ArticleAnnals of translational medicine2022
Bioinformatic identification and analysis of immune-related chromatin regulatory genes as potential biomarkers in idiopathic pulmonary fibrosis.
Article in Annals of translational medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 10 citations in OpenAlex.
- Identification of potential biomarkers and therapeutic targets in psoriasis based on NF-κB pathway-related genes.Archives of dermatological research · 2025Article
- Identification of Five NK Cell-Related Hub Genes in COPD Using Single-Cell RNA Sequencing Analysis.Journal of inflammation research · 2025Article
- Identification and validation of the clinical prediction model and biomarkers based on chromatin regulators in colon cancer by integrated analysis of bulk- and single-cell RNA sequencing data.Translational cancer research · 2024Article
- Identification of abdominal aortic aneurysm subtypes based on mechanosensitive genes.PloS one · 2024Article
- Identification and validation of chemokine system-related genes in idiopathic pulmonary fibrosis.Frontiers in immunology · 2023Article
- A prognostic model based on clusters of molecules related to epithelial-mesenchymal transition for idiopathic pulmonary fibrosis.Frontiers in genetics · 2022Article
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Authors and funding
7 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Idiopathic pulmonary fibrosis (IPF) is a disease with a very poor prognosis. The search for new IPF biomarkers is particularly urgent due to the uncertainty of the mechanisms and treatment. Studies have shown that chromatin regulators (CRs) are involved in the development of IPF and are associated with tumor immunity. However, there are no studies on immune-related CRs in IPF. Therefore, we conducted a systematic study to analyze the expression levels and immune correlation of CRs in IPF tissues and normal tissues and to explore their potential as diagnostic biomarkers. Methods: GSE53845, GSE179781 and GSE24206 datasets from Gene Expression Omnibus (GEO) database were merged into an integrated dataset as the training set; GSE70866 was used as the validation dataset. The cr-related differentially expressed genes (DEGs) between normal and IPF tissues were identified using the "Limma" software package. Weighted gene co-expression network analysis (WGCNA) was performed using the "WGCNA" package to screen eigengenes, which were intersected with DEGs to identify hub genes. The "ggcorrplot" package was used to analyze the correlation between hub genes and immunity, and immune-related hub genes were defined as immHub. A logistic regression model was constructed using immHub as the independent variable and whether the diagnosis was IPF as the dependent variable. Results: One hundred and sixty-nine DEGs were identified between IPF and normal tissues. wGCNA identified 3 key modules in brown, green and yellow genes that were present in all 3 modules and met module membership (MM) >0.8 and gene significance (GS) >0.5 were called signature genes (n=390). Four intersecting genes were obtained by intersecting DEGs with signature genes ( Conclusions: PADI4, IGFBP7 and GADD45A may be biomarkers for IPF, which will provide assistance in the diagnosis, treatment and prognostic assessment of IPF patients, and provide an important basis for future studies on the relationship between CRs genes and IPF.
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