Xia Yuechong, Lei Cheng, Yang Danhui, Luo Hong
Department of Respiratory Medicine, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China.
Research Unit of Respiratory Disease, Central South University, Changsha, Hunan, China.
PeerJ. 2020 Sep 8;8:e9848. doi: 10.7717/peerj.9848. eCollection 2020.
Idiopathic pulmonary fibrosis (IPF) is a chronic and progressive interstitial lung disease, characterized by a decline in lung function. To date, the pathophysiologic mechanisms associated with lung dysfunction remain unclear, and no effective therapy has been identified to improve lung function.
In the present study, we used weighted gene co-expression network analysis (WGCNA) to identify key modules and hub genes associated with lung function in IPF. Three datasets, containing clinical information, were downloaded from Gene Expression Omnibus. WGCNA was performed on the GSE32537 dataset. Differentially expressed gene s (DEGs) between IPF patients and healthy controls were also identified to filter hub genes. The relationship between hub genes and lung function was then validated using the GSE47460 and GSE24206 datasets.
The red module, containing 267 genes, was positively correlated with the St. George's Respiratory Questionnaire score ( = 0.37, < 0.001) and negatively correlated with the percent predicted forced vital capacity (FVC% predicted) ( = - 0.46, < 0.001) and the percent predicted diffusion capacity of the lung for carbon monoxide (Dlco% predicted) ( = - 0.42, < 0.001). Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis suggested that the genes in the red module were primarily involved in inflammation and immune pathways. Based on Module Membership and Gene Significance, 32 candidate hub genes were selected in the red module to construct a protein-protein interaction network . Based on the identified DEGs and the degree of connectivity in the network, we identified three hub genes, including interleukin 6 (), suppressor of cytokine signaling-3 (), and serpin family E member 1 (). In the GSE47460 dataset, Spearman correlation coefficients between Dlco% predicted and expression levels of , , were -0.32, -0.41, and -0.46, respectively. Spearman correlation coefficients between FVC% predicted and expression levels of , , were -0.29, -0.33, and -0.27, respectively. In the GSE24206 dataset, all three hub genes were upregulated in patients with advanced IPF.
We identified three hub genes that negatively correlated with the lung function of IPF patients. Our results provide insights into the pathogenesis underlying the progressive disruption of lung function, and the identified hub genes may serve as biomarkers and potential therapeutictargets for the treatment of IPF patients.
特发性肺纤维化(IPF)是一种慢性进行性间质性肺疾病,其特征为肺功能下降。迄今为止,与肺功能障碍相关的病理生理机制仍不清楚,尚未发现有效的治疗方法来改善肺功能。
在本研究中,我们使用加权基因共表达网络分析(WGCNA)来识别与IPF肺功能相关的关键模块和枢纽基因。从基因表达综合数据库下载了三个包含临床信息的数据集。对GSE32537数据集进行WGCNA分析。还识别了IPF患者和健康对照之间的差异表达基因(DEGs)以筛选枢纽基因。然后使用GSE47460和GSE24206数据集验证枢纽基因与肺功能之间的关系。
包含267个基因的红色模块与圣乔治呼吸问卷评分呈正相关(r = 0.37,P < 0.001),与预测的用力肺活量百分比(FVC%预测)呈负相关(r = -0.46,P < 0.001),与预测的肺一氧化碳弥散量百分比(Dlco%预测)呈负相关(r = -0.42,P < 0.001)。基因本体论和京都基因与基因组百科全书富集分析表明,红色模块中的基因主要参与炎症和免疫途径。基于模块成员度和基因显著性,在红色模块中选择了32个候选枢纽基因来构建蛋白质-蛋白质相互作用网络。基于鉴定出的DEGs和网络中的连接程度,我们确定了三个枢纽基因,包括白细胞介素6(IL-6)、细胞因子信号传导抑制因子3(SOCS3)和丝氨酸蛋白酶抑制剂家族E成员1(SERPINE1)。在GSE47460数据集中,预测的Dlco%与IL-6、SOCS3、SERPINE1表达水平之间的Spearman相关系数分别为-0.32、-0.41和-0.46。预测的FVC%与IL-6、SOCS3、SERPINE1表达水平之间的Spearman相关系数分别为-0.29、-0.33和-0.27。在GSE24206数据集中,所有三个枢纽基因在晚期IPF患者中均上调。
我们确定了三个与IPF患者肺功能呈负相关的枢纽基因。我们的结果为肺功能进行性破坏的潜在发病机制提供了见解,并且鉴定出的枢纽基因可能作为生物标志物和潜在的治疗靶点用于治疗IPF患者。