Modification Patterns of DNA Methylation-Related lncRNAs Regulating Genomic Instability for Improving the Clinical Outcomes and Tumour Microenvironment Characterisation of Lower-Grade Gliomas

Abstract

Background: DNA methylation is an important epigenetic modification that affects genomic instability and regulates gene expression. Long non-coding RNAs (lncRNAs) modulate gene expression by interacting with chromosomal modifications or remodelling factors. It is urgently needed to evaluate the effects of DNA methylation-related lncRNAs (DMlncRNAs) on genome instability and further investigate the mechanism of action of DMlncRNAs in mediating the progression of lower-grade gliomas (LGGs) and their impact on the immune microenvironment.Methods: LGG transcriptome data, somatic mutation profiles and clinical features analysed in the present study were obtained from the CGGA, GEO and TCGA databases. Univariate, multivariate Cox and Lasso regression analyses were performed to establish a DMlncRNA signature. The KEGG and GO analyses were performed to screen for pathways and biological functions associated with key genes. The ESTIMATE and CIBERSORT algorithms were used to determine the level of immune cells in LGGs and the immune microenvironment fraction. In addition, DMlncRNAs were assessed using survival analysis, ROC curves, correlation analysis, external validation, independent prognostic analysis, clinical stratification analysis and qRT-PCR.Results: We identified five DMlncRNAs with prognostic value for LGGs and established a prognostic signature using them. The Kaplan–Meier analysis revealed 10-years survival rate of 10.10% [95% confidence interval (CI): 3.27–31.40%] in high-risk patients and 57.28% (95% CI: 43.17–76.00%) in low-risk patients. The hazard ratio (HR) and 95% CI of risk scores were 1.013 and 1.009–1.017 (p < 0.001), respectively, based on the univariate Cox regression analysis and 1.009 and 1.004–1.013 (p < 0.001), respectively, based on the multivariate Cox regression analysis. Therefore, the five-lncRNAs were identified as independent prognostic markers for patients with LGGs. Furthermore, GO and KEGG analyses revealed that these lncRNAs are involved in the prognosis and tumorigenesis of LGGs by regulating cancer pathways and DNA methylation.Conclusion: The findings of the study provide key information regarding the functions of lncRNAs in DNA methylation and reveal that DNA methylation can regulate tumour progression through modulation of the immune microenvironment and genomic instability. The identified prognostic lncRNAs have high potential for clinical grouping of patients with LGGs to ensure effective treatment and management.

An m6A/m5C/m1A/m7G-Related Long Non-coding RNA Signature to Predict Prognosis and Immune Features of Glioma

“… Chen et al (2022) constructed prognostic signatures of N6-methyladenosine-related lncRNAs in gliomas, demonstrating the feasibility of applying prognostic signatures for patients with glioma. Maimaiti et al (2022) analyzed the relationships between clinical outcomes and methylation-related lncRNAs in LGG and the tumor microenvironment, and found that gliomas are characterized by the tumor microenvironment. Liu X et al (2022) also used m5C-related lncRNAs for prognostic prediction and immune response signature analysis, while Wang et al demonstrated the prognostic value and immune landscape of lncRNAs associated with m6A/m5C/m1A in squamous cell carcinoma of the head and neck.…”

DNA methylation regulator-mediated modification patterns and risk of intracranial aneurysm: a multi-omics and epigenome-wide association study integrating machine learning, Mendelian randomization, eQTL and mQTL data

“…To eliminate batch effects, transcriptomic data from the three training datasets, namely GSE54083, GSE75436, and GSE13353, were combined using the "combats" approach in the "sva" package, and principal component analysis (PCA) was used to verify the successful removal of the batch effect. A total of 19 methylation-related genes (MRGs) were annotated based on previous literature, including 3 erasers (TET1, TET2, TET3), 3 writers (DNMT1, DNMT3A, DNMT3B), and 13 readers (UHRF2, MECP2, UNG, TDG, NTHL1, SMUG1,MBD1, MBD2, MBD3, MBD4, ZBTB33, ZBTB38, ZBTB4) [ 34 , 35 ]. The expression of these MRGs between the samples was compared using either the Kruskal–Wallis test or Wilcox test, visualized using the RCircos package to study their chromosomal location, and their protein levels were explored using the STRING database to understand their relationships.…”