上游突变和代谢组学-分布因果关系的推断确定了癌症的预后生物标志物和治疗靶点
Inference of upstream-mutation and metabolomic-signature causality identifies prognostic biomarkers and therapeutic targets in pancreatic cancer.
摘要(中文译文)
胰腺导管腺癌（PDAC）中的基因组突变被认为通过下游调控网络之间的交互作用，驱动不良预后和对靶向治疗的低应答率。在此，我们应用一种基于因果推断的方法——突变位于代谢组学特征上游（MUMS），证明预后性血清代谢组学特征能够捕捉这种交互作用，并反映突变驱动网络对肿瘤进展的集体影响。我们鉴定出一组包含九种血清代谢物的标志物组合，可在多个独立的PDAC队列中预测生存结局。MUMS分析进一步鉴定并在功能上验证了GRPEL1是一个促肿瘤基因，其下游代谢特征与mTOR/PI3K/Akt信号通路的代谢特征相汇聚。与此一致，GRPEL1使PDAC细胞对mTOR抑制诱导的增殖阻滞更为敏感。总之，我们的发现提供了概念验证性证据，表明血清代谢特征能够反映肿瘤突变景观内部的交互作用。这些共调控模式为发现新的治疗靶点和指导合理的联合治疗设计提供了框架。
Abstract
Genomic mutations in pancreatic ductal adenocarcinoma (PDAC) are hypothesized to drive poor prognosis and low response rates to targeted therapy through crosstalk among downstream regulatory networks. Here, we apply a causal inference-based approach, Mutation-Upstream-of-Metabolomic-Signature (MUMS), to show that prognostic serum metabolomic signatures can capture such crosstalk and reflect the collective impact of mutation-driven networks on tumor progression. We identify a panel of nine serum metabolites that predicts survival outcomes across multiple independent PDAC cohorts. MUMS analysis further identifies and functionally validates GRPEL1 as a tumor-promoting gene whose downstream metabolic signature converges with that of the mTOR/PI3K/Akt signaling pathway. Consistently, GRPEL1 sensitizes PDAC cells to proliferation arrest induced by mTOR inhibition. Together, our findings provide proof-of-concept evidence that serum metabolic signatures can reflect crosstalk within the tumor mutational landscape. These co-regulatory patterns offer a framework for uncovering new therapeutic targets and guiding the design of rational combination therapies.
如何引用
AMA
Feng Chen, Xiaoying Lou, Xingting Guo, Ye Guo, Yuan Huang, Kexin Li, et al. Inference of upstream-mutation and metabolomic-signature causality identifies prognostic biomarkers and therapeutic targets in pancreatic cancer.. Nature communications. 2026; doi:10.1038/s41467-026-73871-x.
APA
Feng Chen, Xiaoying Lou, Xingting Guo, Ye Guo, Yuan Huang, Kexin Li, et al (2026). Inference of upstream-mutation and metabolomic-signature causality identifies prognostic biomarkers and therapeutic targets in pancreatic cancer.. Nature communications. https://doi.org/10.1038/s41467-026-73871-x
GB/T 7714
Feng Chen, Xiaoying Lou, Xingting Guo, Ye Guo, Yuan Huang, Kexin Li, et al. Inference of upstream-mutation and metabolomic-signature causality identifies prognostic biomarkers and therapeutic targets in pancreatic cancer.[J]. Nature communications, 2026 doi:10.1038/s41467-026-73871-x.
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