ArticleFrontiers in oncology2021
A Glycolysis-Related Five-Gene Signature Predicts Biochemical Recurrence-Free Survival in Patients With Prostate Adenocarcinoma.
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 16 papers.
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Who cites it
16 citing papers in PubMed.
- A novel prognostic risk score associated with resistance to docetaxel chemotherapy for predicting biochemical recurrence-free survival in patients with prostate cancer.Discover oncology · 2025Article
- [Research progress in the role of STMN1 in tumor].Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences · 2025Review
- KDELR3 overexpression as a novel prognostic and diagnostic biomarker in glioma: comprehensive bioinformatic analysis insights.Scientific reports · 2024Article
- ANO7 African-ancestral genomic diversity and advanced prostate cancer.Prostate cancer and prostatic diseases · 2024Article
- Construction of lncRNA/Pseudogene-miRNA Network Based on In Silico Approaches for Glycolysis Pathway to Identify Prostate Adenocarcinoma-Related Potential Biomarkers.Applied biochemistry and biotechnology · 2024Article
- Imaging tumor lactate is feasible for identifying intermediate-risk prostate cancer patients with postsurgical biochemical recurrence.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- A Five Glutamine-Associated Signature Predicts Prognosis of Prostate Cancer and Links Glutamine Metabolism with Tumor Microenvironment.Journal of clinical medicine · 2023Article
- Identification of immune-associated genes in diagnosing osteoarthritis with metabolic syndrome by integrated bioinformatics analysis and machine learning.Frontiers in immunology · 2023Article
- PPFIA4 promotes castration-resistant prostate cancer by enhancing mitochondrial metabolism through MTHFD2.Journal of experimental & clinical cancer research : CR · 2022Article
- STMN1 is highly expressed and contributes to clonogenicity in acute promyelocytic leukemia cells.Investigational new drugs · 2022Article
- Identification and validation of an immune-related gene pairs signature for three urologic cancers.Aging · 2022Article
- KDELR3 Is a Prognostic Biomarker Related to the Immune Infiltration and Chemoresistance of Anticancer Drugs in Uveal Melanoma.Disease markers · 2022Article
- Novel Hypoxia-Associated Gene Signature Depicts Tumor Immune Microenvironment and Predicts Prognosis of Colon Cancer Patients.Frontiers in genetics · 2022Article
- Review
- Circβ-catenin promotes tumor growth and Warburg effect of gallbladder cancer by regulating STMN1 expression.Cell death discovery · 2021Article
- An Original Ferroptosis-Related Gene Signature Effectively Predicts the Prognosis and Clinical Status for Colorectal Cancer Patients.Frontiers in oncology · 2021Article
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10 authors.
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Abstract
Prostate cancer (PCa) is one of the most frequently diagnosed cancers in males worldwide. Approximately 25% of all patients experience biochemical recurrence (BCR) after radical prostatectomy (RP) and BCR indicates increased risk for metastasis and castration resistance. PCa patients with highly glycolytic tumors have a worse prognosis. Thus, this study aimed to explore glycolysis-based predictive biomarkers for BCR. Expression data and clinical information of PCa samples were retrieved from three publicly available datasets. One from The Cancer Genome Atlas (TCGA) dataset was used as the training cohort, and two from the Gene Expression Omnibus (GEO) dataset (GSE54460 and GSE70769) were used as validation cohorts. Using the training cohort, univariate Cox regression survival analysis, robust likelihood-based survival model, and stepwise multiply Cox analysis were sequentially applied to explore predictive glycolysis-related candidates. A five-gene risk score was then constructed based on the Cox coefficient as the following: (-0.8367*GYS2) + (0.3448*STMN1) + (0.3595*PPFIA4) + (-0.1940*KDELR3) + (0.4779*ABCB6). Receiver operating characteristic curve (ROC) analysis was used to identify the optimal cut-off point, and patients were divided into low risk and high risk groups. Kaplan-Meier analysis revealed that high risk group had significantly shorter BCR free survival time as compared with that in low risk group in training and validation cohorts. In conclusion, our data support the glycolysis-based five-gene signature as a novel and robust signature for predicting BCR of PCa patients.
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