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BACKGROUND Increasing evidence suggests that the alternative splicing (AS) signature plays a role in the carcinogenesis and prognosis of various cancers. However, the prognostic role of AS in gastric cancer is not clear and needs to be clarified. MATERIAL AND METHODS To identify the differentially expressed AS (DEAS) events, we performed a differential expression analysis between normal and tumor tissue. The DEAS event was further applied to construct a prognostic signature by performing univariate Cox regression analysis and least absolute shrinkage and selection operator (LASSO) analysis. The Kaplan-Meier curve analysis and receiver operating characteristic curve (ROC) analysis were used to evaluate the prognostic value of the AS signature. In addition, the network of the splicing events with splicing factors was constructed using the Cytoscape software. RESULTS A total of 30 005 alternative splicing (AS) events with 372 patients were retrieved from the SpliceSeq database and TCGA database. By performing differential expression analysis, a total of 419 alternative splicing events were screened out, including 56 upregulated and 363 downregulated. We further constructed an AS-related prognostic signature by conducting a series bioinformatics analyses. Moreover, we identified that the AS signature could serve as an independent predictor for the prognosis of GC. We also found that AS signature had a more robust and precise efficacy for prognostic prediction in GC patients. Interestingly, the areas under 3- and 5-year survival curves are similar, both of which are greater than 1-year survival curve, suggesting that the long-term predictive accuracy of our prognostic model built upon AS signature is superior. CONCLUSIONS We performed a comprehensive analysis of overall prognostic-associated AS events concerning GC and constructed a prognostic model to predict the long-term prognostic survival outcomes in GC patients. We also developed a network of splicing events with splicing factors to reveal new potential molecular diagnostic biomarkers and therapeutic targets for GC patients.

The prognostic role of S100A4 in gastric cancer is still under debate. The present meta-analysis aimed to evaluate the relationship between S10A4 levels and the prognosis of gastric cancer. We performed a meta-analysis of published studies assessing the relationship between S100A4 and gastric cancer prognosis. We used the Revman 5.0 software to perform literature retrieval, article selection, data collection, and statistical analysis. A fixed-effect model was used to pool the hazard ratio (HR) and 95% confidence intervals (95%CI). A total of 7 eligible studies that included 1257 gastric cancer patients were analyzed. We did not find a prognostic value for S100A4 in gastric cancer (HR = 1.48, 95%CI = 0.77 to 2.82, P = 0.24). In conclusion, the present study indicated that S100A4 expression level is not a prognostic factor for gastric cancer.