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IDDF2019-ABS-0090 Carboxylomics profiles delineate short-chain fatty acids in colorectal cancer diagnosis and prognosis
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  1. Jian-Lin Wu,
  2. Xiqing Bian,
  3. Na Li
  1. State Key Laboratory for Quality Research in Chinese Medicines, Macau University of Science and Technology, Macau

Abstract

Background Colorectal cancer (CRC) is the third most common type of cancer in the world and is a major cause of worldwide cancer morbidity and mortality. Carboxylic acids widely exist in living systems and are the essential components for life, which mainly contain amino acids, TCA cycle intermediates, short-chain fatty acids, long chain fatty acids, bile acids, acylcarnitine, and so on. Carboxylomics study in biological samples is critical for the understanding of physiological processes and the discovery for the onset of relevant diseases.

Methods In the present study, DIAAA derivatization-UHPLC-Q-TOF/MS approach and caboxylomics study were employed to discover potential novel biomarkers for carboxylic acids in 58 human CRC and 46 healthy samples.

Results 269 carboxylic acids were determined and confirmed their structures. Among of them, 118 carboxylic acids were first reported in CRC serum. Metabolic pathways were constructed by heat maps, Interactive Pathways Explorer, pathway impact and Volcano plot, etc. (IDDF2019-ABS-0090 Figure 1. Heatmap of 134 carboxylic acids which have difference between healthy and CRC patients in training set (A); Interactive Pathways Explorer analysis (B); Metabolomic pathway of CRC samples in training set (C); Volcano plot of the 269 carboxylic acids profiled (D). Mann-Whitney U tests were used to calculate statistical significance, and p values were corrected using Graphpad prism 5.0. Differentially abundant metabolites of different categories were individually color coded.) Short-chain fatty acids were found to be novel diagnostic and prognostic biomarkers for CRC (IDDF2019-ABS-0090 Figure 2. Relative abundance (A) and ROC curves (B) of representative metabolites to differentiate CRC patients from healthy controls.).

Abstract IDDF2019-ABS-0090 Figure 1
Abstract IDDF2019-ABS-0090 Figure 2

Conclusions Overall, using DIAAA derivatization-UHPLC-Q-TOF/MS based carboxylomics study combined with network and ROC curve analysis, a new set of metabolites can be discovered as biomarkers of diseases with diagnostic and prognostic capabilities.

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