Discovery and Validation of Novel Methylation Markers in Helicobacter pylori -Associated Gastric Cancer.
Huan WangNian-Shuang LiCong HeChuan XieYin ZhuNong Hua LuYi HuPublished in: Disease markers (2021)
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.
Keyphrases
- dna methylation
- genome wide
- gene expression
- helicobacter pylori
- gas chromatography
- copy number
- electronic health record
- endothelial cells
- poor prognosis
- genome wide identification
- mass spectrometry
- transcription factor
- risk assessment
- machine learning
- big data
- climate change
- small molecule
- human health
- single cell
- high throughput
- long non coding rna
- high resolution