Abstract
Tumor-infiltrating lymphocytes (TILs) in gastric cancer are closely related to clinical prognosis; however, little is known regarding the immune microenvironment in this disease. Thus, RNA-sequencing data from gastric cancer patients were downloaded from the Gene Expression Omnibus (GEO). The proportion of immune cells was determined based on a deconvolution algorithm (CIBERSORT), and gene expression profiles were analyzed in the context of clinical outcomes to construct an immune risk score. Data were analyzed using least absolute shrinkage and selection operator (LASSO) and multivariable Cox regression, to identify prognostic markers of gastric cancer survival. The model included four immune cell types: neutrophils, plasma cells, activated CD4+ memory T cells, and T follicular helper cells. Patients were classified into two subgroups based on risk score, and a significant difference in overall survival (OS) was seen between the subgroups in both the training and testing cohorts, particularly in patients with tumor stages ≥T3. Multivariable analysis revealed that both T-stage and risk score were independent prognostic factors for gastric cancer survival [hazard ratio (HR) 1.505; 95% confidence interval (CI) 1.043–2.173, HR 1.686; 95% CI 1.367–2.080]. Risk scores and clinical factors were then integrated into a nomogram to build a model with both good discriminatory power and accuracy in predicting clinical outcomes. Further analysis using gene set enrichment analysis (GSEA) identified strong associations of immune risk with TGF-β and tumor metastasis-related pathways, which could inform research on the molecular mechanisms of gastric cancer. Collectively, the data presented here suggest that an immune risk model can make an important contribution to predictions prognosis in gastric cancer patients.
Highlights
Gastric cancer is one of the most common forms of cancer worldwide, with over 1,000,000 new cases diagnosed in 2018, resulting in ∼783,000 deaths [1]
least absolute shrinkage and selection operator (LASSO) and multivariate COX regression analyses of four immune cell types, including activated CD4+ memory T cells, plasma cells, neutrophils, and T follicular helper (Tfh) cells, were performed, with significant correlations with overall survival (OS) seen for all cell types except Tfh cells (p < 0.05)
Due to the strong correlation between infiltrating immune cell types seen in this study, LASSO regression was essential for reducing collinearity through selection of the optimal penalty parameter λ, after which stepwise regression with the Akaike information criterion (AIC) was applied to filter immune cells and select the optimal coefficient for each cell population
Summary
Gastric cancer is one of the most common forms of cancer worldwide, with over 1,000,000 new cases diagnosed in 2018, resulting in ∼783,000 deaths [1]. Recent studies showed that immune cell infiltration phenotypes may be associated with clinical outcomes, including tumor prognosis [5,6,7]. Other studies revealed strong correlations between clinical outcomes and immune cells in gastric cancer, including CD8 T cells, mast cells, and tumor-associated macrophages [8, 9]. The tumor immune response has proven to be an important target of precision therapy for cancer. As the targeting of immune checkpoint inhibitors has proven highly successful for the treatment of various cancers [10, 11], extension of this strategy to other malignancies, including gastric cancer, has become a major topic in clinical research [12]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
