跨网络识别癌症调控基因，精准定位潜在致癌驱动因子。
RegNetAgents: A Multi-Agent Framework for Cross-Network Regulatory Driver Identification in Cancer Genomics
- 构建多智能体框架，统一分析批量与单细胞肿瘤网络。
- 对11个乳腺癌和12个结直肠癌基因验证，候选因子显著富集于癌症基因。
- 支持从发现到假说生成的全流程解释，适合癌症机制研究者使用。
我们提出RegNetAgents，一个面向癌症基因组学的AI多智能体框架，用于在异构基因调控网络中进行结构化、查询驱动的调控候选识别。该系统通过整合TCGA来源的癌症网络与GREmLN项目的大规模单细胞调控网络，实现对批量肿瘤和单细胞衍生ARACNe网络的统一分析。针对特定焦点基因，框架执行双网络分类、基于OncoKB注释的癌症基因过滤及肿瘤来源调控关系的作用模式（MoA）分配。候选因子按跨网络证据一致性（共现、仅TCGA、仅GREmLN）排序。系统以多智能体LangGraph DAG工作流实现，提供统一Python API与模型上下文协议（MCP）客户端，作为预计算调控网络的下游分析层运行。在11个乳腺癌（BRCA）和12个结直肠癌（COAD）焦点基因中，识别出的候选调节因子显著富集于OncoKB标注的癌症基因：TCGA来源候选因子在BRCA（Stouffer Z = 6.69）和COAD（Z = 6.95）中均高度富集，GREmLN来源候选因子在BRCA（Z = 5.51）和COAD（Z = 7.06）中亦显著富集（全部p < 0.0001）。管家基因或非驱动因子对照集无富集，表明信号特异性。扩展模块可结构化评估候选因子的致癌潜力、可药性、临床相关性及网络脆弱性，支持从候选识别到生物学假说生成的端到端解释。RegNetAgents建立了可解释的跨网络调控候选识别框架。
原文摘要 · Abstract (English)
We introduce RegNetAgents, an AI-oriented multi-agent framework for structured, query-driven regulatory candidate identification across heterogeneous gene regulatory networks. The system enables unified analysis of bulk tumor and single-cell-derived ARACNe networks by integrating TCGA-derived cancer networks with large-scale single-cell regulatory networks from the GREmLN project. For a given focal gene, the framework performs dual-network classification, cancer gene filtering using OncoKB annotations, and mode-of-action (MoA) assignment for tumor-derived regulatory relationships. Candidates are ranked by evidence consistency across networks (Both, TCGA-only, GREmLN-only). The system is implemented as a multi-agent LangGraph DAG workflow, accessible through a unified Python API and Model Context Protocol (MCP) client, operating as a downstream analytical layer over precomputed regulatory networks rather than a network inference method. Across eleven breast cancer (BRCA) and twelve colorectal cancer (COAD) focal genes, RegNetAgents identifies candidate regulators significantly enriched for OncoKB-annotated cancer genes. TCGA-derived candidates show strong enrichment (Stouffer Z = 6.69 for BRCA and 6.95 for COAD), while GREmLN-derived candidates also demonstrate significant enrichment (Z = 5.51 for BRCA and 7.06 for COAD; all p < 0.0001). No enrichment is observed in housekeeping or non-driver control gene sets, supporting signal specificity. An extended module enables structured evaluation of oncogenic potential, druggability, clinical relevance, and network vulnerability, supporting end-to-end interpretation from candidate identification to biological hypothesis generation. RegNetAgents establishes an interpretable AI framework for cross-network regulatory candidate identification in cancer genomics.
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