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Background <p>Chronic inflammatory diseases, such as ulcerative colitis (UC), Crohn’s disease (CD), Alzheimer’s disease (AD) and Parkinson’s disease (PD) are clinically related to periodontitis. However, the computation of omic biomarkers regarding these diseases has not leveraged this association.</p> Methods <p>We developed PMGCN, a computational framework that employs optimal percolations on multi-disease gene co-expression networks derived from bulk transcriptomic gene expression profiles to identify a parsimonious set of key nodes as candidate omic biomarkers.</p> Results <p>Evaluation of PMGCN on independent clinical studies of four chronic inflammatory diseases demonstrates improved predictive performance evaluated via cross validation with bootstrapping compared to commonly used univariate differentially expressed genes. Specifically for UC, three key gene biomarkers (CXCL5, FOSB, PTGR1) are identified by PMGCN, and public single-cell RNA-seq datasets confirm that the mainly altered inflammation signaling pathways in three cell clusters are connected to UC and periodontitis progression.</p> Conclusions <p>PMGCN proposes a computational biomarker identification approach leveraging multi-disease association, the discovered gene biomarkers demonstrate improved prediction of chronic inflammatory diseases and provide novel insights into disease progression.</p>

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Identifying omic biomarkers for chronic inflammatory diseases associated with periodontitis using percolation on multi-disease gene co-expression networks

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月21日

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  • Xingyu Wang,
  • Lei Liu,
  • Fan Yang,
  • Chuling Huang,
  • Ziyi Mei,
  • Jie Li,
  • Shiyong Ma

摘要

Background

Chronic inflammatory diseases, such as ulcerative colitis (UC), Crohn’s disease (CD), Alzheimer’s disease (AD) and Parkinson’s disease (PD) are clinically related to periodontitis. However, the computation of omic biomarkers regarding these diseases has not leveraged this association.

Methods

We developed PMGCN, a computational framework that employs optimal percolations on multi-disease gene co-expression networks derived from bulk transcriptomic gene expression profiles to identify a parsimonious set of key nodes as candidate omic biomarkers.

Results

Evaluation of PMGCN on independent clinical studies of four chronic inflammatory diseases demonstrates improved predictive performance evaluated via cross validation with bootstrapping compared to commonly used univariate differentially expressed genes. Specifically for UC, three key gene biomarkers (CXCL5, FOSB, PTGR1) are identified by PMGCN, and public single-cell RNA-seq datasets confirm that the mainly altered inflammation signaling pathways in three cell clusters are connected to UC and periodontitis progression.

Conclusions

PMGCN proposes a computational biomarker identification approach leveraging multi-disease association, the discovered gene biomarkers demonstrate improved prediction of chronic inflammatory diseases and provide novel insights into disease progression.