Revealing potential lipid biomarkers in clear cell renal cell carcinoma using targeted quantitative lipidomics

Lipids in Health and Disease

Xianbin Zhang¹  Peng Gong¹  Xiaobin Wang²  Wen Li²  Degang Ding³  Zhifeng Wang³  Ning Wang³ 

[1] Carson International Cancer Centre, Shenzhen University General Hospital and Shenzhen University Clinical Medical Academy Centre, Shenzhen University, 1098 Xueyuan Road, 518000, Shenzhen, Guangdong, China;Health Science Center, School of Medicine, Shenzhen University, 518060, Shenzhen, China;Department of General Surgery, Shenzhen University General Hospital, Xueyuan Road 1098, 518055, Shenzhen, China;Carson International Cancer Centre, Shenzhen University General Hospital and Shenzhen University Clinical Medical Academy Centre, Shenzhen University, 1098 Xueyuan Road, 518000, Shenzhen, Guangdong, China;Key Laboratory of Optoelectronic Devices and Systems, College of Physics and Optoelectronic Engineering, Shenzhen University, 518060, Shenzhen, China;Health Science Center, School of Medicine, Shenzhen University, 518060, Shenzhen, China;Department of Urology, Henan Provincial People’s Hospital, Zhengzhou University People’s Hospital, Henan University People’s Hospital, No. 7 Weiwu Road, 450003, Zhengzhou City, Henan Province, China;

关键词: Clear cell renal cell carcinoma;    Lipids;    Lipidomics;    Lipid metabolite;    Lipid biomarker;    Lipid quantification;    UPLC-MS/MS;    Differentially expressed lipids;   

DOI  :  10.1186/s12944-021-01572-z

来源: Springer

【 摘 要 】

BackgroundThe high drug resistance and metabolic reprogramming of clear cell renal cell carcinoma (ccRCC) are considered responsible for poor prognosis. In-depth research at multiple levels is urgently warranted to illustrate the lipid composition, distribution, and metabolic pathways of clinical ccRCC specimens.MethodsIn this project, a leading-edge targeted quantitative lipidomic study was conducted using 10 pairs of cancerous and adjacent normal tissues obtained from ccRCC patients. Accurate lipid quantification was performed according to a linear equation calculated using internal standards. Qualitative and quantitative analyses of lipids were performed with multiple reaction monitoring analysis based on ultra-performance liquid chromatography (UPLC) and mass spectrometry (MS). Additionally, a multivariate statistical analysis was performed using data obtained on lipids.ResultsA total of 28 lipid classes were identified. Among them, the most abundant were triacylglycerol (TG), diacylglycerol (DG), phosphatidylcholine (PC), and phosphatidylethanolamine (PE). Cholesteryl ester (CE) was the lipid exhibiting the most considerable difference between normal samples and tumor samples. Lipid content, chain length, and chain unsaturation of acylcarnitine (CAR), CE, and DG were found to be significantly increased. Based on screening for variable importance in projection scores ≥1, as well as fold change limits between 0.5 and 2, 160 differentially expressed lipids were identified. CE was found to be the most significantly upregulated lipid, while TG was observed to be the most significantly downregulated lipid.ConclusionBased on the absolute quantitative analysis of lipids in ccRCC specimens, it was observed that the content and change trends varied in different lipid classes. Upregulation of CAR, CE, and DG was observed, and analysis of changes in the distribution helped clarify the causes of lipid accumulation in ccRCC and possible carcinogenic molecular mechanisms. The results and methods described herein provide a comprehensive analysis of ccRCC lipid metabolism and lay a theoretical foundation for cancer treatment.

【 授权许可】

CC BY