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Background: Gastric cancer (GC) is a common gastrointestinal tumor, and its metastasis has led to a significant increase in the death rate. The mechanisms of GC metastasis remain unclear. Methods: The differentially expressed genes (DmRs) and lncRNAs (DlncRs) of GC were selected from The Cancer Genome Atlas (TCGA) database. We applied the weighted gene co-expression network analysis (WGCNA) to construct co-expression modules related with GC metastasis. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) method analyzed the functional regions and signal pathways of genes in vital modules. DmRs-DlncRs co-expression network were drawn for finding out hub nodes. Survival analyses of significant biomarkers were analyzed by Kaplan-Meier (KM) method. Finally, the expressions of selected biomarkers were validated in cell lines and caner tissues by quantitative real-time PCR (qRT-PCR), in GC tissue microarray by Fluorescence in situ hybridization (FISH). Results: 4776 DmRs and 213 DlncRs were involved the construction of WGCNA network, and MEyellow module was identified to have more significant correlation with GC metastasis. DmRs and DlncRs of MEyellow module were proved to be involved in the processes of cancer pathogenesis by GO and KEGG pathway analysis. Through the DmRs-DlncRs co-expression network, 7 DmRs and 1 DlncRs were considered as hub nodes. Besides, the high expression of TIMD4, CETP, KRT27, PTGDS, FAM30A was worse than low expression in GC patients survival, respectively; However, LRRC26 was opposite trend. FAM30A and TIMD4 were all significant biomarkers of GC survival and hub genes. Simultaneously, TIMD4, CETP, KRT27, PTGDS, FAM30A were increased in GC cell lines and tissues compared with GES-1 and normal tissues, respectively; the expression of LRRC26 was reduced in GC cell lines and tissues. Conclusion: This study identified 6 genes as new biomarkers affecting the metastasis of GC. Especially, FAM30A and TIMD4 might be an effective marker for predicting the prognosis and a potential-therapeutic target in GC.

Use of bioinformatic database analysis and specimen verification to identify novel biomarkers predicting gastric cancer metastasis