Integrating Single-Cell Transcriptomic Data of Metastatic Gastric Cancer to Construct a Tumor-Associated Neutrophil Signature and Clinical Validation in MSS/pMMR Patients

Background: Single-cell sequencing technology has emerged as a powerful tool for investigating the tumor immune microenvironment, enabling a deeper understanding of the heterogeneity of immune cell populations within tumors. Tumor-associated neutrophils (TANs) have become a focal point in the study of the tumor immune microenvironment due to their involvement in tumorigenesis, progression, and drug resistance. Methods: We analyzed scRNA-seq data from metastatic gastric cancer (GC) and bulk RNA-seq data from multiple GC cohorts. Non-negative matrix factorization (NMF) was used to classify molecular subtypes, and a prognostic model was constructed using LASSO Cox regression. Key genes were validated by using qRT-PCR and immunohistochemistry. Molecular docking was utilized to screen small-molecule compounds targeting prognostic genes. Results: scRNA-seq analysis identified neutrophil-associated clusters. Using neutrophil-related genes, we classified two molecular subtypes with distinct immune microenvironment features and established a six-gene prognostic risk model. Internal and external validation confirmed its robust predictive ability. The model also predicted immunotherapy response, with the low-risk group showing better outcomes, preliminarily validated in MSS/pMMR patients treated with Camrelizumab. Additionally, we assessed chemotherapy and targeted drug sensitivities and identified small-molecule compounds targeting the six prognostic genes. Conclusions: This study highlights the significance of TANs in predicting GC prognosis, immunotherapy response, and drug exploration, providing a foundation for personalized treatment strategies.