Fig. 1

The workflow of this study. Step 1: A total of five cohorts comprising 956 gastric cancer (GC) patients were retrieved from public databases, while an additional two cohorts with 56 GC patients were obtained from our institution. Step 2: A geneset containing 31 SiaM-related genes was downloaded from the MsigDB database and a consensus cluster analysis based on this geneset was conducted to establish a SiaM-based classification in the ACRG cohort. Then, we investigated the relationship between the SiaM-based classification with clinicopathological characteristics and survival outcomes. Step 3: The DEGs between different SiaM clusters were identified using limma analyses and then analyzed using multiple pathway enrichment analyses (GO, KEGG, GSEA, and ssGSEA). Step 4: TME features were characterized using multiple algorithms (ESTIMATE, MCPcounter, CIBERSORT, TIDE) and compared in different SiaM clusters. Step 5: A six-gene signature was established using LASSO regression to predict peritoneal metastasis of GC. The predictive and prognostic value of the model was evaluated in multiple independent cohorts, including ACRG, TCGA-STAD, Takeno, ZJUGC-A, and ZJUGC-B. The association between the model and immunotherapy biomarkers was investigated in the Kim cohort. Step 6: The biological function of ST3GAL1 in GC cell proliferation, migration, and invasion was investigated in vitro. Step 7: The biological function of ST3GAL1 in peritoneal metastasis of GC was explored in vivo. ICI, immune checkpoint inhibitor; SiaM, sialic acid metabolism; DEGs, differentially expressed genes; ST, sialyltransferase; GC, gastric cancer