Proteomic landscape of epithelial ovarian cancer

Liujia Qian, Jianqing Zhu, Zhangzhi Xue, Yan Zhou, Nan Xiang, Hong Xu, Rui Sun, Wangang Gong, Xue Cai, Lu Sun, Weigang Ge, Yufeng Liu, Ying Su, Wangmin Lin, Yuecheng Zhan, Junjian Wang, Shuang Song, Xiao Yi, Maowei Ni, Yi Zhu (), Yuejin Hua (), Zhiguo Zheng () and Tiannan Guo ()
Additional contact information
Liujia Qian: Westlake University
Jianqing Zhu: Zhejiang Cancer Hospital
Zhangzhi Xue: Westlake University
Yan Zhou: Westlake University
Nan Xiang: Westlake University
Hong Xu: Zhejiang University
Rui Sun: Westlake University
Wangang Gong: Zhejiang Cancer Hospital
Xue Cai: Westlake University
Lu Sun: Zhejiang Cancer Hospital
Weigang Ge: Ltd.
Yufeng Liu: Zhejiang University
Ying Su: Zhejiang Cancer Hospital
Wangmin Lin: Ltd.
Yuecheng Zhan: Ltd.
Junjian Wang: Zhejiang Cancer Hospital
Shuang Song: Zhejiang University
Xiao Yi: Westlake University
Maowei Ni: Zhejiang Cancer Hospital
Yi Zhu: Westlake University
Yuejin Hua: Zhejiang University
Zhiguo Zheng: Zhejiang Cancer Hospital
Tiannan Guo: Westlake University

Nature Communications, 2024, vol. 15, issue 1, 1-20

Abstract: Abstract Epithelial ovarian cancer (EOC) is a deadly disease with limited diagnostic biomarkers and therapeutic targets. Here we conduct a comprehensive proteomic profiling of ovarian tissue and plasma samples from 813 patients with different histotypes and therapeutic regimens, covering the expression of 10,715 proteins. We identify eight proteins associated with tumor malignancy in the tissue specimens, which are further validated as potential circulating biomarkers in plasma. Targeted proteomics assays are developed for 12 tissue proteins and 7 blood proteins, and machine learning models are constructed to predict one-year recurrence, which are validated in an independent cohort. These findings contribute to the understanding of EOC pathogenesis and provide potential biomarkers for early detection and monitoring of the disease. Additionally, by integrating mutation analysis with proteomic data, we identify multiple proteins related to DNA damage in recurrent resistant tumors, shedding light on the molecular mechanisms underlying treatment resistance. This study provides a multi-histotype proteomic landscape of EOC, advancing our knowledge for improved diagnosis and treatment strategies.

Date: 2024
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DOI: 10.1038/s41467-024-50786-z

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