
Biology Direct, Journal Year: 2024, Volume and Issue: 19(1)
Published: Nov. 12, 2024
Gliomas represent a highly aggressive class of tumors located in the brain. Despite availability multiple treatment modalities, prognosis for patients diagnosed with glioma remains unfavorable. Therefore, further exploration new biomarkers is crucial to enhance prognostic assessment and investigate more effective options. In this research, we utilized machine learning techniques assess significance genes related angiogenesis epithelial-mesenchymal transition (EMT) context patients. The random forest algorithm highlighted CALU, analysis indicated that effect CALU on progression may be regulated by MYC. Different approaches were employed our investigation uncover associated EMT glioma. Our findings verify connection between these glioma, as well results immunotherapeutic interventions. Notably, through experimental verification, identified marker inhibiting expression can impede
Language: Английский