Exploration of the biological mechanisms of CENPA as an oncogene in glioma: Screening based on cancer functional status.

ABSTRACT: Glioma is the most common primary tumour in central nervous system, characterized by high invasiveness, a high recurrence rate and extremely poor prognosis. Machine learning based on cancer functional state helps to combine multi-omics methods to screen for key gene, such as CENPA, that influences the phenotype of glioma and patients' prognosis. Based on 14 CFS, glioma was divided into three subtypes. Bioinformatics and machine learning methods were utilized to develop an enhanced prognostic prediction signature based on three subtypes. We selected CENPA as a hub biomarker and conducted in vitro experiments such as IHC, western blot, Coip, transwell, cck8, flow cytometry, scratch assay, qPCR, AlphaFold, MOE and in vivo experiments. We identified three subtypes of glioma based on the 14 CFS

SUBMITTER: Ling Y 

PROVIDER: S-EPMC11610157 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature