Guizhou University of Traditional Chinese Medicine, Guiyang, 550025, Guizhou, China.
The Second Affiliated Hospital of Guizhou University of Traditional Chinese Medicine, Guiyang, 550003, Guizhou, China.
Sci Rep. 2022 Aug 27;12(1):14632. doi: 10.1038/s41598-022-19061-3.
As one of the most common malignancies, gastric cancer (GC) is the third leading cause of cancer-related deaths in China. GC is asymptomatic in early stages, and the majority of GC mortality is due to delayed symptoms. It is an urgent task to find reliable biomarkers for the identification of GC in order to improve outcomes. A combination of dried blood spot sampling and direct infusion mass spectrometry (MS) technology was used to measure blood metabolic profiles for 166 patients with GC and 183 healthy individuals, and 93 metabolites including amino acids, carnitine/acylcarnitines and their derivatives, and related ratios were quantified. Multiple algorithms were used to characterize the changes of metabolic profiles in patients with GC compared to healthy individuals. A biomarker panel was identified in training set, and assessed by tenfold cross-validation and external test data set. After systematic selection of 93 metabolites, a biomarker panel consisting of Ala, Arg, Gly, Orn, Tyr/Cit, Val/Phe, C4-OH, C5/C3, C10:2 shows the potential to distinguish patients with GC from healthy individuals in tenfold cross-validation model (sensitivity: 0.8750, specificity: 0.9006) and test set (sensitivity: 0.9545, specificity: 0.8636). This metabolomic analysis makes contribution to the identification of disease-associated biomarkers and to the development of new diagnostic tools for patients with GC.
作为最常见的恶性肿瘤之一，胃癌（GC）是中国癌症相关死亡的第三大主要原因。GC 在早期阶段无症状，大多数 GC 死亡率是由于症状延迟。寻找可靠的 GC 标志物以改善预后是当务之急。本研究采用干血斑采样和直接进样质谱（MS）技术，对 166 例 GC 患者和 183 例健康个体进行了血液代谢谱测量，并对 93 种代谢物（包括氨基酸、肉碱/酰基肉碱及其衍生物和相关比值）进行了定量分析。使用多种算法来描述与健康个体相比，GC 患者代谢谱的变化。在训练集中确定了一个生物标志物组合，并通过十折交叉验证和外部测试数据集进行评估。在系统选择 93 种代谢物后，由 Ala、Arg、Gly、Orn、Tyr/Cit、Val/Phe、C4-OH、C5/C3、C10:2 组成的生物标志物组合在十折交叉验证模型（灵敏度：0.8750，特异性：0.9006）和测试集（灵敏度：0.9545，特异性：0.8636）中具有区分 GC 患者和健康个体的潜力。这项代谢组学分析有助于识别与疾病相关的生物标志物，并为 GC 患者开发新的诊断工具。
