BACKGROUND: Gastric cancer is one of the most common malignant tumors worldwide. Increasing studies have indicated that non-coding RNAs (ncRNAs) play critical roles in cancer progression. They have shown great potential to be useful markers and therapeutic targets. METHODS: Bioinformatics analysis was conducted to detect mRNA and ncRNAs' expression changes between tumor samples and adjacent non-tumor samples. The gene expression profiles were obtained from the national center of biotechnology information gene expression omnibus (GEO). The differentially expressed genes (DEGs) were identified through fold change filtering. The interaction between lncRNAs and miRNAs were predicted by DIANA-LncBase, and the interaction between mRNAs and miRNAs were predicted by miRTarBase. Gene ontology (GO) enrichment analysis and pathway analysis were performed using standard enrichment computational methods. An LncRNA-miRNA-mRNA regulation network was constructed based on the gene expression profiles to identify hub genes. A protein-protein interaction (PPI) network was constructed based on STRING database. Survival analysis sourced from The Cancer Genome Atlas (TCGA) data was performed and the log-rank test was conducted to confirm the relationship between gene expression and risk of gastric cancer. RESULTS: With a threshold of p-value < 0.05 and absolute value of fold change (FC) > 2, differentially expressed genes including 70 miRNAs, 3266 mRNAs (4206 probe IDs) and 174 lncRNAs (188 probe IDs) were screened. After the results of predicted interactions and DEGs were intersected, 114 mRNAs, 56 miRNAs, and 68 lncRNAs were selected. GO enrichment analysis and pathway analysis were performed for the 114 mRNAs. A PPI network including 61 nodes and 91 edges was constructed for the selected mRNAs and ncRNAs. Survival analysis was performed for the weighted genes in the network and showed that KRAS, TRAF7, SUCLG2-AS1, IGF1R, UBE2B, AQP4-AS1, LINC00284, LINC01122, and RGMB were closely related with the overall survival coupled with highrisk, and LUM, UBE2D1, HNRNPU, and TOP2A were closely related with the overall survival coupled with lowrisk in gastric cancer. CONCLUSIONS: Our study indicated that KRAS, TRAF7, SUCLG2-AS1, IGF1R, UBE2B, AQP4-AS1, LINC00284, LINC01122, RGMB, LUM, UBE2D1, HNRNPU, and TOP2A might be potential targets for gene therapy for human gastric cancer.
BACKGROUND:Gastric cancer is one of the most common malignant tumors worldwide. Increasing studies have indicated that non-coding RNAs (ncRNAs) play critical roles in cancer progression. They have shown great potential to be useful markers and therapeutic targets. METHODS: Bioinformatics analysis was conducted to detect mRNA and ncRNAs' expression changes between tumor samples and adjacent non-tumor samples. The gene expression profiles were obtained from the national center of biotechnology information gene expression omnibus (GEO). The differentially expressed genes (DEGs) were identified through fold change filtering. The interaction between lncRNAs and miRNAs were predicted by DIANA-LncBase, and the interaction between mRNAs and miRNAs were predicted by miRTarBase. Gene ontology (GO) enrichment analysis and pathway analysis were performed using standard enrichment computational methods. An LncRNA-miRNA-mRNA regulation network was constructed based on the gene expression profiles to identify hub genes. A protein-protein interaction (PPI) network was constructed based on STRING database. Survival analysis sourced from The Cancer Genome Atlas (TCGA) data was performed and the log-rank test was conducted to confirm the relationship between gene expression and risk of gastric cancer. RESULTS: With a threshold of p-value < 0.05 and absolute value of fold change (FC) > 2, differentially expressed genes including 70 miRNAs, 3266 mRNAs (4206 probe IDs) and 174 lncRNAs (188 probe IDs) were screened. After the results of predicted interactions and DEGs were intersected, 114 mRNAs, 56 miRNAs, and 68 lncRNAs were selected. GO enrichment analysis and pathway analysis were performed for the 114 mRNAs. A PPI network including 61 nodes and 91 edges was constructed for the selected mRNAs and ncRNAs. Survival analysis was performed for the weighted genes in the network and showed that KRAS, TRAF7, SUCLG2-AS1, IGF1R, UBE2B, AQP4-AS1, LINC00284, LINC01122, and RGMB were closely related with the overall survival coupled with highrisk, and LUM, UBE2D1, HNRNPU, and TOP2A were closely related with the overall survival coupled with lowrisk in gastric cancer. CONCLUSIONS: Our study indicated that KRAS, TRAF7, SUCLG2-AS1, IGF1R, UBE2B, AQP4-AS1, LINC00284, LINC01122, RGMB, LUM, UBE2D1, HNRNPU, and TOP2A might be potential targets for gene therapy for humangastric cancer.