ArticleBiology direct2024
Machine learning model reveals the role of angiogenesis and EMT genes in glioma patient prognosis and immunotherapy.
Article in Biology direct, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- Progress in the study of molecular markers in the prognosis assessment and recurrence patterns of glioblastoma.Cancer biology & therapy · 2025Review
- The Role of MAPK12 in Prognosis of Patients With Liver Cancer and Effects on Stemness Characteristics.Stem cells international · 2025Article
- Deep learning-based prediction of TERT mutation status from MRI for glioma molecular subtyping.Frontiers in neurologyArticle
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Authors and funding
9 authors.
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Abstract
Gliomas represent a highly aggressive class of tumors located in the brain. Despite the availability of multiple treatment modalities, the prognosis for patients diagnosed with glioma remains unfavorable. Therefore, further exploration of new biomarkers is crucial to enhance the prognostic assessment of glioma and to investigate more effective treatment options. In this research, we utilized multiple machine learning techniques to assess the significance of genes related to angiogenesis and epithelial-mesenchymal transition (EMT) in the context of prognosis and treatment for glioma patients. The random forest algorithm highlighted the significance of CALU, and further analysis indicated that the effect of CALU on glioma progression may be regulated by MYC. Different machine learning approaches were employed in our investigation to uncover crucial genes associated with angiogenesis and EMT in glioma. Our findings verify the connection between these genes and the prognosis of patients with glioma, as well as the results of immunotherapeutic interventions. Notably, through experimental verification, we identified CALU as a new prognostic marker for glioma, and inhibiting the expression of CALU can impede the progression of glioma.
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Registered trials
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