Identification of hub genes and construction of an mRNA-miRNA-lncRNA network of gastric carcinoma using integrated bioinformatics analysis

Abstract

Background Gastric carcinoma (GC) is one of the most common cancer globally. Despite its worldwide decline in incidence and mortality over the past decades, gastric cancer still has a poor prognosis. However, the key regulators driving this process and their exact mechanisms have not been thoroughly studied. This study aimed to identify hub genes to improve the prognostic prediction of GC and construct a messenger RNA-microRNA-long non-coding RNA(mRNA-miRNA-lncRNA) regulatory network. Methods The GSE66229 dataset, from the Gene Expression Omnibus (GEO) database, and The Cancer Genome Atlas (TCGA) database were used for the bioinformatic analysis. Differential gene expression analysis methods and Weighted Gene Co-expression Network Analysis (WGCNA) were used to identify a common set of differentially co-expressed genes in GC. The genes were validated using samples from TCGA database and further validation using the online tools GEPIA database and Kaplan-Meier(KM) plotter database. Gene set enrichment analysis(GSEA) was used to identify hub genes related to signaling pathways in GC. The RNAInter database and Cytoscape software were used to construct an mRNA-miRNA-lncRNA network. Results A total of 12 genes were identified as the common set of differentially co-expressed genes in GC. After verification of these genes, 3 hub genes, namely CTHRC1, FNDC1, and INHBA, were found to be upregulated in tumor and associated with poor GC patient survival. In addition, an mRNA-miRNA-lncRNA regulatory network was established, which included 12 lncRNAs, 5 miRNAs, and the 3 hub genes. Conclusions In summary, the identification of these hub genes and the establishment of the mRNA-miRNA-lncRNA regulatory network provide new insights into the underlying mechanisms of gastric carcinogenesis. In addition, the identified hub genes, CTHRC1, FNDC1, and INHBA, may serve as novel prognostic biomarkers and therapeutic targets.

Identifying Tumor-Associated Genes from Bilayer Networks of DNA Methylation Sites and RNAs

“…To illustrate the fact that the bilayer network could find more tumor-associated DNA methylation sites than the single DNA methylation correlation networks, we further compared the number of prognostically correlated loci among the hub DNA methylation sites found by the two approaches [ 34 ]. We calculated the number of survival-associated loci among the top 100–500 DNA methylation sites with the largest degree and betweenness centralities for the five tumor datasets ( Figure 4 , Table S2 ).…”

“…We obtained the RNA binding relationships from the RNAInter database [ 33 ], which is a comprehensive RNA interactome resource. The database scores the confidence level of each interaction relationship by combining literature support and experimental validation, where a score above 0.75 indicates the existence of strong experimental evidence for the interaction relationship [ 34 ].…”

“…For the RNA layer, we therefore used the binding relationship between RNAs. For the edges of the RNA layer, we used the RNA interactions with confidence scores >0.75 from the RNAInter database, which are supported by strong experimental evidence [ 34 ].…”

Hypoxia-related tumor environment correlated with immune infiltration and therapeutic sensitivity in diffuse large B-cell lymphoma

“…FNDC1, ANTXR1, RARRES2, S100A9, and MT1M were found to be related to prognosis of DLBCL in this study. FNDC1 (fibronectin type III domain containing 1) was found to be related to hypoxia (Zhao et al, 2022) and to be associated with chemoradiation resistance and poor prognosis of gastric cancer, breast cancer, and colorectal cancer by multiple pathways (Ren et al, 2018;Zhong et al, 2018;Liu et al, 2019;Wei et al, 2021;Yunwen et al, 2021;Chen et al, 2022). ANTXR1 is a receptor for anthrax toxin and is highly expressed in tumor endothelial cells.…”

Construction and Investigation of an lncRNA-miRNA-mRNA Regulatory Network of Pancreatic Cancer via bioinformatics analysis

“…In addition, a ferroptosis-related long non-coding RNA (lncRNA) signature and the tumor microenvironment, therapeutic response, and prognosis value in gastric cancer have been discovered previously via bioinformatics analysis [4]. A similar situation that a lncRNA-miRNA-mRNA network was conducted by combining of different kinds of RNAs expression pro les using bioinformatics analysis in gastric carcinoma [5]. In a word, it is necessary to further study the potential tumorigenesis mechanism of pancreatic cancer via integrated bioinformatics.…”