Cancer cell international · 2025
Sialic acid metabolism-based classification reveals novel metabolic subtypes with distinct characteristics of tumor microenvironment and clinical outcomes in gastric cancer.
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Jiang Junjie, Chen Yiran, Zheng Yangyang, Ding Yongfeng, Wang Haiyong, Zhou Quan, Teng Lisong, Zhang Xiaofeng
Аннотация
BACKGROUND: High heterogeneity in gastric cancer (GC) remains a challenge for standard treatments and prognosis prediction. Dysregulation of sialic acid metabolism (SiaM) is recognized as a key metabolic hallmark of tumor immune evasion and metastasis. Herein, we aimed to develop a SiaM-based metabolic classification in GC.
METHODS: SiaM-related genes were obtained from the MsigDB database. Bulk and single-cell transcriptional data of 956 GC patients were acquired from the GEO, TCGA, and MEDLINE databases. Proteomic profiles of 20 GC samples were derived from our institution. The consensus clustering algorithm was applied to identify SiaM-based clusters. The SiaM-based model was established via LASSO regression and evaluated via Kaplan‒Meier curve and ROC curve analyses. In vitro and in vivo experiments were conducted to explore the function of ST3GAL1 in GC.
RESULTS: Three SiaM clusters presented distinct patterns of clinicopathological features, transcriptomic alterations, and tumor immune microenvironment landscapes in GC. Compared with clusters A and B, cluster C presented elevated SiaM activity, higher metastatic potential, more abundant immunosuppressive features, and a worse prognosis. Based on the differentially expressed genes between these clusters, a risk model for six genes (ARHGAP6, ST3GAL1, ADAM28, C7, PLCL1, and TTC28) was then constructed. The model exhibited robust performance in predicting peritoneal metastasis and prognosis in four independent cohorts. As a hub gene in the model, ST3GAL1 promoted GC cell migration and invasion in vitro and in vivo.
CONCLUSIONS: Our study proposed a novel SiaM-based classification that identified three metabolic subtypes with distinct characteristics of tumor microenvironment and clinical outcomes in GC.
METHODS: SiaM-related genes were obtained from the MsigDB database. Bulk and single-cell transcriptional data of 956 GC patients were acquired from the GEO, TCGA, and MEDLINE databases. Proteomic profiles of 20 GC samples were derived from our institution. The consensus clustering algorithm was applied to identify SiaM-based clusters. The SiaM-based model was established via LASSO regression and evaluated via Kaplan‒Meier curve and ROC curve analyses. In vitro and in vivo experiments were conducted to explore the function of ST3GAL1 in GC.
RESULTS: Three SiaM clusters presented distinct patterns of clinicopathological features, transcriptomic alterations, and tumor immune microenvironment landscapes in GC. Compared with clusters A and B, cluster C presented elevated SiaM activity, higher metastatic potential, more abundant immunosuppressive features, and a worse prognosis. Based on the differentially expressed genes between these clusters, a risk model for six genes (ARHGAP6, ST3GAL1, ADAM28, C7, PLCL1, and TTC28) was then constructed. The model exhibited robust performance in predicting peritoneal metastasis and prognosis in four independent cohorts. As a hub gene in the model, ST3GAL1 promoted GC cell migration and invasion in vitro and in vivo.
CONCLUSIONS: Our study proposed a novel SiaM-based classification that identified three metabolic subtypes with distinct characteristics of tumor microenvironment and clinical outcomes in GC.