A Novel TNM Assessing System for Predicting the Prognosis and Immunotherapy Response of Gastric Cancer

Abstract

Background: The classical diagnostic method for the prognosis prediction of gastric cancer is imprecise. Thus, tools that can precisely predict the prognosis of gastric cancer subtypes and guide treatment strategies are needed. Methods: Using publicly available data from The Cancer Genome Atlas and Gene Expression Omnibus, we established tumor-noncoding RNA-microenvironment type (TNM) scores to define a combination of signatures that reflect the expression levels of tumor-related protein-coding genes (tumor score), tumor-related noncoding genes (NC score), and the cellular and stromal composition of the tumor microenvironment (TME score). These signatures were used to predict the prognosis of gastric cancer patients and the therapeutic effect of immunotherapy on gastric cancer. Results: Based on TNM scores, gastric cancer patients were divided into three subgroups and Kaplan-Meier survival curves revealed significant differences among the subgroups (P < 0.001). Tumor score was positively associated with the epithelial-mesenchymal transition signaling pathway and transforming growth factor-beta signaling pathway and negatively associated with DNA damage repair. The TME score showed opposite results and was also positively associated with cytotoxic T-cells and immune check-points. We also explored the predictive ability of TNM scores for the effectiveness of anti-PD-1/PD-L1 treatment. Receiver operating characteristic curves showed that a combination of TME score, tumor score, and tumor mutation burden could predict the treatment effect well (area under the curve = 0.708). Conclusion: Our study showed that the TNM scores could be used as a robust predicting tool for the prognosis of gastric cancer and the treatment effects of check-point inhibitors. Funding: This study was supported by the National Natural Science Foundation of China (No. 81772580, and 81811530024 to Wangjun Liao); Guangzhou Planed Project of Science and Technology (No. 201803010070 to Wangjun Liao). Declaration of Interest: The authors declare that they have no competing interests.