Sun Danxiong, Du Yanhong, Li Rufang, Zhang Yunhui
Faculty of Life Science and Technology, Kunming University of Science and Technology, Kunming, China.
Department of Pulmonary and Critical Care Medicine, The First People's Hospital of Yunnan Province, The Affiliated Hospital of Kunming University of Science and Technology, Kunming, China.
Front Oncol. 2025 Mar 11;15:1535525. doi: 10.3389/fonc.2025.1535525. eCollection 2025.
This study aimed to identify specific metabolic markers in the blood that can diagnose early-stage lung adenocarcinoma.
An untargeted metabolomics study was performed, and the participants were divided into four groups: early-stage lung adenocarcinoma group (E-LUAD; n = 21), healthy control group (HC, n = 17), non-cancerous lung disease group (NCC; n = 17), and advanced lung adenocarcinoma group (A-LUAD; n = 25). Plasma metabolite levels that differed in the E-LUAD group compared to the other three groups were identified via liquid chromatography-mass spectrometry (LC-MS). Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were performed at metaX for statistical analysis. A Venn diagram was constructed to identify overlapping differential metabolites of the class comparisons. The data were randomly divided into a training set and a validation set. Based on the overlapping differential metabolites, the diagnostic model was constructed. The discrimination of the model was evaluated using the area under the curve (AUC).
A total of 527 metabolites were tentatively identified in positive ion mode and 286 metabolites in negative ion mode. Compared with the HC group, 121 differential metabolites were identified. Compared with the NCC group, 67 differential metabolites were identified. Compared with the A-LUAD group, 54 differential metabolites were identified. The Venn diagram showed that 29 metabolites can distinguish E-LUAD from HC and NCC and that four metabolites can distinguish E-LUAD from HC, NCC, and A-LUAD. The feature metabolites were selected to establish the diagnostic model for E-LUAD. The AUC value of the training set was 0.918, and it was 0.983 in the validation set.
Blood metabolomics has potential diagnostic value for E-LUAD. More medical studies are needed to verify whether the metabolic markers identified in the current research can be applied in clinical practice.
本研究旨在识别血液中可诊断早期肺腺癌的特定代谢标志物。
进行了一项非靶向代谢组学研究，参与者被分为四组：早期肺腺癌组（E-LUAD；n = 21）、健康对照组（HC，n = 17）、非癌性肺病组（NCC；n = 17）和晚期肺腺癌组（A-LUAD；n = 25）。通过液相色谱-质谱联用（LC-MS）确定E-LUAD组与其他三组相比存在差异的血浆代谢物水平。在metaX上进行主成分分析（PCA）和偏最小二乘判别分析（PLS-DA）以进行统计分析。构建维恩图以识别类别比较中的重叠差异代谢物。数据被随机分为训练集和验证集。基于重叠差异代谢物构建诊断模型。使用曲线下面积（AUC）评估模型的判别能力。
在正离子模式下初步鉴定出527种代谢物，在负离子模式下鉴定出286种代谢物。与HC组相比，鉴定出121种差异代谢物。与NCC组相比，鉴定出67种差异代谢物。与A-LUAD组相比，鉴定出54种差异代谢物。维恩图显示，29种代谢物可将E-LUAD与HC和NCC区分开，4种代谢物可将E-LUAD与HC、NCC和A-LUAD区分开。选择特征代谢物建立E-LUAD的诊断模型。训练集的AUC值为0.918，验证集的AUC值为0.983。
血液代谢组学对E-LUAD具有潜在诊断价值。需要更多医学研究来验证当前研究中鉴定的代谢标志物是否可应用于临床实践。
