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Urine and serum metabolomic analysis of endometrial cancer diagnosis and classification based on ultra-performance liquid chromatography mass spectrometry

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Abstract

Objective

This study aimed to reveal the urinary and serum metabolic pattern of endometrial cancer (EC) and establish diagnostic models to identify EC from controls, high-risk from low-risk EC, and type II from type I EC.

Method

This study included 146 EC patients (comprising 79 low-risk and 67 high-risk patients, including 124 type I and 22 type II) and 59 controls. The serum and urine samples were analyzed using ultraperformance liquid chromatography mass spectrometry. Analysis was used to elucidate the distinct metabolites and altered metabolic pathways. Receiver operating characteristic (ROC) analyses were employed to discover and validate the potential biomarker models.

Results

Serum and urine metabolomes displayed significant differences between EC and controls, with metabolites related to amino acid and nicotinamide metabolisms. The serum and urine panels distinguished these two groups with Area Under the Curve (AUC) of 0.821 and 0.902, respectively. The panel consisting of serum and urine metabolites demonstrated the best predictive ability (AUC = 0.953 and 0.976 in discovering and validation group). In comparing high-risk and low risk EC, differential metabolites were enriched in purine and glutamine metabolism. The AUC values for serum and urine panels were 0.818, and 0.843, respectively. The combined panel exhibited better predictive accuracy (0.881 in discovering group and 0.936 in external validation). In the comparison between type I and type II group, altered folic acid metabolism was identified. The serum, urine and combined panels discriminated these two groups with the AUC of 0.829, 0.913 and 0.922, respectively.

Conclusion

The combined urine and serum metabolome effectively revealed the metabolic patterns in EC patients, offering valuable diagnostic models for EC diagnosis and classification.

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Data availability

Data and materials are available from the corresponding author by reasonable request.

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Acknowledgements

The authors express their gratitude to the entire faculty, nursing staff, and personnel at the Department of Obstetrics & Gynecology in PUMCH for their outstanding patient care. Additionally, the authors extend their heartfelt thanks to all the patients and their families for their invaluable contributions to this research.

Funding

National High Level Hospital Clinical Research Funding (Grant no. 2022-PUMCH-B-082) and The Natural Science Foundation of Shandong Province (Grant no. ZR2023QH426).

Author information

Author notes

  1. Junyu Chen and Hezhen Lu have contributed equally to the work.

Authors and Affiliations

  1. Department of Obstetrics and Gynecology, National Clinical Research Center for Obstetric & Gynecologic Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China

    Junyu Chen, Dongyan Cao, Jiaxin Yang, Mei Yu, Huimei Zhou, Ninghai Cheng, Jinhui Wang, Ying Zhang, Peng Peng, Tao Wang & Keng Shen

  2. Department of Obstetrics and Gynecology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China

    Junyu Chen

  3. China-Japan Union Hospital of Jilin University, Changchun, China

    Hezhen Lu, Jiaqi Liu & Wei Sun

  4. Core Facility of Instrument, School of Basic Medicine, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China

    Jiameng Sun, Feng Qi & Xiaoyan Liu

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  1. Junyu Chen
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Contributions

Conceptualization: JC, DC, and WS; Data curvation: JC, HL, JS, FQ, XL, JL, DC, JY, MY, HZ, JW, YZ, NC, PP, and KS; Formal analysis: JC, HL, DC, and WS; Software: JC and HL; Writing—original draft: JC; Writing—review and editing: DC, and WS. All authors contributed to the article and approved the submitted version.

Corresponding authors

Correspondence to Dongyan Cao or Wei Sun.

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Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Ethical approval

This study was approved by the Ethics Committee of Peking Union Medical College Hospital (PUMCH) (ZS-2666).

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Supplementary Information

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UPLC-MS system stability assessment. Supplementary file1 (TIF 14400 kb)

11306_2023_2085_MOESM2_ESM.tif

Metabolic analysis of Endometrial cancers and controls. A. Serum metabolic analysis of PCA; B. Urine metabolic analysis of PCA; C. 100 permutation test of OPLS-DA model in serum samples; D. 100 permutation test of the OPLS-DA model in urine samples; E. ROC curve of external validation of the serum metabolites; F. ROC curve of external validation of the urine metabolites; G. ROC curve of external validation of the biomarker panel Supplementary file2 (TIF 28000 kb)

11306_2023_2085_MOESM3_ESM.tif

Metabolic analysis of Endometrial cancers and controls. A. Serum metabolic analysis of PCA; B. Urine metabolic analysis of PCA; C. 100 permutation test of OPLS-DA model in serum samples; D. 100 permutation test of the OPLS-DA model in urine samples; E. ROC curve of external validation of the serum metabolites; F. ROC curve of external validation of the urine metabolites; G. ROC curve of external validation of the biomarker panel. Supplementary file3 (TIF 10800 kb)

11306_2023_2085_MOESM4_ESM.tif

Analysis of metabolic analysis between type I endometrial cancers and type II endometrial cancers. A. Serum metabolic analysis of PCA; B. Urine metabolic analysis of PCA; C. 100 permutation test of OPLS-DA model in serum samples; D. 100 permutation test of the OPLS-DA model in urine samples; E. ROC curve of 10-fold cross-validation of the serum metabolites; F. ROC curve of 10-fold cross-validation of the urine metabolites; G. ROC curve of 10-fold cross-validation of the biomarker panel. Supplementary file4 (TIF 10800 kb)

Supplementary file5 (XLSX 28 kb)

Supplementary file6 (XLSX 18 kb)

Supplementary file7 (XLSX 18 kb)

Supplementary file8 (XLSX 16 kb)

Supplementary file9 (XLSX 11 kb)

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Chen, J., Lu, H., Cao, D. et al. Urine and serum metabolomic analysis of endometrial cancer diagnosis and classification based on ultra-performance liquid chromatography mass spectrometry. Metabolomics 20, 18 (2024). https://doi.org/10.1007/s11306-023-02085-9

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