ArticleFrontiers in oncology2021
Identification of a Metabolism-Related Signature for the Prediction of Survival in Endometrial Cancer Patients.
Article in Frontiers in oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed, 24 citations in OpenAlex.
- Indirect Roles of Immunophenotypes in the Association Between Glycerophospholipid Metabolism and Endometrial Cancer Risk: A Mendelian Randomization Study.International journal of women's health · 2026Article
- Bioinformatics Analysis Identifies Lipid Droplet-Associated Gene Signatures as Promising Prognostic and Diagnostic Models for Endometrial Cancer.Cancer reports (Hoboken, N.J.) · 2025Article
- Exploring the therapeutic potential of H1-antihistamines in endometriosis-A gene regulation-based perspective.Frontiers in medicine · 2025Article
- Article
- Identification and subsequent validation of transcriptomic signature associated with metabolic status in endometrial cancer.Scientific reports · 2023Article
- Glutamine and amino acid metabolism as a prognostic signature and therapeutic target in endometrial cancer.Cancer medicine · 2023Article
- Identification and Characterization of Metabolic Subtypes of Endometrial Cancer Using a Systems-Level Approach.Metabolites · 2023Article
- Role of glycosphingolipid biosynthesis coregulators in malignant progression of thymoma.International journal of biological sciences · 2023Article
- Construction and validation of a prognostic model based on 11 lymph node metastasis-related genes for overall survival in endometrial cancer.Cancer medicine · 2022Article
- Article
- Identification of Metabolism-Related Gene-Based Subgroup in Prostate Cancer.Frontiers in oncology · 2022Article
- CSF2RB Is a Unique Biomarker and Correlated With Immune Infiltrates in Lung Adenocarcinoma.Frontiers in oncology · 2022Article
- Development and Validation of a Novel Prognostic Model for Endometrial Cancer Based on Clinical Characteristics.Cancer management and research · 2021Article
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveEndometrial cancer (EC) is one of the most common gynecologic malignancies. The present study aims to identify a metabolism-related biosignature for EC and explore the molecular immune-related mechanisms underlying the tumorigenesis of EC.
methodsTranscriptomics and clinical data of EC were retrieved from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Common differentially expressed metabolism-related genes were extracted and a risk signature was identified by using the least absolute shrinkage and selection operator (LASSO) regression analysis method. A nomogram integrating the prognostic model and the clinicopathological characteristics was established and validated by a cohort of clinical EC patients. Furthermore, the immune and stromal scores were observed and the infiltration of immune cells in EC cells was analyzed.
resultsSix genes, including CA3, HNMT, PHGDH, CD38, PSAT1, and GPI, were selected for the development of the risk prediction model. The Kaplan-Meier curve indicated that patients in the low-risk group had considerably better overall survival (OS) (P = 7.874e-05). Then a nomogram was constructed and could accurately predict the OS (AUC = 0.827, 0.821, 0.845 at 3-, 5-, and 7-year of OS). External validation with clinical patients showed that patients with low risk scores had a longer OS (p = 0.04). Immune/stromal scores and infiltrating density of six types of immune cells were lower in high-risk group.
conclusionsIn summary, our work provided six potential metabolism-related biomarkers as well as a nomogram for the prognosis of EC patients, and explored the underlying mechanism involved in the progression of EC.
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