No abstract

m6A RNA methylation is an emerging epigenetic modification, and its potential role in immunity and stemness remains unknown. Based on 17 widely recognized m6A regulators, the m6A modification patterns and corresponding characteristics of immune infiltration and stemness of 1152 low-grade glioma samples were comprehensively analyzed. Machine-learning strategies for constructing m6AScores were trained to quantify the m6A modification patterns of individual samples. Here, we reveal a significant correlation between the multi-omics data of regulators and clinicopathological parameters. We identified two distinct m6A modification patterns (an immune-activated differentiation pattern and an immune-desert dedifferentiation pattern) and four regulatory patterns of m6A methylation on immunity and stemness. We show that the m6AScores can predict the molecular subtype of low-grade glioma, the abundance of immune infiltration, the enrichment of signaling pathways, gene variation and prognosis. The concentration of high immunogenicity and clinical benefits in the low-m6AScore group confirmed the sensitive response to radio-chemotherapy and immunotherapy in patients with high-m6AScore. The results of the pan-cancer analyses illustrate the significant correlation between m6AScore and clinical outcome, the burden of neoepitope, immune infiltration and stemness. The assessment of individual tumor m6A modification patterns will guide us in improving treatment strategies and developing objective diagnostic tools.

N6-methyladenosine (m6A) RNA methylation, the most common form of mRNA modification and regulated by the m6A RNA methylation regulators ("writers," "erasers," and "readers"), has been reported to be associated with the progression of the malignant tumor. However, its role in glioblastoma (GBM) has been poorly known. This study aimed to identify the expression, potential functions, and prognostic values of m6A RNA methylation regulators in GBM. Here, we revealed that the 13 central m6A RNA methylation regulators were firmly related to the clinical and molecular phenotype of GBM. Taking advantage of consensus cluster analysis, we obtained two categories of GBM samples and found malignancy-related processes of m6A methylation regulators and compounds that specifically targeted the malignant processes. Besides, we also obtained a list of genes with poor prognosis in GBM. Finally, we derived a risk-gene signature with three selected m6A RNA methylation regulators, which allowed us to extend the in-depth study and dichotomized the OS of patients with GBM into high-and low-risk subgroups. Notably, this risk-gene signature could be used as independent prognostic markers and accurate clinicopathological parameter predictors. In conclusion, m6A RNA methylation regulators are a type of vital participant in the malignant progression of GBM, with a critical potential in the prognostic stratification and treatment strategies of GBM.

The progression of most human cancers mainly involves the gradual accumulation of the loss of differentiated phenotypes and the sequential acquisition of progenitor and stem cell-like features. Glioblastoma multiforme (GBM) stem cells (GSCs), characterized by self-renewal and therapeutic resistance, play vital roles in GBM. However, a comprehensive understanding of GBM stemness remains elusive. Two stemness indices, mRNAsi and EREG-mRNAsi, were employed to comprehensively analyze GBM stemness. We observed that mRNAsi was significantly related to multi-omics parameters (such as mutant status, sample type, transcriptomics, and molecular subtype). Moreover, potential mechanisms and candidate compounds targeting the GBM stemness signature were illuminated. By combining weighted gene co-expression network analysis with differential analysis, we obtained 18 stemness-related genes, 10 of which were significantly related to survival. Moreover, we obtained a prediction model from both two independent cancer databases that was not only an independent clinical outcome predictor but could also accurately predict the clinical parameters of GBM. Survival analysis and experimental data confirmed that the five hub genes (CHI3L2, FSTL3, RPA3, RRM2, and YTHDF2) could be used as markers for poor prognosis of GBM. Mechanistically, the effect of inhibiting the proliferation of GSCs was attributed to the reduction of the ratio of CD133 and the suppression of the invasiveness of GSCs. The results based on an in vivo xenograft model are consistent with the finding that knockdown of the hub gene inhibits the growth of GSCs in vitro. Our approach could be applied to facilitate the development of objective diagnostic and targeted treatment tools