Integrating machine learning and bioinformatics analysis to m6A regulator-mediated methylation modification models for predicting glioblastoma patients' prognosis and immunotherapy response.

ABSTRACT:

Background

Epigenetic regulations of immune responses are essential for cancer development and growth. As a critical step, comprehensive and rigorous explorations of m6A methylation are important to determine its prognostic significance, tumor microenvironment (TME) infiltration characteristics and underlying relationship with glioblastoma (GBM).

Methods

To evaluate m6A modification patterns in GBM, we conducted unsupervised clustering to determine the expression levels of GBM-related m6A regulatory factors and performed differential analysis to obtain m6A-related genes. Consistent clustering was used to generate m6A regulators cluster A and B. Machine learning algorithms were implemented for identifying TME features and predicting the response of GBM patients receiving immu

SUBMITTER: Li C 

PROVIDER: S-EPMC10257999 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature