Bioinformatic identification and analysis of immune-related chromatin regulatory genes as potential biomarkers in idiopathic pulmonary fibrosis

Li, Kaixin; Liu, Pulin; Zhang, Wei; Li, Xue; Tanino, Yoshinori; Koga, Yasuhiko; Yan, Xiaoyan

doi:10.21037/atm-22-3700

Cited by 4 publications

(3 citation statements)

References 47 publications

(51 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A previous study reported that PADI4, IGFBP7, and GADD45A serve as biomarkers for IPF, which may contribute to the diagnosis of IPF (22). This is the first study to establish and validate a diagnostic model using eleven chemokine-associated genes (CXCL2, CCRL2, ARRB1, XCL1, GRK5, PPBP, CCL19, CCL13, CCL11, CXCL6, and CXCL13).…”

Section: Discussionmentioning

confidence: 87%

Identification and validation of chemokine system-related genes in idiopathic pulmonary fibrosis

Zhao

et al. 2023

Front. Immunol.

View full text Add to dashboard Cite

BackgroundIdiopathic pulmonary fibrosis (IPF) is a chronic progressive interstitial lung disease with limited therapeutic options. Recent studies have demonstrated that chemokines play a vital role in IPF pathogenesis. In the present study, we explored whether the gene signature associated with chemokines could be used as a reliable biological marker for patients with IPF.MethodsChemokine-related differentially expressed genes (CR-DEGs) in IPF and control lung tissue samples were identified using data from the Gene Expression Omnibus database. A chemokine-related signature of the diagnostic model was established using the LASSO-Cox regression. In addition, unsupervised cluster analysis was conducted using consensus-clustering algorithms. The CIBERSORT algorithm was used to calculate immune cell infiltration across patient subgroups. Finally, we established a mouse model of bleomycin-induced pulmonary fibrosis and a model of fibroblasts treated with TGFβ1. Expression levels of chemokine-related signature genes were determined using real-time quantitative polymerase chain reaction (RT-qPCR).ResultsWe established a chemokine-related eleven-gene signature of a diagnostic model consisting of CXCL2, CCRL2, ARRB1, XCL1, GRK5, PPBP, CCL19, CCL13, CCL11, CXCL6, and CXCL13, which could easily distinguish between IPF patients and controls. Additionally, we identified two subtypes of IPF samples based on chemokine-related gene expression. Pulmonary function parameters and stromal scores were significantly higher in subtype 1 than in subtype 2. Several immune cell types, especially plasma cells and macrophages, differ significantly between the two subtypes. RT-qPCR results showed that the expression levels of Cxcl2 and Ccl2 increased considerably in bleomycin-induced mice. Meanwhile, Arrb1, Ccrl2, Grk5, and Ppbp expression was significantly reduced. Furthermore, multiple chemokine-related genes were altered in TGFβ1 or TNFα-induced fibroblast cells.ConclusionsA novel chemokine-related eleven-signature of diagnostic model was developed. These genes are potential biomarkers of IPF and may play essential roles in its pathogenesis.

show abstract

Section: Discussionmentioning

confidence: 87%

Identification and validation of chemokine system-related genes in idiopathic pulmonary fibrosis

Zhao

et al. 2023

Front. Immunol.

View full text Add to dashboard Cite

show abstract

“…In this model, the module membership (MM) correlated strongly with gene significance (GS, cor = 0.88, Fig 6F). Genes with high MM and GS are considered to be at the pivotal position of the module or hub genes [41]. In other words, genes plotted on the upper-right quadrant (Fig 6F) may be highly associated with the immune microenvironment and ICS in AAAs.…”

Section: Plos Onementioning

confidence: 99%

Identification of abdominal aortic aneurysm subtypes based on mechanosensitive genes

Sheng,

Zeng,

Huang

et al. 2024

PLoS ONE

View full text Add to dashboard Cite

Background Rupture of abdominal aortic aneurysm (rAAA) is a fatal event in the elderly. Elevated blood pressure and weakening of vessel wall strength are major risk factors for this devastating event. This present study examined whether the expression profile of mechanosensitive genes correlates with the phenotype and outcome, thus, serving as a biomarker for AAA development. Methods In this study, we identified mechanosensitive genes involved in AAA development using general bioinformatics methods and machine learning with six human datasets publicly available from the GEO database. Differentially expressed mechanosensitive genes (DEMGs) in AAAs were identified by differential expression analysis. Molecular biological functions of genes were explored using functional clustering, Protein–protein interaction (PPI), and weighted gene co-expression network analysis (WGCNA). According to the datasets (GSE98278, GSE205071 and GSE165470), the changes of diameter and aortic wall strength of AAA induced by DEMGs were verified by consensus clustering analysis, machine learning models, and statistical analysis. In addition, a model for identifying AAA subtypes was built using machine learning methods. Results 38 DEMGs clustered in pathways regulating ‘Smooth muscle cell biology’ and ‘Cell or Tissue connectivity’. By analyzing the GSE205071 and GSE165470 datasets, DEMGs were found to respond to differences in aneurysm diameter and vessel wall strength. Thus, in the merged datasets, we formally created subgroups of AAAs and found differences in immune characteristics between the subgroups. Finally, a model that accurately predicts the AAA subtype that is more likely to rupture was successfully developed. Conclusion We identified 38 DEMGs that may be involved in AAA. This gene cluster is involved in regulating the maximum vessel diameter, degree of immunoinflammatory infiltration, and strength of the local vessel wall in AAA. The prognostic model we developed can accurately identify the AAA subtypes that tend to rupture.

show abstract

“…At present, with the development of microarray and sequencing technology, genetic testing technology is becoming increasingly common. Based on bioinformatics approach, one study explored a prognostic model for lung adenocarcinoma from the perspective of pyroptosis-related factors (Lin et al, 2022), and one study explored a prognostic model for IPF from the perspective of immune-related chromatin regulatory genes (Li et al, 2022). However, translating the clinical and prognostic value of EMT-related genes to IPF requires extensive research.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2023

Self Cite

View full text Add to dashboard Cite

Background: Most patients with idiopathic pulmonary fibrosis (IPF) have poor prognosis; Effective predictive models for these patients are currently lacking. Epithelial–mesenchymal transition (EMT) often occurs during idiopathic pulmonary fibrosis development, and is closely related to multiple pathways and biological processes. It is thus necessary for clinicians to find prognostic biomarkers with high accuracy and specificity from the perspective of Epithelial–mesenchymal transition.Methods: Data were obtained from the Gene Expression Omnibus database. Using consensus clustering, patients were grouped based on Epithelial–mesenchymal transition-related genes. Next, functional enrichment analysis was performed on the results of consensus clustering using gene set variation analysis. The gene modules associated with Epithelial–mesenchymal transition were obtained through weighted gene co-expression network analysis. Prognosis-related genes were screened via least absolute shrinkage and selection operator (LASSO) regression analysis. The model was then evaluated and validated using survival analysis and time-dependent receiver operating characteristic (ROC) analysis.Results: A total of 239 Epithelial–mesenchymal transition-related genes were obtained from patients with idiopathic pulmonary fibrosis. Six genes with strong prognostic associations (C-X-C chemokine receptor type 7 [CXCR7], heparan sulfate-glucosamine 3-sulfotransferase 1 [HS3ST1], matrix metallopeptidase 25 [MMP25], murine retrovirus integration site 1 [MRVI1], transmembrane four L6 family member 1 [TM4SF1], and tyrosylprotein sulfotransferase 1 [TPST1]) were identified via least absolute shrinkage and selection operator and Cox regression analyses. A prognostic model was then constructed based on the selected genes. Survival analysis showed that patients with high-risk scores had worse prognosis based on the training set [hazard ratio (HR) = 7.31, p < .001] and validation set (HR = 2.85, p = .017). The time-dependent receiver operating characteristic analysis showed that the area under the curve (AUC) values in the training set were .872, .905, and .868 for 1-, 2-, and 3-year overall survival rates, respectively. Moreover, the area under the curve values in the validation set were .814, .814, and .808 for 1-, 2-, and 3-year overall survival rates, respectively.Conclusion: The independent prognostic model constructed from six Epithelial–mesenchymal transition-related genes provides bioinformatics guidance to identify additional prognostic markers for idiopathic pulmonary fibrosis in the future.

show abstract

Bioinformatic identification and analysis of immune-related chromatin regulatory genes as potential biomarkers in idiopathic pulmonary fibrosis

Cited by 4 publications

References 47 publications

Identification and validation of chemokine system-related genes in idiopathic pulmonary fibrosis

Identification and validation of chemokine system-related genes in idiopathic pulmonary fibrosis

Identification of abdominal aortic aneurysm subtypes based on mechanosensitive genes

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

Contact Info

Product

Resources

About