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Non−Invasive Urinary Metabolomic Study for the Diagnostic Biomarkers Discovery of Colorectal Cancer (CRC) Using UPLC−MS Technology
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  • Feng Qi,
  • Yulin Sun,
  • Jiaqi Liu,
  • Xiaoyan Liu,
  • Haidan Sun,
  • Zhengguang Guo,
  • Binbin Zhang,
  • Jiameng Sun,
  • Aiwei Wang,
  • Hezhen Lu,
  • fei Xue,
  • Tingmiao Li,
  • Xin Qi,
  • Xiaohang Zhao,
  • wei sun
Feng Qi
Chinese Academy of Medical Sciences & Peking Union Medical College
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Yulin Sun
State Key Laboratory of Molecular Oncology
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Xiaoyan Liu
Peking Union Medical College
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Haidan Sun
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Zhengguang Guo
Chinese Academy of Medical Sciences & Peking Union Medical College School of Basic Medicine
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Binbin Zhang
Department of Pharmacy,No.79 Army Group Hospital of People's Liberation Army Ground Force
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Jiameng Sun
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Aiwei Wang
Chinese Academy of Medical Sciences & Peking Union Medical College School of Basic Medicine
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Hezhen Lu
Department of Clinical Laboratory, China-Japan Union Hospital of Jilin University
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fei Xue
Department of Clinical Laboratory, China-Japan Union Hospital of Jilin University
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Tingmiao Li
Department of Clinical Laboratory, China-Japan Union Hospital of Jilin University
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Xin Qi
Department of Clinical Laboratory, China-Japan Union Hospital of Jilin University
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Xiaohang Zhao
Cancer Institute of Chiese Academy of Medical Sciences & Peking Union of Medical College
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wei sun
Chinese Academy of Medical Sciences & Peking Union Medical College School of Basic Medicine

Corresponding Author:[email protected]

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Abstract

Background: Colorectal cancer (CRC) ranks as the third most prevalent malignancy globally, presenting a formidable early diagnostic challenge. An effective biomarker with high sensitivity and specificity can help diagnose CRC and improve the chances of successful treatment. Methods: 100 healthy controls and 95 CRC patients( 25 Stage 0/I,30 stage II and 40 stage III based on Clinical stages) were recruited. Subsequently, 195 urine samples were subjected to UPLC-MS analysis. Comparative analysis was employed to elucidate noteworthy metabolic variances, and pathway analysis was conducted to unveil perturbed metabolic functions. Ultimately, metabolic panels for CRC diagnosis were constructed. Result: A total of 82 metabolites exhibited statistical significance between CRC patients and healthy controls. Moreover, pathway analysis revealed that they were associated with Steroid hormone biosynthesis, Nitrogen metabolism, and D-Glutamine and D-glutamate metabolism. A composite panel consisting of Retinol, L-β-aspartyl-L-glycine and 21-Deoxycortisol showed AUCs of 0.933/0.93 in the discovery/validation group. The panel also showed commendable efficacy in indifferent CRC stages, with an AUC of 0.918 for stages 0/I, 0.862 for stage II, and 0.845 for stage III. Conclusions: Urine metabolomecould distinguish CRC from healthy control and reflect the changes in different stages of CRC. Potential biomarkers might be developed by targeted metabolomic analysis.