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GLIOBLASTOMAS ARE CHARACTERIZED BY A REDUCED NUMBER OF M6A SITES IN LNCRNAs

ABSTRACT: Recent studies have suggested the potential role of posttranscriptional N6-methyladenosine (m6A) RNA modifications in diverse biological processes, including tumor development. In contrast to intensively studied mRNA modifications, insufficient information is known so far about the epitranscriptome of long non-coding RNAs (lncRNAs). Furthermore, an epitranscriptome of LncRNAs in glioma pathogenesis has not been previously presented. Thus, this study aimed to profile m6A modifications within lncRNAs in different grades of gliomas using direct RNA long-read sequencing, with the goal of yielding both nucleotide and transcript-resolved m6A hits as biomarkers for tumor progression. Here we explored ONT direct RNA-seq to detect N6-methyladenosine (m6A) sites within lncRNAs' RRACH motifs in 26 glioma tumor tissues. Generally, 98.5% of m6A-modified RRACH motifs were detected in mRNA transcripts, and 1.16% in lncRNAs. We identified 60-748 unique m6A-modified lncRNAs for individual gliomas. On average, 15.84% of all RRACH motifs within lncRNAs were modified in glioblastomas (GB), while the m6A frequency reached 23.73% in the low-grade glioma (LGG) group. Unsupervised clustering analysis based on lncRNAs' m6A status resulted in three m6A clusters. Patients within the highly modified lncRNAs cluster (C3) experienced significantly longer survival compared to patients with lower methylation rates who were clustered into C1/C2 (2901 days in C3 vs 439 days in C1, log-rank test p=0.019).  In summary, we discovered that lncRNAs are highly modified in LGG while multiple epi-marks, found in low-grade gliomas, are absent in GB tissues revealing m6A contribution to glioma pathology.

ORGANISM(S): Homo sapiens

PROVIDER: GSE282642 | GEO | 2025/05/20

REPOSITORIES: GEO

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Project description:Ribba2012 - Low-grade gliomas, tumour growth inhibition model Using longitudinal mean tumour diameter (MTD) data, this model describe the size evolution of low-grade glioma (LGG) in patients treated with chemotherapy or radiotherapy. This model is described in the article: A tumour growth inhibition model for low-grade glioma treated with chemotherapy or radiotherapy Ribba B, Kaloshi G, Peyre M, Ricard D, Calvez V, Tod M, Cajavec-Bernard B, Idbaih A, Psimaras D, Dainese L, Pallud J, Cartalat-Carel S, Delattre JY, Honnorat J, Grenier E, Ducray F. Clin. Cancer Res. 2012 Sep; 18(18): 5071-5080 Abstract: PURPOSE: To develop a tumor growth inhibition model for adult diffuse low-grade gliomas (LGG) able to describe tumor size evolution in patients treated with chemotherapy or radiotherapy. EXPERIMENTAL DESIGN: Using longitudinal mean tumor diameter (MTD) data from 21 patients treated with first-line procarbazine, 1-(2-chloroethyl)-3-cyclohexyl-l-nitrosourea, and vincristine (PCV) chemotherapy, we formulated a model consisting of a system of differential equations, incorporating tumor-specific and treatment-related parameters that reflect the response of proliferative and quiescent tumor tissue to treatment. The model was then applied to the analysis of longitudinal tumor size data in 24 patients treated with first-line temozolomide (TMZ) chemotherapy and in 25 patients treated with first-line radiotherapy. RESULTS: The model successfully described the MTD dynamics of LGG before, during, and after PCV chemotherapy. Using the same model structure, we were also able to successfully describe the MTD dynamics in LGG patients treated with TMZ chemotherapy or radiotherapy. Tumor-specific parameters were found to be consistent across the three treatment modalities. The model is robust to sensitivity analysis, and preliminary results suggest that it can predict treatment response on the basis of pretreatment tumor size data. CONCLUSIONS: Using MTD data, we propose a tumor growth inhibition model able to describe LGG tumor size evolution in patients treated with chemotherapy or radiotherapy. In the future, this model might be used to predict treatment efficacy in LGG patients and could constitute a rational tool to conceive more effective chemotherapy schedules. This model is hosted on BioModels Database and identified by: BIOMD0000000521 . To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models . To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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GLIOBLASTOMAS ARE CHARACTERIZED BY A REDUCED NUMBER OF M6A SITES IN LNCRNAs

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