Construction of miRNA-mRNA-TF Regulatory Network for Diagnosis of Gastric Cancer

Abstract

Gastric cancer (GC), as an epidemic cancer worldwide, has more than 1 million new cases and an estimated 769,000 deaths worldwide in 2020, ranking fifth and fourth in global morbidity and mortality. In mammals, both miRNAs and transcription factors (TFs) play a partial role in gene expression regulation. The mRNA expression profile and miRNA expression profile of GEO database were screened by GEO2R for differentially expressed genes (DEGs) and differentially expressed miRNAs (DEMs). Then, DAVID annotated the functions of DEGs to understand the functions played in biological processes. The prediction of potential target genes of miRNA and key TFs of mRNA was performed by mipathDB V2.0 and CHEA3, respectively, and the gene list comparison was performed to look for overlapping genes coregulated by key TFs and DEMs. Finally, the obtained miRNAs, TF, and overlapping genes were used to construct the miRNA-mRNA-TF regulatory network, which was verified by RT-qPCR. 76 upregulated DEGs, 199 downregulated DEGs, and 3 upregulated miRNAs (miR-199a-3p/miR-199b-3p, miR-125b-5p, and miR-199a-5p) were identified from the expression profiles of mRNA (GSE26899, GSE29998, GSE51575, and GSE13911) and miRNA (GSE93415), respectively. Through database prediction and gene list comparison, it was found that among the 199 downregulated DEGs, 61, 71, and 69 genes were the potential targets of miR-199a-3p/miR-199b-3p, miR-125b-5p, and miR-199a-5p, respectively. 199 downregulated DEGs were used as the gene list for the prediction of key TFs, and the results showed that RFX6 ranked the highest. The potential target overlap genes of miR-199a-3p/miR-199b-3p, miR-125b-5p, and miR-199a-5p were 4 genes (SH3GL2, ATP4B, CTSE, and SORBS2), 7 genes (SLC7A8, RNASE4, ESRRG, PGC, MUC6, Fam3B, and FMO5), and 6 genes (CHGA, PDK4, TMPRSS2, CLIC6, GPX3, and PSCA), respectively. Finally, we constructed a miRNA-mRNA-TF regulatory network based on the above 17 mRNAs, 3 miRNAs, and 1 TF and verified by RT-qPCR and western blot results that the expression of RFX6 was downregulated in GC tissues. These identified miRNAs, mRNAs, and TF have a certain reference value for further exploration of the regulatory mechanism of GC.

Establishment of a Combined Diagnostic Model of Abdominal Aortic Aneurysm with Random Forest and Artificial Neural Network

“…To further understand the function of DEGs, they were subject to GO enrichment, which categorizes genes into biological process (BP), cellular component (CC), and molecular function (MF) [ 24 , 25 ]. Moreover, KEGG analysis [ 26 ] was used to describe metabolic pathways, using DAVID 6.8 ( https://david.ncifcrf.gov/home.jisp ).…”

Autophagy-Related Genes Are Involved in the Progression and Prognosis of Asthma and Regulate the Immune Microenvironment

“…The transcriptional regulatory networks of prognosis-related genes were predicted from the ChEA3 database ( ). The database integrated ENCODE; ReMap; some independently published CHIP-seq data; and transcription factor co-expression data from GTEx, TCGA, and ARCHS4 RNA-seq data ( 31 ). The target miRNA was first predicted using the starBase ( ) database to predict the regulation of target genes by noncoding RNAs, and prediction results included the analysis of RNA22, miRanda, and TargetScan ( 32 ).…”

Identification of pyroptosis-related subtypes and establishment of prognostic model and immune characteristics in asthma

“…The module with the maximum correlation with the trait was selected for subsequent analysis. The ChEA3 database ( ) includes a large number of independently published ChIP-seq data and integrates transcription factor coexpression data on the basis of RNA-seq data ( 18 ). The transcriptional regulatory network of key modules is predicted from this database.…”