Department of Respiratory Medicine, Yue Bei People's Hospital, Shantou University Medical College, Shaoguan, Guangdong, China.
Department of Radiotherapy, Yue Bei People's Hospital, Shantou University Medical College, Shaoguan, Guangdong, China.
Front Immunol. 2022 Dec 19;13:1010345. doi: 10.3389/fimmu.2022.1010345. eCollection 2022.
The role of inflammation in the formation of idiopathic pulmonary fibrosis (IPF) has gained a lot of attention recently. However, the involvement of genes related to inflammation and immune exchange environment status in the prognosis of IPF remains to be further clarified. The objective of this research is to establish a new model for the prediction of the overall survival (OS) rate of inflammation-related IPF.
Gene Expression Omnibus (GEO) was employed to obtain the three expression microarrays of IPF, including two from alveolar lavage fluid cells and one from peripheral blood mononuclear cells. To construct the risk assessment model of inflammation-linked genes, least absolute shrinkage and selection operator (lasso), univariate cox and multivariate stepwise regression, and random forest method were used. The proportion of immune cell infiltration was evaluated by single sample Gene Set Enrichment Analysis (ssGSEA) algorithm.
The value of genes linked with inflammation in the prognosis of IPF was analyzed, and a four-genes risk model was constructed, including tpbg, Myc, ffar2, and CCL2. It was highlighted by Kaplan Meier (K-M) survival analysis that patients with high-risk scores had worse overall survival time in all training and validation sets, and univariate and multivariate analysis highlighted that it has the potential to act as an independent risk indicator for poor prognosis. ROC analysis showed that the prediction efficiency of 1-, 3-, and 5-year OS time in the training set reached 0.784, 0.835, and 0.921, respectively. Immune infiltration analysis showed that Myeloid-Derived Suppressor Cells (MDSC), macrophages, regulatory T cells, cd4+ t cells, neutrophils, and dendritic cells were more infiltrated in the high-risk group than in the low-risk group.
Inflammation-related genes can be well used to evaluate the IPF prognosis and impart a new idea for the treatment and follow-up management of IPF patients.
炎症在特发性肺纤维化（IPF）形成中的作用最近引起了广泛关注。然而，炎症相关基因及其免疫交换环境状态在 IPF 预后中的参与仍需进一步阐明。本研究旨在建立一种新的炎症相关 IPF 总生存率（OS）预测模型。
利用基因表达综合数据库（GEO）获取三个 IPF 的表达微阵列，包括两个肺泡灌洗液细胞和一个外周血单核细胞的表达微阵列。使用最小绝对收缩和选择算子（lasso）、单因素 cox 回归、多因素逐步回归和随机森林方法构建炎症相关基因风险评估模型。采用单样本基因集富集分析（ssGSEA）算法评估免疫细胞浸润比例。
分析了与炎症相关的基因在 IPF 预后中的价值，并构建了一个包含 tpbg、Myc、ffar2 和 CCL2 的四个基因风险模型。Kaplan-Meier（K-M）生存分析表明，高风险评分患者在所有训练集和验证集中的总生存时间更差，单因素和多因素分析突出了其作为不良预后独立风险指标的潜力。ROC 分析表明，训练集中 1 年、3 年和 5 年 OS 时间的预测效率分别达到 0.784、0.835 和 0.921。免疫浸润分析表明，高风险组中髓源性抑制细胞（MDSC）、巨噬细胞、调节性 T 细胞、CD4+T 细胞、中性粒细胞和树突状细胞的浸润程度高于低风险组。
炎症相关基因可用于评估 IPF 预后，并为 IPF 患者的治疗和随访管理提供新的思路。
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