Dataset Information

Identification of Hub Genes and Pathways Associated With Idiopathic Pulmonary Fibrosis via Bioinformatics Analysis.

ABSTRACT: Idiopathic pulmonary fibrosis (IPF) is a progressive disease whose etiology remains unknown. The purpose of this study was to explore hub genes and pathways related to IPF development and prognosis. Multiple gene expression datasets were downloaded from the Gene Expression Omnibus database. Weighted correlation network analysis (WGCNA) was performed and differentially expressed genes (DEGs) identified to investigate Hub modules and genes correlated with IPF. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, and protein-protein interaction (PPI) network analysis were performed on selected key genes. In the PPI network and cytoHubba plugin, 11 hub genes were identified, including ASPN, CDH2, COL1A1, COL1A2, COL3A1, COL14A1, CTSK, MMP1, MMP7, POSTN, and SPP1. Correlation between hub genes was displayed and validated. Expression levels of hub genes were verified using quantitative real-time PCR (qRT-PCR). Dysregulated expression of these genes and their crosstalk might impact the development of IPF through modulating IPF-related biological processes and signaling pathways. Among these genes, expression levels of COL1A1, COL3A1, CTSK, MMP1, MMP7, POSTN, and SPP1 were positively correlated with IPF prognosis. The present study provides further insights into individualized treatment and prognosis for IPF.

SUBMITTER: Wan H

PROVIDER: S-EPMC8406749 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Identification of Hub Genes and Pathways Associated With Idiopathic Pulmonary Fibrosis <i>via</i> Bioinformatics Analysis.

Wan Hanxi H Huang Xinwei X Cong Peilin P He Mengfan M Chen Aiwen A Wu Tingmei T Dai Danqing D Li Wanrong W Gao Xiaofei X Tian Li L Liang Huazheng H Xiong Lize L

Frontiers in molecular biosciences 20210812

Idiopathic pulmonary fibrosis (IPF) is a progressive disease whose etiology remains unknown. The purpose of this study was to explore hub genes and pathways related to IPF development and prognosis. Multiple gene expression datasets were downloaded from the Gene Expression Omnibus database. Weighted correlation network analysis (WGCNA) was performed and differentially expressed genes (DEGs) identified to investigate Hub modules and genes correlated with IPF. Gene Ontology (GO), Kyoto Encyclopedi ...[more]

PMID: 34476240

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Project description:Background: Lung cancer is the most common comorbidity of idiopathic pulmonary fibrosis. Thus there is an urgent need for the research of IPF and carcinogenesis Objective: The objective of this study was to explore hub genes which are common in pulmonary fibrosis and lung cancer progression through bioinformatic analysis. Methods: All the analysis was performed in R software. Differentially expressed genes (DEGs) were explored by comparing gene expression profiles between IPF tissues and healthy lung tissues from GSE24206, GSE53845, GSE101286 and GSE110147 datasets. Venn Diagram analysis was used to identify the overlapping genes, while GO and KEGG pathway enrichment analysis were used to explore the biological functions of the DEGs using clusterprofiler package. Hub genes were identified by analyzing protein-protein interaction networks using Cytoscape software. Nomogram was constructed using the rms package. Tumor immune dysfunction and exclusion (TIDE) and Genomics of Drug Sensitivity in Cancer (GDSC) analysis was used to quantify the immunotherapy and chemotherapy sensitivity of non-small cell lung cancer (NSCLC) patients. Results: COL1A1, COL3A1, MMP1, POSTN1 and TIMP3 were identified as the top five hub genes. The five hub genes were used to construct a diagnostic nomogram that was validated in another IPF dataset. Since the hub genes were also associated with lung cancer progression, we found that the nomogram also had diagnostic value in NSCLC patients. These five genes achieved a statistically difference of overall survival in NSCLC patients (p < 0.05). The expression of the five hub genes was mostly enriched in fibroblasts. Fibroblasts and the hub genes also showed significant ability to predict the susceptibility of NSCLC patients to chemotherapy and immunotherapy. Conclusion: We identified five hub genes as potential biomarkers of IPF and NSCLC progression. This finding may give insight into the underlying molecular mechanisms of IPF and lung cancer progression and provides potential targets for developing new therapeutic agents for IPF patients.

Project description: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.

Project description:BackgroundIdiopathic 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.MethodsIn 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.ResultsThe 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.ConclusionWe 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.

Project description:Background: Idiopathic pulmonary fibrosis (IPF) is a fatal and irreversible interstitial lung disease. The specific mechanisms involved in the pathogenesis of IPF are not fully understood, while metabolic dysregulation has recently been demonstrated to contribute to IPF. This study aims to identify key metabolism-related genes involved in the progression of IPF, providing new insights into the pathogenesis of IPF. Methods: We downloaded four datasets (GSE32537, GSE110147, GSE150910, and GSE92592) from the Gene Expression Omnibus (GEO) database and identified differentially expressed metabolism-related genes (DEMRGs) in lung tissues of IPF by comprehensive analysis. Then, we performed GO, KEGG, and Reactome enrichment analyses of the DEMRGs. Subsequently, key DEMRGs were identified by machine-learning algorithms. Next, miRNAs regulating these key DEMRGs were predicted by integrating the GSE32538 (IPF miRNA dataset) and the miRWalk database. The Cytoscape software was used to visualize miRNA-mRNA regulatory networks. In addition, the relative levels of immune cells were assessed by the CIBERSORT algorithm, and the correlation of key DEMRGs with immune cells was calculated. Finally, the mRNA expression of the key DEMRGs was validated in two external independent datasets and an in vivo experiment. Results: A total of 101 DEMRGs (51 upregulated and 50 downregulated) were identified. Six key DEMRGs (ENPP3, ENTPD1, GPX3, PDE7B, PNMT, and POLR3H) were further identified using two machine-learning algorithms (LASSO and SVM-RFE). In the lung tissue of IPF patients, the expression levels of ENPP3, ENTPD1, and PDE7B were upregulated, and the expression levels of GPX3, PNMT, and POLR3H were downregulated. In addition, the miRNA-mRNA regulatory network of key DEMRGs was constructed. Then, the expression levels of key DEMRGs were validated in two independent external datasets (GSE53845 and GSE213001). Finally, we verified the key DEMRGs in the lung tissue of bleomycin-induced pulmonary fibrosis mice by qRT-PCR. Conclusion: Our study identified key metabolism-related genes that are differentially expressed in the lung tissue of IPF patients. Our study emphasizes the critical role of metabolic dysregulation in IPF, offers potential therapeutic targets, and provides new insights for future studies.

Dataset Information

Identification of Hub Genes and Pathways Associated With Idiopathic Pulmonary Fibrosis via Bioinformatics Analysis.

Publications

Identification of Hub Genes and Pathways Associated With Idiopathic Pulmonary Fibrosis <i>via</i> Bioinformatics Analysis.

Similar Datasets

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