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.