Gastric cancer (GC) is the fifth most common malignancy and the third leading cause of cancer-associated mortality in the world. However, its mechanisms of occurrence and development have not been clearly elucidated. Furthermore, there is no effective tumor marker for GC. Using DNA microarray analysis, the present study revealed genetic alterations, screened out core genes as novel markers and discovered pathways for potential therapeutic targets. Differentially expressed genes (DEGs) between GC and adjacent normal tissues were identified, followed by pathway enrichment analysis of DEGs. Next, the protein-protein interaction (PPI) network of DEGs was built and visualized. Analyses of modules in the PPI network were then performed to identify the functional core genes. Finally, survival analysis of core genes was conducted. A total of 256 genes were identified as DEGs between the GC samples and normal samples, including 169 downregulated and 87 upregulated genes. Through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis, the present study identified a total of 143 GO terms and 21 pathways. Six clusters of functional modules were identified, and the genes associated with these modules were screened out as the functional core genes. Certain core genes, including collagen type 12 α1 chain (COL12A1), glutathione S-transferase α3 (GSTA3), fibrinogen α chain (FGA) and fibrinogen γ chain (FGG), were the first reported to be associated with GC. Survival analysis suggested that these four genes, COL12A1 (P=0.002), GSTA3 (P=3.4×10−6), FGA (P=0.00075) and FGG (P=1.4×10-5), were significant poor prognostic factors and therefore, potential targets to improve diagnosis, optimize chemotherapy and predict prognostic outcomes.
Gastric cancer is still one of the most common and deadly malignancies in the world. Not all patients could benefit from chemotherapy or chemoradiotherapy due to tumor heterogeneity. Therefore, identifying different subgroups of patients is an important trend for obtaining more effective responses. However, few molecular classifications associated with chemosensitivity are based on immune–risk status. In this study, we obtained six key immune–related genes. Using these genes, we constructed a molecular model related to immune–risk status and calculated an individual immune–risk score. The score showed great efficiency and stability in predicting prognosis and identifying different subgroups where persons could benefit from postoperative adjuvant therapy. The patients could be divided into different risk groups based on the immune–related score. For patients in the low–risk group, both postoperative chemoradiotherapy and chemotherapy could significantly improve prognosis on overall survival (OS) and disease–free survival (DFS) (DFS, P < 0.001 and P = 0.041, respectively; OS, P < 0.001, P = 0.006, respectively) and chemoradiotherapy was significantly superior than simple chemotherapy (DFS, P = 0.031; OS, P = 0.027). For patients with an intermediate–risk score, postoperative chemoradiotherapy showed a statistically significant survival advantage over no anticancer treatment ( P = 0.004 and P = 0.002, respectively), while chemotherapy did not. Compared with no adjuvant treatment, neither postoperative chemoradiotherapy nor chemotherapy made significant difference for patients in the high–risk group. Combining the value of immune–risk status and chemosensitivity, the immune–risk score could not only offer us prognostic evaluation and adjuvant treatment guidance, but also improve our understanding about the binding point between chemotherapy or chemoradiotherapy and the immune system, which may be helpful for further expanding the application of immunotherapy.
Glutamate receptor, ionotropic, kainate 3 (GRIK3), as a member of the glutamate kainate receptor family, mainly participated in neuroactive ligand receptor interaction pathway. Other members of GRIK family were previously reported to regulate cellular migration, transformation, and proliferation in tumor. However, the mechanism of GRIK3 in tumor is still unclear. Therefore, the purpose of our study was to reveal the expression and clinical significance of GRIK3 in gastric cancer (GC). First, we performed the expression analysis and survival analysis of GRIK3 using The Cancer Genome Atlas (TCGA) database, and the results showed that the GRIK3 expressed differentially between gastric cancer tissues and the adjacent normal tissues and that higher expression of GRIK3 was associated with poor survival outcomes. And the gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis suggested that GRIK3 mainly took part in cancer-related process. Subsequently, the validated immunohistochemistry showed that GRIK3 expressed higher in the GC tissues than in the matched normal tissues and the patients with overexpressed GRIK3 had worse survival outcomes. The univariate and multivariate analyses suggested that the expression of GRIK3 was an independent prognostic factor to predict GC prognosis. Furthermore, additional experiment showed that the lymph node metastasis tissues had higher GRIK3 expression than their matched primary GC tissues. These findings suggested that elevated GRIK3 expression could serve as an independent prognostic biomarker and a novel potential treatment target for patients with GC.
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