Clinical assay for the early detection of colorectal cancer using mass spectrometric wheat germ agglutinin multiple reaction monitoring

I. Jung Tsai, Emily Chia Yu Su, I. Lin Tsai, Ching Yu Lin*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

13 Scopus citations

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 languageEnglish
Article number2190
JournalCancers
Volume13
Issue number9
DOIs
StatePublished - 1 May 2021

Keywords

  • Colorectal cancer
  • Machine learning algorithm
  • Multiple reaction monitoring
  • Plasma
  • Tandem mass spectrometer
  • Wheat germ agglutinin

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