Identification and Analysis of Key Immune- and Inflammation-Related Genes in Idiopathic Pulmonary Fibrosis
Yan Tan,1,* Baojiang Qian,1,* Qiurui Ma,2 Kun Xiang,1 Shenglan Wang1 1Department of Respiratory and Critical Care Medicine, the First People’s Hospital of Yunnan Province, Kunming, People’s Republic of China; 2Medical School of Kunming University of Science and Technolog, Kunming, Pe...
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Dove Medical Press
2025-02-01
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| author | Tan Y Qian B Ma Q Xiang K Wang S |
| author_facet | Tan Y Qian B Ma Q Xiang K Wang S |
| author_sort | Tan Y |
| collection | DOAJ |
| description | Yan Tan,1,* Baojiang Qian,1,* Qiurui Ma,2 Kun Xiang,1 Shenglan Wang1 1Department of Respiratory and Critical Care Medicine, the First People’s Hospital of Yunnan Province, Kunming, People’s Republic of China; 2Medical School of Kunming University of Science and Technolog, Kunming, People’s Republic of China*These authors contributed equally to this workCorrespondence: Shenglan Wang, Email [email protected]: Studies suggest that immune and inflammation processes may be involved in the development of idiopathic pulmonary fibrosis (IPF); however, their roles remain unclear. This study aims to identify key genes associated with immune response and inflammation in IPF using bioinformatics.Methods: We identified differentially expressed genes (DEGs) in the GSE93606 dataset and GSE28042 dataset, then obtained differentially expressed immune- and inflammation-related genes (DE-IFRGs) by overlapping DEGs. Two machine learning algorithms were used to further screen key genes. Genes with an area under curve (AUC) of > 0.7 in receiver operating characteristic (ROC) curves, significant expression and consistent trends across datasets were considered key genes. Based on these key genes, we carried out nomogram construction, enrichment and immune analyses, regulatory network mapping, drug prediction, and expression verification.Results: 27 DE-IFRGs were identified by intersecting 256 DEGs, 1793 immune-related genes, and 1019 inflammation-related genes. Three genes (RNASE3, S100A12, S100A8) were obtained by crossing two machine algorithms (Boruta and LASSO),which had good diagnostic performance with AUC values. These key genes were all enriched in the same pathways, such as GOCC_azurophil_granule, IL-12 signalling and production in macrophages is the pathway with the strongest role for key genes. Six distinct immune cells, including naive CD4 T cells, T cells CD4 memory resting, T cells regulatory (Tregs), Monocytes, Macrophages M2, Neutrophils were identified. Real-time quantitative polymerase chain reaction (RT-qPCR) results were consistent with the training and validation sets, and the expression of these key genes was significantly upregulated in the IPF samples.Conclusion: This study identified three key genes (RNASE3, S100A12 and S100A8) associated with immune response and inflammation in IPF, providing valuable insights into the diagnosis and treatment of IPF.Summary: This study focuses on cutting-edge research in IPF, with special attention to the development dynamics and application potential of IPF. By systematically reviewing the research results in recent years, this paper aims to provide a comprehensive perspective on the latest field. It is submitted to the journal not only because of its high international recognition, but also because of its emphasis on a progress in this field. The research scope covers a wide range of content from basic theory to practical cases, which has a guiding effect on clinical research.This manuscript is closely related to the core purpose and readership of Journal of Inflammation Research, a journal known for its in-depth and comprehensive coverage of the immune system and encouragement of original research. The content of this study is highly aligned with the journal’s advocacy of pushing the boundaries of knowledge in the immune system and is expected to bring new insights to journal readers and positively influence the future direction of research in the field.Keywords: bioinformatics, idiopathic pulmonary fibrosis, immunity, inflammation, machine learning |
| format | Article |
| id | doaj-art-075696cef3654259965d10cc3a657082 |
| institution | Kabale University |
| issn | 1178-7031 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Dove Medical Press |
| record_format | Article |
| series | Journal of Inflammation Research |
| spelling | doaj-art-075696cef3654259965d10cc3a6570822025-02-11T17:30:56ZengDove Medical PressJournal of Inflammation Research1178-70312025-02-01Volume 1819932009100049Identification and Analysis of Key Immune- and Inflammation-Related Genes in Idiopathic Pulmonary FibrosisTan YQian BMa QXiang KWang SYan Tan,1,* Baojiang Qian,1,* Qiurui Ma,2 Kun Xiang,1 Shenglan Wang1 1Department of Respiratory and Critical Care Medicine, the First People’s Hospital of Yunnan Province, Kunming, People’s Republic of China; 2Medical School of Kunming University of Science and Technolog, Kunming, People’s Republic of China*These authors contributed equally to this workCorrespondence: Shenglan Wang, Email [email protected]: Studies suggest that immune and inflammation processes may be involved in the development of idiopathic pulmonary fibrosis (IPF); however, their roles remain unclear. This study aims to identify key genes associated with immune response and inflammation in IPF using bioinformatics.Methods: We identified differentially expressed genes (DEGs) in the GSE93606 dataset and GSE28042 dataset, then obtained differentially expressed immune- and inflammation-related genes (DE-IFRGs) by overlapping DEGs. Two machine learning algorithms were used to further screen key genes. Genes with an area under curve (AUC) of > 0.7 in receiver operating characteristic (ROC) curves, significant expression and consistent trends across datasets were considered key genes. Based on these key genes, we carried out nomogram construction, enrichment and immune analyses, regulatory network mapping, drug prediction, and expression verification.Results: 27 DE-IFRGs were identified by intersecting 256 DEGs, 1793 immune-related genes, and 1019 inflammation-related genes. Three genes (RNASE3, S100A12, S100A8) were obtained by crossing two machine algorithms (Boruta and LASSO),which had good diagnostic performance with AUC values. These key genes were all enriched in the same pathways, such as GOCC_azurophil_granule, IL-12 signalling and production in macrophages is the pathway with the strongest role for key genes. Six distinct immune cells, including naive CD4 T cells, T cells CD4 memory resting, T cells regulatory (Tregs), Monocytes, Macrophages M2, Neutrophils were identified. Real-time quantitative polymerase chain reaction (RT-qPCR) results were consistent with the training and validation sets, and the expression of these key genes was significantly upregulated in the IPF samples.Conclusion: This study identified three key genes (RNASE3, S100A12 and S100A8) associated with immune response and inflammation in IPF, providing valuable insights into the diagnosis and treatment of IPF.Summary: This study focuses on cutting-edge research in IPF, with special attention to the development dynamics and application potential of IPF. By systematically reviewing the research results in recent years, this paper aims to provide a comprehensive perspective on the latest field. It is submitted to the journal not only because of its high international recognition, but also because of its emphasis on a progress in this field. The research scope covers a wide range of content from basic theory to practical cases, which has a guiding effect on clinical research.This manuscript is closely related to the core purpose and readership of Journal of Inflammation Research, a journal known for its in-depth and comprehensive coverage of the immune system and encouragement of original research. The content of this study is highly aligned with the journal’s advocacy of pushing the boundaries of knowledge in the immune system and is expected to bring new insights to journal readers and positively influence the future direction of research in the field.Keywords: bioinformatics, idiopathic pulmonary fibrosis, immunity, inflammation, machine learninghttps://www.dovepress.com/identification-and-analysis-of-key-immune--and-inflammation-related-ge-peer-reviewed-fulltext-article-JIRbioinformaticsidiopathic pulmonary fibrosisimmunityinflammationmachine learning |
| spellingShingle | Tan Y Qian B Ma Q Xiang K Wang S Identification and Analysis of Key Immune- and Inflammation-Related Genes in Idiopathic Pulmonary Fibrosis Journal of Inflammation Research bioinformatics idiopathic pulmonary fibrosis immunity inflammation machine learning |
| title | Identification and Analysis of Key Immune- and Inflammation-Related Genes in Idiopathic Pulmonary Fibrosis |
| title_full | Identification and Analysis of Key Immune- and Inflammation-Related Genes in Idiopathic Pulmonary Fibrosis |
| title_fullStr | Identification and Analysis of Key Immune- and Inflammation-Related Genes in Idiopathic Pulmonary Fibrosis |
| title_full_unstemmed | Identification and Analysis of Key Immune- and Inflammation-Related Genes in Idiopathic Pulmonary Fibrosis |
| title_short | Identification and Analysis of Key Immune- and Inflammation-Related Genes in Idiopathic Pulmonary Fibrosis |
| title_sort | identification and analysis of key immune and inflammation related genes in idiopathic pulmonary fibrosis |
| topic | bioinformatics idiopathic pulmonary fibrosis immunity inflammation machine learning |
| url | https://www.dovepress.com/identification-and-analysis-of-key-immune--and-inflammation-related-ge-peer-reviewed-fulltext-article-JIR |
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