Metabolic Landscape of Endometrial Cancer: Insights into Pathway Dysregulation and Metabolic Features

Yang, Qing; Tian, Xiaoli; Hu, Min; Ma, Wenjing; Xie, Qingzhen; Liu, Jingchun; Hong, Li

doi:10.3390/biomedicines14010202

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Metabolic Landscape of Endometrial Cancer: Insights into Pathway Dysregulation and Metabolic Features

by

Qing Yang

^1,†,

Xiaoli Tian

^2,†,

Min Hu

^1,†,

Wenjing Ma

¹,

Qingzhen Xie

^3,*,

Jingchun Liu

^1,* and

Li Hong

^1,*

¹

Department of Gynecology and Obstetrics, Renmin Hospital of Wuhan University, Wuhan 430060, China

²

Department of Pathology, Renmin Hospital of Wuhan University, Wuhan 430060, China

³

Centre for Reproductive Medicine, Renmin Hospital of Wuhan University, Wuhan 430060, China

^*

Authors to whom correspondence should be addressed.

^†

These authors contributed equally to this work.

Biomedicines 2026, 14(1), 202; https://doi.org/10.3390/biomedicines14010202 (registering DOI)

Submission received: 14 December 2025 / Revised: 5 January 2026 / Accepted: 16 January 2026 / Published: 17 January 2026

(This article belongs to the Section Cancer Biology and Oncology)

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Abstract

Background: Metabolic reprogramming is increasingly recognized as a hallmark of endometrial cancer, yet tissue-based metabolic signatures remain insufficiently defined. Methods: Untargeted metabolomics was performed on paired endometrial cancer (n = 10) and adjacent normal tissues (n = 10). Differential metabolites were identified through multivariate and univariate analyses. KEGG enrichment characterized altered pathways, while Random Forest and SVM were used for machine-learning-based feature prioritization. ROC analyses were conducted to evaluate the discriminative potential of selected metabolites. Results: 300 metabolites were significantly altered. Tumor tissues showed increased sphingolipid metabolism, glutathione metabolism, and arachidonic acid metabolism, alongside decreased bile acid, phenylalanine, and steroid biosynthesis. Machine learning converged on six key metabolites that demonstrate strong tissue-discriminative capacity. Conclusions: Endometrial cancer exhibits a distinct metabolic profile characterized by lipid remodeling and redox adaptation. The six metabolites identified through machine-learning-based analyses represent candidate metabolic features associated with endometrial cancer and provide a foundation for future mechanistic studies and validation in larger, independent cohorts.

Keywords: endometrial cancer; metabolism; machine learning

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MDPI and ACS Style

Yang, Q.; Tian, X.; Hu, M.; Ma, W.; Xie, Q.; Liu, J.; Hong, L. Metabolic Landscape of Endometrial Cancer: Insights into Pathway Dysregulation and Metabolic Features. Biomedicines 2026, 14, 202. https://doi.org/10.3390/biomedicines14010202

AMA Style

Yang Q, Tian X, Hu M, Ma W, Xie Q, Liu J, Hong L. Metabolic Landscape of Endometrial Cancer: Insights into Pathway Dysregulation and Metabolic Features. Biomedicines. 2026; 14(1):202. https://doi.org/10.3390/biomedicines14010202

Chicago/Turabian Style

Yang, Qing, Xiaoli Tian, Min Hu, Wenjing Ma, Qingzhen Xie, Jingchun Liu, and Li Hong. 2026. "Metabolic Landscape of Endometrial Cancer: Insights into Pathway Dysregulation and Metabolic Features" Biomedicines 14, no. 1: 202. https://doi.org/10.3390/biomedicines14010202

APA Style

Yang, Q., Tian, X., Hu, M., Ma, W., Xie, Q., Liu, J., & Hong, L. (2026). Metabolic Landscape of Endometrial Cancer: Insights into Pathway Dysregulation and Metabolic Features. Biomedicines, 14(1), 202. https://doi.org/10.3390/biomedicines14010202

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Metabolic Landscape of Endometrial Cancer: Insights into Pathway Dysregulation and Metabolic Features

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