School of Medicine and Pharmacy, Ocean University of China, Qingdao, Shandong 266003, P. R. China.
Department of Thoracic Surgery, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing 100730, P. R. China.
J Proteome Res. 2022 Aug 5;21(8):2011-2022. doi: 10.1021/acs.jproteome.2c00316. Epub 2022 Jul 20.
Non-small cell lung cancer (NSCLC) is the prevalent histological subtype of lung cancer. In this study, we performed ultraperformance liquid chromatography-high-resolution mass spectrometry (UPLC-HRMS)-based metabolic profiling of 227 tissue samples from 79 lung cancer patients with adenocarcinoma (AC) or squamous cell carcinoma (SCC). Orthogonal partial least squares-discriminant analysis (oPLS-DA) analyses showed that AC, SCC, and NSCLC tumors were discriminated from adjacent noncancerous tissue (ANT) and distant noncancerous tissue (DNT) samples with good accuracies (91.3, 100, and 88.3%), sensitivities (85.7, 100, and 83.9%), and specificities (94.3, 100, and 90.7%), using 12, 4, and 7 discriminant metabolites, respectively. The discriminant panel for AC detection included valine, sphingosine, glutamic acid γ-methyl ester, and lysophosphatidylcholine (LPC) (16:0), levels of which in tumor tissues were significantly altered. Valine, sphingosine, LPC (18:1), and leucine derivatives were used for SCC detection. The discrimination between AC and SCC had 96.8% accuracy, 98.2% sensitivity, and 85.7% specificity using a five-metabolite panel, of which valine and creatine had significant differences. The classification models were further verified with external validation sets, showing a promising prospect for NSCLC tissue detection and subtyping.
非小细胞肺癌（NSCLC）是肺癌中最常见的组织学亚型。在这项研究中，我们对 79 名肺癌患者的 227 个组织样本进行了基于超高效液相色谱-高分辨率质谱（UPLC-HRMS）的代谢组学分析，这些患者中包括腺癌（AC）或鳞状细胞癌（SCC）患者。正交偏最小二乘法判别分析（oPLS-DA）分析表明，AC、SCC 和 NSCLC 肿瘤可以与相邻非癌组织（ANT）和远处非癌组织（DNT）样本很好地区分，准确率分别为 91.3%、100%和 88.3%，灵敏度分别为 85.7%、100%和 83.9%，特异性分别为 94.3%、100%和 90.7%，使用了 12、4 和 7 个判别代谢物。AC 检测的判别面板包括缬氨酸、神经鞘氨醇、谷氨酸γ-甲酯和溶血磷脂酰胆碱（LPC）（16:0），其在肿瘤组织中的水平发生了显著改变。缬氨酸、神经鞘氨醇、LPC（18:1）和亮氨酸衍生物用于 SCC 检测。使用五代谢物面板对 AC 和 SCC 进行区分的准确率为 96.8%，灵敏度为 98.2%，特异性为 85.7%，其中缬氨酸和肌酸有显著差异。通过外部验证集进一步验证了分类模型，为 NSCLC 组织检测和亚型分类提供了有前景的前景。
