High-resolution metabolomic biomarkers for lung cancer diagnosis and prognosis

Scientific Reports

Kaining Mao¹  Wei Zhang¹  Jie Chen¹  Shi-ang Qi²  Yuanyuan Li³  Qian Wu⁴  Jia Li⁵  Yunchao Huang⁵  Youguang Huang⁵  Yongchun Zhou⁵  Zhenpu Chen⁵ 

[1] Electrical and Computer Engineering, University of Alberta, Edmonton, Canada;Electrical and Computer Engineering, University of Alberta, Edmonton, Canada;Third Affiliated Hospital of Kunming Medical University (Yunnan Cancer Hospital), 650118, Kunming, Yunnan, China;Shanghai Center for Bioinformation Technology and Shanghai Engineering Research Center of Pharmaceutical Translation, Shanghai Industrial Technology Institute, 201203, Shanghai, China;Shanghai Center for Bioinformation Technology and Shanghai Engineering Research Center of Pharmaceutical Translation, Shanghai Industrial Technology Institute, 201203, Shanghai, China;Shanghai Fenglin Clinical Laboratory Co., Ltd, 200231, Shanghai, China;Third Affiliated Hospital of Kunming Medical University (Yunnan Cancer Hospital), 650118, Kunming, Yunnan, China;

DOI  :  10.1038/s41598-021-91276-2

来源: Springer

【 摘 要 】

Lung cancer is the leading cause of human cancer mortality due to the lack of early diagnosis technology. The low-dose computed tomography scan (LDCT) is one of the main techniques to screen cancers. However, LDCT still has a risk of radiation exposure and it is not suitable for the general public. In this study, plasma metabolic profiles of lung cancer were performed using a comprehensive metabolomic method with different liquid chromatography methods coupled with a Q-Exactive high-resolution mass spectrometer. Metabolites with different polarities (amino acids, fatty acids, and acylcarnitines) can be detected and identified as differential metabolites of lung cancer in small volumes of plasma. Logistic regression models were further developed to identify cancer stages and types using those significant biomarkers. Using the Variable Importance in Projection (VIP) and the area under the curve (AUC) scores, we have successfully identified the top 5, 10, and 20 metabolites that can be used to differentiate lung cancer stages and types. The discrimination accuracy and AUC score can be as high as 0.829 and 0.869 using the five most significant metabolites. This study demonstrated that using 5 + metabolites (Palmitic acid, Heptadecanoic acid, 4-Oxoproline, Tridecanoic acid, Ornithine, and etc.) has the potential for early lung cancer screening. This finding is useful for transferring the diagnostic technology onto a point-of-care device for lung cancer diagnosis and prognosis.

【 授权许可】

CC BY