Identification of three hub genes related to the prognosis of idiopathic pulmonary fibrosis using bioinformatics analysis

Abstract

Background: Idiopathic pulmonary fibrosis (IPF) is a chronic respiratory disease characterized by peripheral distribution of bilateral pulmonary fibrosis that is more pronounced at the base. IPF has a short median survival time and a poor prognosis. Therefore, it is necessary to identify effective prognostic indicators to guide the treatment of patients with IPF. Methods: We downloaded microarray data of bronchoalveolar lavage cells from the Gene Expression Omnibus (GEO), containing 176 IPF patients and 20 controls. The top 5,000 genes in the median absolute deviation were classified into different color modules using weighted gene co-expression network analysis (WGCNA), and the modules significantly associated with both survival time and survival status were identified as prognostic modules. We used Lasso Cox regression and multivariate Cox regression to search for hub genes related to prognosis from the differentially expressed genes (DEGs) in the prognostic modules and constructed a risk model and nomogram accordingly. Moreover, based on the risk model, we divided IPF patients into high-risk and low-risk groups to determine the biological functions and immune cell subtypes associated with the prognosis of IPF using gene set enrichment analysis and immune cell infiltration analysis. Results: A total of 153 DEGs located in the prognostic modules, three (TPST1, MRVI1, and TM4SF1) of which were eventually defined as prognostic hub genes. A risk model was constructed based on the expression levels of the three hub genes, and the accuracy of the model was evaluated using time-dependent receiver operating characteristic (ROC) curves. The areas under the curve for 1-, 2-, and 3-year survival rates were 0.862, 0.885, and 0.833, respectively. The results of enrichment analysis showed that inflammation and immune processes significantly affected the prognosis of patients with IPF. The degree of mast and natural killer (NK) cell infiltration also increases the prognostic risk of IPF. Conclusions: We identified three hub genes as independent molecular markers to predict the prognosis of patients with IPF and constructed a prognostic model that may be helpful in promoting therapeutic gains for IPF patients.

Identification of immune-related genes in diagnosing atherosclerosis with rheumatoid arthritis through bioinformatics analysis and machine learning

“…Protein-tyrosine sulfotransferase 1 (TPST1) catalyzes the sulfuration of tyrosine residues within the acidic motif of polypeptides ( 65 ). It has been proven that TPST1 can regulate immune and inflammatory response through catalyzing sulfation ( 66 ) involved in regulating immune and inflammatory responses through tyrosine sulfation ( 67 ). Additionally, tyrosine sulfation contributes to monocyte recruitment, a major factor in AS development, making drugs inhibiting TPST1 favorable in AS treatment ( 68 , 69 ).…”

A prognostic model based on clusters of molecules related to epithelial–mesenchymal transition for idiopathic pulmonary fibrosis

“…Based on the LASSO regression, we obtained the EMT-related prognostic genes and their corresponding coefficients. We multiplied the gene expressions with the corresponding coefficients and summed all of them (Wang et al, 2022). The risk score formula was constructed as follows:…”

“…Bronchoalveolar lavage (BAL) is the subject of a common ancillary test for IPF diagnosis (Meyer et al, 2012;Patel et al, 2021). Since bronchoalveolar lavage fluid (BALF) better reflects the exudation of inflammatory factors and mediators in IPF and improves the accuracy of IPF biomarker construction, BAL cell samples were selected for both the training and validation sets of this study (Xia et al, 2021;Wang et al, 2022). First, differential EMT-related genes were identified in patients with IPF via consensus clustering and weighted co-expression network analysis (WGCNA).…”

Identification of Molecular Subtypes and Key Gene of Pulmonary Fibrosis Through Gene Expression Profiles

“…The ROC curve of the target gene was delineated using the "pROC" R package based on the expression pro le of validation dataset. [12] Declarations Ethical Approval…”