Sialic acid metabolism-based classification reveals novel metabolic subtypes with distinct characteristics of tumor microenvironment and clinical outcomes in gastric cancer.

ABSTRACT:

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 vi

SUBMITTER: Jiang J 

PROVIDER: S-EPMC11847363 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature