# The potential role of biomarkers CD28 and PF4 in the pathogenesis of idiopathic pulmonary fibrosis and their impact on the prognosis: an immune microenvironment analysis

**Authors:** Li Yan, Jiang-Han Li, Ai-Li Zhang, He Li, Bo Pang, De-Yang Meng, Qian Fu, Li-Juan Du, Yan Su

PMC · DOI: 10.1186/s41065-025-00464-x · Hereditas · 2025-06-07

## TL;DR

This study identifies CD28 and PF4 as potential biomarkers in idiopathic pulmonary fibrosis, linking them to disease progression and prognosis through immune microenvironment analysis.

## Contribution

The study introduces CD28 and PF4 as novel biomarkers for IPF prognosis and explores their roles in immune-related pathways and regulatory networks.

## Key findings

- CD28 and PF4 were identified as significant biomarkers associated with IPF progression and prognosis.
- The biomarkers correlate with specific immune cell populations and pathways like hemostasis and prion diseases.
- RT-qPCR confirmed reduced CD28 and PF4 expression in IPF samples compared to controls.

## Abstract

This study aims to identify and investigate biomarkers associated with mitochondrial-related genes (MRGs) and programmed cell death-related genes (PCDRGs) that concurrently influence the progression of idiopathic pulmonary fibrosis (IPF) and to explore the underlying biological mechanisms involved.

The GSE28042 and GSE27957 datasets, comprising 1,136 MRGs and 1,548 PCDRGs, were utilized in this study. Differentially expressed genes (DEGs) between the IPF and control groups were initially identified through differential expression analysis. Subsequently, key module genes closely associated with IPF samples were selected using Weighted Gene Co-expression Network Analysis (WGCNA). Intersection genes 1 and 2 were then identified by overlapping DEGs with key module genes, MRGs, and PCDRGs. Candidate genes were further selected through Spearman correlation analysis involving intersection genes 1 and 2. Additionally, biomarkers were identified, and a risk model was developed using Cox regression analysis, proportional hazards (PH) assumption testing, and machine learning methods. Patients with IPF were stratified into high- and low-risk cohorts. Finally, functional enrichment analysis, immune infiltration analysis, regulatory network construction, and reverse transcription quantitative PCR (RT-qPCR) were conducted separately to validate the findings.

CD28 and PF4 were identified as biomarkers, and a risk model was established. The distinct risk cohorts exhibited differences in pathways related to hemostasis, prion diseases, and other biological processes. A significant positive correlation with was observed between CD28 and native CD4 T cells, while PF4 showed a negative correlation with activated NK cells. Based on these two biomarkers, 30 miRNAs and 532 lncRNAs were predicted, resulting in the construction of a lncRNA–miRNA–biomarker network. Additionally, 11 chemicals associated with these biomarkers were identified. RT-qPCR analysis further confirmed that expression levels of CD28 and PF4 were significantly reduced in IPF samples (P < 0.05).

The results of this study suggested that the biomarkers CD28 and PF4 might play a potential role in the pathogenesis of IPF and might have an impact on the prognosis of the disease. These findings might offer valuable insights for future treatment strategies and prognostic evaluation for patients with IPF.

The online version contains supplementary material available at 10.1186/s41065-025-00464-x.

## Linked entities

- **Genes:** CD28 (CD28 molecule) [NCBI Gene 940], PF4 (platelet factor 4) [NCBI Gene 5196]
- **Diseases:** idiopathic pulmonary fibrosis (MONDO:0800029)

## Full-text entities

- **Genes:** CD28 (CD28 molecule) [NCBI Gene 940] {aka IMD123, Tp44}, PF4 (platelet factor 4) [NCBI Gene 5196] {aka CXCL4, PF-4, SCYB4}, CD4 (CD4 molecule) [NCBI Gene 920] {aka CD4mut, IMD79, Leu-3, OKT4D, T4}
- **Diseases:** IPF (MESH:D054990), prion diseases (MESH:D017096)
- **Species:** Homo sapiens (human, species) [taxon 9606]

## Full text

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## Figures

9 figures with captions in the complete paper: https://tomesphere.com/paper/PMC12144798/full.md

## References

2 references — full list in the complete paper: https://tomesphere.com/paper/PMC12144798/full.md

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Source: https://tomesphere.com/paper/PMC12144798