Qiu Lingxiao, Gong Gencheng, Wu Wenjuan, Li Nana, Li Zhaonan, Chen Shanshan, Li Ping, Chen Tengfei, Zhao Huasi, Hu Chunling, Fang Zeming, Wang Yan, Liu Hongping, Cui Panpan, Zhang Guojun
Department of Respiratory Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Academy of Medical Sciences, Zhengzhou University, Zhengzhou, China.
Ann Transl Med. 2021 Oct;9(20):1570. doi: 10.21037/atm-21-4545.
Idiopathic pulmonary fibrosis (IPF) is a highly fatal lung disease of unknown etiology with a median survival after diagnosis of only 2-3 years. Its poor prognosis is due to the limited therapy options available as well as the lack of effective prognostic indicators. This study aimed to construct a novel prognostic signature for IPF to assist in the personalized management of IPF patients during treatment.
Differentially-expressed genes (DEGs) in IPF patients versus healthy individuals were analyzed using the "limma" package of R software. Immune-related genes (IRGs) were obtained from the ImmPort database. Univariate Cox regression analysis was adopted to screen significantly prognostic IRGs for IPF patients. Multiple Cox regression analysis was used to identify optimal prognostic IRGs and construct a prognostic signature.
Compared with healthy individuals, there were a total of 52 prognosis-related DEGs in the bronchoalveolar lavage (BAL) samples of IPF patients, of which 37 genes were identified as IRGs. Of these, five genes ( and ) were significantly associated with overall survival (OS) in IPF patients, and were utilized for establishment of the prognostic signature. IPF patients were divided into high- and low-risk groups based on the prognostic signature. Marked differences in the OS probability were observed between high- and low-risk IPF patients. The area under curves (AUCs) of the receiver operating characteristic (ROC) curve for the prognostic signature in the training and validation cohorts were 0.858 and 0.837, respectively. The expression levels between and (P<0.01, r=0.394), between and (P<0.01, r=-0.355), between and (P<0.01, r=0.258), as well as between and (P<0.01, r=0.293) were significantly correlated.
We developed a validated and reproducible IRG-based prognostic signature that should be helpful in the personalized management of patients with IPF, providing new insights into the relationship between the immune system and IPF.
特发性肺纤维化（IPF）是一种病因不明的高致死性肺部疾病，诊断后的中位生存期仅为2至3年。其预后较差是由于可用的治疗选择有限以及缺乏有效的预后指标。本研究旨在构建一种用于IPF的新型预后特征，以协助IPF患者治疗期间的个性化管理。
使用R软件的“limma”包分析IPF患者与健康个体之间的差异表达基因（DEG）。从ImmPort数据库中获取免疫相关基因（IRG）。采用单变量Cox回归分析筛选IPF患者的显著预后IRG。使用多变量Cox回归分析确定最佳预后IRG并构建预后特征。
与健康个体相比，IPF患者的支气管肺泡灌洗（BAL）样本中共有52个与预后相关的DEG，其中37个基因被鉴定为IRG。其中，五个基因（和）与IPF患者的总生存期（OS）显著相关，并用于建立预后特征。根据预后特征将IPF患者分为高风险和低风险组。高风险和低风险IPF患者的OS概率存在显著差异。训练和验证队列中预后特征的受试者工作特征（ROC）曲线下面积（AUC）分别为0.858和0.837。与之间（P<0.01，r=0.394）、与之间（P<0.01，r=-0.355）、与之间（P<0.01，r=0.258）以及与之间（P<0.01，r=0.293）的表达水平显著相关。
我们开发了一种经过验证且可重复的基于IRG的预后特征，这应该有助于IPF患者的个性化管理，为免疫系统与IPF之间的关系提供新的见解。
