A Novel Prognostic Signature Integrating Immune and Glycolytic Pathways for Enhanced Prognosis and Immunotherapy Prediction in Hepatocellular Carcinoma
- Zeyu Zhang 1, Hongxi Zhao 1, Pengyu Wang 2, Xueyan Geng 1, Maopeng Yin 1, Yingjie Liu 1, Shoucai Zhang 1, Yongyuan Liang 1, Jian Ji 1, Guixi Zheng 1,3
- Zeyu Zhang 1, Hongxi Zhao 1, Pengyu Wang 2
- 1Department of Clinical Laboratory, Qilu Hospital of Shandong University, Jinan, Shandong, 250012, People's Republic of China.
- 2Faculty of Science, University of Alberta, Edmonton, Alberta, T6G 2E9, Canada.
- 3Shandong Engineering Research Center of Biomarker and Artificial Intelligence Application, Jinan, Shandong, 250012, People's Republic of China.
- 0Department of Clinical Laboratory, Qilu Hospital of Shandong University, Jinan, Shandong, 250012, People's Republic of China.
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View abstract on PubMed
Summary
This summary is machine-generated.This study developed an immune-glycolysis-related prognostic signature (IGRPS) to predict hepatocellular carcinoma (HCC) outcomes. The IGRPS accurately forecasts patient prognosis and response to immunotherapy, aiding treatment strategies.
Area Of Science
- Oncology
- Immunology
- Metabolic pathways
Background
- Hepatocellular carcinoma (HCC) poses a significant challenge in clinical oncology.
- Predicting HCC patient prognosis and response to therapy remains complex.
- Integrating immune and glycolytic factors may offer novel prognostic insights.
Purpose Of The Study
- To establish an immune-glycolysis-related prognostic signature (IGRPS) for hepatocellular carcinoma (HCC).
- To evaluate the IGRPS's predictive capability for patient survival and immunotherapy response.
- To explore the association between the IGRPS and the tumor immune microenvironment (TIME) and glycolytic pathways.
Main Methods
- Utilized RNA-sequencing and single-cell sequencing data from TCGA and GEO.
- Employed weighted gene co-expression network analysis (WGCNA) and Cox regression to identify survival-related immune and glycolysis genes (SRIGRGs).
- Validated the IGRPS using Kaplan-Meier analysis, ROC curves, and prognostic nomograms; assessed in vitro gene function.
Main Results
- Identified 13 SRIGRGs to construct the IGRPS.
- The IGRPS demonstrated strong predictive performance (AUC > 0.7) and identified patients with significantly longer survival.
- Low-risk patients showed improved responses to anti-CTLA4 and anti-PD-1 therapies; in vitro studies confirmed gene roles in HCC progression.
Conclusions
- The developed IGRPS serves as a robust, independent prognostic biomarker for HCC.
- IGRPS effectively predicts immunotherapy response in HCC patients.
- This signature offers valuable insights into the interplay of immunity and glycolysis in HCC progression and treatment.
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