@Article{oncologie.2020.012376,
AUTHOR = {Dong Zhang, Lingxia Tong, Qingjun Wang, Yunfeng Cao, Yu Gao, Donghua Yang, Tianhao Bao, Zhitu Zhu},
TITLE = {Diagnosis of Lung Cancer Based on Plasma Metabolomics Combined with  Serum Markers},
JOURNAL = {Oncologie},
VOLUME = {22},
YEAR = {2020},
NUMBER = {2},
PAGES = {75--82},
URL = {http://www.techscience.com/oncologie/v22n2/40107},
ISSN = {1765-2839},
ABSTRACT = {Predicting the onset and metastasis of early tumor is the primary 
means of improving lung cancer prognosis. The purpose of this study was to 
identify the ability of plasma metabolomics combined with blood markers to 
establish benign lung disease versus lung cancer regression models. Blood 
samples were collected from 174 lung cancer patients, 350 benign lung disease 
patients and 100 healthy volunteers and the metabolites were analyzed by mass 
spectrometry. The target metabolites consisted of 23 amino acids, 26 
acylcarnitines and 45 conventional blood markers. A regression analysis model 
was established based on 12 metabolites and five blood markers selected by 
elastic network analysis. Two-thirds of the data were used in a training set for 
modeling and signature construction, and the remaining one-third were used in a 
validation set to test the model. This model was identified to have good 
specificity and sensitivity in distinguishing between lung cancer and lung disease. 
The performance of the model was evaluated using the area under the receiver 
operating curve, which was 0.915 in training set and 0.875 in validation set. In 
conclusion, this study demonstrates that regression model established by plasma 
metabolomics in combination with conventional serum markers has potential for 
the diagnosis of lung cancer.},
DOI = {10.32604/oncologie.2020.012376}
}