Unravelling the molecular landscape of endometrial cancer subtypes: insights from multiomics analysis

Author:

Shen Yufei123,Tian Yan123,Ding Jiashan123,Chen Zhuo123,Zhao Rong123,Lu Yingnan123,Li Lucia123,Zhang Hui123,Wu Haiyue123,Li Xi425,Zhang Yu123

Affiliation:

1. Department of Gynecology, Xiangya Hospital, Central South University

2. National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University

3. Gynecological Oncology Research and Engineering Center of Hunan Province

4. Department of Clinical Pharmacology, Xiangya Hospital, Central South University

5. Institute of Clinical Pharmacology, Central South University, Hunan Key Laboratory of Pharmacogenetics, Changsha Hunan, People’s Republic of China

Abstract

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 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. 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 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. Results: 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. Conclusions: 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.

Publisher

Ovid Technologies (Wolters Kluwer Health)

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3