Abstract
BACKGROUND: Gastric cancer (GC) is the third leading cause of cancer-related deaths globally. Despite advancements in treatment, the overall 5-year survival rate remains below 30%, particularly in advanced stages. Copper metabolism, vital for various cellular processes, has been linked to cancer progression, but its role in GC, especially at the single-cell level, is not well understood. OBJECTIVE: This study aims to investigate copper metabolism in GC by integrating single-cell RNA sequencing (scRNA-seq) data and developing a prognostic model based on copper metabolism-related gene (CMRG) expression. The study explores how copper metabolism affects the tumor microenvironment and identifies potential therapeutic targets. METHODS: scRNA-seq data from gastric cancer and normal tissues were analyzed using the Seurat package. Principal Component Analysis (PCA) and Uniform Manifold Approximation and Projection (UMAP) were used for dimensionality reduction and clustering. Non-negative matrix factorization (NMF) was employed for T cell subpopulation analysis. A high-dimensional weighted gene co-expression network analysis (HdWGCNA) identified key molecular features. LASSO regression and Random Survival Forest (RSF) techniques were used to create and validate a prognostic model. Survival analysis, immune microenvironment assessment, and drug sensitivity analysis were conducted. RESULTS: Sixteen cell clusters and nine distinct cell types were identified, with T cells showing significant roles in cell communication. The NMF analysis of CD8 +T cells revealed five copper metabolism-related subtypes. The prognostic model based on nine CMRGs indicated significant survival differences between high- and low-risk groups. High-risk patients showed shorter survival times, increased immune cell infiltration, and altered immune responses. Drug sensitivity analysis suggested higher efficacy of certain drugs in high-CMRG patients.