ABCC2 作为肺腺癌的新型治疗靶点：一项连接氨代谢与预后及耐药性的机器学习驱动发现
ABCC2 as a novel therapeutic target in lung adenocarcinoma: a machine learning-driven discovery linking ammonia metabolism to prognosis and drug resistance.
摘要(中文译文)
氨曾长期被视为一种代谢废物，但近年来已被认识到是肿瘤微环境中一种关键的肿瘤代谢物，参与促进癌症进展和免疫逃逸。然而，其在肺腺癌（LUAD）中的预后价值及治疗相关性仍未被充分阐明。研究从公共数据库获取了多个LUAD队列的转录组数据。将十种机器学习算法整合为101种组合，以构建预测模型并识别与预后相关的关键基因。采用成熟的计算方法评估肿瘤免疫微环境（TIME）和免疫治疗敏感性。通过体外和体内实验验证了首要候选基因的功能影响。此外，筛选了疗效与ABCC2表达相关的候选药物，并进行分子对接以分析结合亲和力及相互作用模式。氨代谢评分（AMs）成为LUAD的独立预后指标。高AMs与受抑制的TIME相关，其特征为CD8⁺ T细胞等肿瘤浸润淋巴细胞减少，并表现出对免疫治疗的耐药性。与此一致，ABCC2本身也显示出作为LUAD预后生物标志物的显著潜力。ABCC2过表达改变了核心氨代谢基因的表达，并在体外和体内驱动了肺腺癌细胞系的增殖。本研究确定肿瘤氨代谢是LUAD预后和免疫治疗耐药的关键决定因素。ABCC2进一步被确立为这一不良表型的关键驱动因子，使其成为新型预后生物标志物和有前景的治疗靶点。
Abstract
Ammonia, long regarded as a metabolic waste product, has recently been recognized as a pivotal oncometabolite in the tumor microenvironment, contributing to cancer progression and immune evasion. However, its prognostic value and therapeutic relevance in lung adenocarcinoma (LUAD) remain insufficiently characterized. Transcriptomic data from multiple LUAD cohorts were obtained from public databases. Ten machine learning algorithms were integrated into 101 combinations to construct predictive models and identify key genes associated with prognosis. The tumor immune microenvironment (TIME) and immunotherapy sensitivity were evaluated using established computational methods. The functional impact of the top candidate gene was validated through in vitro and in vivo experiments. Additionally, candidate agents whose efficacy correlates with ABCC2 expression were screened, and molecular docking was performed to analyze binding affinity and interaction modes. The Ammonia Metabolism Score (AMs) emerged as an independent prognostic index for LUAD. A high AMs was associated with a suppressed TIME, characterized by fewer tumor-infiltrating lymphocytes such as CD8⁺ T cells, and showed resistance to immunotherapy. Consistently, ABCC2 itself also demonstrated significant potential as a prognostic biomarker in LUAD. Overexpression of ABCC2 altered the expression of core ammonia-metabolism genes and drove the proliferation of lung adenocarcinoma cell lines both in vitro and in vivo. This study identifies tumor ammonia metabolism as a critical determinant of prognosis and immunotherapy resistance in LUAD. ABCC2 is further established as a key driver of this adverse phenotype, positioning it as a novel prognostic biomarker and a promising therapeutic target.
如何引用
AMA
Shuangqing Liao, Ziqi Huang, Xiaobing Liu, Kai Wang, Zhi Zheng, Yanqi Li, et al. ABCC2 as a novel therapeutic target in lung adenocarcinoma: a machine learning-driven discovery linking ammonia metabolism to prognosis and drug resistance.. Journal of translational medicine. 2026; doi:10.1186/s12967-026-08357-x.
APA
Shuangqing Liao, Ziqi Huang, Xiaobing Liu, Kai Wang, Zhi Zheng, Yanqi Li, et al (2026). ABCC2 as a novel therapeutic target in lung adenocarcinoma: a machine learning-driven discovery linking ammonia metabolism to prognosis and drug resistance.. Journal of translational medicine. https://doi.org/10.1186/s12967-026-08357-x
GB/T 7714
Shuangqing Liao, Ziqi Huang, Xiaobing Liu, Kai Wang, Zhi Zheng, Yanqi Li, et al. ABCC2 as a novel therapeutic target in lung adenocarcinoma: a machine learning-driven discovery linking ammonia metabolism to prognosis and drug resistance.[J]. Journal of translational medicine, 2026 doi:10.1186/s12967-026-08357-x.
在 PubMed增强版 阅读全文
翻译全文 · AI 文献问答 · PDF 原文下载 · 引用导出