Abstract
Background. Peritoneal metastatic gastric cancer (PMGC) is very common, and usually, the prognosis is poor. There is currently an absence of accurate methods for the early diagnosis and prediction of peritoneal metastasis (PM). This highlights the need to develop strategies to identify the risk of PMGC. Methods. We performed a comprehensive discovery of biomarkers to predict PM by analyzing profiling datasets from GSE62254. The prognostic PM-related genes were obtained using the univariate Cox regression analysis, followed by a least absolute shrinkage and selection operator regression (LASSO) to establish a risk score model. The gene set enrichment analysis (GSEA) was used to determine the pathway enrichment in both the high- and low-risk groups. The 1-, 3-, and 5-year overall survival (OS) rates and area under the receiver operating characteristic curve (ROC) were used to compare the predictive accuracy-based risk stratification. In addition, an unsupervised clustering algorithm was applied to divide patients into subgroups according to the PM-related genes. Results. We identified 10 genes (MMP12, TAC1, TSPYL5, PPP1R14A, TMSB15B, NPY1R, PCDH9, EPM2AIP1, TIG7, and DYNC1I1) for PMGC diagnosis. The OS rates between the high- and low-risk groups at 1-, 3-, and 5-years were significantly different in the training and validation sets. The AUCs at 1-, 3-, and 5-years in the training set were 0.71, 0.74, and 0.73, respectively. In the validation set, the AUCs at 1-, 3-, and 5-years were 0.68, 0.66, and 0.69, respectively. The 10 gene signatures were correlated with immune cell infiltration in both the high- and low-risk groups. In addition, based on the GSEA, several significant pathways were enriched in the high-risk PMGC group, such as the Wnt and transforming growth factor beta (TGF-β) signaling pathway and leukocyte transendothelial migration pathway. Furthermore, unsupervised cluster analysis showed that the model could distinguish the level of risk among patients with PMGC. Conclusions. Overall, 10 gene signatures were identified for PMGC risk prediction. These may be valuable in making clinical decisions to improve treatment outcomes in patients with PMGC.
Prognostic value of gastric cancer-associated gene signatures: Evidence based on a meta-analysis using integrated bioinformatics methods
