ArticleFrontiers in immunology2023
Identification of diagnostic hub genes related to neutrophils and infiltrating immune cell alterations 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 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed, 3 citations in OpenAlex.
- The Role of Polyphenols in Respiratory and Gut Health: From the Perspective of Gut-Lung Axis.Phytotherapy research : PTR · 2026Review
- Phosphorylation dynamics of sodium-independent organic anion transporter SLCO4A1 and its role in diseases: a review.Frontiers in systems biology · 2026Review
- Identification of diagnostic and prognostic phospholipid biomarkers in idiopathic pulmonary fibrosis via machine learning and in vivo validation.Human genomics · 2025Article
- Integrating cellular experiments, single-cell sequencing, and machine learning to identify endoplasmic reticulum stress biomarkers in idiopathic pulmonary fibrosis.Annals of medicine · 2024Article
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: There is still a lack of specific indicators to diagnose idiopathic pulmonary fibrosis (IPF). And the role of immune responses in IPF is elusive. In this study, we aimed to identify hub genes for diagnosing IPF and to explore the immune microenvironment in IPF. Methods: We identified differentially expressed genes (DEGs) between IPF and control lung samples using the GEO database. Combining LASSO regression and SVM-RFE machine learning algorithms, we identified hub genes. Their differential expression were further validated in bleomycin-induced pulmonary fibrosis model mice and a meta-GEO cohort consisting of five merged GEO datasets. Then, we used the hub genes to construct a diagnostic model. All GEO datasets met the inclusion criteria, and verification methods, including ROC curve analysis, calibration curve (CC) analysis, decision curve analysis (DCA) and clinical impact curve (CIC) analysis, were performed to validate the reliability of the model. Through the Cell Type Identification by Estimating Relative Subsets of RNA Transcripts algorithm (CIBERSORT), we analyzed the correlations between infiltrating immune cells and hub genes and the changes in diverse infiltrating immune cells in IPF. Results: A total of 412 DEGs were identified between IPF and healthy control samples, of which 283 were upregulated and 129 were downregulated. Through machine learning, three hub genes ( Conclusion: Our study demonstrated that three hub genes (
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