用AI和网络分析找出治疗胃癌最有效的三药组合。
Utilizing the RAIN method and Graph SAGE Model to Identify Effective Drug Combinations for Gastric Neoplasm Treatment
- 结合图神经网络与文献挖掘，构建药物-基因关系图
- 三药联用使疗效显著提升，p值降至0.0069
- 适合肿瘤治疗研究者与精准医疗决策参考
胃癌（主要为腺癌）是一种侵袭性强、死亡率高的癌症，常因晚期诊断导致转移等并发症。有效药物组合对克服疾病异质性、增强疗效、降低耐药性至关重要。本研究采用RAIN方法，融合Graph SAGE模型，构建以p值加权的药物-基因-蛋白关系图，通过NLP与系统性文献回顾（PubMed、Scopus等）验证候选药物，并利用网络荟萃分析评估疗效，全部在Python中实现。结果发现奥沙利铂、氟尿嘧啶和曲妥珠单抗组合最有效，有61项研究支持。单独使用氟尿嘧啶的p值为0.0229，加入曲妥珠单抗后降至0.0099，三药联合进一步降至0.0069，表明疗效显著提升。结论：RAIN方法结合人工智能与网络荟萃分析，能有效识别胃癌最优药物组合，为改善治疗效果和指导卫生政策提供新策略。
原文摘要 · Abstract (English)
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.
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