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.
背景/目的：我们近期发现肿瘤依赖血液脂肪酸作为生长能量来源。因此，我们研究了胰腺导管腺癌（PDAC）患者血浆脂质组分中的生物标志物，以用于PDAC的筛查诊断。 方法：采用非靶向方法筛选人类（正常99例，PDAC 103例）和小鼠（正常7例，KPC模型22例）血浆样本中的常见脂肪酸类型。随后，我们在人类血浆样本集A（正常68例，胰腺癌102例）中鉴定出能够区分健康个体与癌症患者的目标代谢物。接着，我们在新的人类样本集B（正常96例，胰腺癌78例）中验证了已识别目标物的有效性。将集合A和B合并为集合C，并进一步将其划分为训练集（7:3比例；正常115例，胰腺癌126例）和验证集（正常49例，PC 54例）。利用识别出的目标物训练了三种统计模型：逻辑回归（LR）、随机森林（RF）以及采用径向基函数（RBF）核的支持向量机（SVM）。 结果：通过比较人类和小鼠血浆，鉴定出八种共有的脂质代谢物。我们进一步确定了包含这些代谢物的四个分析平台：酰基肉碱、磷脂、脂肪酸酰胺和鞘脂。使用集合A、B和C对这四个平台进行分析，发现20种脂质（1种酰基肉碱、1种鞘脂和18种磷脂）在所有三个集合中均满足曲线下面积（AUC）≥0.75的标准。基于包含11种及以上磷脂的LR模型平均AUC值，健康个体与癌症患者的区分性能达到0.9207（灵敏度90.74%；特异度86.22%；阳性预测值87.90%；阴性预测值89.42%），而相同分组中验证集的CA19-9 AUC值为0.7354。将CA19-9加入LR模型后，验证集的区分性能提升至0.9427（90.74%；88.01%；89.32%；89.61%）。 结论：我们鉴定出18种可作为胰腺癌患者血清脂质组分生物标志物的候选脂肪酸代谢物，并证实它们在患者体内均呈下降趋势。此外，我们开发了利用这些标志物的算法，与FDA批准的胰腺癌生物标志物CA19-9的AUC值相比，其区分能力提高了25%。总之，我们发现了可作为胰腺癌患者血浆脂质组分生物标志物的候选代谢物及相关算法。
肿瘤(癌症)患者之家