Machine learning model reveals the role of angiogenesis and EMT genes in glioma patient prognosis and immunotherapy.

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 approa

SUBMITTER: Feng S 

PROVIDER: S-EPMC11555840 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature