Academic Journal
Comprehensive analysis of an endoplasmic reticulum stress-related gene prediction model and immune infiltration in idiopathic pulmonary fibrosis
| العنوان: | Comprehensive analysis of an endoplasmic reticulum stress-related gene prediction model and immune infiltration in idiopathic pulmonary fibrosis |
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| المؤلفون: | Zhu, Honglan, Zhou, Aiming, Zhang, Menglin, Pan, Lin, Wu, Xiao, Fu, Chenkun, Gong, Ling, Yang, Wenting, Liu, Daishun, Cheng, Yiju |
| المساهمون: | National Natural Science Foundation of China |
| المصدر: | Frontiers in Immunology ; volume 14 ; ISSN 1664-3224 |
| بيانات النشر: | Frontiers Media SA |
| سنة النشر: | 2024 |
| المجموعة: | Frontiers (Publisher - via CrossRef) |
| الوصف: | 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 in vivo and in vitro via RT-qPCR. 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. |
| نوع الوثيقة: | article in journal/newspaper |
| اللغة: | unknown |
| DOI: | 10.3389/fimmu.2023.1305025 |
| DOI: | 10.3389/fimmu.2023.1305025/full |
| الاتاحة: | http://dx.doi.org/10.3389/fimmu.2023.1305025 https://www.frontiersin.org/articles/10.3389/fimmu.2023.1305025/full |
| Rights: | https://creativecommons.org/licenses/by/4.0/ |
| رقم الانضمام: | edsbas.C089DD15 |
| قاعدة البيانات: | BASE |
| DOI: | 10.3389/fimmu.2023.1305025 |
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