{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,5]],"date-time":"2026-10-05T19:48:10Z","timestamp":1791229690642,"version":"4.1.0"},"reference-count":39,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T00:00:00Z","timestamp":1781481600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Key R&D Program in Xinjiang Uygur Autonomous Region","award":["2022B03019-6"],"award-info":[{"award-number":["2022B03019-6"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Biomolecules"],"abstract":"<jats:p>Metastasis remains a major cause of cancer mortality, making reliable primary\u2013metastatic state prediction from somatic genomic alterations clinically important yet technically difficult. We present TF-GateNet, a biologically constrained neural network that combines TF-aware feature integration based on TRRUST and DoRothEA TF\u2013gene regulatory priors with sample-specific dynamic gating on a Reactome-defined hierarchical sparse backbone. The model was evaluated on multi-center prostate and breast-cancer cohorts using mutation and copy-number features across 10 repeated runs on a fixed 80\/10\/10 split, together with independent prostate external validation, and was compared with biologically informed neural-network baselines (P-NET, BKGNet-Pathway, and BKGNet-Protein), a dense feed-forward neural network (FNN), and conventional machine-learning baselines (LR, SVM, RF, DT, and XGBoost). On prostate, TF-GateNet achieved the best internal performance (AUROC 0.954 \u00b1 0.005; AUPRC 0.925 \u00b1 0.007) and the best combined external performance (AUROC 0.952 \u00b1 0.009; AUPRC 0.898 \u00b1 0.018). On breast, TF-GateNet achieved the strongest internal ranking performance, reaching AUROC 0.893 \u00b1 0.004 and AUPRC 0.835 \u00b1 0.006. Ablation analysis indicated that TF-aware integration accounted for the larger prostate gain, whereas within the TF-GateNet family on breast, both TF-aware integration and dynamic gating contributed positively. Interpretability analysis further supported a cross-level route from TF-related genomic perturbation cues to genes, pathways, and phenotype-associated predictions. These results position TF-GateNet as a biologically grounded and interpretable framework for primary\u2013metastatic state prediction, with the strongest overall evidence in prostate cancer and favorable internal evidence in breast cancer.<\/jats:p>","DOI":"10.3390\/biom16060879","type":"journal-article","created":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T00:49:42Z","timestamp":1781743782000},"page":"879","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["TF-GateNet: An Interpretable and Biologically Guided Framework for Primary\u2013Metastatic State Prediction from Somatic Genomic Alterations"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-3639-327X","authenticated-orcid":false,"given":"Hao","family":"Zhou","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Xinjiang University, Urumqi 830017, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenjia","family":"Guo","sequence":"additional","affiliation":[{"name":"Cancer Institute, Affiliated Cancer Hospital of Xinjiang Medical University, Urumqi 830017, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4076-7479","authenticated-orcid":false,"given":"Liang","family":"He","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Xinjiang University, Urumqi 830017, China"},{"name":"School of Intelligence Science and Technology, Xinjiang University, Urumqi 830017, China"},{"name":"Xinjiang Multimodal Information Technology Engineering Research Center, Urumqi 830017, China"},{"name":"Xinjiang Key Laboratory of Signal Detection and Processing, Urumqi 830017, China"},{"name":"Department of Electronic Engineering, Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing 100084, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,6,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1038\/s41591-020-01195-4","article-title":"Targeting metastatic cancer","volume":"27","author":"Ganesh","year":"2021","journal-title":"Nat. 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