Table_4_The Pyroptosis-Related Signature Predicts Prognosis and Indicates Immune Microenvironment Infiltration in Gastric Cancer.XLSX

Gastric cancer (GC) is one of the leading causes of cancer-related deaths and shows high levels of heterogeneity. The development of a specific prognostic model is important if we are to improve treatment strategies. Pyroptosis can arise in response to H. pylori, a primary carcinogen, and also in response to chemotherapy drugs. However, the prognostic evaluation of GC to pyroptosis is insufficient. Consensus clustering by pyroptosis-related regulators was used to classify 618 patients with GC from four GEO cohorts. Following Cox regression with differentially expressed genes, our prognosis model (PS-score) was built by LASSO-Cox analysis. The TCGA-STAD cohort was used as the validation set. ESTIMATE, CIBERSORTx, and EPIC were used to investigate the tumor microenvironment (TME). Immunotherapy cohorts by blocking PD1/PD-L1 were used to investigate the treatment response. The subtyping of GC based on pyroptosis-related regulators was able to classify patients according to different clinical traits and TME. The difference between the two subtypes identified in this study was used to develop a prognosis model which we named “PS-score.” The PS-score could predict the prognosis of patients with GC and his/her overall survival time. A low PS-score implies greater inflammatory cell infiltration and better response of immunotherapy by PD1/PD-L1 blockers. Our findings provide a foundation for future research targeting pyroptosis and its immune microenvironment to improve prognosis and responses to immunotherapy.

胃癌（Gastric cancer, GC）是癌症相关死亡的主要诱因之一，且具有高度异质性。若要优化临床治疗策略，构建专属的预后评估模型至关重要。细胞焦亡（Pyroptosis）可由主要致癌物幽门螺杆菌（H. pylori）诱导发生，亦可在化疗药物作用下激活，但目前针对胃癌与细胞焦亡相关的预后评估研究仍较为匮乏。本研究借助细胞焦亡相关调控因子的共识聚类算法，对来自4个GEO队列的618例胃癌患者进行分型；随后通过差异表达基因的Cox回归分析，结合LASSO-Cox算法构建了预后模型（"PS-score"），并以TCGA-STAD队列作为验证集。研究采用ESTIMATE、CIBERSORTx及EPIC算法分析肿瘤微环境（Tumor Microenvironment, TME）特征，同时纳入阻断PD1/PD-L1的免疫治疗队列以评估模型的治疗响应预测能力。结果显示，基于细胞焦亡调控因子的胃癌分型可根据患者临床特征及肿瘤微环境特征进行精准分组；本研究基于鉴定出的两类胃癌亚型间的表达差异，构建了上述"PS-score"预后模型。该模型可有效预测胃癌患者的预后情况与总生存时长：PS-score分值较低的患者往往伴随更显著的炎症细胞浸润，且对PD1/PD-L1阻断剂的免疫治疗响应更佳。本研究结果为后续以细胞焦亡及其免疫微环境为靶点、改善胃癌患者预后与免疫治疗响应的相关研究提供了理论基础。