解码子宫腺肌症:来自遗传学、多组学与高级计算分析的整合见解
Decoding adenomyosis: integrative insights from genetics, multiomics, and advanced computational analyses
摘要
该综述整合近期关于子宫腺肌症的遗传学、多组学与先进计算研究。子宫腺肌症是雌激素驱动、炎症性子宫疾病,导致异常出血、疼痛及不良生殖结局。研究显示特定基因与该病存在因果关联,基因组与表观遗传机制促进雌激素信号增强、孕激素信号减弱及相关炎症,并获新型3D体外细胞模型等正交验证支持。AI驱动的多组学整合平台进一步揭示有前景的治疗候选物,有望缓解疾病进展与症状并改善生育与妊娠结局。原文为叙述性综述,未提供具体样本量或效应数据。
原文摘要
Adenomyosis is an oestrogen-driven, inflammatory uterine disorder characterised by endometrial glands and stroma in the myometrium resulting in abnormal bleeding, pain, and poor reproductive outcomes. Multiple contributors to its pathogenesis and pathophysiology have been identified over the years using a variety of experimental approaches and technologies, with gaps in integrating these approaches into a comprehensive paradigm of adenomyosis pathobiology. This review provides integrative insights from recent studies focused on genetics, multiomics, and advanced computational approaches. These demonstrate that select genes are causally linked to adenomyosis and that genomic and epigenetic mechanisms promote the enhanced oestrogen and diminished progesterone signalling and associated inflammation that are pathognomonic of the disorder, supported by orthogonal validation including novel 3D in vitro cell models. In addition, advanced computational tools, including AI-driven platforms that integrate multiomics data, reveal promising therapeutic candidates for mitigating disease progression and symptoms and improving fertility and pregnancy success.
推荐理由:以多组学与AI整合视角系统梳理子宫腺肌症的遗传与表观遗传机制,并指向3D模型与治疗候选物,对内膜相关不孕与ART临床决策有参考价值;属叙述性综述,证据强度受限。
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