Identification of an Immune Gene-Associated Prognostic Signature and Its Association with a Poor Prognosis in Gastric Cancer Patients

Background: A better understanding of the tumor-immune system interactions in gastric cancer (GC) may provide promising diagnostic, prognostic, and therapeutic biomarkers for patients with this disease. We aimed to identify a prognostic signature of GC through a comprehensive bioinformatics analysis on the tumor-immune interactions as well as the molecular characteristics. Methods: We employed the single sample Gene Sets Enrichment Analysis (ssGSEA) to define immune-related subtypes and investigated the complicated biological functions and regulatory networks of these subtypes. We then developed a risk model using Lasso Cox regression and verified it via the external validation set and correlated the immune signature with GC clinicopathologic features and genomic characteristics. Findings: We observed two immune subgroups depending on the activity and level of immune cell in GC patients. We also identified a six-immune-gene signature as a promising independent prognostic biomarker for GC. A nomogram was constructed based on the immune signature and clinical characteristics and showed high potential for GC prognosis prediction. The receiver operating characteristic (ROC) curve analysis distinguishing patients with distinct prognosis yielded an area under the curve (AUC) of 0.779 for 5 years. Interpretation: Our work supports the clinical significance of this immune gene-associated signature for predicting the prognosis of GC patients. This study also shed light on the treatment strategies for GC patients from the perspective of immunology. Funding: This study was supported by Natural Science Foundation of Zhejiang Province (LY18H290006), National Natural Science Foundation of China (81903842, 81973634), and Program of Zhejiang Provincial TCM Sci-tech Plan (2018ZY006, 2020ZZ005). Declaration of Interests: The authors declare that they have no competing interests. Ethics Approval Statement: The patient data in this work were acquired from the publicly available datasets whose informed consent of patients were complete.