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Peer-reviewedCC BY-NC 4.0PeerJ

Identifying potential targets in endothelial to mesenchymal transition-mediated atherosclerosis using bioinformatics and machine learning

  • Zhengjun Hou1
  • Sihan Chen1
  • Yuliang Cui2
  • Qinfeng Ma3
  • Jiandong Chen1
  • Kunxian Shu1
  1. 1Chongqing University of Post and Telecommunications, Chongqing, China
  2. 2Chongqing Industry Polytechnic University, Chongqing, China
  3. 3Chongqing Blood Center, Chongqing, China
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Abstract

Background: Endothelial to mesenchymal transition (EndMT) is a key driver of atherosclerosis (AS). This study integrated multiple RNA transcriptomic and single-cell transcriptomic datasets to identify shared gene expression signatures between EndMT and AS.

Methods: GEO expression profiles were analyzed via differential expression, weighted gene co-expression network analysis (WGCNA), and functional enrichment analyses. A Random Forest model evaluated key genes, validating their expression and diagnostic performance. Single-cell RNA sequencing (scRNA-seq) further assessed their expression patterns, and a Transcription Factor (TF)-mRNA-miRNA regulatory network.

Results: Fifteen overlapping genes were identified, from which six candidates were screened through a Protein-Protein Interaction (PPI) network and validated in vitro . The application of a Random Forest model identified two key hub genes, CCL5 and IL1B , which were significantly upregulated. Their diagnostic Area Under the Curve (AUC) was >0.7 when assessed separately and increased to >0.85 in a combined model. Three shared TFs (NFKBIA, NFKB1, RELA) of the Nuclear Factor-kappa B (NF-κB) pathway were identified, highlighting its central role in disease progression.

Conclusions: CCL5 and IL1B are key genes potentially regulating EndMT-mediated AS via inflammatory pathways including Nucleotide-binding oligomerization domain-like (NOD-like) receptor, Tumor Necrosis Factor (TNF), and NF-κB signaling, offering promising diagnostic and therapeutic targets for AS.

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Cite

BibTeXRIS
BibTeX
@article{hou2026identifying,
  title = {{Identifying potential targets in endothelial to mesenchymal transition-mediated atherosclerosis using bioinformatics and machine learning}},
  author = {Zhengjun Hou and Sihan Chen and Yuliang Cui and Qinfeng Ma and Jiandong Chen and Kunxian Shu},
  journal = {PeerJ},
  year = {2026},
  month = nov,
  doi = {10.7717/peerj.21863},
  url = {https://peerj.com/articles/21863},
  note = {CC BY-NC 4.0},
}
RIS
TY  - JOUR
TI  - Identifying potential targets in endothelial to mesenchymal transition-mediated atherosclerosis using bioinformatics and machine learning
AU  - Zhengjun Hou
AU  - Sihan Chen
AU  - Yuliang Cui
AU  - Qinfeng Ma
AU  - Jiandong Chen
AU  - Kunxian Shu
PY  - 2026
DA  - 2026/11/03
JO  - PeerJ
DO  - 10.7717/peerj.21863
UR  - https://peerj.com/articles/21863
AB  - Background: Endothelial to mesenchymal transition (EndMT) is a key driver of atherosclerosis (AS). This study integrated multiple RNA transcriptomic and single-cell transcriptomic datasets to identify shared gene expression signatures between EndMT and AS.  Methods: GEO expression profiles were analyzed via differential expression, weighted gene co-expression network analysis (WGCNA), and functional enrichment analyses. A Random Forest model evaluated key genes, validating their expression and diagnostic performance. Single-cell RNA sequencing (scRNA-seq) further assessed their expression patterns, and a Transcription Factor (TF)-mRNA-miRNA regulatory network.  Results: Fifteen overlapping genes were identified, from which six candidates were screened through a Protein-Protein Interaction (PPI) network and validated in vitro . The application of a Random Forest model identified two key hub genes, CCL5 and IL1B , which were significantly upregulated. Their diagnostic Area Under the Curve (AUC) was >0.7 when assessed separately and increased to >0.85 in a combined model. Three shared TFs (NFKBIA, NFKB1, RELA) of the Nuclear Factor-kappa B (NF-κB) pathway were identified, highlighting its central role in disease progression.  Conclusions: CCL5 and IL1B are key genes potentially regulating EndMT-mediated AS via inflammatory pathways including Nucleotide-binding oligomerization domain-like (NOD-like) receptor, Tumor Necrosis Factor (TNF), and NF-κB signaling, offering promising diagnostic and therapeutic targets for AS.
SN  - 2167-8359
ER  -