ArticleJournal of translational medicine2024
Integration of Graph Neural Networks and multi-omics analysis identify the predictive factor and key gene for immunotherapy response and prognosis of bladder cancer.
Article in Journal of translational medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Artificial Intelligence for Alzheimer's Disease Diagnosis: From Traditional Machine Learning to Large Language Models.Biosensors · 2026Review
- Leveraging Advanced AI Frameworks for Dual PPAR α/γ Agonist Discovery in Alzheimer's Disease.ACS chemical neuroscience · 2026Review
- GraphTME: graph-based framework for predicting immunotherapy response by interpreting tumour microenvironment interactions using spatial transcriptomics.NPJ systems biology and applications · 2026Article
- Article
- Multi Omics Integration in Colorectal Cancer: From Molecular Insights to Precision Oncology.Cancers · 2026Review
- Organ-Specific Migration License (OSML) theory: a novel paradigm for spatiotemporal regulation and intervention of cross-organ immune cell migration in tumor immune responses.Cell communication and signaling : CCS · 2026Review
- Targeting MDSCs in cancer: emerging immunotherapeutic and metabolic strategies.Frontiers in immunology · 2026Review
- Deep learning for prognostic stratification and biomarker exploration in upper tract urothelial carcinoma: a multicenter retrospective cohort study.International journal of surgery (London, England) · 2026Article
- MOGEDN: small-sample cancer subtype classification with encoder-decoder networks for missing-omics recovery and biomarker discovery.Briefings in bioinformatics · 2025Article
- The clinical application of artificial intelligence in cancer precision treatment.Journal of translational medicine · 2025Review
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7 authors.
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Abstract
objectiveThe evaluation of the efficacy of immunotherapy is of great value for the clinical treatment of bladder cancer. Graph Neural Networks (GNNs), pathway analysis and multi-omics analysis have shown great potential in the field of cancer diagnosis and treatment.
methodsA GNNs model was constructed to predict the immunotherapy response and identify key pathways. Based on the genes of key pathways, bioinformatic methods were used to generate a simple linear scoring model, namely responseScore. The intrinsic mechanism of responseScore was explored from the perspectives of multi-omics analysis. The relationship between each gene involved in responseScore and prognosis was also explored. Transfection experiments with human bladder cancer cells were used to investigate the biological effects of PSMB9 gene.
resultsThe final GNNs model had an AUC of 0.785 on the training set and an AUC of 0.839 on the validation set. R-HSA-69620 and others were identified as key pathways. ResponseScore had a good performance in predicted the immunotherapy response and prognosis. Analysis results from genetic variation, pathways and tumor microenvironment, showed that responseScore was significantly associated with immune cell infiltration and anti-tumor immunity. The results of single-cell analysis showed that responseScore was closely related to the functional state of natural killer cells. Compared with the PCDH-NC group, cell migration and proliferation were significantly inhibited while cell apoptosis increased in the PCDH-PSMB9 group.
conclusionThe GNNs predictive model and responseScore constructed in this study can reflect the immunotherapy response and prognosis of bladder cancer patients. ResponseScore can also reflect features such as tumor microenvironment, antitumor immunity, and natural killer cell function status in bladder cancer. PSMB9 was identified as a significant gene for prognosis. High expression of PSMB9 can inhibit bladder cancer cell migration and proliferation while increasing cell apoptosis.
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