{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T06:07:47Z","timestamp":1788847667964,"version":"build-2803163510"},"reference-count":42,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T00:00:00Z","timestamp":1788825600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Syst. Biol."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>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) \u2013 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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>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 \u2248 30% in PRAD but only \u22486% in LUAD. Escape probability also depends on tumor stage and on the spontaneous activation rate.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fsysb.2026.1868020","type":"journal-article","created":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T05:35:30Z","timestamp":1788845730000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Bidirectional network hubs: NT-genes as candidate targets for partial cancer reversal"],"prefix":"10.3389","volume":"6","author":[{"given":"Gabriel","family":"Gil","sequence":"first","affiliation":[{"name":"Institute of Cybernetics, Mathematics and Physics","place":["Havana, Cuba"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rolando","family":"Perez","sequence":"additional","affiliation":[{"name":"Center for Molecular Immunology","place":["Havana, Cuba"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Augusto","family":"Gonzalez","sequence":"additional","affiliation":[{"name":"Institute of Cybernetics, Mathematics and Physics","place":["Havana, Cuba"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,9,8]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"1657","DOI":"10.1038\/s41592-025-02772-6","article-title":"Deep-learning-based gene perturbation effect prediction does not yet outperform simple linear baselines","volume":"22","author":"Ahlmann-Elze","year":"2025","journal-title":"Nat. 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