Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Nanchang University, National Regional Center for Respiratory Medicine, Nanchang, 330006, China.
Division of Thoracic and Endocrine Surgery, University Hospitals and University of Geneva, Geneva, Switzerland.
Oncol Res. 2023 Jul 21;31(5):697-714. doi: 10.32604/or.2023.029458. eCollection 2023.
Pancreatic cancer is associated with high mortality and is one of the most aggressive of malignancies, but studies have not fully evaluated its molecular subtypes, prognosis and response to immunotherapy of different subtypes. The purpose of this study was to explore the molecular subtypes and the key genes associated with the prognosis of pancreas cancer patients and study the clinical phenotype, prognosis and response to immunotherapy using single-cell seq data and bulk RNA seq data, and data retrieved from GEO and TCGA databases.
Single-cell seq data and bioinformatics methods were used in this study. Pancreatic cancer data were retrieved from GEO and TCGA databases, the molecular subtypes of pancreatic cancer were determined using the six cGAS-STING related pathways, and the clinical phenotype, mutation, immunological characteristics and pathways related to pancreatic cancer were evaluated.
Pancreatic cancer was classified into 3 molecular subtypes, and survival analysis revealed that patients in Cluster3 (C3) had the worst prognosis, whereas Cluster1 (C1) had the best prognosis. The clinical phenotype and gene mutation were statistically different among the three molecular subtypes. Analysis of immunotherapy response revealed that most immune checkpoint genes were differentially expressed in the three subtypes. A lower risk of immune escape was observed in Cluster1 (C1), indicating higher sensitivity to immunotherapeutic drugs and subjects in this Cluster are more likely to benefit from immunotherapy. The pathways related to pancreatic cancer were differentially enriched among the three subtypes. Five genes, namely SFRP1, GIPR, EMP1, COL17A and CXCL11 were selected to construct a prognostic signature.
Single-cell seq data were to classify pancreatic cancer into three molecular subtypes based on differences in clinical phenotype, mutation, immune characteristics and differentially enriched pathways. Five prognosis-related genes were identified for prediction of survival of pancreatic cancer patients and to evaluate the efficacy of immunotherapy in various subtypes.
胰腺癌死亡率高，是最具侵袭性的恶性肿瘤之一，但目前对其分子亚型、预后以及不同亚型免疫治疗反应的研究尚未完全阐明。本研究旨在通过单细胞测序(scRNA-seq)和批量 RNA 测序(bulk RNA-seq)数据，以及从 GEO 和 TCGA 数据库中检索的数据，探讨胰腺癌患者的分子亚型及其与预后相关的关键基因，并研究其临床表型、预后和免疫治疗反应。
本研究采用 scRNA-seq 数据和生物信息学方法。从 GEO 和 TCGA 数据库中检索胰腺癌数据，采用 6 条 cGAS-STING 相关通路确定胰腺癌的分子亚型，评估其临床表型、突变、免疫特征和与胰腺癌相关的通路。
将胰腺癌分为 3 个分子亚型，生存分析显示，Cluster3 (C3)患者预后最差，而 Cluster1 (C1)患者预后最好。三个分子亚型的临床表型和基因突变存在统计学差异。免疫治疗反应分析显示，三种亚型中大多数免疫检查点基因表达存在差异。Cluster1 (C1)中观察到免疫逃逸风险较低，表明对免疫治疗药物的敏感性较高，此类患者更有可能从免疫治疗中获益。三种亚型中与胰腺癌相关的通路存在差异富集。筛选出 SFRP1、GIPR、EMP1、COL17A 和 CXCL11 五个基因构建预后模型。
根据临床表型、突变、免疫特征和差异富集通路的不同，利用 scRNA-seq 数据将胰腺癌分为 3 个分子亚型。筛选出与预后相关的 5 个基因，用于预测胰腺癌患者的生存，并评估不同亚型免疫治疗的疗效。
