Integrated Non-targeted and Targeted Metabolomics Uncovers Amino Acid Markers of Oral Squamous Cell Carcinoma.

Abstract

Purpose: It is very important to develop potential molecular associated with oral squamous cell carcinoma (OSCC) malignant transformation and progression. Thus, the aim of our study was to determine the amino acid metabolic characteristics of OSCC patients and test their diagnostic value. Experimental Design: Eight pairs of matched tumor and normal samples were collected for gas chromatography-mass spectrometry (GC-MS) high-throughput untargeted analysis. Another 20 cases (each case including tumor and normal tissues) were also enrolled for ultrahigh-performance liquid chromatography-tandem mass spectrometer (UHPLC-MS/MS) amino acid quantitative analysis. T-test and receiver operating characteristic (ROC) curve analysis were used to determine candidate markers. Principal component analysis, partial least squares discriminant analysis, and heat map analysis were used to verify the ability of candidate markers to distinguish tumors from normal tissues. Results: A total of 10 amino acids biomarker were selected as OSCC candidate diagnostic biomarkers by GC-MS high-throughput untargeted metabolomics analyses [area under the curve (AUC) >0.80]. We further measured the specific concentration of these candidate amino acids biomarkers in another batch of 20 cases by UHPLC-MS/MS quantitative analysis. The result validated that nine amino acids had been detected, which had statistically significant difference (t-test, p < 0.05). Moreover, three of nine amino acid markers (glutamate, aspartic acid, and proline) displayed high sensitivity and specificity (AUC >0.90) by ROC curve analysis and obtained optimal sensitivity and specificity by binary logistic regression in the Glmnet package (AUC = 0.942). Conclusions: In conclusion, a panel including three amino acids (glutamate, aspartic acid, and proline) was identified as potential diagnostic biomarkers of OSCC by a combination of non-targeted and targeted metabolomics methods.

• Authors: Xi-Hu Yang, Yue Jing, Shuai Wang, Feng Ding, Xiao-Xin Zhang, Sheng Chen, Lei Zhang, Qin-Gang Hu, Yan-Hong Ni
• Journal: Frontiers in oncology (Front Oncol) Vol. 10 Pg. 426 ( 2020) ISSN: 2234-943X [Print] Switzerland
• PMID: 32351881 (Publication Type: Journal Article)
• Copyright: Copyright © 2020 Yang, Jing, Wang, Ding, Zhang, Chen, Zhang, Hu and Ni.