医学
计算生物学
代谢物
子宫内膜癌
孟德尔随机化
转录组
代谢组学
癌症
基因
生物信息学
癌症研究
遗传学
内科学
基因表达
基因型
生物
遗传变异
作者
Yufei Shen,Yan Tian,Jiashan Ding,Zhuo Chen,Zhao Rong,Yingnan Lu,Lucia Li,Hui Zhang,Haiyue Wu,X Y Li,Yu Zhang
标识
DOI:10.1097/js9.0000000000001685
摘要
Background: 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 multi-omics 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. Methods: 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 non-endometrioid). Pathway analysis, transcriptomic analysis, and network analysis were further employed to illustrate gene-protein-metabolites interactions underlying the pathogenesis of distinct EC histological types. Results: We 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 non-endometrioid EC, and 125 causal relationships between plasma proteins and non-endometrioid EC were observed. Substantial differences were observed between endometrioid and non-endometrioid histological types of EC at both the metabolite and protein levels. We identified 7 overlapping proteins (RGMA, NRXN2, EVA1C, SLC14A1, SLC6A14, SCUBE1, FGF8) in endometrioid subtype and 6 overlapping proteins (IL32, GRB7, L1CAM, CCL25, GGT2, PSG5) in non-endometrioid subtype and network analysis of above proteins and metabolites to identify coregulated nodes. Conclusions: Our findings observed substantial differences between endometrioid and non-endometrioid EC at the metabolite and protein levels, providing novel insights into gene-protein-metabolites interactions that could influence future EC treatments.
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