Abstract
Glioblastoma (GBM) is a group of intracranial neoplasms with intra-tumoral heterogeneity. RNA N6-methyladenosine (m6A) methylation modification reportedly plays roles in immune response. The relationship between the m6A modification pattern and immune cell infiltration in GBM remains unknown. Utilizing expression data of GBM patients, we thoroughly explored the potential m6A modification pattern and m6A-related signatures based on 21 regulators. Thereafter, the m6A 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 m6A modification characteristics. Based on differentially expressed genes (DEGs) within two clusters, m6A-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 m6A regulators and specific immune cell infiltration was demonstrated in our study and the m6A regulator ELAVL1 was determined to play an important role in the anticancer response to PD-L1 therapy. Our findings indicated the relationship between m6A 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 m6A-related signatures.
Highlights
Glioblastoma (GBM) is the most common lethal neoplasm of the central nervous system, accounting for approximately half of primary brain tumors and almost 60% of all types of gliomas [1]
From Gliovis, a published data visualization web tool for brain tumor expression profile data uploaded on Gene-Expression Omnibus (GEO) and the Cancer Genome Atlas (TCGA) [34], six glioblastoma datasets (Donson et al, n = 21; Ducray et al, n = 48; Gravendeel et al, n = 163; Kamoun et al, n = 19; Murat et al, n = 84; Rembrandt et al, n = 209); and corresponding clinical data were obtained for downstream analysis, which was sequenced using Affymetrix expression arrays (HG-U133_Plus_2, HGU133A, HG_U95Av2, and HuGene-1_0-st)
To illustrate the process by which we constructed the modification-based prognostic assessment pipeline (MPAP) and what datasets were applied in our study, a schematic workflow was developed dividing the overall work into four steps broadly (Figure 1A)
Summary
Glioblastoma (GBM) is the most common lethal neoplasm of the central nervous system, accounting for approximately half of primary brain tumors and almost 60% of all types of gliomas [1]. Despite the killing effect of systemic therapy after complete resection, infiltrating cancer cells can often escape, resulting in tumor recurrence, progression, and even death [5]. Recent advances in precision oncology, immunology, and other disciplines have uncovered multiple experimental therapies, such as immunotherapy, gene therapy, and novel drug-delivery technologies, which are emerging as powerful tools to solve the complicated GBM treatment difficulties, including low permeability of the blood-brain barrier, complex tumor signaling pathways, and the absence of specific biomarkers [6]. Since multimodality therapy heralds promise in achieving durable and broad anticancer responses, it is urgent to establish a reliable tumor classification and prognosis model for cancer treatment strategy planning
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