Background: Gastric cancer (GC) is a leading cause of cancer-related deaths worldwide, with late-stage diagnoses frequently leading to poor outcomes. This underscores the need for effective early-stage gastric cancer (ESGC) diagnostics.
Methods: We introduce ESGCmiRD, an innovative artificial intelligence-driven strategy that identifies a miRNA signature for ESGC detection by integrating robust expression patterns, ESGC relevance, and regulatory capabilities of microRNA (miRNA) based on multiple networks. Expression and biological roles of miRNAs in GC were validated and explored via bioinformatics analysis and in vitro studies. miRNA-target interaction was confirmed by dual-luciferase reporter assay. Molecular docking predicted miRNA-drug binding affinities, assessing the miRNA signature's therapeutic potential.
Results: ESGCmiRD identified a blood miRNA signature (miR-320b, miR-222-3p, miR-181a-5p, miR-103a-3p, miR-107) for ESGC detection, demonstrated high diagnostic accuracy with AUC values of 0.986, 0.977, 0.815, and 0.811 in the test and three validation sets (GSE211692, TCGA-STAD, and our cohort), respectively. The five miRNAs were overexpressed in ESGC plasma and directly target PTEN, promoting GC cell proliferation, migration, and invasion. Molecular docking suggested Paclitaxel had the strongest potential interaction with these miRNAs.
Conclusion: This method identifies a robust miRNA signature for ESGC detection and sheds light on gastric carcinogenesis mechanisms, opening doors for potential therapeutic strategies.
© 2025. The Author(s), under exclusive licence to Springer Nature Limited.