Lin Hao, Zeng Zhen, Zhang Hong, Jia Yongbin, Pang Jiangmei, Chen Jingjing, Zhang Hu
Department of Gastroenterology, West China Hospital, Sichuan University, 37 Guoxue Lane, Chengdu 610041, China.
Centre for Inflammatory Bowel Disease, West China Hospital, Sichuan University, Chengdu 610041, China.
Pathogens. 2025 Jun 25;14(7):635. doi: 10.3390/pathogens14070635.
Ovarian cancer remains a formidable global health burden, characterized by frequent late-stage diagnosis and elevated mortality rates attributable to its elusive pathogenesis and the critical lack of reliable early-detection biomarkers. Emerging investigations into the gut-vaginal microbiome axis have unveiled novel pathogenic mechanisms and potential diagnostic targets in ovarian carcinogenesis. This comprehensive review systematically examines the compositional alterations in and functional interplay between vaginal and intestinal microbial communities in ovarian cancer patients. We elucidate three principal mechanistic pathways through which microbial dysbiosis may drive oncogenesis: (1) estrogen-mediated metabolic reprogramming via β-glucuronidase activity; (2) chronic activation of pro-inflammatory cascades (particularly NF-κB and STAT3 signaling); (3) epigenetic silencing of tumor suppressor genes through DNA methyltransferase modulation. We propose an integrative diagnostic framework synthesizing multi-omics data-incorporating microbial profiles, metabolic signatures, pathway-specific molecular alterations, established clinical biomarkers, and imaging findings-within a multifactorial etiological paradigm. This innovative approach aims to enhance early-detection accuracy through machine learning-enabled multidimensional pattern recognition. By bridging microbial ecology with tumor biology, this review provides novel perspectives for understanding ovarian cancer etiology and advancing precision oncology strategies through microbiome-targeted diagnostic innovations.
卵巢癌仍然是一个巨大的全球健康负担,其特点是晚期诊断频繁,且由于其难以捉摸的发病机制以及严重缺乏可靠的早期检测生物标志物,死亡率居高不下。对肠道 - 阴道微生物群轴的新研究揭示了卵巢癌发生过程中的新致病机制和潜在诊断靶点。本综述系统地研究了卵巢癌患者阴道和肠道微生物群落的组成变化及其功能相互作用。我们阐明了微生物失调可能驱动肿瘤发生的三个主要机制途径:(1)通过β - 葡萄糖醛酸酶活性介导的雌激素代谢重编程;(2)促炎级联反应(特别是NF - κB和STAT3信号)的慢性激活;(3)通过DNA甲基转移酶调节对肿瘤抑制基因进行表观遗传沉默。我们提出了一个综合诊断框架,在多因素病因范式内整合多组学数据,包括微生物谱、代谢特征、特定途径的分子改变、已确立的临床生物标志物和影像学发现。这种创新方法旨在通过机器学习实现的多维模式识别提高早期检测准确性。通过将微生物生态学与肿瘤生物学联系起来,本综述为理解卵巢癌病因以及通过微生物群靶向诊断创新推进精准肿瘤学策略提供了新的视角。