Folate Metabolism–Related Gene Signature Reveals Prognostic and Immunological Characteristics in Idiopathic Pulmonary Fibrosis

Xuan  Chen; Junzhi  Zhang; Yuankai  Lv; Yiping Chen

doi:10.36141/svdld.2026.18589

Authors

Xuan Chen Department of Respiratory and Critical Care Medicine,The First Affiliated Hospital of Lishui University, Lishui People's Hospital
Junzhi Zhang Department of Respiratory and Critical Care Medicine, Lishui Hospital of Wenzhou Medical University, The First Affiliated Hospital of Lishui University, Lishui People's Hospital，Lishui 323000, Zhejiang , China
Yuankai Lv Department of Respiratory and Critical Care Medicine, Lishui Hospital of Wenzhou Medical University, The First Affiliated Hospital of Lishui University, Lishui People's Hospital，Lishui 323000, Zhejiang , China
Yiping Chen Department of Respiratory and Critical Care Medicine, Lishui Hospital of Wenzhou Medical University, The First Affiliated Hospital of Lishui University, Lishui People's Hospital，Lishui 323000, Zhejiang , China

Keywords:

Idiopathic pulmonary fibrosis, folate metabolism, prognostic model

Abstract

Background

Idiopathic pulmonary fibrosis (IPF) is a progressive interstitial lung disease characterized by irreversible lung remodeling and poor prognosis. Metabolic dysregulation, has emerged as a critical contributor to fibroblast activation and immune dysfunction.

Methods

Transcriptome datasets from the Gene Expression Omnibus were used, with GSE70866-GPL14550 as the training cohort and GSE70866-GPL17077 as an independent validation cohort. Differential expression analysis and weighted gene co-expression network analysis were performed to identify IPF-associated modules. Intersection of differentially expressed genes, hub genes from key modules, and curated FMRGs yielded candidate metabolic signatures. Cox regression and least absolute shrinkage and selection operator analysis were applied to construct a multi-gene prognostic model. Functional enrichment, Gene Set Enrichment Analysis, single-sample Gene Set Enrichment Analysis, and Cell-type deconvolution was performed using CIBERSORT were used to characterize biological pathways and immune infiltration patterns. A nomogram integrating clinical features was built and validated. Upstream transcription factor and competing endogenous RNA regulatory networks, potential drug–gene interactions, and molecular subtypes were also identified.

Results

Ten IPF-related FMRGs were identified through integrated analyses. A four-gene prognostic signature was constructed in the training cohort and externally validated in the independent cohort, effectively stratifying patients into high- and low-risk groups with distinct survival outcomes. High-risk patients exhibited enrichment of epithelial–mesenchymal transition, extracellular matrix remodeling, and cytokine-mediated pathways, accompanied by heightened infiltration of pro-inflammatory and pro-fibrotic immune cells and elevated immune checkpoint expression. The nomogram demonstrated high predictive accuracy and clinical utility. Two metabolic subtypes (Cluster 1/2) were identified, showing significant differences in survival, immune infiltration, checkpoint expression, and pathway activation.

Conclusion

This study establishes a folate metabolism–based prognostic signature and reveals distinct metabolic–immune phenotypes and molecular subtypes in IPF, offering novel biomarkers and therapeutic insights. Clinically, the risk score/nomogram may support risk-adapted monitoring and prioritization of high-risk patients for treatment optimization.

References

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