基于液相色谱-质谱联用技术的肺癌细胞代谢组学分析

通过高效液相色谱-四极杆飞行时间质谱(HPLC-Q-TOF/MS)分别对肺癌细胞与正常细胞的极性与非极性代谢物进行指纹图谱分析,进一步应用偏最小二乘判别分析(PLS-DA)对代谢组学数据进行多维统计分析。研究结果显示,与正常细胞相比,肿瘤细胞存在异常的蛋白质、脂肪酸、磷脂代谢,并发现31种对分类有显著贡献的代谢小分子物质。通过本研究,建立了基于液相色谱-质谱联用技术的肺癌细胞代谢组学分析方法,发现了肺癌潜在疾病标记物,可为肺癌分子标记物的发现及其早期诊断提供新思路和新方法。

Metabonomics study of lung cancer cells based on liquid chromatography-mass spectrometry

The metabolic profiles of the polar metabolites and the non-polar metabolites in lung tumor cell lines H358, A549, HCC827, H1299, Calu-3, Calu-1, PC-9 and normal cell line MRC-5 were analyzed separately using high performance liquid chromatography-quadrupole time-of-flight mass spectrometry (HPLC-Q-TOF/MS). Partial least square discriminant analysis (PLS-DA) was used to process the metabolic data. The results showed that the metabolites of the lung cancer cell lines and normal cell line have significant differences. Further, 10 polar metabolites and 21 non-polar metabolites which had a significant contribution to classification were selected and preliminarily identified due to the accurate mass. Comparing with the normal cell line, the lung tumor cell lines present an abnormal metabolism in protein, fatty acid, and phospholipids. These results may provide important information for the early diagnosis of lung cancer.