ArticleComputational and mathematical methods in medicine2022
Construction of Glycolytic Regulator Gene Signature to Predict the Prognosis and Tumor Immune Cell Infiltration Levels for Prostate Cancer.
Article in Computational and mathematical methods in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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9 citing papers in PubMed, 10 citations in OpenAlex.
- Orchestrating glucose metabolism: PFKFB2 as a signal-integrating conductor in homeostasis and disease.The Journal of biological chemistry · 2026Review
- Bidirectional Feedback Between Metabolic Reprogramming and Epithelial-Mesenchymal Transition: From Mechanisms to Therapeutic Interventions.Molecules (Basel, Switzerland) · 2026Review
- Integrating Radiosensitivity Index and Radiation Resistance Related Index Improves Prostate Cancer Outcome Prediction.Advances in radiation oncology · 2025Article
- A PSMA-Targeted Tri-Specific Killer Engager Enhances NK Cell Cytotoxicity against Prostate Cancer.Cancer immunology research · 2025Article
- Role of ENO1 and its targeted therapy in tumors.Journal of translational medicine · 2024Review
- Triosephosphate isomerase 1 may be a risk predictor in laryngeal squamous cell carcinoma: a multi-centered study integrating bulk RNA, single-cell RNA, and protein immunohistochemistry.European journal of medical research · 2023Article
- Druggable Metabolic Vulnerabilities Are Exposed and Masked during Progression to Castration Resistant Prostate Cancer.Biomolecules · 2022Review
- Identification ofFrontiers in cardiovascular medicine · 2022Article
- Comprehensive Analysis of the Role ofAnalytical cellular pathology (Amsterdam) · 2022Article
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Prostate cancer (PCa) is the commonly generated noncutaneous neoplasm among men worldwide. Glycolysis had been validated to promote cancer progression. However, the clinical significance of glycolytic regulators in PCa was not well understood. Here, we discovered that glycolytic regulators were dysregulated in PCa samples using GSE8511, GSE6919, and GEPIA. By detecting the expression of these regulators in PCa samples, we found that SLC2A1, SLC2A3, HK2, PFKFB2, TPI1, PKM2, and LDHA had higher expression in PCa compared with normal tissues. Moreover, both higher expression of TPI1, ALDOA, ENO1, LDHA, and PKM and lower expression of LDHB and HK2 were significantly related to shorter progression-free survival time in PCa. Of note, an 8 gene-based risk score was further constructed and confirmed to have a good performance in predicting progression-free survival (PFS) time in PCa. The signature risk score significantly correlated with NK cell, neutrophil cell, macrophage M2 cell, and myeloid dendritic cell infiltration levels in PCa. After bioinformatics analysis, our data suggested glycolytic regulators participated in the regulation of multiple nonmetabolic biological processes, such as RNA transport, biosynthesis of antibiotics, and cell cycle. We recapitulate that the glycolytic regulator signature was a prospective indicator for prognosis and immune cell infiltration levels in PCa.
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