Genetics and Computational Biology Department, QIMR Berghofer Medical Research Institute, Brisbane, QLD 4006, Australia.
Genetics and Computational Biology Department, QIMR Berghofer Medical Research Institute, Brisbane, QLD 4006, Australia.
EBioMedicine. 2022 Mar;77:103895. doi: 10.1016/j.ebiom.2022.103895. Epub 2022 Feb 23.
Endometrial cancer is a common gynaecological cancer with increasing incidence and mortality. In the last decade, endometrial cancer genome-wide association studies (GWAS) have provided a resource to explore aetiology and for functional interpretation of heritable risk variation, informing endometrial cancer biology. Indeed, GWAS data have been used to assess relationships with other traits through correlation and Mendelian randomisation analyses, establishing genetic relationships and potential risk factors. Cross-trait GWAS analyses have increased statistical power and identified novel endometrial cancer risk variation related to other traits. Functional analysis of risk loci has helped prioritise candidate susceptibility genes, revealing molecular mechanisms and networks. Lastly, risk scores generated using endometrial cancer GWAS data may allow for clinical translation through identification of patients at high risk of disease. In the next decade, this knowledge base should enable substantial progress in our understanding of endometrial cancer and, potentially, new approaches for its screening and treatment.
子宫内膜癌是一种常见的妇科癌症,发病率和死亡率呈上升趋势。在过去的十年中,子宫内膜癌全基因组关联研究(GWAS)为探索病因和遗传风险变异的功能解释提供了资源,为子宫内膜癌生物学提供了信息。事实上,GWAS 数据已被用于通过相关性和孟德尔随机化分析来评估与其他特征的关系,从而确定遗传关系和潜在的风险因素。跨特征 GWAS 分析增加了统计效力,并确定了与其他特征相关的新的子宫内膜癌风险变异。风险位点的功能分析有助于优先考虑候选易感性基因,揭示分子机制和网络。最后,使用子宫内膜癌 GWAS 数据生成的风险评分可以通过识别疾病高危患者来实现临床转化。在未来十年,这一知识库应该能够使我们对子宫内膜癌的认识取得实质性进展,并可能为其筛查和治疗提供新的方法。