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Identification of Potential Pathogenic Super-Enhancers-Driven Genes in Pulmonary Fibrosis.

ABSTRACT: Abnormal fibroblast differentiation into myofibroblast is a crucial pathological mechanism of pulmonary fibrosis (PF). Super-enhancers, a newly discovered cluster of regulatory elements, are regarded as the regulators of cell identity. We speculate that abnormal activation of super-enhancers must be involved in the pathological process of PF. This study aims to identify potential pathogenic super-enhancer-driven genes in PF. Differentially expressed genes (DEGs) in PF mouse lungs were identified from a GEO dataset (GDS1492). We collected super-enhancers and their associated genes in human lung fibroblasts and mouse embryonic fibroblasts from SEA version 3.0, a network database that provides comprehensive information on super-enhancers. We crosslinked upregulated DEGs and super-enhancer-associated genes in fibroblasts to predict potential super-enhancer-driven pathogenic genes in PF. A total of 25 genes formed an overlap, and the protein-protein interaction network of these genes was constructed by the STRING database. An interaction network of transcription factors (TFs), super-enhancers, and associated genes was constructed using the Cytoscape software. Gene enrichment analyses, including KEGG pathway and GO analysis, were performed for these genes. Latent transforming growth factor beta (TGF-β) binding protein 2 (LTBP2), one of the predicted super-enhancer-driven pathogenic genes, was used to verify the predicted network's accuracy. LTBP2 was upregulated in the lungs of the bleomycin-induced PF mouse model and TGF-β1-stimulated mouse and human fibroblasts. Myc is one of the TFs binding to the LTBP2 super-enhancer. Knockout of super-enhancer sequences with a CRISPR/Cas9 plasmid or inhibition of Myc all decreased TGF-β1-induced LTBP2 expression in NIH/3 T3 cells. Identifying and interfering super-enhancers might be a new way to explore possible therapeutic methods for PF.

SUBMITTER: Li H

PROVIDER: S-EPMC8153712 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundIdiopathic pulmonary fibrosis (IPF) is a disease with a very poor prognosis. The search for new IPF biomarkers is particularly urgent due to the uncertainty of the mechanisms and treatment. Studies have shown that chromatin regulators (CRs) are involved in the development of IPF and are associated with tumor immunity. However, there are no studies on immune-related CRs in IPF. Therefore, we conducted a systematic study to analyze the expression levels and immune correlation of CRs in IPF tissues and normal tissues and to explore their potential as diagnostic biomarkers.MethodsGSE53845, GSE179781 and GSE24206 datasets from Gene Expression Omnibus (GEO) database were merged into an integrated dataset as the training set; GSE70866 was used as the validation dataset. The cr-related differentially expressed genes (DEGs) between normal and IPF tissues were identified using the "Limma" software package. Weighted gene co-expression network analysis (WGCNA) was performed using the "WGCNA" package to screen eigengenes, which were intersected with DEGs to identify hub genes. The "ggcorrplot" package was used to analyze the correlation between hub genes and immunity, and immune-related hub genes were defined as immHub. A logistic regression model was constructed using immHub as the independent variable and whether the diagnosis was IPF as the dependent variable.ResultsOne hundred and sixty-nine DEGs were identified between IPF and normal tissues. wGCNA identified 3 key modules in brown, green and yellow genes that were present in all 3 modules and met module membership (MM) >0.8 and gene significance (GS) >0.5 were called signature genes (n=390). Four intersecting genes were obtained by intersecting DEGs with signature genes (PADI4, IGFBP7, GADD45A, and SETBP1) all associated with immunity were defined as immHub genes Logistic regression models were constructed based on immHub genes. The area under the curve (AUC) of the ROC curve is used to evaluate the diagnostic accuracy of the logistic regression model for IPF. The AUC in the ROC analysis was 0.771 for the training dataset, and 0.759 for the validation dataset.ConclusionsPADI4, IGFBP7 and GADD45A may be biomarkers for IPF, which will provide assistance in the diagnosis, treatment and prognostic assessment of IPF patients, and provide an important basis for future studies on the relationship between CRs genes and IPF.

Project description:Aging plays a significant role in the occurrence and development of idiopathic pulmonary fibrosis (IPF). In this study, we aimed to identify and verify potential aging-associated genes involved in IPF using bioinformatic analysis. The mRNA expression profile dataset GSE150910 available in the Gene Expression Omnibus (GEO) database and R software were used to identify the differentially expressed aging-related genes involved in IPF. Hub gene expression was validated by other GEO datasets. Gene ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed on differentially expressed aging-related genes. Subsequently, aging-related genes were further screened using three techniques (least absolute shrinkage and selection operator (LASSO) regression, support vector machine, and random forest), and the receiver operating characteristic curves were plotted based on screening results. Finally, real-time quantitative polymerase chain reaction (qRT-PCR) was performed to verify the RNA expression of the six differentially expressed aging-related genes using the blood samples of patients with IPF and healthy individuals. Sixteen differentially expressed aging-related genes were detected, of which the expression of 12 were upregulated and four were downregulated. GO and KEGG enrichment analyses indicated the presence of several enriched terms related to senescence and apoptotic mitochondrial changes. Further screening by LASSO regression, support vector machine, and random forest identified six genes (IGF1, RET, IGFBP2, CDKN2A, JUN, and TFAP2A) that could serve as potential diagnostic biomarkers for IPF. Furthermore, qRT-PCR analysis indicated that among the above-mentioned six aging-related genes, only the expression levels of IGF1, RET, and IGFBP2 in patients with IPF and healthy individuals were consistent with the results of bioinformatic analysis. In conclusion, bioinformatics analysis identified 16 potential aging-related genes associated with IPF, and clinical sample validation suggested that among these, IGF1, RET, and IGFBP2 might play a role in the incidence and prognosis of IPF. Our findings may help understand the pathogenesis of IPF.

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Identification of Potential Pathogenic Super-Enhancers-Driven Genes in Pulmonary Fibrosis.

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