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Metabolism pathway-based subtyping in endometrial cancer: An integrated study by multi-omics analysis and machine
Xiaodie Liu1,2, Wenhui Wang3, Xiaolei Zhang4
1Department of Obstetrics and Gynecology, China-Japan Friendship Hospital (Institute of Clinical Medical Sciences), Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100029, China.
This study classifies endometrial cancer (EC) into three metabolic subgroups, revealing distinct prognoses and treatment responses. A novel 13-hub gene classifier aids in predicting subtypes for targeted therapies and immunotherapy.
Area of Science:
- Oncology
- Metabolomics
- Genomics
Background:
- Endometrial cancer (EC) is a common female reproductive malignancy with known genomic heterogeneity.
- Understanding the metabolic landscape of EC is crucial for developing effective precision therapies.
Purpose of the Study:
- To comprehensively analyze metabolic dysfunctions in EC using multi-omics data.
- To classify EC patients into distinct metabolic subgroups for improved prognostic prediction and therapeutic guidance.
Main Methods:
- Integrated analysis of RNA-seq, proteomics, and metabolomics data from TCGA, CCLE, and CPTAC.
- Unsupervised consensus clustering to define metabolism-pathway-based subgroups (MPSs).
- Machine learning algorithms (LASSO, random forest, Cox regression) to develop a prognostic metagene signature.
Main Results:
- Identified three distinct MPS subgroups with varying clinical prognoses, genomic alterations, and immune microenvironments.
- MPS2 subgroup demonstrated a superior response to immunotherapy.
- Developed a 13-hub gene classifier for predicting MPS subtypes.
Conclusions:
- A metabolism-based classification system for EC enhances prognostic accuracy.
- This classification guides personalized immunotherapy and metabolism-targeted treatment strategies.
- The 13-hub gene classifier provides a practical tool for clinical application in endometrial cancer.
