ArticleFrontiers in immunology2024
A novel artificial intelligence network to assess the prognosis of gastrointestinal cancer to immunotherapy based on genetic mutation features.
Article in Frontiers in immunology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
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Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence networks for assessing the prognosis of gastrointestinal cancer to immunotherapy based on genetic mutation features: a systematic review and meta-analysis.BMC gastroenterology · 2025Pooled it
- Artificial intelligence and transforming cancer care.Discover oncology · 2026Review
- Artificial intelligence and machine learning-driven advancements in gastrointestinal cancer: Paving the way for precision medicine.World journal of gastroenterology · 2026Review
- A New Paradigm of Bispecific Antibodies in Clinical Management of Gastrointestinal Cancers.Oncology research · 2026Review
- Artificial intelligence-enabled multi-omics biomarkers for immune checkpoint blockade: mechanisms, predictive modeling, and clinical translation.Frontiers in immunology · 2026Review
- AI-derived prognostic model identifies high-risk gene signatures in pediatric gliomas.Frontiers in immunology · 2026Article
- Artificial intelligence in biology and medicine.Die Naturwissenschaften · 2025Review
- Machine Learning-Based Glycolipid Metabolism Gene Signature Predicts Prognosis and Immune Landscape in Oesophageal Squamous Cell Carcinoma.Journal of cellular and molecular medicine · 2025Article
- Integrating AI into Cancer Immunotherapy-A Narrative Review of Current Applications and Future Directions.Diseases (Basel, Switzerland) · 2025Review
- The role of artificial intelligence in enhancing breast cancer screening and diagnosis: A review of current advances.BioImpacts : BI · 2025Review
- Construction and validation of a transmembrane 4 superfamily-related genes prognostic model for esophageal squamous cell carcinoma.Frontiers in oncology · 2025Article
- Comprehensive Single-Cell Characterization of LDL in the Ovarian Cancer Microenvironment and Its Prognostic Implications.Mediators of inflammation · 2025Article
- SPP1International journal of genomics · 2025Article
- Applications and advances of multi-omics technologies in gastrointestinal tumors.Frontiers in medicine · 2025Review
- Prognostic value of positive lymph node ratio, tumor deposit, and perineural invasion in advanced colorectal signet-ring cell carcinoma.Frontiers in molecular biosciences · 2025Article
- Single-cell and spatial transcriptomics integration reveals FAM49B promotes tumor-associated macrophages polarization in colorectal cancer via the MK pathway.Frontiers in immunology · 2025Article
- Integrating necroptosis into pan-cancer immunotherapy: a new era of personalized treatment.Frontiers in immunology · 2024Article
- Leveraging single-cell and multi-omics approaches to identify MTOR-centered deubiquitination signatures in esophageal cancer therapy.Frontiers in immunology · 2024Article
- Comprehensive multi-omics analysis identifies chromatin regulator-related signatures and TFF1 as a therapeutic target in lung adenocarcinoma through a 429-combination machine learning approach.Frontiers in immunology · 2024Article
- From single-cell to spatial transcriptomics: decoding the glioma stem cell niche and its clinical implications.Frontiers in immunology · 2024Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Immune checkpoint inhibitors (ICIs) have revolutionized gastrointestinal cancer treatment, yet the absence of reliable biomarkers hampers precise patient response prediction. Methods: We developed and validated a genomic mutation signature (GMS) employing a novel artificial intelligence network to forecast the prognosis of gastrointestinal cancer patients undergoing ICIs therapy. Subsequently, we explored the underlying immune landscapes across different subtypes using multiomics data. Finally, UMI-77 was pinpointed through the analysis of drug sensitization data from the Genomics of Drug Sensitivity in Cancer (GDSC) database. The sensitivity of UMI-77 to the AGS and MKN45 cell lines was evaluated using the cell counting kit-8 (CCK8) assay and the plate clone formation assay. Results: Using the artificial intelligence network, we developed the GMS that independently predicts the prognosis of gastrointestinal cancer patients. The GMS demonstrated consistent performance across three public cohorts and exhibited high sensitivity and specificity for 6, 12, and 24-month overall survival (OS) in receiver operating characteristic (ROC) curve analysis. It outperformed conventional clinical and molecular features. Low-risk samples showed a higher presence of cytolytic immune cells and enhanced immunogenic potential compared to high-risk samples. Additionally, we identified the small molecule compound UMI-77. The half-maximal inhibitory concentration (IC50) of UMI-77 was inversely related to the GMS. Notably, the AGS cell line, classified as high-risk, displayed greater sensitivity to UMI-77, whereas the MKN45 cell line, classified as low-risk, showed less sensitivity. Conclusion: The GMS developed here can reliably predict survival benefit for gastrointestinal cancer patients on ICIs therapy.
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