Yuqin Dai (Presenter)
Agilent Technologies
Authorship: Xiaotao Shen (1), Tao Zhang (2), Fuzhong Xue (2), Yuqin Dai (3), Zheng-Jiang Zhu (1),
(1) Interdisciplinary Research Center of Biology and Chemistry, Chinese Academy of Sciences, China (2) Department of Biostatistics, Shandong University, China
| Short Abstract A high-throughput untargeted metabolomics workflow is developed using an UHPLC/Q-TOFMS technique. The untargeted metabolic profiling of one sample can be completed within 12 minutes, which enables an analytical throughput of >100 samples per day. This workflow is used for the metabolic profiling of serum samples collected from esophageal squamous cell carcinoma (ESCC) subjects with the aim to discover potential metabolite biomarkers for early identification. This metabolomics study includes a total of 97 ESCC cases and 105 healthy controls (HCs). 16 differential metabolites (potential biomarkers) were identified and found to be disturbed in several metabolic pathways among ESCC donors, which may possess great potential for early identification of ESCC. |
Long Abstract
Metabolites are defined as a collection of small molecules that are produced during metabolism. Mass spectrometry-based untargeted metabolomics has enabled simultaneous measurements of thousands of metabolites using minimal amounts of biological samples, providing functional readouts of physiological and pathological states of biological individuals at the systems level. To better understand physiological and pathological changes related to diseases, as well as to define biomarkers, here, a high-throughput untargeted metabolomics workflow is developed using UHPLC/Q-TOFMS technique. The untargeted metabolic profiling of one sample can be completed within 12 minutes, which enables an analytical throughput of >100 samples per day. This capability of high-throughput analysis using UHPLC/Q-TOFMS technique provides an opportunity to analyze thousands to tens of thousands of samples within several months to one year. Further, we developed a machine learning algorithm-based method for large-scale metabolomics data normalization and integration. The unwanted intra- and inter-batch variations can be effectively removed, and we demonstrated that the portion of metabolic peaks with RSDs of less than 30% increased to more than 90% of the total peaks after normalization.
Esophageal squamous cell carcinoma (ESCC) remains the most predominant type of esophageal cancer. Identification of ESCC at early stages (0/I/II) is difficult because it is often asymptotic. Here, a high-throughput metabolomics workflow is used for the metabolic profiling of serum sample collected from ESCC subjects with an aim to discover potential metabolite biomarker for early identification of ESCC. The metabolomics study includes a total of 97 ESCC cases and 105 healthy controls (HCs). The enrolled subjects were randomly separated into training data (77 ESCC cases, 84 HCs) and validation data (20 ESCC cases, 21 HCs). The global metabolomic profiles could clearly distinguish ESCC from HC in the training data. 16 differential metabolites (potential biomarker) were identified and found to be disturbed in several metabolic pathways among ESCC subjects, mainly characterized by deregulated fatty acid biosynthesis, glycerophospholipid metabolism, choline metabolism in cancer and linoleic acid metabolism. The value of AUC in the external validation data was 0.895, with sensitivity of 85.0% and specificity of 90.5%. In summary, the findings suggest serum metabolomics, performed in a minimally noninvasive and convenient manner, may possess great potential for early identification of ESCC.
For Research Use Only. Not for use in diagnostic procedures.
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