Complex impact of DNA methylation on transcriptional dysregulation across 22 human cancer types

Wang, Zishan; Yin, Jiaqi; Zhou, Weiwei; Bai, Jing; Xie, Yunjin; Xu, Kang; Zheng, Xiangyi; Xiao, Jun; Zhou, Li; Qi, Xiaolin; Li, Yongsheng; Li, Xia; Xu, Juan

doi:10.1093/nar/gkaa041

Cited by 34 publications

(36 citation statements)

References 69 publications

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“…Although inverse correlation between promoter methylation and gene expression has been observed in different tissues [ 32 ], the exact regulatory role of methylation on transcriptome has yet to be uncovered [ 33 ]. In particular, transcriptional regulation by methylation in cancer has been shown to be a complex molecular mechanism, characterized by the intricate interplay between SNPs, transcription factors and DNA methylation in regulating gene expression [ 34 – 36 ].…”

Section: Resultsmentioning

confidence: 99%

RNAAgeCalc: A multi-tissue transcriptional age calculator

Kuan

2020

PLoS ONE

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Biological aging reflects decline in physiological functions and is an effective indicator of morbidity and mortality. Numerous epigenetic age calculators are available, however biological aging calculators based on transcription remain scarce. Here, we introduce RNAAgeCalc, a versatile across-tissue and tissue-specific transcriptional age calculator. By performing a meta-analysis of transcriptional age signature across multi-tissues using the GTEx database, we identify 1,616 common age-related genes, as well as tissue-specific age-related genes. Based on these genes, we develop new across-tissue and tissue-specific age predictors. We show that our transcriptional age calculator outperforms other prior age related gene signatures as indicated by the higher correlation with chronological age as well as lower median and median error. Our results also indicate that both racial and tissue differences are associated with transcriptional age. Furthermore, we demonstrate that the transcriptional age acceleration computed from our within-tissue predictor is significantly correlated with mutation burden, mortality risk and cancer stage in several types of cancer from the TCGA database, and offers complementary information to DNA methylation age. RNAAgeCalc is available at http://www.ams.sunysb.edu/~pfkuan/softwares. html#RNAAgeCalc, both as Bioconductor and Python packages, accompanied by a userfriendly interactive Shiny app.

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Section: Resultsmentioning

confidence: 99%

RNAAgeCalc: A multi-tissue transcriptional age calculator

Kuan

2020

PLoS ONE

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“…DNA methylation is an epigenetic modification that plays a crucial role in regulating the growth, development, gene expression pattern, and stability of the genome without changing the DNA sequence [12,13]. Many studies in recent years have found that abnormal DNA methylation is closely related to tumorigenesis, development, and cell canceration [14][15][16]. In particular, alterations in DNA methylation can supply crucial information for the early diagnosis and prognosis of cancer [17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Prognostic Value of DNA Methylation-Driven Genes in Clear Cell Renal Cell Carcinoma: A Study Based on Methylation and Transcriptome Analyses

Xie

Hou

et al. 2020

Disease Markers

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Background. Few previous studies have comprehensively explored the level of DNA methylation and gene expression in ccRCC. The purpose of this study was to identify the key clear cell renal cell carcinoma- (ccRCC-) related DNA methylation-driven genes (MDG) and to build a prognostic model based on the level of DNA methylation. Methods. RNA-seq transcriptome data and DNA methylation data were obtained from The Cancer Genome Atlas. Based on the MethylMix algorithm, we obtain ccRCC-related MDG. The univariate and multivariate Cox regression analyses were employed to investigate the correlation between patient overall survival and the methylation level of each MDG. Finally, a prognosis risk score was established based on a linear combination of the regression coefficient derived from the multivariate Cox regression model (β) multiplied with the methylation level of the gene. Results. 19 ccRCC-related MDG were identified. Three MDG (NCKAP1L, EVI2A, and BATF) were further screened and integrated into a prognostic risk score model, risk score=3.710∗methylation level of NCKAP1L+−3.892∗methylation level of EVI2A+−3.907∗methylation level of BATF. The risk model was independent from conventional clinical characteristics as a prognostic factor for ccRCC (HR=1.221, 95% confidence interval: 1.063–1.402, and P=0.005). The joint survival analysis showed that the gene expression and methylation levels of the prognostic genes EVI2A and BATF were significantly related with prognosis. Conclusion. This study provided an important bioinformatics foundation for in-depth studies of ccRCC DNA methylation.

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“…All patients with glioma were divided into a training set and a testing set of similar gender and age. We next used the Cox hazard analysis to identify the genes associated with survival 32 , 33 . Genes with p < 0.05 were identified based on the expression profiles in the training set.…”

Section: Methodsmentioning

confidence: 99%

Systematic analysis of enhancer regulatory circuit perturbation driven by copy number variations in malignant glioma

Xiao¹,

Jin²,

Zhang³

et al. 2021

Theranostics

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Background: Enhancers are emerging regulatory regions controlling gene expression in diverse cancer types. However, the functions of enhancer regulatory circuit perturbations driven by copy number variations (CNVs) in malignant glioma are unclear. Therefore, we aimed to investigate the comprehensive enhancer regulatory perturbation and identify potential biomarkers in glioma. Results: We performed a meta-analysis of the enhancer centered regulatory circuit perturbations in 683 gliomas by integrating CNVs, gene expression, and transcription factors (TFs) binding. We found widespread CNVs of enhancers during glioma progression, and CNVs were associated with the perturbations of enhancer activities. In particular, the degree of perturbations for amplified enhancers was much greater accompanied by the glioma malignant progression. In addition, CNVs and enhancers cooperatively regulated the expressions of cancer-related genes. Genome-wide TF binding profiles revealed that enhancers were pervasively regulated by TFs. A network-based analysis of TF-enhancer-gene regulatory circuits revealed a core TF-gene module (58 interactions including seven genes and 14 TFs) that was associated survival of patients with glioma (p < 0.001). Finally, we validated this prognosis-associated TF-gene regulatory module in an independent cohort. In summary, our analyses provided new molecular insights for enhancer-centered transcriptional perturbation in glioma therapy. Conclusion: Integrative analysis revealed enhancer regulatory perturbations in glioma and also identified a network module that was associated with patient survival, thereby providing novel insights into enhancer-centered cancer therapy.

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Complex impact of DNA methylation on transcriptional dysregulation across 22 human cancer types

Cited by 34 publications

References 69 publications

RNAAgeCalc: A multi-tissue transcriptional age calculator

RNAAgeCalc: A multi-tissue transcriptional age calculator

Prognostic Value of DNA Methylation-Driven Genes in Clear Cell Renal Cell Carcinoma: A Study Based on Methylation and Transcriptome Analyses

Systematic analysis of enhancer regulatory circuit perturbation driven by copy number variations in malignant glioma

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