To explore the possibility of Pumilio (PUM) protein as a potential biomarker in gastric carcinoma (GC) by bioinformatics methods.

The TCGA-STAD queue was downloaded from UCSC Xena’s official website as the training set, and GSE15459 was downloaded from Gene Expression Omnibus (GEO) as the verification set. Based on the survival time and state of GC samples in the training set, GC samples were divided into high and low expression groups according to the optimal cut-off values of PUM1 and PUM2. Survival analysis was performed at PUM1 and PUM2 respectively, and PUM associated with GC prognostic survival significantly were selected. PUM-differentially expressed genes (PUM-DEGs) and DEGs were entially screened out to obtain candidate genes in the high and low expression groups of PUM, as well as in the GC and normal samples, respectively. Through univariate Cox and least absolute shrinkage and selection operator (LASSO) analyses, the risk model genes were further analyzed, and a prognosis prediction model was established. Finally, immunocyte infiltration analysis and chemotherapy drug sensitivity analysis were performed.

There were significant differences in survival rates between the groups with high and low PUM2-based expression (P<0.05), indicating that PUM2 was significantly correlated with the prognostic survival of GC. A total of 307 PUM2-DEGs and 4 176 DEGs were obtained by difference analysis, and 209 candidate genes were obtained, 33 prognostic related genes and 4 risk model genes (LBP, CST2, IGFBP1, C5orf46) were screened out. The expressions of LBP, CST2, IGFBP1 and C5orf46 in GC samples were higher than those in normal samples. A prognostic prediction model for predicting effectively the survival rates of GC patients at 1-, 3- and 5-year was constructed and verified. Immunocyte infiltration analysis showed significant differences in the abundance of six immune cells such as memory B cells, M0 macrophages, plasma cells and 18 immune checkpoint molecules between the high and low risk groups (P<0.05). The inhibitory concentration (IC₅₀) of drugs including dasatinib, docetaxel, parthenolide in the low risk group was higher than that in the high risk group.

PUM2 is associated with the prognosis of patients with GC and is a potential biomarker of GC. A prognostic prediction model constructed with four PUM2-DEGs can be used to predict the prognosis of GC. Immunoanalysis and drug sensitivity analysis based on the prognostic prediction model suggest the potential direction of immune and chemotherapy drug treatment of GC.