Abstract
Colorectal cancer (CRC) is currently the third leading cause of cancer-related mortality in the world. U.S. Food and Drug Administration-approved circulating tumor markers, including carcinoembryonic antigen, carbohydrate antigen (CA) 19-9 and CA125 were used as prognostic biomarkers of CRC that attributed to low sensitivity in diagnosis of CRC. Therefore, our purpose is to develop a novel strategy for novel clinical biomarkers for early CRC diagnosis. We used mass spectrometry (MS) methods such as nanoLC-MS/MS, targeted LC-MS/MS, and stable isotope-labeled multiple reaction monitoring (MRM) MS coupled to test machine learning algorithms and logistic regression to analyze plasma samples from patients with early-stage CRC, late-stage CRC, and healthy controls (HCs). On the basis of our methods, 356 peptides were identified, 6 differential expressed peptides were verified, and finally three peptides corresponding wheat germ agglutinin (WGA)-captured proteins were semi-quantitated in 286 plasma samples (80 HCs and 206 CRCs). The novel peptide biomarkers combination of PF454–62, ITIH4429–438, and APOE198–207 achieved sensitivity 84.5%, specificity 97.5% and an AUC of 0.96 in CRC diagnosis. In conclusion, our study demonstrated that WGA-captured plasma PF454–62, ITIH4429–438, and APOE198–207 levels in combination may serve as highly effective early diagnostic biomarkers for patients with CRC.
| Original language | English |
|---|---|
| Article number | 2190 |
| Journal | Cancers |
| Volume | 13 |
| Issue number | 9 |
| DOIs | |
| Publication status | Published - May 1 2021 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
Keywords
- Colorectal cancer
- Machine learning algorithm
- Multiple reaction monitoring
- Plasma
- Tandem mass spectrometer
- Wheat germ agglutinin
ASJC Scopus subject areas
- Oncology
- Cancer Research
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