ArticleFrontiers in medicine2022
Identification of diagnostic gene biomarkers related to immune infiltration in patients with idiopathic pulmonary fibrosis based on bioinformatics strategies.
Article in Frontiers in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it, 17 citations in OpenAlex.
- Identification of Hub Genes in Idiopathic Pulmonary Fibrosis and Their Association with Lung Cancer by Bioinformatics Analysis.Advances in respiratory medicine · 2023Pooled it
- Global RNA expression analysis of patient samples identified potential diagnostic biomarkers specific for peritoneal, ovarian and deep endometriosis.Scientific reports · 2026Article
- Network toxicology reveals key genes of amiodarone induced pulmonary fibrosis: based on machine learning and SHAP analysis.Frontiers in pharmacology · 2026Article
- T Regulatory Mechanisms in Airway and Interstitial Lung Disease.Seminars in respiratory and critical care medicine · 2025Review
- Multi-omics analysis identified macrophages as key contributors to sex-related differences in ulcerative colitis.Frontiers in immunology · 2025Article
- Commensal microbiome dysbiosis elicits interleukin-8 signaling to drive fibrotic skin disease.PNAS nexus · 2024Article
- Antifibrotic Drugs against Idiopathic Pulmonary Fibrosis and Pulmonary Fibrosis Induced by COVID-19: Therapeutic Approaches and Potential Diagnostic Biomarkers.International journal of molecular sciences · 2024Review
- Development of a Novel Biomarker for the Progression of Idiopathic Pulmonary Fibrosis.International journal of molecular sciences · 2024Article
- Updates on the controversial roles of regulatory lymphoid cells in idiopathic pulmonary fibrosis.Frontiers in immunology · 2024Review
- Transcriptomics in idiopathic pulmonary fibrosis unveiled: a new perspective from differentially expressed genes to therapeutic targets.Frontiers in immunology · 2024Article
- Regulatory T cells in lung disease and transplantation.Bioscience reports · 2023Review
- Artificial neural network identified the significant genes to distinguish Idiopathic pulmonary fibrosis.Scientific reports · 2023Article
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Authors and funding
10 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: The study aims to identify potential diagnostic markers of idiopathic pulmonary fibrosis (IPF) and analyze the significance of immune cell infiltration in this pathology. Materials and methods: Download two publicly available gene expression profiles (GSE10667 and GSE24206 datasets) from the GEO database including 48 Idiopathic pulmonary fibrosis (IPF) samples and 21 human control samples and select for distinctly expressed genes (DEG) from them. Lasso regression model and support vector machine recursive feature elimination S,V,R,F analysis were used to check candidate biomarkers. The area under the subject's work characteristic curve (AUC) value is used to evaluate its recognition ability. The GSE53845 dataset (40 IPF patients and 8 controls) continue to validate the expression level and diagnostic value of biomarkers in IPF. Comprehensive analysis of immune infiltrated cells of IPF was performed using R software and immune cell infiltration estimation analysis tool- deconvolution algorithm (CIBERSORT). Results: 43 DEGs were identified in total. The identified DEGs mostly involve pneumonia, lung disease, collagen disease, obstructive pulmonary disease and other diseases. The activation of IL-17 signaling pathways, amoebic disease, interaction of viral proteins with cytokines and cytokine receptors, protein digestion and absorption, and flaccid hormone signaling pathways in IPF were different from the control group. The expression degree of CRTAC1, COL10A1, COMP, RPS4Y1, IGFL2, NECAB1, SCG5, SLC6A4, and SPP1 in IPF tissue were prominently higher than the normal group. Immune cell infiltration analysis showed that CRTAC1, COL10A1, COMP, IGFL2, NECAB1, SCG5, SLC6A4, and SPP1 were associated with monocytes, plasma cells, neutrophils, and regulatory (treg) T cells. Conclusion: CRTAC1, COL10A1, COMP, IGFL2, NECAB1, SCG5, SLC6A4, and SPP1 can be used as diagnostic markers for IPF, providing new ideas for the future study of IPF occurrence and molecular mechanisms.
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