-gene interventions mainly alter the T-network, while pure N-gene interventions create a mixed normal-tumor state. In contrast, high-frequency NT-genes are predicted to simultaneously deactivate T-cascades and reactivate N-programs. The unreached fraction U varies across cancers: for perfect diagnostic panels, U ≈ 30% in PRAD but only ≈6% in LUAD. Escape probability also depends on tumor stage and on the spontaneous activation rate. Conclusion: NT-genes with high dual frequency are predicted optimal targets for partial phenotype reversal. The combination of composed coverage, unreached fraction and normal-state relevance provides a quantitative guide for designing multi-target therapies. The framework is general and yields testable predictions for intervention outcomes across cancer types.