Glycoproteomic Analysis Reveals Aberrant Expression of Complement C9 and Fibronectin in the Plasma of Patients with Colorectal Cancer

Colorectal cancer (CRC) is a major cause of cancer mortality. Currently used CRC biomarkers provide insufficient sensitivity and specificity; therefore, novel biomarkers are needed to improve the CRC detection. Label-free quantitative proteomics were used to identify and compare glycoproteins, enriched by wheat germ agglutinin, from plasma of CRC patients and age-matched healthy controls. Among 189 identified glycoproteins, the levels of 7 and 15 glycoproteins were significantly altered in the non-metastatic and metastatic CRC groups, respectively. Protein-protein interaction analysis revealed that they were predominantly involved in immune responses, complement pathways, wound healing and coagulation. Of these, the levels of complement C9 (C9) was increased and fibronectin (FN1) was decreased in both CRC states in comparison to those of the healthy controls. Moreover, their levels detected by immunoblotting were validated in another independent cohort and the results were consistent with in the study cohort. Combination of CEA, a commercial CRC biomarker, with C9 and FN1 showed better diagnostic performance. Interestingly, predominant glycoforms associated with acetylneuraminic acid were obviously detected in alpha-2 macroglobulin, haptoglobin, alpha-1-acid glycoprotein 1, and complement C4-A of CRC patient groups. This glycoproteomic approach provides invaluable information of plasma proteome profiles of CRC patients and identification of CRC biomarker candidates

Translational Proteomic Approach for Cholangiocarcinoma Biomarker Discovery, Validation, and Multiplex Assay Development: A Pilot Study

Cholangiocarcinoma (CCA) is a highly lethal disease because most patients are asymptomatic until they progress to advanced stages. Current CCA diagnosis relies on clinical imaging tests and tissue biopsy, while specific CCA biomarkers are still lacking. This study employed a translational proteomic approach for the discovery, validation, and development of a multiplex CCA biomarker assay. In the discovery phase, label-free proteomic quantitation was performed on nine pooled plasma specimens derived from nine CCA patients, nine disease controls (DC), and nine normal individuals. Seven proteins (S100A9, AACT, AFM, and TAOK3 from proteomic analysis, and NGAL, PSMA3, and AMBP from previous literature) were selected as the biomarker candidates. In the validation phase, enzyme-linked immunosorbent assays (ELISAs) were applied to measure the plasma levels of the seven candidate proteins from 63 participants: 26 CCA patients, 17 DC, and 20 normal individuals. Four proteins, S100A9, AACT, NGAL, and PSMA3, were significantly increased in the CCA group. To generate the multiplex biomarker assays, nine machine learning models were trained on the plasma dynamics of all seven candidates (All-7 panel) or the four significant markers (Sig-4 panel) from 45 of the 63 participants (70%). The best-performing models were tested on the unseen values from the remaining 18 (30%) of the 63 participants. Very strong predictive performances for CCA diagnosis were obtained from the All-7 panel using a support vector machine with linear classification (AUC = 0.96; 95% CI 0.88–1.00) and the Sig-4 panel using partial least square analysis (AUC = 0.94; 95% CI 0.82–1.00). This study supports the use of the composite plasma biomarkers measured by clinically compatible ELISAs coupled with machine learning models to identify individuals at risk of CCA. The All-7 and Sig-4 assays for CCA diagnosis should be further validated in an independent prospective blinded clinical study