ArticleFrontiers in oncology2023
PI3K/AKT/mTOR pathway-derived risk score exhibits correlation with immune infiltration in uveal melanoma patients.
Article in Frontiers in oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed, 21 citations in OpenAlex.
- Single-cell transcriptomic atlas of the human fetal uveal tract reveals heterogeneity in melanocyte populations.iScience · 2026Article
- Fatty Acid-Binding Protein 5 Potentially Regulates the Proliferation and Metastasis of Uveal Melanoma Cells Through the PI3K/AKT/mTOR Signaling Pathway.CNS neuroscience & therapeutics · 2026Article
- Network-based analysis reveals potential microRNA regulation of oncogenic pathways in SOX10-depleted uveal melanoma.Cellular and molecular life sciences : CMLS · 2026Article
- Research on the molecular mechanism of celastrol targeting CTNNB1/STAT3 to inhibit uveal melanoma based on network pharmacology and multi-omics analysis.Scientific reports · 2026Article
- Immunohistochemical and molecular profiling of uveal melanoma: clinicopathological correlations from an Italian cohort.Pathologica · 2025Article
- An Optimized NGS Workflow Defines Genetically Based Prognostic Categories for Patients with Uveal Melanoma.Biomolecules · 2025Article
- Lactylation modulation identifies key biomarkers and therapeutic targets in KMT2A-rearranged AML.Scientific reports · 2025Article
- Oxidative stress-related genes in uveal melanoma: the role of CALM1 in modulating oxidative stress and apoptosis and its prognostic significance.Frontiers in oncology · 2025Article
- Global trends and emerging insights in BRAF and MEK inhibitor resistance in melanoma: a bibliometric analysis.Frontiers in molecular biosciences · 2025Article
- Exploring recent advances in signaling pathways and hallmarks of uveal melanoma: a comprehensive review.Exploration of targeted anti-tumor therapy · 2025Review
- Exploring Bioinformatics Tools to Analyze the Role of CDC6 in the Progression of Polycystic Ovary Syndrome to Endometrial Cancer by Promoting Immune Infiltration.International journal of molecular sciences · 2024Article
- The impact of Benzophenone-3 on osteoarthritis pathogenesis: a network toxicology approach.Toxicology research · 2024Article
- Advances in Melanoma: From Genetic Insights to Therapeutic Innovations.Biomedicines · 2024Review
- Machine Learning Methods for Gene Selection in Uveal Melanoma.International journal of molecular sciences · 2024Article
- Uncovering the Role of Anoikis-Related Genes in Modulating Immune Infiltration and Pathogenesis of Diabetic Kidney Disease.Journal of inflammation research · 2024Article
- Impact of Physical Exercise on Melanoma Hallmarks: Current Status of Preclinical and Clinical Research.Journal of Cancer · 2024Review
- Clinical Applications of Machine Learning in the Management of Intraocular Cancers: A Narrative Review.Investigative ophthalmology & visual science · 2023Review
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Uveal melanoma (UVM) is a rare but highly aggressive intraocular tumor with a poor prognosis and limited therapeutic options. Recent studies have implicated the PI3K/AKT/mTOR pathway in the pathogenesis and progression of UVM. Here, we aimed to explore the potential mechanism of PI3K/AKT/mTOR pathway-related genes (PRGs) in UVM and develop a novel prognostic-related risk model. Using unsupervised clustering on 14 PRGs profiles, we identified three distinct subtypes with varying immune characteristics. Subtype A demonstrated the worst overall survival and showed higher expression of human leukocyte antigen, immune checkpoints, and immune cell infiltration. Further enrichment analysis revealed that subtype A mainly functioned in inflammatory response, apoptosis, angiogenesis, and the PI3K/AKT/mTOR signaling pathway. Differential analysis between different subtypes identified 56 differentially expressed genes (DEGs), with the major enrichment pathway of these DEGs associated with PI3K/AKT/mTOR. Based on these DEGs, we developed a consensus machine learning-derived signature (RSF model) that exhibited the best power for predicting prognosis among 76 algorithm combinations. The novel signature demonstrated excellent robustness and predictive ability for the overall survival of patients. Moreover, we observed that patients classified by risk scores had distinguishable immune status and mutation. In conclusion, our study identified a consensus machine learning-derived signature as a potential biomarker for prognostic prediction in UVM patients. Our findings suggest that this signature is correlated with tumor immune infiltration and may serve as a valuable tool for personalized therapy in the clinical setting.
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