Liquid Chromatography-Mass Spectrometry-Based Tissue Metabolic Profiling Reveals Major Metabolic Pathway Alterations and Potential Biomarkers of Lung Cancer

被引:38
作者
You, Lei [1 ,5 ]
Fan, Yingying [2 ]
Liu, Xinyu [1 ]
Shao, Shujuan [3 ]
Guo, Lei [4 ]
Noreldeen, Hamada A. A. [1 ,5 ]
Li, Zaifang [1 ,5 ]
Ouyang, Yang [1 ,5 ]
Li, Enyou [4 ]
Pan, Xue [2 ]
Liu, Tianyang [2 ]
Tian, Xin [2 ]
Ye, Fei [2 ]
Li, Xiangnan [2 ]
Xu, Guowang [1 ,5 ]
机构
[1] Chinese Acad Sci, CAS Key Lab Separat Sci Analyt Chem, Dalian Inst Chem Phys, Dalian 116023, Peoples R China
[2] Zhengzhou Univ, Affiliated Hosp 1, Zhengzhou 450052, Peoples R China
[3] Dalian Med Univ, Key Lab Prote, Dalian 116044, Peoples R China
[4] Harbin Med Univ, Affiliated Hosp 1, Dept Anesthesiol, Harbin 150001, Peoples R China
[5] Univ Chinese Acad Sci, Beijing 100049, Peoples R China
基金
中国国家自然科学基金;
关键词
non-small-cell lung cancer; metabolomics; metabolic reprogramming; biomarkers; GLUTAMINE-METABOLISM; OXIDATIVE STRESS; LIPID-METABOLISM; CELL; CYCLE; GLUTATHIONE; HALLMARKS; CARCINOMA;
D O I
10.1021/acs.jproteome.0c00285
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
Unclarified molecular mechanism and lack of practical diagnosis biomarkers hinder the effective treatment of non-small-cell lung cancer. Herein, we performed liquid chromatography-mass spectrometry-based nontargeted metabolomics analysis in 131 patients with their lung tissue pairs to study the metabolic characteristics and disordered metabolic pathways in lung tumor. A total of 339 metabolites were identified in metabolic profiling. Also, 241 differential metabolites were found between lung carcinoma tissues (LCTs) and paired distal noncancerous tissues; amino acids, purine metabolites, fatty acids, phospholipids, and most of lysophospholipids significantly increased in LCTs, while 3-phosphoglyceric acid, phosphoenolpyruvate, 6-phosphogluconate, and citrate decreased. Additionally, pathway enrichment analysis revealed that energy, purine, amino acid, lipid, and glutathione metabolism are markedly disturbed in lung cancer (LCa). Using binary logistic regression, we further defined candidate biomarkers for different subtypes of lung tumor. Xanthine and PC 35:2 were selected as combinational biomarkers for distinguishing benign from malignant lung tumors with a 0.886 area under curve (AUC) value, and creatine, myoinositol and LPE 16:0 were defined as combinational biomarkers for discriminating adenocarcinoma from squamous cell lung carcinoma with a 0.934 AUC value. Overall, metabolic characterization and pathway disturbance demonstrated apparent metabolic reprogramming in LCa. The defined candidate metabolite marker panels are useful for subtyping of lung tumors to assist clinical diagnosis.
引用
收藏
页码:3750 / 3760
页数:11
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