Transplantation Center, The 3rd Xiangya Hospital, Central South University, Changsha, China.
Xiangya School of Medicine, Central South University, Changsha, China.
Front Immunol. 2021 Jun 14;12:629854. doi: 10.3389/fimmu.2021.629854. eCollection 2021.
There is growing evidence found that the role of hypoxia and immune status in idiopathic pulmonary fibrosis (IPF). However, there are few studies about the role of hypoxia and immune status in the lung milieu in the prognosis of IPF. This study aimed to develop a hypoxia-immune-related prediction model for the prognosis of IPF.
Hypoxia and immune status were estimated with microarray data of a discovery cohort from the GEO database using UMAP and ESTIMATE algorithms respectively. The Cox regression model with the LASSO method was used for identifying prognostic genes and developing hypoxia-immune-related genes. Cibersort was used to evaluate the difference of 22 kinds of immune cell infiltration. Three independent validation cohorts from GEO database were used for external validation. Peripheral blood mononuclear cell (PBMC) and bronchoalveolar lavage fluid (BALF) were collected to be tested by Quantitative reverse transcriptase-PCR (qRT-PCR) and flow cytometry from 22 clinical samples, including 13 healthy controls, six patients with non-fibrotic pneumonia and three patients with pulmonary fibrosis.
Hypoxia and immune status were significantly associated with the prognosis of IPF patients. High hypoxia and high immune status were identified as risk factors for overall survival. CD8+ T cell, activated CD4+ memory T cell, NK cell, activated mast cell, M1 and M0 macrophages were identified as key immune cells in hypoxia-immune-related microenvironment. A prediction model for IPF prognosis was established based on the hypoxia-immune-related one protective and nine risk DEGs. In the independent validation cohorts, the prognostic prediction model performed the significant applicability in peripheral whole blood, peripheral blood mononuclear cell, and lung tissue of IPF patients. The preliminary clinical specimen validation suggested the reliability of most conclusions.
The hypoxia-immune-based prediction model for the prognosis of IPF provides a new idea for prognosis and treatment.
越来越多的证据表明，缺氧和免疫状态在特发性肺纤维化（IPF）中起作用。然而，关于缺氧和免疫状态在 IPF 肺环境中的预后作用的研究较少。本研究旨在建立一个用于预测 IPF 预后的缺氧-免疫相关预测模型。
使用 UMAP 和 ESTIMATE 算法分别从 GEO 数据库中的发现队列的微阵列数据中评估缺氧和免疫状态。使用 Cox 回归模型和 LASSO 方法识别预后基因并开发缺氧-免疫相关基因。Cibersort 用于评估 22 种免疫细胞浸润的差异。使用 GEO 数据库中的三个独立验证队列进行外部验证。从 22 个临床样本中收集外周血单核细胞（PBMC）和支气管肺泡灌洗液（BALF），包括 13 名健康对照者、6 名非纤维化性肺炎患者和 3 名肺纤维化患者，进行定量逆转录酶-PCR（qRT-PCR）和流式细胞术检测。
缺氧和免疫状态与 IPF 患者的预后显著相关。高缺氧和高免疫状态被确定为总生存率的危险因素。CD8+T 细胞、活化的 CD4+记忆 T 细胞、NK 细胞、活化的肥大细胞、M1 和 M0 巨噬细胞被确定为缺氧-免疫相关微环境中的关键免疫细胞。基于缺氧-免疫相关的一个保护基因和九个风险 DEGs 建立了 IPF 预后预测模型。在独立验证队列中，该预后预测模型在 IPF 患者的外周全血、外周血单核细胞和肺组织中具有显著的适用性。初步的临床标本验证表明了大多数结论的可靠性。
基于缺氧-免疫的 IPF 预后预测模型为预后和治疗提供了新的思路。
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