Artificial intelligence-driven microRNA signature for early detection of gastric cancer: discovery and clinical functional exploration
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces ESGCmiRD, an AI tool identifying a five-microRNA signature for early-stage gastric cancer (ESGC) detection. The signature shows high accuracy and potential therapeutic applications.
Area Of Science
- Biomarkers and Diagnostics
- Bioinformatics and Computational Biology
- Oncology
Background
- Gastric cancer (GC) is a major global health concern, often diagnosed late, necessitating improved early detection methods.
- Effective diagnostics for early-stage gastric cancer (ESGC) are crucial for improving patient outcomes.
- Current diagnostic strategies require enhancement for timely and accurate ESGC identification.
Purpose Of The Study
- To develop an artificial intelligence-driven strategy (ESGCmiRD) for identifying a microRNA (miRNA) signature for early-stage gastric cancer (ESGC) detection.
- To validate the diagnostic accuracy and explore the biological roles and therapeutic potential of the identified miRNA signature.
- To investigate the underlying mechanisms of gastric carcinogenesis involving the identified miRNAs.
Main Methods
- Utilized an AI-driven approach (ESGCmiRD) integrating miRNA expression patterns, ESGC relevance, and network-based regulatory capabilities.
- Performed comprehensive bioinformatics analysis and in vitro studies to validate miRNA expression and biological roles in GC.
- Confirmed miRNA-target interactions using dual-luciferase reporter assays and predicted therapeutic potential via molecular docking.
Main Results
- Identified a five-blood miRNA signature (miR-320b, miR-222-3p, miR-181a-5p, miR-103a-3p, miR-107) with high diagnostic accuracy (AUCs ranging from 0.811 to 0.986) across multiple cohorts.
- Demonstrated that these overexpressed miRNAs in ESGC plasma target PTEN, promoting GC cell proliferation, migration, and invasion.
- Molecular docking indicated Paclitaxel as a potential therapeutic agent interacting strongly with the identified miRNA signature.
Conclusions
- The ESGCmiRD strategy successfully identified a robust miRNA signature for ESGC detection.
- The findings provide insights into gastric carcinogenesis mechanisms and highlight potential therapeutic strategies.
- This AI-driven approach offers a promising avenue for developing novel diagnostics and therapeutics for early-stage gastric cancer.
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