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Background <p>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.</p> Methods <p>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.</p> Result <p>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 across different CRC stages when these stages were compared with the healthy group,with an AUC of 0.918 for stages 0/I, 0.862 for stage II, and 0.845 for stage III.</p> Conclusions <p>Urine metabolome could distinguish CRC from healthy controls and reflect the changes in different stages of CRC. Potential biomarkers might be developed by targeted metabolomic analysis.</p>

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Investigating stage-specific metabolic alterations in colorectal cancer through urine metabolomics

  • 期刊论文

  • 发表日期：

    2025年10月22日

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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

摘要

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 across different CRC stages when these stages were compared with the healthy group,with an AUC of 0.918 for stages 0/I, 0.862 for stage II, and 0.845 for stage III.

Conclusions

Urine metabolome could distinguish CRC from healthy controls and reflect the changes in different stages of CRC. Potential biomarkers might be developed by targeted metabolomic analysis.