Department of Respiratory and Critical Care Medicine, Second Affiliated Hospital of Harbin Medical University, Harbin, 150086, China.
Department of Respiration, The First Hospital of Harbin, Harbin, 150010, China.
BMC Pulm Med. 2022 Jan 5;22(1):15. doi: 10.1186/s12890-021-01799-7.
With the rapid advances of genetic and genomic technologies, the pathophysiological mechanisms of idiopathic pulmonary fibrosis (IPF) were gradually becoming clear, however, the prognosis of IPF was still poor. This study aimed to systematically explore the ferroptosis-related genes model associated with prognosis in IPF patients.
Datasets were collected from the Gene Expression Omnibus (GEO). The least absolute shrinkage and selection operator (LASSO) Cox regression analysis was applied to create a multi-gene predicted model from patients with IPF in the Freiburg cohort of the GSE70866 dataset. The Siena cohort and the Leuven cohort were used for validation.
Nineteen differentially expressed genes (DEGs) between the patients with IPF and control were associated with poor prognosis based on the univariate Cox regression analysis (all P < 0.05). According to the median value of the risk score derived from an 8-ferroptosis-related genes signature, the three cohorts' patients were stratified into two risk groups. Prognosis of high-risk group (high risk score) was significantly poorer compared with low-risk group in the three cohorts. According to multivariate Cox regression analyses, the risk score was an independently predictor for poor prognosis in the three cohorts. Receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA) confirmed the signature's predictive value in the three cohorts. According to functional analysis, inflammation- and immune-related pathways and biological process could participate in the progression of IPF.
These results imply that the 8-ferroptosis-related genes signature in the bronchoalveolar lavage samples might be an effective model to predict the poor prognosis of IPF.
随着遗传和基因组技术的快速发展，特发性肺纤维化（IPF）的病理生理机制逐渐明晰，但 IPF 的预后仍然较差。本研究旨在系统探讨与 IPF 患者预后相关的铁死亡相关基因模型。
从基因表达综合数据库（GEO）中收集数据集。应用最小绝对收缩和选择算子（LASSO）Cox 回归分析，从 GSE70866 数据集的弗赖堡队列中创建一个与 IPF 患者相关的多基因预测模型。用锡耶纳队列和鲁汶队列进行验证。
基于单因素 Cox 回归分析，在 Freiburg 队列的 GSE70866 数据集中，有 19 个差异表达基因（DEGs）与预后不良相关（均 P<0.05）。根据 8 个铁死亡相关基因特征衍生的风险评分的中位数，三个队列的患者被分为两个风险组。在三个队列中，高风险组（高风险评分）的预后明显差于低风险组。根据多因素 Cox 回归分析，风险评分是三个队列中预后不良的独立预测因子。受试者工作特征（ROC）曲线分析和决策曲线分析（DCA）证实了该特征在三个队列中的预测价值。根据功能分析，炎症和免疫相关途径和生物学过程可能参与了 IPF 的进展。
这些结果表明，支气管肺泡灌洗液中 8 个铁死亡相关基因特征可能是预测 IPF 不良预后的有效模型。
