Dataset Information

Prediction of the Prognosis Based on Chromosomal Instability-Related DNA Methylation Patterns of ELOVL2 and UBAC2 in PTCs.

ABSTRACT: Papillary thyroid carcinoma (PTC) is the most common malignant tumor of endocrine systems. Chromosomal instability (CIN) is crucial to the clinical prognoses of tumor patients. DNA methylation plays an important role in the regulation of gene expression and CIN. Based on PTC samples from The Cancer Genome Atlas database, we used multiple regression analyses to identify methylation patterns of CpG sites with the strongest correlation with gene expression. A total of 4,997 genes were obtained through combining the CpG sites, which were represented as featured DNA methylation patterns. In order to identify CIN-related epigenetic markers of PTC survival, we developed a method to characterize CIN based on DNA methylation patterns of genes using the Student's t statistics. We found that 1,239 genes were highly associated with CIN. With the use of the log-rank test, univariate Cox regression analyses, and the Kaplan-Meier method, DNA methylation patterns of UBAC2 and ELOVL2, highly correlated with CIN, provided potential prognostic values for PTC. The higher these two genes, risk scores were correlated with worse PTC patient prognoses. Moreover, the ELOVL2 risk score was significantly different in the four stages of PTC, suggesting that it was related to the progress of PTC. The DNA methylation pattern associated with CIN may therefore be a good predictor of PTC survival.

SUBMITTER: Han J

PROVIDER: S-EPMC6906861 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

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Prediction of the Prognosis Based on Chromosomal Instability-Related DNA Methylation Patterns of ELOVL2 and UBAC2 in PTCs.

Han Jun J Chen Meijun M Fang Qingxiao Q Zhang Yanqing Y Wang Yihan Y Esma Jamaspishvili J Qiao Hong H

Molecular therapy. Nucleic acids 20191007

Papillary thyroid carcinoma (PTC) is the most common malignant tumor of endocrine systems. Chromosomal instability (CIN) is crucial to the clinical prognoses of tumor patients. DNA methylation plays an important role in the regulation of gene expression and CIN. Based on PTC samples from The Cancer Genome Atlas database, we used multiple regression analyses to identify methylation patterns of CpG sites with the strongest correlation with gene expression. A total of 4,997 genes were obtained thro ...[more]

PMID: 31698312

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Project description: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.

Dataset Information

Prediction of the Prognosis Based on Chromosomal Instability-Related DNA Methylation Patterns of ELOVL2 and UBAC2 in PTCs.

Publications

Prediction of the Prognosis Based on Chromosomal Instability-Related DNA Methylation Patterns of ELOVL2 and UBAC2 in PTCs.

Similar Datasets

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