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. 2022 Mar 10:9:844973.
doi: 10.3389/fmolb.2022.844973. eCollection 2022.

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

Aierpati Maimaiti  1 , Yirizhati Aili  1 , Mirzat Turhon  2   3 , Kaheerman Kadeer  1 , Paziliya Aikelamu  1 , Zhitao Wang  1 , Weiwei Niu  1 , Maimaitili Aisha  1 , Maimaitijiang Kasimu  1 , Yongxin Wang  1 , Zengliang Wang  1
Affiliations

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

Aierpati Maimaiti et al. Front Mol Biosci. .

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.

Keywords: DNA methylation; biomarker; genomic instability; long non-coding RNA; lower-grade glioma; tumour microenvironment.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
DNA methylation-related lncRNAs (DMlncRNAs) in patients with LGGs. (A) Flow chart of the study design. (B) Sankey relational diagram for 20 regulatory genes of DNA methylation and DMlncRNAs. (C) Heat map representing the correlation among the 20 DNA methylation-related genes and the 5 prognostic DMlncRNAs.
FIGURE 2
FIGURE 2
DNA methylation-related lncRNA (DMlncRNA) signature for predicting outcomes in the training set. (A) Lasso–Cox analysis suggests a significant correlation between the five DMlncRNAs and patient survival. (B) Optimal values of penalty parameters determined via cross-validation with 1,000 replicates. (C) Multivariate Cox regression analysis suggests a high correlation between the selected DMlncRNAs and clinical prognosis. (D) Distribution of the DMlncRNA model-based risk signature. (E) Various profiles of survival time and survival status in the low- and high-risk groups. (F) Heat map showing the expression levels of the five prognostic lncRNAs in all patients. (G) The overall survival of high- and low-risk patients in the training cohort determined based on DNA methylation-related lncRNAs via Kaplan–Meier analysis. (H) 1-, 2- and 3-years ROC curves of patients.
FIGURE 3
FIGURE 3
Validation of the 5-DNA-methylation-related-lncRNA (DMlncRNA) risk model in the test and TCGA cohorts. (A) DMlncRNA model-based risk signature profile in the test cohort. (B) Survival status and survival time profiles in the low- and high-risk groups in the test cohort. (C) Heat map showing the expression profiles of the five prognostic DMlncRNAs for each patient in the test cohort. (D) Overall survival of patients in the low- and high-risk groups based on Kaplan–Meier analysis in the test cohort. (E) ROC curves for predicting 1-, 2- and 3-years survival rates based on DMlncRNAs in the test cohort. (F–J) Validation of these findings in TCGA cohort.
FIGURE 4
FIGURE 4
(A,B) Prognostic value of the five DNA methylation-related lncRNAs (DMlncRNAs) in the training (A) and test (B) cohorts. (C–F) Nomogram based on the five DMlncRNAs for every feature in the training cohort to predict 1-, 3- and 5-years survival. Nomograms were verified via DCA and calibration curves. (G–J) Nomogram based on the five DMlncRNAs for every feature in the test cohort to predict 1-, 3- and 5-years survival and verification of nomogram via DCA and calibration curves.
FIGURE 5
FIGURE 5
Validation of DNA methylation-related lncRNAs (DMlncRNAs) as an independent prognostic biomarker. (A–C) Sex, grade, age, diagnoses, tumour type (primary/recurrent), IDH1 R132 mutation status, PTEN status, EGFR status, ATRX status, TP53 status and DMlncRNAs are accurate markers for predicting the prognosis of patients with LGGs. (D–F) Multivariate Cox regression analysis suggests that DMlncRNAs and age are correlated with overall survival, thus indicating that DMlncRNAs are independent predictors of survival in patients with LGGs. (G–I) ROC curve analysis suggests high prognostic accuracy of clinicopathological parameters, such as age, sex, tumour grade, diagnoses, tumour type (primary/recurrent), IDH1 R132 mutation status, PTEN status, EGFR status, ATRX status, TP53 status and DMlncRNA-based prognostic risk score, in predicting 1-, 3- and 5-years survival.
FIGURE 6
FIGURE 6
(A–J) Overall survival patterns of patients grouped based on tumour grade, sex, age radiation therapy and tumour type (primary/recurrent) in low- and high-risk patients in TCGA cohort.
FIGURE 7
FIGURE 7
Prognostic value of DNA methylation-related lncRNAs (DMlncRNAs) evaluated using the CGGA mRNA-seq-693 and GSE16011 datasets as external independent cohorts. (A–L) Box plots showing the expression levels of CRNDE and CYTOR in patients grouped based on 1p19q codeletion status, tumour grade, age (<40 and ≥40 years), PRS type, chemotherapy status and IDH1 mutation status in the CGGA mRNA-seq-693 cohort (RNA-seq data). (M–O) Box graphs showing the expression level of SNHG18 in patients grouped based on IDH1 mutation status, 1p19q codeletion status and age in the CGGA mRNA-seq-693 cohort. (P) Box plots showing the expression level of CRNDE in patients grouped based on IDH1 (R132) mutation status in the GSE16011 cohort.
FIGURE 8
FIGURE 8
Correlation of risk scores with immune cell infiltration and overall survival in LGGs. (A–C) Relationship among the ESTIMATE, immune and stromal scores in the high- and low-risk groups. (D) Prognostic value of ESTIMATE scores as determined via Kaplan–Meier survival analysis. (E) Prognostic significance of immune scores as determined via Kaplan–Meier survival analysis. (F) Prognostic significance of stromal scores as determined via Kaplan–Meier survival analysis. (G) Distribution diagram of 22 types of TICs in different risk patients. (H) The Heatmap of expression of 22 types of TICs in different risk patients. The deepening of the red color indicates an increased level of expression. (I) Differentiation ratio of 22 immune cell types between the low- and high-DMlncRNA-expression groups. (J–L) Kaplan–Meier survival analysis for activated NK cells, resting mast cells and activated mast cells in the low- and high-risk groups.
FIGURE 9
FIGURE 9
(A–D) KEGG and GO enrichment analyses, with significant enrichment indicated by p and q < 0.05.
FIGURE 10
FIGURE 10
Validation of the expression of DMlncRNAs. (A–C) Comparison of the expression profiles of three lncRNAs (CRNDE, CYTOR and SNHG18) between TCGA (518 LGG samples) and GTEx (207 normal brain samples) cohorts via GEPIA. (D–F) Disease-free survival evaluated based on three lncRNAs via Kaplan-Meier survival curves. (G–I) Bar plots demonstrating the expression of three lncRNAs in LGG and normal brain samples evaluated via qRT-PCR (***p < 0.001).
FIGURE 11
FIGURE 11
Two- and three-dimensional conformers of the selected compounds obtained via CMap analysis. (A) Calmidazolium, (B) Etacrynic acid, (C) Megestrol, (D) Lomustine, (E) Triamterene, (F) Monobenzone, (G) Parthenolide, (H) Amiprilose, (I) Ciclopirox, (J) Gelsemine.
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