Department of Gynecology, Xiangya Hospital, Central South University.
National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University.
Int J Surg. 2024 Sep 1;110(9):5385-5395. doi: 10.1097/JS9.0000000000001685.
Endometrial cancer (EC) as one of the most common gynecologic malignancies is increasing in incidence during the past 10 years. Genome-Wide Association Studies (GWAS) extended to metabolic and protein phenotypes inspired us to employ multiomics methods to analyze the causal relationships of plasma metabolites and proteins with EC to advance our understanding of EC biology and pave the way for more targeted approaches to its diagnosis and treatment by comparing the molecular profiles of different EC subtypes.
Two-sample mendelian randomization (MR) was performed to investigate the effects of plasma metabolites and proteins on risks of different subtypes of EC (endometrioid and nonendometrioid). Pathway analysis, transcriptomic analysis, and network analysis were further employed to illustrate gene-protein-metabolites interactions underlying the pathogenesis of distinct EC histological types.
The authors identified 66 causal relationships between plasma metabolites and endometrioid EC, and 132 causal relationships between plasma proteins and endometrioid EC. Additionally, 40 causal relationships between plasma metabolites and nonendometrioid EC, and 125 causal relationships between plasma proteins and nonendometrioid EC were observed. Substantial differences were observed between endometrioid and nonendometrioid histological types of EC at both the metabolite and protein levels. The authors identified seven overlapping proteins (RGMA, NRXN2, EVA1C, SLC14A1, SLC6A14, SCUBE1, FGF8) in endometrioid subtype and six overlapping proteins (IL32, GRB7, L1CAM, CCL25, GGT2, PSG5) in nonendometrioid subtype and conducted network analysis of above proteins and metabolites to identify coregulated nodes.
Our findings observed substantial differences between endometrioid and nonendometrioid EC at the metabolite and protein levels, providing novel insights into gene-protein-metabolites interactions that could influence future EC treatments.
子宫内膜癌（EC）作为最常见的妇科恶性肿瘤之一，在过去 10 年中发病率不断上升。全基因组关联研究（GWAS）扩展到代谢和蛋白质表型，这启发我们采用多组学方法分析血浆代谢物和蛋白质与 EC 的因果关系，以增进我们对 EC 生物学的理解，并通过比较不同 EC 亚型的分子谱，为其诊断和治疗开辟更有针对性的方法。
采用两样本孟德尔随机化（MR）研究方法，探究血浆代谢物和蛋白质对不同 EC 亚型（子宫内膜样和非子宫内膜样）风险的影响。进一步进行途径分析、转录组分析和网络分析，以阐明不同 EC 组织学类型发病机制下的基因-蛋白-代谢物相互作用。
作者鉴定出 66 种与子宫内膜样 EC 相关的血浆代谢物因果关系，132 种与子宫内膜样 EC 相关的血浆蛋白质因果关系。此外，还观察到 40 种与非子宫内膜样 EC 相关的血浆代谢物因果关系和 125 种与非子宫内膜样 EC 相关的血浆蛋白质因果关系。在代谢物和蛋白质水平上，子宫内膜样和非子宫内膜样 EC 组织学类型之间存在显著差异。作者鉴定出 7 种在子宫内膜样亚型中重叠的蛋白质（RGMA、NRXN2、EVA1C、SLC14A1、SLC6A14、SCUBE1、FGF8）和 6 种在非子宫内膜样亚型中重叠的蛋白质（IL32、GRB7、L1CAM、CCL25、GGT2、PSG5），并对上述蛋白质和代谢物进行网络分析，以识别共同调控节点。
本研究在代谢物和蛋白质水平上观察到子宫内膜样和非子宫内膜样 EC 之间存在显著差异，为影响未来 EC 治疗的基因-蛋白-代谢物相互作用提供了新的见解。
