Gu Xiaoqiang, Zhang Qiqi, Wu Xueying, Fan Yue, Qian Jianxin
Department of Oncology, Longhua Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China.
Department of Integrated traditional Chinese and Western Medicine, Zhongshan Hospital of Fudan University, Shanghai, China.
World J Surg Oncol. 2021 Apr 12;19(1):112. doi: 10.1186/s12957-021-02201-w.
Pancreatic adenocarcinoma (PAAD) is a nonimmunogenic tumor, and very little is known about the relationship between the host immune response and patient survival. We aimed to develop an immune prognostic model (IPM) and analyze its relevance to the tumor immune profiles of patients with PAAD.
We investigated differentially expressed genes between tumor and normal tissues in the TCGA PAAD cohort. Immune-related genes were screened from highly variably expressed genes with weighted gene correlation network analysis (WGCNA) to construct an IPM. Then, the influence of IPM on the PAAD immune profile was comprehensively analyzed.
A total of 4902 genes highly variably expressed among primary tumors were used to construct a weighted gene coexpression network. One hundred seventy-five hub genes in the immune-related module were used for machine learning. Then, we established an IPM with four core genes (FCGR2B, IL10RA, and HLA-DRA) to evaluate the prognosis. The risk score predicted by IPM was an independent prognostic factor and had a high predictive value for the prognosis of patients with PAAD. Moreover, we found that the patients in the low-risk group had higher cytolytic activity and lower innate anti-PD-1 resistance (IPRES) signatures than patients in the high-risk group.
Unlike the traditional methods that use immune-related genes listed in public databases to screen prognostic genes, we constructed an IPM through WGCNA to predict the prognosis of PAAD patients. In addition, an IPM prediction of low risk indicated enhanced immune activity and a decreased anti-PD-1 therapeutic response.
胰腺腺癌（PAAD）是一种非免疫原性肿瘤，关于宿主免疫反应与患者生存率之间的关系，人们知之甚少。我们旨在开发一种免疫预后模型（IPM），并分析其与PAAD患者肿瘤免疫特征的相关性。
我们研究了TCGA PAAD队列中肿瘤组织与正常组织之间差异表达的基因。通过加权基因共表达网络分析（WGCNA）从高变表达基因中筛选免疫相关基因，以构建IPM。然后，全面分析IPM对PAAD免疫特征的影响。
共有4902个在原发性肿瘤中高变表达的基因用于构建加权基因共表达网络。免疫相关模块中的175个核心基因用于机器学习。然后，我们建立了一个包含四个核心基因（FCGR2B、IL10RA和HLA - DRA）的IPM来评估预后。IPM预测的风险评分是一个独立的预后因素，对PAAD患者的预后具有较高的预测价值。此外，我们发现低风险组患者比高风险组患者具有更高的细胞溶解活性和更低的先天性抗PD - 1抗性（IPRES）特征。
与使用公共数据库中列出的免疫相关基因筛选预后基因的传统方法不同，我们通过WGCNA构建了一个IPM来预测PAAD患者的预后。此外，IPM预测低风险表明免疫活性增强，抗PD - 1治疗反应降低。
