Department of Respiratory Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, china.
Department of Pharmacy, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, china.
PLoS One. 2020 May 6;15(5):e0232272. doi: 10.1371/journal.pone.0232272. eCollection 2020.
Non-small cell lung cancer (NSCLC) remains a leading cause of cancer death globally. More accurate and reliable diagnostic methods/biomarkers are urgently needed. Joint application of metabolomics and transcriptomics technologies possesses the high efficiency of identifying key metabolic pathways and functional genes in lung cancer patients. In this study, we performed an untargeted metabolomics analysis of 142 NSCLC patients and 159 healthy controls; 35 identified metabolites were significantly different between NSCLC patients and healthy controls, of which 6 metabolites (hypoxanthine, inosine, L-tryptophan, indoleacrylic acid, acyl-carnitine C10:1, and lysoPC(18:2)) were chosen as combinational potential biomarkers for NSCLC. The area under the curve (AUC) value, sensitivity (SE), and specificity (SP) of these six biomarkers were 0.99, 0.98, and 0.99, respectively. Potential diagnostic implications of the metabolic characteristics in NSCLC was studied. The metabolomics results were further verified by transcriptomics analysis of 1027 NSCLC patients and 108 adjacent peritumoral tissues from TCGA database. This analysis identified 2202 genes with significantly different expressions in cancer cells compared to normal controls, which in turn defined pathways implicated in the metabolism of the compounds revealed by metabolomics analysis. We built a fully connected network of metabolites and genes, which shows a good correspondence between the transcriptome analysis and the metabolites selected for diagnosis. In conclusion, this work provides evidence that the metabolic biomarkers identified may be used for NSCLC diagnosis and screening. Comprehensive analysis of metabolomics and transcriptomics data offered a validated and comprehensive understanding of metabolism in NSCLC.
非小细胞肺癌（NSCLC）仍然是全球癌症死亡的主要原因。迫切需要更准确和可靠的诊断方法/生物标志物。代谢组学和转录组学技术的联合应用具有高效识别肺癌患者关键代谢途径和功能基因的能力。在这项研究中，我们对 142 名 NSCLC 患者和 159 名健康对照者进行了非靶向代谢组学分析；35 种鉴定出的代谢物在 NSCLC 患者和健康对照者之间存在显著差异，其中 6 种代谢物（次黄嘌呤、肌苷、L-色氨酸、吲哚丙烯酸、酰基辅酶 C10:1 和溶血磷脂酰胆碱（18:2））被选为 NSCLC 的联合潜在生物标志物。这六种生物标志物的曲线下面积（AUC）值、敏感性（SE）和特异性（SP）分别为 0.99、0.98 和 0.99。研究了 NSCLC 代谢特征的潜在诊断意义。代谢组学结果通过 TCGA 数据库中 1027 名 NSCLC 患者和 108 个癌旁组织的转录组学分析进一步验证。该分析确定了与正常对照相比癌细胞中表达差异显著的 2202 个基因，进而定义了代谢组学分析揭示的化合物代谢所涉及的途径。我们构建了一个代谢物和基因的全连接网络，该网络显示转录组分析和选择用于诊断的代谢物之间有很好的对应关系。总之，这项工作提供了证据，表明鉴定的代谢生物标志物可用于 NSCLC 的诊断和筛查。代谢组学和转录组学数据的综合分析为 NSCLC 的代谢提供了验证和全面的理解。
