Wu Bin, Zheng Min, Guo Qingkui, Wang Ning, Zhu Chen, Zhao Wen, Xu Ye
Department of Thoracic Surgery, Tongren Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China.
Front Mol Biosci. 2025 Apr 25;12:1544774. doi: 10.3389/fmolb.2025.1544774. eCollection 2025.
This study aims to investigate serum metabolite changes in patients with early-stage (T1) lung adenocarcinoma, identify potential diagnostic biomarkers, and establish an early warning mechanism for T1 stage lung adenocarcinoma.
The study included two groups: a lung adenocarcinoma group and a healthy control group. Serum samples underwent non-targeted metabolomics analysis. Total ion chromatograms (TIC) were generated to assess system stability. Chromatographic data were analyzed using multivariate statistical methods, including principal component analysis (PCA) for dimensionality reduction. Partial least squares discriminant analysis (PLS-DA) further validated PCA findings. Variables with VIP scores >1.0 in the PLS-DA model were selected, combined with ANOVA and T-tests (P < 0.05), to identify differentially expressed metabolites. Receiver operating characteristic (ROC) curve analysis was conducted to evaluate the diagnostic performance of selected metabolites.
Serum metabolites significantly differed between the lung adenocarcinoma group and the healthy control group. Multivariate statistical analysis and ROC curve evaluation identified four potential diagnostic biomarkers: Cortisol, 3-Oxo-OPC4-CoA, PE-NMe(14:1(9Z)/14:1(9Z)), and Ceramide (d18:1/9Z-18:1), with AUC values of 0.930, 0.895, 0.890, and 0.795, respectively.
Cortisol,3-Oxo-OPC4-CoA,PE-NMe(14:1(9Z)/14:1(9Z)), and Ceramide (d18:1/9Z-18:1) exhibit significantly altered metabolic levels in T1 stage lung adenocarcinoma patients and can serve as metabolic biomarkers. These markers may enhance the sensitivity and specificity of early diagnosis, facilitating improved detection of T1 stage lung adenocarcinoma.
本研究旨在调查早期（T1）肺腺癌患者血清代谢物变化，识别潜在诊断生物标志物，并建立T1期肺腺癌预警机制。
本研究包括两组：肺腺癌组和健康对照组。对血清样本进行非靶向代谢组学分析。生成总离子色谱图（TIC）以评估系统稳定性。使用多变量统计方法分析色谱数据，包括主成分分析（PCA）进行降维。偏最小二乘判别分析（PLS-DA）进一步验证PCA结果。选择PLS-DA模型中VIP得分>1.0的变量，结合方差分析和T检验（P<0.05），以识别差异表达的代谢物。进行受试者工作特征（ROC）曲线分析以评估所选代谢物的诊断性能。
肺腺癌组和健康对照组血清代谢物存在显著差异。多变量统计分析和ROC曲线评估确定了四种潜在诊断生物标志物：皮质醇；3-氧代-OPC4-CoA；PE-NMe(14:1(9Z)/14:1(9Z))；神经酰胺（d18:1/9Z-18:1），其AUC值分别为0.930、0.895、0.890和0.795。
皮质醇、3-氧代-OPC4-CoA、PE-NMe(14:1(9Z)/14:1(9Z))和神经酰胺（d18:1/9Z-18:1）在T1期肺腺癌患者中表现出显著改变的代谢水平，可作为代谢生物标志物。这些标志物可能提高早期诊断的敏感性和特异性，有助于改善T1期肺腺癌的检测。
