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可解释的机器学习驱动的前列腺癌生物标志物识别和验证.

Jianxu Yuan1, Dalin Zhou1, Shengjie Yu1

  • 1Department of Surgery, The Second Affiliated Hospital of Chongqing Medical University, Chongqing Medical University, Chongqing, China.

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|July 21, 2025
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概括
此摘要是机器生成的。

这项研究确定了八个关键基因 (TRPM4,EDN3,EFCAB4A,FAM83B,PENK,NUDT10,KRT14,CXCL13) 作为前列腺癌 (PCa) 诊断和进展的潜在生物标志物. 这些发现为PCa治疗策略提供了理论基础.

关键词:
前列腺癌 (PCa) 是一种癌症.沙普利的添加式扩展 (SHAP)最小绝对收缩和选择操作员回归 (LASSO回归)随机森林 (RF) 是一个随机的森林.支持矢量机器 (SVM) 的使用.

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科学领域:

  • 在瘤学瘤学.
  • 基因组学就是基因组学.
  • 生物信息学是一种生物信息学.

背景情况:

  • 前列腺癌 (PCa) 是全球流行的一种恶性瘤.
  • 早期诊断和进展预测需要可靠的生物标志物.
  • 识别关键基因对于理解PCa发育至关重要.

研究的目的:

  • 确定涉及前列腺癌发生和发展的关键基因.
  • 发现潜在的生物标志物用于早期诊断和PCa的进展预测.
  • 探索PCa.背后的分子机制.

主要方法:

  • 来自基因表达综合 (GEO) 数据库的集成多芯片数据集.
  • 应用差异表达分析和丰富分析以确定PCa相关基因.
  • 构建并评估了机器学习模型 (LASSO,SVM,RF),使用基因贡献的SHAP分析,并利用GSEA和免疫细胞透分析.

主要成果:

  • 鉴定了222个富含PCa相关功能和通路的差异表达基因 (DEG).
  • 已经确定了八个核心PCa相关基因:TRPM4,EDN3,EFCAB4A,FAM83B,PENK,NUDT10,KRT14和CXCL13.
  • 通过GSEA和免疫细胞透分析,发现PCa和正常组织之间的显著区别.

结论:

  • 确定了八个核心基因作为前列腺癌的潜在生物标志物.
  • 为PCa的诊断和治疗提供了理论基础.
  • 强调了机器学习和生物信息学在癌症研究中的实用性.