Abstract
Gastric cancer (GC) is a common malignant tumor worldwide, characterized by complex biological processes. The distribution of various cell types and gene expression profiles in the GC microenvironment remains unclear. This study uses single-cell RNA sequencing to explore gene expression patterns and identify differentially expressed genes in GC samples, offering new insights into cellular diversity and potential molecular mechanisms. We conducted temporal and clustering analyses with single-cell sequencing, followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses to clarify their functions. Using machine learning, we identified relevant genes to create highly accurate prediction models. Additionally, ssGSEA analysis provided detailed insights into the immunosuppressive tumor microenvironment, revealing complex gene expression interactions and diverse immune infiltrates in cancer. Correlation analysis highlighted TIMP1 as having significant prognostic value across different immune cell subtypes. Single-cell RNA sequencing revealed the cellular landscape and gene expression profiles of the GC microenvironment, offering crucial data on how cell heterogeneity is regulated in relation to the tumor microenvironment. Moreover, new insights into the expression levels of AGT, INHBA, and TIMP1 showed distinct sex-biased gene functions within the tumor microenvironment. These findings enhance our understanding of the molecular mechanisms associated with gastric cancer development and may lay the groundwork for identifying novel therapeutic targets and diagnostic strategies.