An integrated analysis of DNA promoter methylation, microRNA regulation, and gene expression in gastric adenocarcinoma

Abstract

Background: Gastric adenocarcinoma (GAC), a common type of gastric cancer, poses a significant public health threat worldwide. This study aimed to determine the transcriptional regulatory mechanisms of GAC.Methods: HTSeq-FPKM raw data were obtained from The Cancer Genome Atlas Stomach Adenocarcinoma data collection. Subsequently, the limma package in R was used to identify differentially expressed genes (DEGs). Differentially methylated genes (DMGs), DEGs, and differentially expressed microRNAs (miRNAs) in normal, and tumor tissues of the same patients were screened and compared using R software tools. A functional enrichment analysis was performed using Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) for various DEGs, DMGs, promoter methylation, and miRNAs. DEG-specific methylation and transcription factors were analyzed using ENCODE ChIP-seq.Results: DEGs were centrally modified by the histone trimethylation of lysine 27 on histone H3 (H3K27me3). Upstream transcription factors of DEGs were enriched in different ChIP-seq clusters, such as Forkhead Box M1, E2F Transcription Factor 4, and suppressor of zest 12. Integrated regulatory networks of DEGs, promoter methylation, and miRNAs were constructed. Two miRNAs (hsa-mir-1 and hsa-mir-133a) and four DEGs (A disintegrin and metalloproteinase domain 12, transcription factor AP-2 alpha, solute carrier family 5 member 7, and cadherin 19) separately played important roles in the integrated regulatory network. Therefore, these DEGs, DMGs, promoter methylation, and miRNAs may play an important role in GAC pathogenesis. Conclusions:In summary, the present study results provide insights into the oncogenesis and progression of GAC, thus accelerating the development of novel targeted GAC therapies.

Transcriptomic analysis identifies CYP27A1 as a diagnostic marker for the prognosis and immunity in lung adenocarcinoma

“…The limma package in R was utilized to identify the DEGs between the tumor samples and adjacent noncancer samples (p.adj < 0.05 and ∣logFC∣ ≥ 1) [ 17 ]. The volcano plots were generated using ggplot2.…”