Utilizing the RAIN method and Graph SAGE Model to Identify Effective Drug Combinations for Gastric Neoplasm Treatment

2025-08-16

AI Summary

To address the clinical challenges of high intratumoral heterogeneity, rapid development of monotherapy resistance, and poor prognosis in gastric cancer, this study proposes an AI-driven drug synergy discovery framework. We integrate the RAIN algorithm with the GraphSAGE graph neural network to construct a p-value-weighted multimodal drug–gene–protein interaction network. Candidate combinations are systematically validated via NLP-enhanced literature review (PubMed/Scopus), and therapeutic efficacy is quantitatively assessed using a Python-implemented network meta-analysis. This work represents the first synergistic integration of RAIN and graph neural networks, introducing a statistical significance (p-value)-driven edge weighting mechanism that substantially enhances model interpretability and evidentiary rigor. The framework identifies oxaliplatin + fluorouracil + trastuzumab as a highly promising triple-combination regimen (p = 0.0069), supported by 61 independent studies and demonstrating superior efficacy over mono- or dual-agent therapies—indicating strong translational potential for clinical application.

Technology Category

• Search and Optimization: Combinatorial Optimization
• Humans and AI: Other Foundations of Human Computation & AI
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

Abstract

Background: Gastric neoplasm, primarily adenocarcinoma, is an aggressive cancer with high mortality, often diagnosed late, leading to complications like metastasis. Effective drug combinations are vital to address disease heterogeneity, enhance efficacy, reduce resistance, and improve patient outcomes. Methods: The RAIN method integrated Graph SAGE to propose drug combinations, using a graph model with p-value-weighted edges connecting drugs, genes, and proteins. NLP and systematic literature review (PubMed, Scopus, etc.) validated proposed drugs, followed by network meta-analysis to assess efficacy, implemented in Python. Results: Oxaliplatin, fluorouracil, and trastuzumab were identified as effective, supported by 61 studies. Fluorouracil alone had a p-value of 0.0229, improving to 0.0099 with trastuzumab, and 0.0069 for the triple combination, indicating superior efficacy. Conclusion: The RAIN method, combining AI and network meta-analysis, effectively identifies optimal drug combinations for gastric neoplasm, offering a promising strategy to enhance treatment outcomes and guide health policy.

Problem

• Identifying effective drug combinations for gastric cancer treatment
• Addressing disease heterogeneity and drug resistance issues
• Integrating AI and network analysis for optimal therapy selection

Innovation

• RAIN method integrates Graph SAGE model
• NLP and literature review validate drug combinations
• Network meta-analysis assesses combination efficacy