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, Yi Xiao, Maowei Ni, Yi Zhu, Yuejin Hua, Zhiguo Zheng, Tiannan Guo
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. It remains essential to find clinically relevant biomarkers in epithelial ovarian cancer (EOC). Here, the authors perform a comprehensive proteomic profiling of tissue and plasma samples from EOC and control patients; they find potential biomarkers for EOC early detection and develop methods for tumour recurrence prediction.