|
Type |
Symposium |
Area |
Analytical Technologies to Improve Human Health |
Room No. |
Room 202 |
Time |
FRI 15:20-15:45 |
Code |
ANAL2-3 |
Subject |
Gastric Cancer Ascites Proteome for Potential Gastric Cancer Biomarkers Using Targeted Quantitative Methods |
Authors |
Jonghwa Jin Division of Convergence Technology, New Drug Development Center, Korea |
Abstract |
Gastric cancer is one of the most lethal malignancies, and the lack of specific early screening, diagnostic, and prognostic methods for such patients has necessitated the development of gastric cancer-specific biomarkers. Ascites is an important source of biomarkers, because it contains secreted proteins from malignant cells, growth factors, and cytokines.
We performed a comprehensive proteome study using the ascites of patients with inflammatory diseases and gastric cancer. In the discovery stage, we identified 2761 ascites-specific proteins, 234 of which were quantitated by label-free quantitation using normalized spectral abundance factors (NSAFs)—152 and 82 proteins were up- and downregulated, respectively. In the verification stage (68 target proteins), we developed 2 quantitative targeted methods using stable isotope standard (SIS) peptide-based approaches—parallel reaction monitoring (PRM) and multiple reaction monitoring (MRM)—which target peptide fragments selectively and sensitively in complex samples; the PRM-targeted method showed high correlation with the MRM results (approximately 90% of peptides with r > 0.9).
In the linear mixed effects analysis of the 2 methods, the expression of 36 proteins differed significantly between benign (N: 15) and cancer ascites (N: 33). Further, we performed a multiplex assay (linear discriminant analysis) using 7 biomarker candidates from the PRM and MRM analysis, resulting in a merged AUC value of 0.97 for PRM and 0.96 for MRM. Although our model requires further validation in a larger sample size, our 6- and 4-protein marker panel can be used as baseline references for the discovery of novel gastric cancer-specific biomarkers. |
E-mail |
jichang011@kbiohealth.kr |
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