Potential roles of small metabolites in the differential diagnosis between lung cancer and pneumonia
10.3969/j.issn.1674-8115.2020.08.007
- VernacularTitle: 小分子代谢物在肺癌和肺炎鉴别诊断中的潜在作用
- Author:
Chen ZOU
1
Author Information
1. Department of Immunology and Microbiology, College of Basic Medical Sciences, Shanghai Jiao Tong University
- Publication Type:Journal Article
- Keywords:
Amino acids;
Bile acids;
High performance liquid chromatography tandem mass spectrometry;
Lung cancer;
Metabolites;
Pneumonia
- From:
Journal of Shanghai Jiaotong University(Medical Science)
2020;40(8):1041-1047
- CountryChina
- Language:Chinese
-
Abstract:
Objective: To study on the changes of serum small metabolites in patients with lung cancer and pneumonia, assess the performance of these metabolites on differential diagnosis between lung cancer and pneumonia, and establish diagnostic model. Methods: Liquid chromatography tandem mass spectrometry(LC-MS/MS) was applied to test the serum metabolites, including 13 amino acids and 15 bile acids, of 95 patients with lung cancer, 69 patients with pneumonia and 90 healthy people. T test and Mann-Whitney U test were used to analyze the differences among the three groups. Binary logistic regression was applied to screen valuable indexes and establish diagnostic model. The receiver operating characteristic curve (ROC) was used to compare the differential diagnostic value of single index and diagnostic model. Results: Compared with the control group, 3 kinds of amino acids and 7 kinds of bile acids were decreased significantly, while 1 kind of amino acid and 2 kinds of bile acids were increased significantly in pneumonia patients. 6 kinds of amino acids and 2 kinds of bile acids were decreased significantly, while 2 kinds of amino acids and 4 kinds of bile acids were increased significantly in lung cancer patients. Deoxycholic acid was the most valuable metabolite in differential diagnosis. Citrulline, phenylalanine and deoxycholic acid were screened to establish differential diagnostic model of lung cancer and pneumonia. The area under curve (AUC) of the model was 0.829, and the Cut-Off value was 0.55, while the sensitivity was 76.8%, the specificity was 79.7%, and the coincidence rate was 78.3%. Conclusion: This study revealed that some small metabolites in serum of the patients with pneumonia and lung cancer have changed significantly. The diagnostic model composed of some metabolites has the potential value to assist differential diagnosis of lung cancer and pneumonia.
