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Published on: January 22, 2013
PANoptosis-related gene clusters and prognostic risk model in clear cell renal cell carcinoma
Qiyue Zhao1, Huadong Xie1, Chaofu Li2
1Department of Urology, Liuzhou Workers' Hospital, Liuzhou, Guangxi, China.
Background:
Despite advancements in targeted therapies, the prognosis for clear cell renal cell carcinoma (ccRCC) remains poor, particularly for metastatic cases. PANoptosis, a newly discovered programmed cell death pathway involving crosstalk among pyroptosis, apoptosis, and necroptosis, has an undefined role in ccRCC pathogenesis and prognosis, representing a critical knowledge gap.
Methods:
We conducted a bioinformatics analysis of the expression PANoptosis-related genes (PRGs) in 524 ccRCC patients from the TCGA and GEO databases. Three ccRCC clusters were identified based on PRG expression. Innovatively, we developed a prognostic risk model using LASSO and Cox regression on three hub genes (WDR72, ANLN, SLC16A12), integrating multi-omics data for immune microenvironment, tumor mutation burden (TMB), cancer stem cell (CSC) index, and drug sensitivity assessment. Expression of these hub genes was further validated by RT-qPCR.
Results:
We found that most of the PRGs were upregulated in ccRCC tumors with low mutation rates, and 18 PRGs exhibited a significant correlation with ccRCC patient survival. Patients were stratified into three PRG clusters and two gene clusters, which were significantly associated with ccRCC prognosis. We constructed a prognostic risk model based on three genes, dividing ccRCC patients into high- and low-risk groups. The predictive value of this risk model was confirmed by ROC curves. High-risk scores were associated with an increased stromal score, immune score, and tumor mutation burden (TMB), but they were associated with a decrease in the cancer stem cell (CSC) index. RT-qPCR confirmed the expression of WDR72, ANLN, and SLC16A12 in ccRCC tissues and cell lines. Additionally, the PRG risk score model exhibited significant associations with sensitivity to multiple drugs.
Conclusion:
This novel PANoptosis-based model addresses the knowledge gap by providing enhanced prognostic accuracy and clinical utility for personalized ccRCC management, potentially guiding targeted and immunotherapeutic strategies.
Insights
This study introduces a new PANoptosis-related gene (PRG) risk model for clear cell renal cell carcinoma (ccRCC). The model improves prognosis prediction and guides personalized treatment strategies for ccRCC patients.
Area of Science:
- Oncology
- Molecular Biology
- Bioinformatics
Background:
- Clear cell renal cell carcinoma (ccRCC) prognosis remains poor, especially for metastatic disease.
- PANoptosis, a programmed cell death pathway, has an unclear role in ccRCC.
- A knowledge gap exists regarding PANoptosis's impact on ccRCC pathogenesis and patient outcomes.
Purpose of the Study:
- To investigate the role of PANoptosis-related genes (PRGs) in ccRCC.
- To develop a prognostic risk model for ccRCC based on PRGs.
- To assess the clinical utility of this model for personalized ccRCC management.
Main Methods:
- Bioinformatics analysis of PRG expression in 524 ccRCC patients from TCGA and GEO databases.
- Identification of ccRCC clusters based on PRG expression.
- Development of a prognostic risk model using LASSO and Cox regression on hub genes (WDR72, ANLN, SLC16A12).
- Integration of multi-omics data, including immune microenvironment, tumor mutation burden (TMB), cancer stem cell (CSC) index, and drug sensitivity.
- Validation of hub gene expression via RT-qPCR.
Main Results:
- Most PRGs were upregulated in ccRCC tumors with low mutation rates.
- 18 PRGs significantly correlated with ccRCC patient survival.
- A prognostic risk model based on WDR72, ANLN, and SLC16A12 stratified patients into high- and low-risk groups.
- High-risk scores correlated with increased stromal and immune scores, TMB, and decreased CSC index.
- The risk model showed significant associations with drug sensitivity.
Conclusions:
- A novel PANoptosis-based prognostic model was developed for ccRCC.
- This model enhances prognostic accuracy and clinical utility for personalized ccRCC management.
- The findings may guide targeted and immunotherapeutic strategies in ccRCC treatment.
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