Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China.
Tianjin University of Traditional Chinese Medicine, Tianjin, China.
Sci Rep. 2022 Sep 2;12(1):9123. doi: 10.1038/s41598-022-13045-z.
To establish a prognostic model based on immune-related microRNA (miRNA) for pancreatic carcinoma. Weighted correlation network analysis (WGCNA) was performed using the "WGCNA" package to find the key module genes involved in pancreatic carcinoma. Spearman correlation analysis was conducted to screen immune-related miRNAs. Uni- and multi-variate COX regression analyses were carried out to identify miRNAs prognostic for overall survival (OS) of pancreatic carcinoma, which were then combined to generate a prognostic model. Kaplan-Meier survival analysis, receiver operating characteristic (ROC) analysis, distribution plot of survival status in patients and regression analysis were collectively performed to study the accuracy of the model in prognosis. Target genes of the miRNAs in the model were intersected with the key module genes, and a miRNA-mRNA network was generated and visualized by Cytoscape3.8.0. TIMER analysis was conducted to study the abundance of immune infiltrates in tumor microenvironment of pancreatic carcinoma. Expression levels of immune checkpoint genes in subgroups stratified by the model were compared by Wilcoxon test. Gene Set Enrichment Analysis (GSEA) was performed to analyze the enriched signaling pathways between subgroups. Differential analysis revealed 1826 genes differentially up-regulated in pancreatic carcinoma and 1276 genes differentially down-regulated. A total of 700 immune-related miRNAs were obtained, of which 7 miRNAs were significantly associated with OS of patients and used to establish a prognostic model with accurate predictive performance. There were 99 mRNAs overlapped from the 318 target genes of the 7 miRNAs and the key modules genes analyzed by WGCNA. Patient samples were categorized as high or low risk according to the prognostic model, which were significantly associated with dendritic cell infiltration and expression of immune checkpoint genes (TNFSF9, TNFRSF9, KIR3DL1, HAVCR2, CD276 and CD80). GSEA showed remarkably enriched signaling pathways in the two subgroups. This study identified an immune-related 7-miRNA based prognostic model for pancreatic carcinoma, which could be used as a reliable tool for prognosis.
建立基于免疫相关 microRNA（miRNA）的胰腺癌预后模型。使用“WGCNA”包进行加权相关网络分析（WGCNA），以找到涉及胰腺癌的关键模块基因。进行 Spearman 相关性分析以筛选免疫相关 miRNA。进行单变量和多变量 COX 回归分析，以鉴定与胰腺癌总生存（OS）相关的 miRNA，然后将其组合以生成预后模型。进行 Kaplan-Meier 生存分析、接受者操作特征（ROC）分析、患者生存状态分布图和回归分析，以研究模型在预后中的准确性。模型中 miRNA 的靶基因与关键模块基因相交，通过 Cytoscape3.8.0 生成并可视化 miRNA-mRNA 网络。通过 TIMER 分析研究胰腺癌肿瘤微环境中免疫浸润的丰度。通过 Wilcoxon 检验比较模型分层的亚组中免疫检查点基因的表达水平。进行基因集富集分析（GSEA）以分析亚组间富集的信号通路。差异分析显示，胰腺癌中上调基因有 1826 个，下调基因有 1276 个。获得了 700 个免疫相关 miRNA，其中 7 个 miRNA与患者 OS 显著相关，用于建立具有准确预测性能的预后模型。从 WGCNA 分析的 318 个 miRNA 的靶基因和关键模块基因中获得了 99 个 mRNAs。根据预后模型将患者样本分为高风险或低风险，与树突状细胞浸润和免疫检查点基因（TNFSF9、TNFRSF9、KIR3DL1、HAVCR2、CD276 和 CD80）的表达显著相关。GSEA 显示两个亚组中显著富集的信号通路。本研究确定了一个基于免疫相关的 7-miRNA 的胰腺癌预后模型，可作为一种可靠的预后工具。
