ArticleComputers in biology and medicine2025
Integrating AI/ML and multi-omics approaches to investigate the role of TNFRSF10A/TRAILR1 and its potential targets in pancreatic cancer.
Article in Computers in biology and medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Multi-omics and artificial intelligence for precision drug discovery and potential clinical applications.Signal transduction and targeted therapy · 2026Review
- Multi Omics Integration in Colorectal Cancer: From Molecular Insights to Precision Oncology.Cancers · 2026Review
- Artificial intelligence in drug discovery from advanced molecular representation to pipeline applications.Frontiers in bioinformatics · 2026Review
- Thymoquinone Enhances Tamoxifen Efficacy Against Triple-Negative Breast Cancer by Targeting EMT Signaling.International journal of breast cancer · 2026Article
- Spatial AI in cancer: mapping immune evasion topology through multi-modal omics and deep learning.Frontiers in oncology · 2026Review
- Artificial Intelligence-Driven Innovations in Oncology Drug Discovery: Transforming Traditional Pipelines and Enhancing Drug Design.Drug design, development and therapy · 2025Review
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4 authors.
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Abstract
Pancreatic ductal adenocarcinoma (PDAC) remains one of the most lethal malignancies, with a five-year survival of under 10 % despite current therapies. Aggressive tumor biology, a desmoplastic stroma that limits drug delivery and immune cell infiltration, and profound resistance to apoptosis make it more complex to treat. Here, we describe a multi-layered system biology and drug discovery pipeline that integrates bulk genomics, single-cell spatial transcriptomics, proteomics, competing endogenous RNA (ceRNA) network analysis, and deep learning-driven quantitative structure-activity relationship (QSAR) modeling. By implementing this pipeline, we predicted that TNFRSF10A encodes for the TRAILR1 death receptor as a potential therapeutic target in PDAC. Mutational and expressional analysis also confirmed TNFRSF10A as a putative target in PDAC. Cancer cells within the PDAC microenvironment exhibit aberrantly elevated TNFRSF10A expression. Immune-excluded tumor niches and pro-survival signaling link this elevated expression. Using an advanced transformer-based deep learning approach, SELFormer, combined with QSAR analysis-based virtual screening, we identified previously unexplored FDA-approved drugs and natural compounds, i.e., Temsirolimus, Ergotamine, and capivasertib, with potential TRAILR1 modulatory effects. During molecular dynamics simulations, these repurposed candidates showed the highest binding affinities against TNFRSF10A for 300 ns. These showed favorable binding energies (MM-PBSA), minimal RMSD drift, PCA, and SASA. We propose TNFRSF10A as a therapeutically important PDAC vulnerability nurtured by spatially resolved expression patterns and dynamic molecular modeling. This study has used a novel integration of AI-implemented chemical modeling, high-throughput screening, and a multi-omics approach to unravel and pharmacologically target a cancer compartment-specific weakness in a notoriously drug-resistant cancer.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.