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Updated: May 5, 2026
Facilitating Drug Discovery: An Automated High-content Inflammation Assay in Zebrafish
Published on: July 16, 2012
Integrative bioinformatics and machine learning approaches identify inflammation-related genes and drug candidates
Jialu Yuan1, Haiyang Fu2, Weidong Han1
1Department of Clinical Laboratory, Affiliated Nantong Hospital of Shanghai University (The Sixth People's Hospital of Nantong), Nantong, 226011, Jiangsu, China.
Background:
Inflammation plays a critical role in ischemic stroke (IS). This study aimed to identify inflammation-related genes and explore potential pharmacological agents for future preclinical validation in IS.
Methods:
Transcriptome data were integrated to identify inflammation-related genes, which were functionally characterized and evaluated for diagnostic potential, with single-cell analysis and computational drug prediction.
Results:
Four inflammation-related genes, C-C chemokine receptor type 7 (CCR7), CD7, CD96, and interleukin-7 receptor (IL-7R), were identified from integrated transcriptome analyses. These genes showed promising diagnostic potential (area under the curve (AUC) > 0.8) and were functionally associated with cytokine signaling, immune interactions, and calcium homeostasis. Drug-gene interaction and molecular docking analyses indicated that capecitabine and ruxolitinib are potential candidates for modulating CD96 and IL-7R.
Conclusion:
This study reveals four inflammation-related genes with preliminary diagnostic value and proposes capecitabine and ruxolitinib as candidate drugs for future preclinical research on IS.
