Identification of novel prognostic targets in Gastric Cancer using bioinformatics analysis
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Background: Gastric cancer (GC) is a deadly malignancy with an ever-increasing incidence worldwide. The potential prognostic biomarkers and novel therapeutic targets remain to be explored. This study builds up predictive gene targets of expression alterations in GC utilizing bioinformatics analysis. Methods and results: Themicroarray datasets (GSE19826, GSE54129 and GSE79973) associated with GC were obtained from Gene Expression Omnibus (GEO) database to identify the differentially expressed genes (DEGs) between GC and non-tumor tissues. Among all these up-regulated genes, we focused on WISP1 belong to the cellular communication network (CCN) family. We analyzed the mRNA and protein expressions of CCN family in GC utilizing GEPIA, ONCOMINE, and HPA databases. A significant correlation was found between the expression of WISP1 and the pathological stage of patients with GC. The prognostic value of distinct CCN family members in patients were analyzed using the Kaplan–Meier plotter database. Patients with low transcription levels of WISP1 were significantly associated with better prognosis. To furtherly clarify the important role of WISP1 in GC cells, we performed colony formation assay and scratch test. The experimental data showed that inhibiting the expression of WISP1 led to attenuatedcell proliferation and migration. Conclusions: Analysis of multiple datasets combined with global network information and experimental verification provides a successful approach to uncover the potential prognostic biomarkers and therapeutic targets of GC. Our investigation identified the risk and prognostic related gene WISP1 and presented a novel perspective to improve the prognostic and therapeutic outcomes of GC.
