Comprehensive molecular characterization of high-stemness gastric cancer cells using single-cell transcriptomics, spatial mapping, and machine learning.

ABSTRACT: Gastric cancer (GC) remains a global clinical challenge due to late diagnosis, high heterogeneity, and poor prognosis. Tumor stemness has emerged as a key factor driving tumor aggressiveness and therapeutic resistance. However, the systematic characterization of high-stemness GC cells and their molecular features remains limited. We integrated single-cell RNA sequencing (scRNA-seq), spatial transcriptomics, and bulk RNA-seq data to identify and characterize high-stemness GC cells. Stemness scores were calculated using CytoTRACE, and malignant cells were classified into high stemness (top 25% CytoTRACE-scored cells, HighStem), dynamic transition stemness (DTStem), and low stemness (LowStem) subpopulations based on the quartile method cutoff. ScPagwas and cell-cell communication profiling we

SUBMITTER: Wang Z 

PROVIDER: S-EPMC12711953 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature