Diagnostic Biomarker Candidates Proposed Using Targeted Lipid Metabolomics Analysis of the Plasma of Patients with...

Background/Objectives: We recently discovered that tumors rely on blood fatty acids as an energy source for growth. Therefore, we investigated biomarkers in the lipid fractions of plasma from patients with pancreatic ductal adenocarcinoma (PDAC) for the screening diagnosis of PDAC. Methods: We screened common fatty acid types in human (normal 99, PDAC 103) and mouse (normal 7, KPC 22) plasma samples using a non-targeted approach. Subsequently, we identified targets in human plasma (set A: normal 68, PC 102) that could distinguish between healthy individuals and patients with cancer. Next, we verified whether the identified targets were useful in a new human set (set B: 96 normal, 78 PC). We combined sets A and B to create set C and further divided it into a training set (7:3 ratio; normal 115, pancreatic cancer 126) and a validation set (normal 49, PC 54). The identified targets were used to train three statistical models (logistic regression (LR), random forest (RF), and support vector machine (SVM) with a radial basis function (RBF) kernel). Results: The comparison of human and mouse plasma identified eight common lipid metabolites. We further identified four platforms containing these metabolites for target analysis: acylcarnitines, phospholipids, fatty acid amides, and sphingolipids. We analyzed the four platforms using sets A, B, and C and found 20 lipids (1 acylcarnitine, 1 sphingolipid, and 18 phospholipids) that met the criterion of AUC ≥ 0.75 in all three sets. Based on an average AUC for LR models with 11 or more phospholipids, the separation performance between healthy individuals and patients with cancer was 0.9207 (sensitivity, 90.74%; specificity, 86.22%; PPV, 87.90%; NPV, 89.42%), and the AUC of the validation set for CA19-9 in the same groups was 0.7354. The addition of CA19-9 to the LR models resulted in a separation performance of 0.9427 (90.74%; 88.01%; 89.32%; 89.61%) for the validation set. Conclusions: We identified 18 candidate fatty acid metabolites that could serve as biological markers in the serum lipid fractions of pancreatic cancer patients and confirmed that all of them decreased in patients. Additionally, we developed an algorithm utilizing these markers, which demonstrated a 25% increase in discriminatory power compared to the AUC value of CA19-9, an FDA-approved biomarker for pancreatic cancer. In summary, we identified candidate metabolites and algorithms that could serve as biomarkers in the lipid fractions of plasma from patients with pancreatic cancer.