MedicineCancer BiologyOncologyEpigeneticsEpigenomicsMolecular OncologyCancer BiomarkersCancer EpigenomicsCancer GenomicsCancer TranscriptomicsDna Methylation ProfilingGlioblastomaDna Methylation PatternsGlioblastoma EpigeneticsGlioblastoma PrognosisImmune Infiltration HeterogeneityLasso RegressionN6-methyladenosine RegulationPrognostic Expression SignaturesTumor Immune Cell Infiltration
Glioblastoma (GBM) is a group of intracranial neoplasms with intra-tumoral heterogeneity. RNA N6-methyladenosine (m 6 A) methylation modification reportedly plays roles in immune response. The relationship between the m 6 A modification pattern and immune cell infiltration in GBM remains unknown. Utilizing expression data of GBM patients, we thoroughly explored the potential m 6 A modification pattern and m 6 A-related signatures based on 21 regulators. Thereafter, the m 6 A methylation modification-based prognostic assessment pipeline (MPAP) was constructed to quantitatively assess GBM patients’ clinical prognosis combining the Robustness and LASSO regression. Single-sample gene-set enrichment analysis (ssGSEA) was used to estimate the specific immune cell infiltration level. We identified two diverse clusters with diverse m 6 A modification characteristics. Based on differentially expressed genes (DEGs) within two clusters, m 6 A-related signatures were identified to establish the MPAP, which can be used to quantitatively forecast the prognosis of GBM patients. In addition, the relationship between 21 m 6 A regulators and specific immune cell infiltration was demonstrated in our study and the m 6 A regulator ELAVL1 was determined to play an important role in the anticancer response to PD-L1 therapy. Our findings indicated the relationship between m 6 A methylation modification patterns and tumor microenvironment immune cell infiltration, through which we could comprehensively understand resistance to multiple therapies in GBM, as well as accomplish precise risk stratification according to m 6 A-related signatures.
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clusterProfiler: an R Package for Comparing Biological Themes Among Gene Clusters
Guangchuang Yu, Li-Gen Wang, Yanyan Han et al. · OMICS A Journal of Integrative Biology · 2012 · 38.3K citations