ArticleCancer medicine2021
Identification of a glycolysis-related gene signature for survival prediction of ovarian cancer patients.
Article in Cancer medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- Survival prediction and risk stratification in R0-resected ovarian cancer: a multi-modal deep learning approach.NPJ precision oncology · 2026Article
- Pan cohort immune biomarker of CD8 lymphocyte activation enabling HGSOC outcome prediction and treatment response.Discover oncology · 2025Article
- "Friends or foes": a new perspective of tumour metabolic transcriptional modification.Cell death & disease · 2025Review
- TCEB2/HIF1A signaling axis promotes chemoresistance in ovarian cancer cells by enhancing glycolysis and angiogenesis.European journal of medical research · 2024Article
- FOSL2-mediated transcription of ISG20 induces M2 polarization of macrophages and enhances tumorigenic ability of glioblastoma cells.Journal of neuro-oncology · 2024Article
- A lactate metabolism-related signature predicting patient prognosis and immune microenvironment in ovarian cancer.Frontiers in endocrinology · 2024Article
- Identification and validation of a prognostic model based on immune-related genes in ovarian carcinoma.PeerJ · 2024Article
- Transcription factor LHX9 (LIM Homeobox 9) enhances pyruvate kinase PKM2 activity to induce glycolytic metabolic reprogramming in cancer stem cells, promoting gastric cancer progression.Journal of translational medicine · 2023Article
- HNRNPC promotes collagen fiber alignment and immune evasion in breast cancer via activation of the VIRMA-mediated TFAP2A/DDR1 axis.Molecular medicine (Cambridge, Mass.) · 2023Article
- Article
- Review
- An artificial intelligence prediction model based on extracellular matrix proteins for the prognostic prediction and immunotherapeutic evaluation of ovarian serous adenocarcinoma.Frontiers in molecular biosciences · 2023Article
- Identification of disulfidptosis-related subtypes, characterization of tumor microenvironment infiltration, and development of a prognosis model in breast cancer.Frontiers in immunology · 2023Article
- PTH2R is related to cell proliferation and migration in ovarian cancer: a multi-omics analysis of bioinformatics and experiments.Cancer cell international · 2022Article
- Integrated clinical characteristics and omics analysis identifies a ferroptosis and iron-metabolism-related lncRNA signature for predicting prognosis and therapeutic responses in ovarian cancer.Journal of ovarian research · 2022Article
- Identification of a Recurrence Gene Signature for Ovarian Cancer Prognosis by Integrating Single-Cell RNA Sequencing and Bulk Expression Datasets.Frontiers in genetics · 2022Article
- Computed Tomography Imaging-Based Radiogenomics Analysis Reveals Hypoxia Patterns and Immunological Characteristics in Ovarian Cancer.Frontiers in immunology · 2022Article
- Derivation, Comprehensive Analysis, and Assay Validation of a Pyroptosis-Related lncRNA Prognostic Signature in Patients With Ovarian Cancer.Frontiers in oncology · 2022Article
- The cuproptosis-related signature predicts prognosis and indicates immune microenvironment in breast cancer.Frontiers in genetics · 2022Article
- Integrating cell cycle score for precise risk stratification in ovarian cancer.Frontiers in genetics · 2022Article
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13 authors.
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Abstract
backgroundOvarian cancer (OV) is deemed the most lethal gynecological cancer in women. The aim of this study was to construct an effective gene prognostic model for predicting overall survival (OS) in patients with OV.
methodsThe expression profiles of glycolysis-related genes (GRGs) and clinical data of patients with OV were extracted from The Cancer Genome Atlas (TCGA) database. Univariate, multivariate, and least absolute shrinkage and selection operator Cox regression analyses were conducted, and a prognostic signature based on GRGs was constructed. The predictive ability of the signature was analyzed using training and test sets.
resultsA gene risk signature based on nine GRGs (ISG20, CITED2, PYGB, IRS2, ANGPTL4, TGFBI, LHX9, PC, and DDIT4) was identified to predict the survival outcome of patients with OV. The signature showed a good prognostic ability for OV, particularly high-grade OV, in the TCGA dataset, with areas under the curve (AUC) of 0.709 and 0.762 for 3- and 5-year survival, respectively. Similar results were found in the test sets, and the AUCs of 3-, 5-year OS were 0.714 and 0.772 in the combined test set. And our signature was an independent prognostic factor. Moreover, a nomogram combining the prediction model and clinical factors was developed.
conclusionOur study established a nine-GRG risk model and nomogram to better predict OS in patients with OV. The risk model represents a promising and independent prognostic predictor for patients with OV. Moreover, our study on GRGs could offer guidance for the elucidation of underlying mechanisms in future studies.
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