DNA and Cell Biology · 2020 · 25 citations · 24 references
Life SciencesMolecular BiologyOncologyGene ExpressionGene Expression AnalysisMolecular OncologyCancer GenomicsCancer TranscriptomicsDifferential Gene Expression AnalysisGastric CancerGene Expression ProfilingNoncoding Rna In CancerBiomarkers Of Gastric CancerComparative Cancer Gene Expression ProfilesCompeting Endogenous Rna AnalysisEnrichment AnalysisGastric Cancer GeneticsMicroarray AnalysisMolecular Diagnostic MarkersNoncoding Rna Expression Profiling
Integrated analysis of accumulated data is an effective way to obtain reliable potential diagnostic molecular in gastric cancer (GC). The study aimed to identify potential lncRNAs associated with the pathogenesis and prognosis in GC. Raw noncoding RNA microarray data (GSE53137, GSE95667, and GSE111762) was downloaded from Gene Expression Omnibus (GEO) database. Differentially expressed genes between GC and adjacent normal gastric tissue samples were screened by an integrated analysis of multiple gene expression profile after gene reannotation and batch normalization. Differentially expressed genes were further confirmed by the cancer genome atlas (TCGA) database. Competing endogenous RNA (ceRNA) network, survival analysis, and gene set enrichment analysis (GSEA) were extensively applied to identify hub lncRNAs and discover potential biomarkers related to diagnosis and prognosis of GC. qPCR was applied to confirm hub lncRNA expression levels in GC tissues. In total, 17 integrated differential lncRNAs were obtained after intersections of differential genes between GEO and TCGA database. Four lncRNAs (HMGA1P4, UBE2Q1-AS1, MAGI2-AS3, MIR22HG) concentrated in ceRNA network were validated by qPCR in GC tissues, which were consistent with informatics results. The clinicopathological association revealed that four lncRNAs might be effective in GC progression. Further study revealed that GC patients with lower MAGI2-AS3 expression was evidently longer than those with higher MAGI2-AS3 expression (p = 0.015). Multivariate analysis revealed MAGI2-AS3 was independently associated with overall survival in GC. GSEA showed GC samples were differentially enriched in pyrimidine metabolism, RNA degradation, cell cycle, oxidative phosphorylation etc., and most significantly enriched in ribosome pathway in MAGI2-AS3 low expression phenotype. Four lncRNAs, including HMGA1P4, UBE2Q1-AS1, MAGI2-AS3, and MIR22HG may contribute to GC development, and MAGI2-AS3 might be associated with the prognosis of GC.
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