Guo Run, Zhou Yuefei, Lin Fang, Li Mengxing, Tan Chunting, Xu Bo
Department of Respiratory Medicine, Beijing Friendship Hospital of Capital Medical University, Beijing, China.
Department of Orthopedics Medicine, The First Hospital of China Medical University, Shenyang, China.
Front Pharmacol. 2022 Sep 6;13:981604. doi: 10.3389/fphar.2022.981604. eCollection 2022.
Increasing evidence has demonstrated that there was a strong correlation between COVID-19 and idiopathic pulmonary fibrosis (IPF). However, the studies are limited, and the real biological mechanisms behind the IPF progression were still uncleared. GSE70866 and GSE 157103 datasets were downloaded. The weight gene co-expression network analysis (WGCNA) algorithms were conducted to identify the most correlated gene module with COVID-19. Then the genes were extracted to construct a risk signature in IPF patients by performing Univariate and Lasso Cox Regression analysis. Univariate and Multivariate Cox Regression analyses were used to identify the independent value for predicting the prognosis of IPF patients. What's more, the Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Ontology (GO), and gene set enrichment analysis (GSEA) were conducted to unveil the potential biological pathways. CIBERSORT algorithms were performed to calculate the correlation between the risk score and immune cells infiltrating levels. Two hundred thirty three differentially expressed genes were calculated as the hub genes in COVID-19. Fourteen of these genes were identified as the prognostic differentially expressed genes in IPF. Three (MET, UCHL1, and IGF1) of the fourteen genes were chosen to construct the risk signature. The risk signature can greatly predict the prognosis of high-risk and low-risk groups based on the calculated risk score. The functional pathway enrichment analysis and immune infiltrating analysis showed that the risk signature may regulate the immune-related pathways and immune cells. We identified prognostic differentially expressed hub genes related to COVID-19 in IPF. A risk signature was constructed based on those genes and showed great value for predicting the prognosis in IPF patients. What's more, three genes in the risk signature may be clinically valuable as potential targets for treating IPF patients and IPF patients with COVID-19.
越来越多的证据表明,2019冠状病毒病(COVID-19)与特发性肺纤维化(IPF)之间存在密切关联。然而,相关研究有限,IPF进展背后真正的生物学机制仍不清楚。下载了GSE70866和GSE157103数据集。运用加权基因共表达网络分析(WGCNA)算法来识别与COVID-19最相关的基因模块。然后通过单变量和套索Cox回归分析提取基因,以构建IPF患者的风险特征。使用单变量和多变量Cox回归分析来确定预测IPF患者预后的独立价值。此外,进行了京都基因与基因组百科全书(KEGG)、基因本体论(GO)和基因集富集分析(GSEA),以揭示潜在的生物学途径。运用CIBERSORT算法计算风险评分与免疫细胞浸润水平之间的相关性。计算出233个差异表达基因作为COVID-19中的枢纽基因。其中14个基因被确定为IPF中的预后差异表达基因。从这14个基因中选择3个(MET、UCHL1和IGF1)来构建风险特征。基于计算出的风险评分,该风险特征能够很好地预测高风险和低风险组的预后。功能通路富集分析和免疫浸润分析表明,该风险特征可能调节免疫相关通路和免疫细胞。我们在IPF中鉴定出与COVID-19相关的预后差异表达枢纽基因。基于这些基因构建了一个风险特征,该特征在预测IPF患者的预后方面显示出巨大价值。此外,风险特征中的三个基因作为治疗IPF患者和合并COVID-19的IPF患者的潜在靶点可能具有临床价值。