Table_5_Reveal the Heterogeneity in the Tumor Microenvironment of Pancreatic Cancer and Analyze the Differences in Prognosis and Immunotherapy Responses of Distinct Immune Subtypes.xlsx

PurposeThe current clinical classification of pancreatic ductal adenocarcinoma (PDAC) cannot well predict the patient’s possible response to the treatment plan, nor can it predict the patient’s prognosis. We use the gene expression patterns of PDAC patients to reveal the heterogeneity of the tumor microenvironment of pancreatic cancer and analyze the differences in the prognosis and immunotherapy response of different immune subtypes.MethodsFirstly, use ICGC’s PACA-AU PDAC expression profile data, combined with the ssGSEA algorithm, to analyze the immune enrichment of the patient’s tumor microenvironment. Subsequently, the spectral clustering algorithm was used to extract different classifications, the PDAC cohort was divided into four subtypes, and the correlation between immune subtypes and clinical characteristics and survival prognosis was established. The patient’s risk index is obtained through the prognostic prediction model, and the correlation between the risk index and immune cells is prompted.ResultsWe can divide the PDAC cohort into four subtypes: immune cell and stromal cell enrichment (Immune-enrich-Stroma), non-immune enrichment but stromal cell enrichment (Non-immune-Stroma), immune-enriched Collective but non-matrix enrichment (Immune-enrich-non-Stroma) and non-immune enrichment and non-stromal cell enrichment (Non-immune-non-Stroma). The five-year survival rate of immune-enrich-Stroma and non-immune-Stroma of PACA-CA is quite different. TCGA-PAAD’s immune-enrich-Stroma and immune-enrich-non-Stroma groups have a large difference in productivity in one year. The results of the correlation analysis between the risk index and immune cells show that the patient’s disease risk is significantly related to epithelial cells, megakaryocyte-erythroid progenitor (MEP), and Th2 cells.ConclusionThe tumor gene expression characteristics of pancreatic cancer patients are related to immune response, leading to morphologically recognizable PDAC subtypes with prognostic/predictive significance.

目的：当前胰腺导管腺癌（PDAC）的临床分类难以准确预测患者对治疗方案的潜在反应，亦无法预测患者的预后。本研究通过分析PDAC患者的基因表达模式，揭示胰腺癌肿瘤微环境的异质性，并探讨不同免疫亚型在预后及免疫治疗反应上的差异。方法：首先，利用ICGC的PACA-AU PDAC表达谱数据，结合ssGSEA算法，分析患者肿瘤微环境的免疫富集情况。随后，运用光谱聚类算法提取不同分类，将PDAC队列划分为四个亚型，并建立免疫亚型与临床特征及生存预后的相关性。通过预后预测模型获得患者的风险指数，并提示风险指数与免疫细胞之间的相关性。结果：可以将PDAC队列划分为四个亚型：免疫细胞和基质细胞富集型（Immune-enrich-Stroma）、非免疫富集但基质细胞富集型（Non-immune-Stroma）、免疫富集集体但非基质富集型（Immune-enrich-non-Stroma）以及非免疫富集和非基质细胞富集型（Non-immune-non-Stroma）。PACA-CA的免疫富集-Stroma和非免疫富集-Stroma的五年生存率存在显著差异。TCGA-PAAD的免疫富集-Stroma和免疫富集-non-Stroma组在一年内的生产力存在较大差异。风险指数与免疫细胞的相关性分析结果显示，患者的疾病风险与上皮细胞、巨核细胞-红细胞祖细胞（MEP）和Th2细胞显著相关。结论：胰腺癌患者的肿瘤基因表达特征与免疫反应相关，导致形态上可识别的、具有预后/预测意义的PDAC亚型。