Identification of key modules and hub genes associated with lung function in idiopathic pulmonary fibrosis

Abstract

Background 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. Methods 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. Results The red module, containing 267 genes, was positively correlated with the St. George’s Respiratory Questionnaire score (r = 0.37, p < 0.001) and negatively correlated with the percent predicted forced vital capacity (FVC% predicted) (r = − 0.46, p < 0.001) and the percent predicted diffusion capacity of the lung for carbon monoxide (Dlco% predicted) (r = − 0.42, p < 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 (IL6), suppressor of cytokine signaling-3 (SOCS3), and serpin family E member 1 (SERPINE1). In the GSE47460 dataset, Spearman correlation coefficients between Dlco% predicted and expression levels of IL6, SERPINE1, SOCS3 were –0.32, –0.41, and –0.46, respectively. Spearman correlation coefficients between FVC% predicted and expression levels of IL6, SERPINE1, SOCS3 were –0.29, –0.33, and –0.27, respectively. In the GSE24206 dataset, all three hub genes were upregulated in patients with advanced IPF. Conclusion 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.

Bioinformatics-based investigation on the genetic influence between SARS-CoV-2 infections and idiopathic pulmonary fibrosis (IPF) diseases, and drug repurposing

“…To investigate the common pathogenetic processes of cHubGs, we selected top five common GO terms for each of BPs, MFs and CCs, and KEGG pathways that are significantly enriched by cHubGs in at least two of three databases (see Table 3). Among them, the association of the detected BPs (inflammatory response, response to virus, regulation of inflammatory response, response to tumor necrosis factor, response to cytokine) with COVID-19 and IPF diseases were supported by several independent studies [60][61][62][63][64][65][66][67][68][69][70][71][72][73][74] . The SARS-CoV-2 infection is influenced by a severe inflammatory response with the release of a huge amount of cytokine storm known as pro-inflammatory cytokines.…”

Core genes involved in the regulation of acute lung injury and their association with COVID-19 and tumor progression: A bioinformatics and experimental study

“…Moreover, it is clearly shown that most of the studied genes ( IL-6 , CXC13 , TIMP1 , CCL2 , ADAM8 , THBS1 , SOCS3 , SERPINA3 , SERPINE1 , PLAUR , TNC , MMP8 , IFI44 , RSAD2 , PTX3 , LRG1 , ELN ) are involved in pathogenesis and have prognostic value for a wide variety of non-tumour chronic lung pathologies which can serve as a background for malignant transformation, such as idiopathic pulmonary fibrosis [ 100 , 101 ], chronic bronchitis, emphysema and chronic obstructive pulmonary disease (COPD) [ 102 , 103 ], bronchiectasis and cystic fibrosis [ 104 , 105 ], systemic sclerosis accompanied with the pulmonary fibrosis [ 106 , 107 ] (all found associations of ALI-related core genes/proteins with nonmalignant chronic lung diseases were summarized in S2 Table ).…”

A computational approach based on weighted gene co-expression network analysis for biomarkers analysis of Parkinson’s disease and construction of diagnostic model

“…Gene significance (GS) is used to incorporate external information into the co-expression network, with a higher absolute value of GS indicating the increased biological significance of a gene for a given clinical trait. We selected hub genes in key modules based on the criteria: | MM| > 0.8 and | GS| > 0.2 ( Xia et al, 2020 ). We performed the LASSO regression analysis to extract genes strongly associated with PD in the training dataset via glmnet package in R.…”