ArticleBioscience reports2020
Identification and validation of a prognostic index based on a metabolic-genomic landscape analysis of ovarian cancer.
Article in Bioscience reports, 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, 18 citations in OpenAlex.
- BCAA metabolism: the Achilles' heel of ovarian cancer, polycystic ovary syndrome, and premature ovarian failure.Frontiers in endocrinology · 2025Review
- An 11-gene glycosyltransferases-related model for the prognosis of patients with bladder urothelial carcinoma: development and validation based on TCGA and GEO datasets.Translational andrology and urology · 2024Article
- Role of ENPP1 in cancer pathogenesis: Mechanisms and clinical implications (Review).Oncology letters · 2024Review
- Validation of ESM1 Related to Ovarian Cancer and the Biological Function and Prognostic Significance.International journal of biological sciences · 2023Article
- Identification of an Individualized Prognostic Biomarker for Serous Ovarian Cancer: A Qualitative Model.Diagnostics (Basel, Switzerland) · 2022Article
- Article
- Identification of key genes associated with polycystic ovary syndrome (PCOS) and ovarian cancer using an integrated bioinformatics analysis.Journal of ovarian research · 2022Article
- Identification of an Amino Acid Metabolism Signature Participating in Immunosuppression in Ovarian Cancer.Evidence-based complementary and alternative medicine : eCAM · 2022Article
- HPGDS is a novel prognostic marker associated with lipid metabolism and aggressiveness in lung adenocarcinoma.Frontiers in oncology · 2022Article
- Identification of a glycolysis-related gene signature for survival prediction of ovarian cancer patients.Cancer medicine · 2021Article
- Identification and validation of a novel glycolysis-related gene signature for predicting the prognosis in ovarian cancer.Cancer cell international · 2021Article
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Authors and funding
9 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTumour metabolism has become a novel factor targeted by personalised cancer drugs. This research evaluated the prognostic significance of metabolism-related genes (MRGs) in ovarian serous cystadenocarcinoma (OSC).
methodsMRGs in 379 women surviving OSC were obtained using The Cancer Genome Atlas (TCGA) database. Then, several biomedical computational algorithms were employed to identify eight hub prognostic MRGs that were significantly relevant to OSC survival. These eight genes have important clinical significance and prognostic value in OSC. Subsequently, a prognostic index was constructed. Drug sensitivity analysis was used to screen the key genes in eight MRGs. Immunohistochemistry (IHC) staining confirmed the expression levels of key genes and their correlations with clinical parameters and prognosis for patients.
resultsA total of 701 differentially expressed MRGs were confirmed in women with OSC by the TCGA database. The random walking with restart (RWR) algorithm and the univariate Cox and lasso regression analyses indicated a prognostic signature based on eight MRGs (i.e., ENPP1, FH, CYP2E1, HPGDS, ADCY9, NDUFA5, ADH1B and PYGB), which performed moderately well in prognostic predictions. Drug sensitivity analysis indicated that PYGB played a key role in the progression of OSC. Also, IHC staining confirmed that PYGB has a close correlation with clinical parameters and poor prognosis in OSC.
conclusionThe results of the present study may help to establish a foundation for future research attempting to predict the prognosis of OSC patients and to characterise OSC metabolism.
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