ArticleMolecular therapy. Nucleic acids2024
Metabolism pathway-based subtyping in endometrial cancer: An integrated study by multi-omics analysis and machine learning algorithms.
Article in Molecular therapy. Nucleic acids, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed, 17 citations in OpenAlex.
- Real-time NMR analysis of glutamine metabolism for screening anti-cancer agents in living cells.Magnetic resonance letters · 2027Article
- EMMA-STRAT: a multi-omics based machine learning framework for stratification of endometrial carcinoma molecular subtypes and MSI status.BioData mining · 2026Article
- Integrating artificial intelligence and multi-omics data for precision oncology in endometrial cancer: a narrative review.Functional & integrative genomics · 2026Review
- Artificial intelligence-driven integration of multi-biofluid omics and clinical phenotype enables stratification of endometrial cancer.Cell reports. Medicine · 2026Article
- Development of an interpretable machine learning model for lymphovascular space invasion prediction in patients with endometrioid endometrial carcinoma: A prospective study.Chinese journal of cancer research = Chung-kuo yen cheng yen chiu · 2026Article
- Article
- Machine learning-based radiomics model: prognostic prediction and mechanism exploration in patients with endometrial cancer.Biomarker research · 2025Article
- Integrating Multi-Omics in Endometrial Cancer: From Molecular Insights to Clinical Applications.Cells · 2025Review
- Metabolic interplay between endometrial cancer and tumor-associated macrophages: lactate-induced M2 polarization enhances tumor progression.Journal of translational medicine · 2025Article
- Machine Learning-Driven Insights in Cancer Metabolomics: From Subtyping to Biomarker Discovery and Prognostic Modeling.Metabolites · 2025Review
- Detecting metabolic signatures in endometrial cancer: potential applications of Raman spectroscopy.Future oncology (London, England) · 2025Review
- Risk factors and prognostic analysis of endometrial cancer with para-aortic lymph node metastasis.Discover oncology · 2025Article
- SIM2, associated with clinicopathologic features, promotes the malignant biological behaviors of endometrial carcinoma cells.BMC cancer · 2025Article
- Relevance of proteomics and metabolomics approaches to overview the tumorigenesis and better management of cancer.3 Biotech · 2025Review
- Sialylation-associated long non-coding RNA signature predicts the prognosis, tumor microenvironment, and immunotherapy and chemotherapy options in uterine corpus endometrial carcinoma.Cancer cell international · 2024Article
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Authors and funding
8 authors at 3 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
Endometrial cancer (EC), the second most common malignancy in the female reproductive system, has garnered increasing attention for its genomic heterogeneity, but understanding of its metabolic characteristics is still poor. We explored metabolic dysfunctions in EC through a comprehensive multi-omics analysis (RNA-seq datasets from The Cancer Genome Atlas [TCGA], Cancer Cell Line Encyclopedia [CCLE], and GEO datasets; the Clinical Proteomic Tumor Analysis Consortium [CPTAC] proteomics; CCLE metabolomics) to develop useful molecular targets for precision therapy. Unsupervised consensus clustering was performed to categorize EC patients into three metabolism-pathway-based subgroups (MPSs). These MPS subgroups had distinct clinical prognoses, transcriptomic and genomic alterations, immune microenvironment landscape, and unique patterns of chemotherapy sensitivity. Moreover, the MPS2 subgroup had a better response to immunotherapy. Finally, three machine learning algorithms (LASSO, random forest, and stepwise multivariate Cox regression) were used for developing a prognostic metagene signature based on metabolic molecules. Thus, a 13-hub gene-based classifier was constructed to predict patients' MPS subtypes, offering a more accessible and practical approach. This metabolism-based classification system can enhance prognostic predictions and guide clinical strategies for immunotherapy and metabolism-targeted therapy in EC.
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