Association between vaginal microbiota and the progression of ovarian cancer

Author:

Li Chanyuan123,Feng Yanling1,Yang Cai2,Wang Dachi2,Zhang Dailiang24,Luo Xiaolin1,Zhang Han1,Huang He1,Zhang Hongyu3,Jiang Yanhui3,Tan Weihong2,Liu Jihong13

Affiliation:

1. Sun Yat‐Sen University Cancer Center State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine Guangzhou China

2. The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences Hangzhou Zhejiang China

3. Cancer Center, The Fifth Affiliated Hospital of Sun Yat‐Sen University Zhuhai Guangdong China

4. College of Chemistry and Chemical Engineering, Hunan Provincial Key Laboratory of Micro & Nano Materials Interface Science Central South University Changsha China

Abstract

AbstractOvarian cancers, especially high‐grade serous ovarian cancer (HGSOC), are one of the most lethal age‐independent gynecologic malignancies. Although pathogenic microorganisms have been demonstrated to participate in the pathogenesis of multiple types of tumors, their potential roles in the development of ovarian cancer remain unclear. To gain an insight into the microbiome‐associated pathogenesis of ovarian cancer and identify potential diagnostic biomarkers, we applied different techniques to analyse the microbiome and serum metabolome of different resources. We found that the vaginal microbiota in ovarian cancer mouse models was under dysbiosis, with altered metabolite configurations that may result from amino acid or lysophospholipid metabolic processes. Local therapeutic intervention with a broad spectrum of antibiotics was effective in reversing microbiota dysbiosis and suppressing carcinogenic progression. As the ovary is situated deeply in the pelvis, it is difficult to directly monitor the ovarian microbial community. Our findings provide alternative options for utilizing the vaginal bacteria as noninvasive biomarkers, such as Burkholderia (area under the curve = 0.8843, 95% confidence interval: 0.743–1.000), which supplement the current invasive diagnostic methods for monitoring ovarian cancer progression and contribute to the development of advanced microbe‐based diagnosis and adjuvant therapies.

Publisher

Wiley

Subject

Infectious Diseases,Virology

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