Abstract
Gastric cancer is an aggressive malignancy characterized by significant clinical heterogeneity arising from complex genetic and environmental interactions. This study employed single-cell RNA sequencing, using the 10 × Genomics platform, to analyze 262,532 cells from gastric cancer samples, identifying 32 distinct clusters and 10 major cell types, including immune cells (e.g., T cells, monocytes) and epithelial subpopulations. Among 27 epithelial subgroups, five malignant subpopulations were identified, each defined by unique marker gene expressions and playing diverse roles in tumor progression. Developmental trajectory analysis revealed potential stem-like characteristics in certain clusters, suggesting their involvement in therapeutic resistance and disease recurrence. Cell-cell communication analysis uncovered a dynamic network of interactions within the tumor microenvironment, potentially influencing tumor growth and metastasis. Differential gene expression analysis identified key genes (LDHA, GPC3, MIF, CD44, and TFF3) that were used to construct a prognostic risk score model. This model demonstrated robust predictive power, achieving AUC values of 0.77, 0.77, and 0.76 for 1-, 3-, and 5-year overall survival in the TCGA training dataset, with validation across independent cohorts. These findings deepen our understanding of gastric cancer's cellular and molecular heterogeneity, offering insights into potential therapeutic targets and biomarkers. By facilitating the development of targeted therapies and personalized treatment strategies, these results hold promise for improving clinical outcomes in gastric cancer patients.