Identification of glycolysis-related genes and analysis of potential prognostic clinical application in oral squamous cell carcinoma
- Rongxi Chen 1,2, Yuhui Li 2,3, Wenhao Sun 1,2, Junhui Wang 1,2, Tianjun Lan 1,2, Jinsong Li 1,2, Fan Wu 1,2, Zijing Huang 4
- Rongxi Chen 1,2, Yuhui Li 2,3, Wenhao Sun 1,2
- 1Department of Oral and Maxillofacial Surgery, Sun Yat-sen Memorial Hospital of Sun Yat-sen University, Guangzhou, China.
- 2Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Medical Research Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China.
- 3Department of Pathology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China.
- 4Department of Endodontics, Stomatological Hospital, Southern Medical University, Guangzhou, China.
- 0Department of Oral and Maxillofacial Surgery, Sun Yat-sen Memorial Hospital of Sun Yat-sen University, Guangzhou, China.
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View abstract on PubMed
Summary
This summary is machine-generated.This study identifies six glycolysis-related genes (GRGs) as key predictors for oral squamous cell carcinoma (OSCC) prognosis. A novel risk score and nomogram model can predict patient outcomes and guide potential immunotherapeutic strategies.
Area Of Science
- Oncology
- Metabolic pathways
- Bioinformatics
Background
- Glycolysis is a critical metabolic pathway fueling tumor cell energy production.
- Oral squamous cell carcinoma (OSCC) relies heavily on glycolysis.
- Identifying prognostic biomarkers for OSCC is crucial for improving patient outcomes.
Purpose Of The Study
- To identify glycolysis-related genes (GRGs) associated with OSCC prognosis.
- To develop and validate a predictive model for OSCC patient outcomes based on GRGs.
- To explore the relationship between the GRG-based risk score and tumor immune microenvironment.
Main Methods
- Transcriptomic data from TCGA and GEO databases were analyzed.
- Gene set enrichment analysis (GSEA), LASSO, and Cox regression identified prognostic GRGs.
- A risk score and nomogram were constructed and validated.
- Immune cell infiltration was assessed using CIBERSORT and TIMER.
Main Results
- Six GRGs (ADH1A, ADH1B, ADH1C, PGK1, IER3, B4GALT1) were identified as independent prognostic factors for OSCC.
- A risk score model based on these six GRGs demonstrated excellent predictive performance.
- The risk score correlated with the infiltration levels of specific immune cells, including regulatory T cells.
Conclusions
- A GRG-based risk score and nomogram can effectively predict clinical outcomes in OSCC patients.
- Enhanced glycolysis in OSCC is linked to immune cell infiltration, suggesting potential for immunotherapy.
- These findings offer novel targets for anti-cancer strategies in OSCC.
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