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Updated: Apr 23, 2026
Robust Ligature-Induced Model of Murine Periodontitis for the Evaluation of Oral Neutrophils
Published on: January 21, 2020
Identifying omic biomarkers for chronic inflammatory diseases associated with periodontitis using percolation on
Xingyu Wang1, Lei Liu2, Fan Yang1
1Basic Medicine Research and Innovation Center for Novel Target and Therapeutic Intervention, The Ministry of Education, College of Pharmacy, Chongqing Medical University, Chongqing, China.
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.
