ArticleFrontiers in genetics2020
Construction and Validation of a Novel Glycometabolism-Related Gene Signature Predicting Survival in Patients With Ovarian Cancer.
Article in Frontiers in genetics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Relationship between nutritional-inflammatory markers and postoperative outcomes in ovarian cancer: a retrospective study.Frontiers in oncology · 2025Article
- Article
- Identification of Copper Homeostasis-Related Gene Signature for Predicting Prognosis in Patients with Epithelial Ovarian Cancer.Cancer informatics · 2024Article
- Article
- A macrophage related signature for predicting prognosis and drug sensitivity in ovarian cancer based on integrative machine learning.BMC medical genomics · 2023Article
- Genetics of enzymatic dysfunctions in metabolic disorders and cancer.Frontiers in oncology · 2023Review
- Nomogram for predicting postoperative cancer-specific early death in patients with epithelial ovarian cancer based on the SEER database: a large cohort study.Archives of gynecology and obstetrics · 2022Article
- Article
- Construction and validation of four-metabolism related-long non-coding RNAs as potential signature in prognosis of colon cancer.Translational cancer research · 2022Article
- Glycometabolism-related gene signature of hepatocellular carcinoma predicts prognosis and guides immunotherapy.Frontiers in cell and developmental biology · 2022Article
- Current status and future directions of ovarian cancer prognostic models.Journal of gynecologic oncology · 2021Article
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Among all fatal gynecological malignant tumors, ovarian cancer has the highest mortality rate. The purpose of this study was to develop a stable and personalized glycometabolism-related prognostic signature to predict the overall survival of ovarian cancer patients. The gene expression profiles and clinical information of ovarian cancer patients were derived from four public GEO datasets, which were divided into training and testing cohorts. Glycometabolism-related genes significantly associated with prognosis were selected. A risk score model was established and validated to evaluate its predictive value. We found 5 genes significantly related to prognosis and established a five-mRNA signature. The five-mRNA signature significantly divided patients into a low-risk group and a high-risk group in the training set and validation set. Survival analysis showed that high risk scores obtained by the model were significantly correlated with adverse survival outcomes and could be regarded as an independent predictor for patients with ovarian cancer. In addition, the five-mRNA signature can predict the overall survival of ovarian cancer patients in different subgroups. In summary, we successfully constructed a model that can predict the prognosis of patients with ovarian cancer, which provides new insights into postoperative treatment strategies, promotes individualized therapy, and provides potential new targets for immunotherapy.
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