ArticleFrontiers in immunology2023
Comprehensive analysis of an endoplasmic reticulum stress-related gene prediction model and immune infiltration in idiopathic pulmonary fibrosis.
Article in Frontiers in immunology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 8 citations in OpenAlex.
- Identification of diagnostic and prognostic phospholipid biomarkers in idiopathic pulmonary fibrosis via machine learning and in vivo validation.Human genomics · 2025Article
- Machine learning identifies lipid-associated genes and constructs diagnostic and prognostic models for idiopathic pulmonary fibrosis.Orphanet journal of rare diseases · 2025Article
- Exploring ABHD5 as a Lipid-Related Biomarker in Idiopathic Pulmonary Fibrosis: Integrating Machine Learning, Bioinformatics, and In Vitro Experiments.Inflammation · 2025Article
- Endothelin receptor antagonists target EDNRB and modulate the progression of idiopathic pulmonary fibrosis via anoikis-related genes.Frontiers in medicine · 2025Article
- Integrating cellular experiments, single-cell sequencing, and machine learning to identify endoplasmic reticulum stress biomarkers in idiopathic pulmonary fibrosis.Annals of medicine · 2024Article
- Integrating machine learning with bioinformatics for predicting idiopathic pulmonary fibrosis prognosis: developing an individualized clinical prediction tool.Experimental biology and medicine (Maywood, N.J.) · 2024Article
- Investigates the Role of PANoptosis in Idiopathic Pulmonary Fibrosis and Potential Therapeutic Targets.Journal of inflammation research · 2024Article
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Authors and funding
10 authors at 8 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Idiopathic pulmonary fibrosis (IPF) is a chronic progressive interstitial lung disease. This study aimed to investigate the involvement of endoplasmic reticulum stress (ERS) in IPF and explore its correlation with immune infiltration. Methods: ERS-related differentially expressed genes (ERSRDEGs) were identified by intersecting differentially expressed genes (DEGs) from three Gene Expression Omnibus datasets with ERS-related gene sets. Gene Set Variation Analysis and Gene Ontology were used to explore the potential biological mechanisms underlying ERS. A nomogram was developed using the risk signature derived from the ERSRDEGs to perform risk assessment. The diagnostic value of the risk signature was evaluated using receiver operating characteristics, calibration, and decision curve analyses. The ERS score of patients with IPF was measured using a single-sample Gene Set Enrichment Analysis (ssGSEA) algorithm. Subsequently, a prognostic model based on the ERS scores was established. The proportion of immune cell infiltration was assessed using the ssGSEA and CIBERSORT algorithms. Finally, the expression of ERSRDEGs was validated Results: This study developed an 8-ERSRDEGs signature. Based on the expression of these genes, we constructed a diagnostic nomogram model in which agouti-related neuropeptide had a significantly greater impact on the model. The area under the curve values for the predictive value of the ERSRDEGs signature were 0.975 and 1.000 for GSE70866 and GSE110147, respectively. We developed a prognostic model based on the ERS scores of patients with IPF. Furthermore, we classified patients with IPF into two subtypes based on their signatures. The RT-qPCR validation results supported the reliability of most of our conclusions. Conclusion: We developed and verified a risk model using eight ERSRDEGs. These eight genes can potentially affect the progression of IPF by regulating ERS and immune responses.
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