Ji Guoqin, Yu Di, Li Luhan, Zhao Jinhui, Wang Xiaolin, Zhu Siqi, Luo Shiheng, Li Xiaodong, Xu Guowang, Cao Penglong, Liu Xinyu
State Key Laboratory of Medical Proteomics, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, 116023, China.
Liaoning Province Key Laboratory of Metabolomics, Dalian, 116023, China.
Anal Bioanal Chem. 2025 Sep 12. doi: 10.1007/s00216-025-06092-8.
Lung adenocarcinoma, the most prevalent subtype of non-small cell lung cancer, is often diagnosed at advanced stages due to the lack of effective early screening methods, leading to poor patient outcomes. In this study, an enhanced pseudotargeted metabolomics approach was developed using liquid chromatography-mass spectrometry, combining untargeted-level coverage with targeted quantitative accuracy while enabling simplified clinical implementation. Serum samples from early-stage lung adenocarcinoma (LUAD) patients and healthy controls were analyzed using this method to identify potential biomarkers and establish a diagnostic model for early LUAD detection. A total of 329 serum samples were divided into discovery, internal validation, and external validation cohorts. Through non-parametric tests and machine learning algorithms, 113 differential metabolites were identified. Glycerophosphocholine and glutamine were validated as potential biomarkers for early LUAD diagnosis; the diagnostic model based on these biomarkers demonstrated good discriminative power, with AUCs of 0.972 and 0.867 in the internal and external validations, respectively. Additionally, comparative analysis between stage I and stage II patients revealed significant metabolic changes including elevated levels of choline, and sphingosine, and decreased levels of 3-dehydroteasterone and PC 31:0. These findings provided new insights into the metabolic alterations associated with LUAD progression and highlighted the potential of pseudotargeted metabolomics in discovering the metabolite biomarkers for early diagnosis of LUAD.
肺腺癌是非小细胞肺癌最常见的亚型，由于缺乏有效的早期筛查方法，往往在晚期才被诊断出来，导致患者预后较差。在本研究中，开发了一种增强的伪靶向代谢组学方法，使用液相色谱-质谱联用技术，将非靶向水平的覆盖范围与靶向定量准确性相结合，同时简化临床应用。使用该方法分析早期肺腺癌（LUAD）患者和健康对照的血清样本，以识别潜在的生物标志物，并建立早期LUAD检测的诊断模型。总共329份血清样本被分为发现队列、内部验证队列和外部验证队列。通过非参数检验和机器学习算法，鉴定出113种差异代谢物。甘油磷酸胆碱和谷氨酰胺被验证为早期LUAD诊断的潜在生物标志物；基于这些生物标志物的诊断模型具有良好的判别能力，在内部和外部验证中的AUC分别为0.972和0.867。此外，I期和II期患者之间的比较分析显示出显著的代谢变化，包括胆碱和鞘氨醇水平升高，以及3-脱氢睾酮和PC 31:0水平降低。这些发现为与LUAD进展相关的代谢改变提供了新的见解，并突出了伪靶向代谢组学在发现LUAD早期诊断代谢物生物标志物方面的潜力。
