Scholarlink

<p>Apolipoprotein B mRNA-editing enzyme, catalytic polypeptide-like (APOBEC) cytidine deaminases are a highly evolutionarily conserved protein family. Their members are closely associated with DNA damage repair and involved in the genesis and progression of various tumors. Additionally, DNA damage contributes to the onset and progression of kidney renal clear cell carcinoma (KIRC). We collected KIRC samples from public datasets and applied clustering and machine learning methods to analyze the correlation between APOBEC families and KIRC prognosis. Based on APOBEC family expression, we classified KIRC patients into two clusters, which showed significant differences in drug sensitivity and survival. Subsequently, we compared the difference between the traditional coxph model and the machine learning model in predicting the prognosis of KIRC patients, and the results showed that the machine learning has better predictive ability. In addition, the study also predicted potential small-molecule drugs for KIRC treatment. This study proposes a method for KIRC classification and prognosis prediction based on APOBEC family expression. It may help clinicians predict patient drug sensitivity and survival, thereby guiding clinical medication and long-term treatment follow-up.</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

Machine learning-driven classification and prognostic prediction of kidney renal clear cell carcinoma using APOBEC family expression signatures

  • 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
  • Zhen Ren,
  • Yaru Zhu,
  • Xiaochen Qi,
  • Jianing Zhao,
  • Shuhong Tang,
  • Qichao Wang,
  • Haizhe Ji,
  • Jia He,
  • Wei Tian,
  • Xinru Tang,
  • Peizhi Yang,
  • Huanhuan Wu,
  • Tianliang Zheng,
  • Jingyuan Zhao,
  • Hong Yuan,
  • Jundong Li

摘要

Apolipoprotein B mRNA-editing enzyme, catalytic polypeptide-like (APOBEC) cytidine deaminases are a highly evolutionarily conserved protein family. Their members are closely associated with DNA damage repair and involved in the genesis and progression of various tumors. Additionally, DNA damage contributes to the onset and progression of kidney renal clear cell carcinoma (KIRC). We collected KIRC samples from public datasets and applied clustering and machine learning methods to analyze the correlation between APOBEC families and KIRC prognosis. Based on APOBEC family expression, we classified KIRC patients into two clusters, which showed significant differences in drug sensitivity and survival. Subsequently, we compared the difference between the traditional coxph model and the machine learning model in predicting the prognosis of KIRC patients, and the results showed that the machine learning has better predictive ability. In addition, the study also predicted potential small-molecule drugs for KIRC treatment. This study proposes a method for KIRC classification and prognosis prediction based on APOBEC family expression. It may help clinicians predict patient drug sensitivity and survival, thereby guiding clinical medication and long-term treatment follow-up.