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Updated: Aug 15, 2025

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Quantitative Mass Spectrometric Profiling of Cancer-cell Proteomes Derived From Liquid and Solid Tumors
Published on: February 27, 2015
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Cancer Serum Atlas-Supported Precise Pan-Targeted Proteomics Enable Multicancer Detection
Anqi Hu1, Lei Zhang1, Zhenxin Wang2
1Institutes of Biomedical Sciences and Minhang Hospital, Fudan University, Shanghai 200032, China.
Analytical Chemistry
|December 30, 2022
Summary
A new Cancer Serum Atlas and proteomic strategy enable efficient discovery of cancer biomarkers from serum. This approach aids in early cancer detection and multicancer diagnosis, offering a powerful tool for liquid biopsy.
Area of Science:
- Biochemistry
- Proteomics
- Cancer Research
Background:
- Serum proteome's wide dynamic range hinders biomarker discovery in large studies.
- Efficient and sensitive methods are needed for cancer serum proteomic research.
Purpose of the Study:
- To develop a high-sensitivity, high-throughput pan-targeted proteomic strategy for cancer biomarker discovery.
- To create a comprehensive resource for cancer-secreted proteins and their standard assays.
Main Methods:
- Construction of the Cancer Serum Atlas with over 2000 cancer-secreted proteins and standard MS assays.
- Development of the standard peptide-anchored parallel reaction monitoring (SPA-PRM) method.
- Direct quantification of 325 cancer-related proteins in 288 serum samples from four cancer types and controls.
Main Results:
- The SPA-PRM method achieved precise quantification of cancer-secreted proteins with high throughput and sensitivity.
- Identified potential biomarkers for early cancer detection.
- Developed a proteomic-based multicancer detection model with 87.2% sensitivity, 100% specificity, and 73.8% localization accuracy.
Conclusions:
- The Cancer Serum Atlas and SPA-PRM strategy support efficient biomarker discovery and multicancer detection.
- This approach is a powerful tool for systematic serological studies and liquid biopsy.
- The developed model shows high accuracy for multicancer detection.

