ArticleiScience2023
Deep learning untangles the resistance mechanism of p53 reactivator in lung cancer cells.
Article in iScience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Targeting p53 in Cancer: Functional States, Therapeutic Strategies, and Clinical Progress.Cancers · 2026Review
- Causality-aware graph neural networks for functional stratification and phenotype prediction at scale.NPJ systems biology and applications · 2025Article
- Mechanism of THBS1 Regulation of MDCK Cell Proliferation and Apoptosis Through TGF-β/Smad Signalling.International journal of molecular sciences · 2025Article
- Spatial deciphering of the transcriptomic heterogeneity of tumor spread through air spaces in lung cancer.Frontiers in pharmacology · 2025Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Tumor suppressor p53 plays a pivotal role in suppressing cancer, so various drugs has been suggested to upregulate its function. However, drug resistance is still the biggest hurdle to be overcome. To address this, we developed a deep learning model called AnoDAN (anomalous gene detection using generative adversarial networks and graph neural networks for overcoming drug resistance) that unravels the hidden resistance mechanisms and identifies a combinatorial target to overcome the resistance. Our findings reveal that the TGF-β signaling pathway, alongside the p53 signaling pathway, mediates the resistance, with THBS1 serving as a core regulatory target in both pathways. Experimental validation in lung cancer cells confirms the effects of THBS1 on responsiveness to a p53 reactivator. We further discovered the positive feedback loop between THBS1 and the TGF-β pathway as the main source of resistance. This study enhances our understanding of p53 regulation and offers insights into overcoming drug resistance.
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Registered trials
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