Scholarlink

Background <p>Manganese modulates tumorigenesis and immune regulation. High levels of manganese may promote cancer progression. While manganese toxicity causes renal tubular damage and chronic impairment, its association with kidney cancer remains poorly understood.</p> Methods <p>We systematically analyzed manganese metabolism genes in KIRC using the TCGA dataset. Through integrated bioinformatics approaches, including differential expression analysis, univariate Cox regression, and three machine learning algorithms (Boruta, GBM, and RFS), we identified prognosis-related MMCG. The Ward.D2 method was used to identify MMCG subtypes, while Lasso-cox regression analysis was performed to establish the MMCG risk model. The predictive performance was validated through time-dependent ROC analysis, calibration curves, and decision curve analysis.</p> Results <p>We identified 11 prognosis-related manganese metabolism core genes (MMCGs). KIRC patients were stratified into two clusters based on MMCG expression levels. Patients in Cluster I showed poorer outcomes, which were associated with tumour progression. The MMCG risk score was subsequently developed using LASSO-Cox regression analysis, and patients were classified into high- and low-risk groups. Survival analysis revealed that the outcomes of high-risk group patients were poorer than those of the low-risk group. Univariate and multivariate analyses confirmed the MMCG risk score as an independent prognostic biomarker. Pathway enrichment analysis showed differential enrichment of immune and metabolic pathways across subtypes and risk groups. We constructed a clinical nomogram incorporating the MMCG risk score and other clinical parameters, which demonstrated highly accurate predictive capabilities. Immune infiltration analysis and immune therapy response predictions indicated that patients in Cluster I and the high-risk group showed low responses to immune therapy.</p> Conclusion <p>Our findings provide a basis for clinical stratification strategies and future research on manganese-based interventions for renal cell carcinoma (RCC).</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

A manganese metabolism-related gene signature stratifies prognosis and immunotherapy efficacy in kidney cancer

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月1日

查看全文
  • Yang Liu,
  • Hao Ye,
  • Ruoxuan Zhang,
  • Xiaolong Liu,
  • Ranlu Liu

摘要

Background

Manganese modulates tumorigenesis and immune regulation. High levels of manganese may promote cancer progression. While manganese toxicity causes renal tubular damage and chronic impairment, its association with kidney cancer remains poorly understood.

Methods

We systematically analyzed manganese metabolism genes in KIRC using the TCGA dataset. Through integrated bioinformatics approaches, including differential expression analysis, univariate Cox regression, and three machine learning algorithms (Boruta, GBM, and RFS), we identified prognosis-related MMCG. The Ward.D2 method was used to identify MMCG subtypes, while Lasso-cox regression analysis was performed to establish the MMCG risk model. The predictive performance was validated through time-dependent ROC analysis, calibration curves, and decision curve analysis.

Results

We identified 11 prognosis-related manganese metabolism core genes (MMCGs). KIRC patients were stratified into two clusters based on MMCG expression levels. Patients in Cluster I showed poorer outcomes, which were associated with tumour progression. The MMCG risk score was subsequently developed using LASSO-Cox regression analysis, and patients were classified into high- and low-risk groups. Survival analysis revealed that the outcomes of high-risk group patients were poorer than those of the low-risk group. Univariate and multivariate analyses confirmed the MMCG risk score as an independent prognostic biomarker. Pathway enrichment analysis showed differential enrichment of immune and metabolic pathways across subtypes and risk groups. We constructed a clinical nomogram incorporating the MMCG risk score and other clinical parameters, which demonstrated highly accurate predictive capabilities. Immune infiltration analysis and immune therapy response predictions indicated that patients in Cluster I and the high-risk group showed low responses to immune therapy.

Conclusion

Our findings provide a basis for clinical stratification strategies and future research on manganese-based interventions for renal cell carcinoma (RCC).