Comprehensive analysis based on IFN-γ and SASP related genes, bulk RNA and single-cell sequencing to evaluate the prognosis and immune landscape of stomach adenocarcinoma.

[BACKGROUND] Stomach adenocarcinoma (STAD) represents the predominant subtype of gastric cancer, known for its drug resistance, unfavorable prognosis, and low cure rates. IFN-γ serves as a cytokine generated by immune cells, instrumental in tumor immune clearance and essential to the tumor microenvironment. The aging-associated secretory phenotype (SASP) can modify the local tissue environment, facilitating gastric cancer progression and chemotherapy resistance.

[OBJECTIVE] This study intends to identify STAD subtypes based on IFN-γ and SASP-related genes and to develop a risk prognostic model for predicting patient survival, tumor immune microenvironment, and responses to drug treatment.

[METHODS] The genomic and clinical datasets originate from the Cancer Genome Atlas (TCGA) database, while the genes associated with IFN-γ and SASP come from pertinent scholarly articles. We discovered the prognostic genes linked to IFN-γ and SASP in STAD using Cox regression analysis. Next, we applied non-negative matrix factorization (NMF) to categorize LIHC into distinct molecular subtypes, identifying differentially expressed genes across these subtypes. Following this, we developed a predictive model using Cox and LASSO regression analyses to stratify patients into specific risk categories, validating the model to assess the prognostic significance of the identified signatures. Lastly, we integrated single-cell data to elucidate the immune landscape of STAD and identified potential drugs along with their sensitivity profiles.

[RESULTS] We identified 17 prognostic genes related to IFN-γ and SASP, successfully classifying patients into two distinct molecular subtypes. These subtypes exhibited notable differences in immune profiles and prognostic outcomes. We pinpointed three differentially expressed genes to establish risk characteristics and created a prognostic model capable of accurately predicting patient outcomes. Our findings revealed a strong association between STAD and the extracellular matrix, low-risk group exhibited favorable prognosis, and may derive greater benefits from immunotherapy.

[CONCLUSION] We developed a risk model using IFN-γ and SASP-associated genes to predict the prognosis of STAD patients more accurately. Additionally, we assessed the immune landscape of STAD by integrating bulk RNA and single-cell sequencing analyses. This approach may yield valuable insights for clinical decision-making and immunotherapy strategies in STAD.