scholarly journals A network-based pathway-extending approach using DNA methylation and gene expression data to identify altered pathways

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Jie Li ◽  
Qiaosheng Zhang ◽  
Zhuo Chen ◽  
Dechen Xu ◽  
Yadong Wang
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Gene Expression Data ◽  
Expression Data

scholarly journals A Genome-Wide Integrative Association Study of DNA Methylation and Gene Expression Data and Later Life Cognitive Functioning in Monozygotic Twins

2020 ◽  
Vol 14 ◽  
Author(s):  
Mette Soerensen ◽  
Dominika Marzena Hozakowska-Roszkowska ◽  
Marianne Nygaard ◽  
Martin J. Larsen ◽  
Veit Schwämmle ◽  
...  
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Cognitive Functioning ◽  
Association Study ◽  
Gene Expression Data ◽  
Monozygotic Twins ◽  
Later Life ◽  
Expression Data ◽  
Genome Wide ◽  
A Genome

Identification of functionally methylated regions based on discriminant analysis through integrating methylation and gene expression data

2015 ◽  
Vol 11 (7) ◽  
pp. 1786-1793 ◽  
Author(s):  
Yuanyuan Zhang ◽  
Junying Zhang
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Discriminant Analysis ◽  
Gene Expression Data ◽  
Cellular Differentiation ◽  
Expression Data

DNA methylation is essential not only in cellular differentiation but also in diseases.

scholarly journals PiiL: visualization of DNA methylation and gene expression data in gene pathways

BMC Genomics ◽  
2017 ◽  
Vol 18 (1) ◽  
Author(s):  
Behrooz Torabi Moghadam ◽  
Neda Zamani ◽  
Jan Komorowski ◽  
Manfred Grabherr
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Gene Expression Data ◽  
Expression Data

scholarly journals Integrating DNA Methylation and Gene Expression Data in the Development of the Soybean-Bradyrhizobium N2-Fixing Symbiosis

2016 ◽  
Vol 7 ◽  
Author(s):  
Austin G. Davis-Richardson ◽  
Jordan T. Russell ◽  
Raquel Dias ◽  
Andrew J. McKinlay ◽  
Ronald Canepa ◽  
...  
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Gene Expression Data ◽  
Expression Data

scholarly journals An integrated analysis of genome‐wide DNA methylation and gene expression data in hepatocellular carcinoma

FEBS Open Bio ◽  
2018 ◽  
Vol 8 (7) ◽  
pp. 1093-1103 ◽  
Author(s):  
Xiang‐Jun Sun ◽  
Ming‐Chun Wang ◽  
Feng‐Hua Zhang ◽  
Xiao Kong
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Dna Methylation ◽  
Gene Expression Data ◽  
Integrated Analysis ◽  
Expression Data ◽  
Genome Wide

scholarly journals Discovery and Validation of Novel Methylation Markers in Helicobacter pylori-Associated Gastric Cancer

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Huan Wang ◽  
Nian-Shuang Li ◽  
Cong He ◽  
Chuan Xie ◽  
Yin Zhu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gastric Cancer ◽  
Dna Methylation ◽  
Helicobacter Pylori ◽  
Gene Expression Data ◽  
Functional Enrichment ◽  
Expression Data ◽  
H Pylori

Previous studies have shown that abnormal methylation is an early key event in the pathogenesis of most human cancers, contributing to the development of tumors. However, little attention has been given to the potential of DNA methylation patterns as markers for Helicobacter pylori- (H. pylori-) associated gastric cancer (GC). In this study, an integrated analysis of DNA methylation and gene expression was conducted to identify some potential key epigenetic markers in H. pylori-associated GC. DNA methylation data of 28 H. pylori-positive and 168 H. pylori-negative GC samples were compared and analyzed. We also analyzed the gene expression data of 18 H. pylori-positive and 145 H. pylori-negative GC cases. Finally, the results were verified by in vitro and in vivo experiments. A total of 5609 differentially methylated regions associated with 2454 differentially methylated genes were identified. A total of 228 differentially expressed genes were identified from the gene expression data of H. pylori-positive and H. pylori-negative GC cases. The screened genes were analyzed for functional enrichment. Subsequently, we obtained 28 genes regulated by methylation through a Venn diagram, and we identified five genes (GSTO2, HUS1, INTS1, TMEM184A, and TMEM190) downregulated by hypermethylation. HUS1, GSTO2, and TMEM190 were expressed at lower levels in GC than in adjacent samples ( P < 0.05 ). Moreover, H. pylori infection decreased HUS1, GSTO2, and TMEM190 expression in vitro and in vivo. Our study identified HUS1, GSTO2, and TMEM190 as novel methylation markers for H. pylori-associated GC.

Sign in / Sign up

Export Citation Format

Share Document