ArticleMedical science monitor : international medical journal of experimental and clinical research2017
Pathway Cross-Talk Analysis in Detecting Significant Pathways in Barrett's Esophagus Patients.
Article in Medical science monitor : international medical journal of experimental and clinical research, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Multidimensional mechanisms and therapies underlying gastroesophageal reflux disease: focus on immunity, signaling pathways, and the microbiota-gut-brain axis.Frontiers in immunology · 2025Review
- GraphPath: a graph attention model for molecular stratification with interpretability based on the pathway-pathway interaction network.Bioinformatics (Oxford, England) · 2024Article
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Authors and funding
5 authors.
Funding
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Abstract
BACKGROUND The pathological mechanism of Barrett's esophagus (BE) is still unclear. In the present study, pathway cross-talks were analyzed to identify hub pathways for BE, with the purpose of finding an efficient and cost-effective detection method to discover BE at its early stage and take steps to prevent its progression. MATERIAL AND METHODS We collected and preprocessed gene expression profile data, original pathway data, and protein-protein interaction (PPI) data. Then, we constructed a background pathway cross-talk network (BPCN) based on the original pathway data and PPI data, and a disease pathway cross-talk network (DPCN) based on the differential pathways between the PPI data and the BE and normal control. Finally, a comprehensive analysis was conducted on these 2 networks to identify hub pathway cross-talks for BE, so as to better understand the pathological mechanism of BE from the pathway level. RESULTS A total of 12 411 genes, 300 pathways (6919 genes), and 787 896 PPI interactions (16 730 genes) were separately obtained from their own databases. Then, we constructed a BPCN with 300 nodes (42 293 interactions) and a DPCN with 296 nodes (15 073 interactions). We identified 4 hub pathways: AMP signaling pathway, cGMP-PKG signaling pathway, natural killer cell-mediated cytotoxicity, and osteoclast differentiation. We found that these pathways might play important roles during the occurrence and development of BE. CONCLUSIONS We predicted that these pathways (such as AMP signaling pathway and cAMP signaling pathway) could be used as potential biomarkers for early diagnosis and therapy of BE.
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