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<p>Mass-spectrometry-based proteomic data of tumour patient plasma samples present opportunities for improving cancer detection. Here we generate plasma proteomic profiles from 2,251 pan-cancer patient samples and investigate potential diagnostic biomarkers. Proteomic subtyping with different dominant tumour types links proteomic features and clinical indicators such as tumour stage. The highly immune-activated subtype, consisting of renal and bladder cancers, shows elevated glucose–insulin metabolism and reduced lipid metabolism. Comparison of the plasma proteome before and after surgery indicates that proteome patterns could be used to monitor post-surgery therapeutic effects. We also develop a binary classified model that distinguishes between tumour types and healthy controls, as well as a multicancer model for pan-cancer classification of proteins that could be useful biomarkers, and validate their performance in an independent cohort. In addition, we find that the plasma proteome, along with clinical indicators, whole blood cells and so on, can distinguish the pathological subtypes of specific tumour types. This study portrays a pan-cancer plasma proteomic landscape, providing information on plasma biomarkers that could help in discovering diagnostic opportunities.</p>

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Cancer biomarkers discovered using pan-cancer plasma proteomic profiling

  • 期刊论文

  • 发表日期：

    2025年7月2日

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  • Lin Bai,
  • Jiacheng Lyu,
  • Jinwen Feng,
  • Xiaoqiang Qiao,
  • Yuanyuan Qu,
  • Guojian Yang,
  • Yuanxue Zhu,
  • Lingxiao Liao,
  • Hui Gao,
  • Aimin Zang,
  • Zeya Xu,
  • Tao Ji,
  • Peng Ran,
  • Wencong Ding,
  • Hailiang Zhang,
  • Lingli Zhu,
  • Yan Wang,
  • Liang Wang,
  • Xiaofang Wang,
  • Yumiao Li,
  • Jinghua Li,
  • Xiaoping Yin,
  • Guofa Zhao,
  • Dan Liu,
  • Xiangpeng Gao,
  • Sha Tian,
  • Subei Tan,
  • Yan Pu,
  • Lingling Li,
  • Zizheng Song,
  • Jin Song,
  • Wenjia Guo,
  • Yongshi Liao,
  • Dingwei Ye,
  • Wenjun Yang,
  • Youchao Jia,
  • Chen Ding

摘要

Mass-spectrometry-based proteomic data of tumour patient plasma samples present opportunities for improving cancer detection. Here we generate plasma proteomic profiles from 2,251 pan-cancer patient samples and investigate potential diagnostic biomarkers. Proteomic subtyping with different dominant tumour types links proteomic features and clinical indicators such as tumour stage. The highly immune-activated subtype, consisting of renal and bladder cancers, shows elevated glucose–insulin metabolism and reduced lipid metabolism. Comparison of the plasma proteome before and after surgery indicates that proteome patterns could be used to monitor post-surgery therapeutic effects. We also develop a binary classified model that distinguishes between tumour types and healthy controls, as well as a multicancer model for pan-cancer classification of proteins that could be useful biomarkers, and validate their performance in an independent cohort. In addition, we find that the plasma proteome, along with clinical indicators, whole blood cells and so on, can distinguish the pathological subtypes of specific tumour types. This study portrays a pan-cancer plasma proteomic landscape, providing information on plasma biomarkers that could help in discovering diagnostic opportunities.