Abstract
Weighted correlation network analysis (WGCNA) is a statistical method that has been widely used in recent years to explore gene co-expression modules. Competing endogenous RNA (ceRNA) is commonly involved in the cancer gene expression regulation mechanism. Some ceRNA networks are recognized in gastric cancer; however, the prognosis-associated ceRNA network has not been fully identified using WGCNA. We performed WGCNA using datasets from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) to identify cancer-associated modules. The criteria of differentially expressed RNAs between normal stomach samples and gastric cancer samples were set at the false discovery rate (FDR) < 0.01 and |fold change (FC)| > 1.3. The ceRNA relationships obtained from the RNAinter database were examined by both the Pearson correlation test and hypergeometric test to confirm the mRNA–lncRNA regulation. Overlapped genes were recognized at the intersections of genes predicted by ceRNA relationships, differentially expressed genes, and genes in cancer-specific modules. These were then used for univariate and multivariate Cox analyses to construct a risk score model. The ceRNA network was constructed based on the genes in this model. WGCNA-uncovered genes in the green and turquoise modules are those most associated with gastric cancer. Eighty differentially expressed genes were observed to have potential prognostic value, which led to the identification of 12 prognosis-related mRNAs (KIF15, FEN1, ZFP69B, SP6, SPARC, TTF2, MSI2, KYNU, ACLY, KIF21B, SLC12A7, and ZNF823) to construct a risk score model. The risk genes were validated using the GSE62254 and GSE84433 datasets, with 0.82 as the universal cutoff value. 12 genes, 12 lncRNAs, and 35 miRNAs were used to build a ceRNA network with 86 dysregulated lncRNA–mRNA ceRNA pairs. Finally, we developed a 12-gene signature from both prognosis-related and tumor-specific genes, and then constructed a ceRNA network in gastric cancer. Our findings may provide novel insights into the treatment of gastric cancer.
Highlights
Gastric cancer is a major cause of cancer-related mortality worldwide (Van Cutsem et al, 2016)
Thirty pathways were generated after the genes were applied to the Kyoto Encyclopedia of Genes and Genomes (KEGG)–gene set enrichment analysis (GSEA) (Figure 1A)
2347 upregulated and 1548 downregulated genes were subjected to Gene Ontology (GO) term and KEGG pathway enrichment analyses
Summary
Gastric cancer is a major cause of cancer-related mortality worldwide (Van Cutsem et al, 2016). It is a serious form of cancer characterized by limited chemotherapy regimens and complex patterns of tumorigenesis and progression in different subtypes (Erdem et al, 2018; Kubota et al, 2020). There have been exceptional advancements in the interpretation of the molecular pattern of gastric cancer through research projects including the Cancer Genome Atlas (TCGA) (Cancer Genome Atlas Research N, 2014) and the Asian Cancer Research Group (ACRG) (Cristescu et al, 2015) in recent years; current classifications are not sufficient to describe the vast differences in prognoses and summarize overall genomic characteristics, even for patients who are recognized as belonging to the same molecular subtypes. Many recent surveys have used WGCNA for both non-neoplastic and neoplastic diseases, including gastric cancer
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