Scholarlink

<p>Clear cell renal cell carcinoma (ccRCC) is the most common type of RCC. Apoptosis, pyroptosis, and necroptosis are key regulatory processes in carcinogenesis. Accumulating evidence indicated significant crosstalk among three forms of cell death, which is termed as the PANoptosis. However, the connection between PANoptosis and ccRCC remains uncertain. Here, we utilized the TCGA and GEO database to explore distinct PANoptosis patterns based on 62 PANoptosis genes and investigated the clinical, biological, and immune cell infiltration characteristics of PANoptosis patterns. Then, we identified prognosis-related genes from PANoptosis patterns and developed a scoring system that effectively predicts clinical outcomes of ccRCC patients. We also explored the expression of key regulators to confirm these identifications. Immunological analyses revealed a positive correlation between risk score and M0 type macrophages, activated mast cells, follicular helper T cells, and regulatory T cells. Finally, the risk model demonstrated the ability to predict drug sensitivity for ccRCC, such as sorafenib, rapamycin and pazopanib. In conclusion, our findings offer novel insights into the role of PANoptosis in ccRCC and identify potential targets for controlling ccRCC.</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

Integrated bioinformatics analysis reveals novel PANoptosis biomarkers and therapeutic response in clear cell renal cell carcinoma

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年10月31日

查看全文
  • Bin Zheng,
  • Kan Liu,
  • Qing Ouyang,
  • Shengpan Wu,
  • Tongyu Jia,
  • Ji Feng,
  • Jichen Wang,
  • Yuhao Dong,
  • Xiubin Li,
  • Xin Ma,
  • Xu Zhang

摘要

Clear cell renal cell carcinoma (ccRCC) is the most common type of RCC. Apoptosis, pyroptosis, and necroptosis are key regulatory processes in carcinogenesis. Accumulating evidence indicated significant crosstalk among three forms of cell death, which is termed as the PANoptosis. However, the connection between PANoptosis and ccRCC remains uncertain. Here, we utilized the TCGA and GEO database to explore distinct PANoptosis patterns based on 62 PANoptosis genes and investigated the clinical, biological, and immune cell infiltration characteristics of PANoptosis patterns. Then, we identified prognosis-related genes from PANoptosis patterns and developed a scoring system that effectively predicts clinical outcomes of ccRCC patients. We also explored the expression of key regulators to confirm these identifications. Immunological analyses revealed a positive correlation between risk score and M0 type macrophages, activated mast cells, follicular helper T cells, and regulatory T cells. Finally, the risk model demonstrated the ability to predict drug sensitivity for ccRCC, such as sorafenib, rapamycin and pazopanib. In conclusion, our findings offer novel insights into the role of PANoptosis in ccRCC and identify potential targets for controlling ccRCC.