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综合机器学习和生物信息学方法用于识别胆囊癌诊断和进展中的关键生物标志物.

Rabea Khatun1,2, Wahia Tasnim3, Maksuda Akter1

  • 1Department of Computer Science and Engineering, Jagannath University, Dhaka, Bangladesh.

IET systems biology
|June 17, 2025
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概括
此摘要是机器生成的。

这项研究确定了包括SLIT3,COL7A1和CLDN4在内的关键基因,作为胆囊癌 (GBC) 的潜在生物标志物. 这些发现可以改善早期GBC诊断和预后.

关键词:
个人个人信息网络PPI网络.生物信息学是一种生物信息学.功能选择 功能选择胆囊癌 胆囊癌 是一种胆囊癌.核心基因 核心基因 核心基因机器学习是机器学习.

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科学领域:

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

背景情况:

  • 胆囊癌 (GBC) 是最常见的胆道瘤.
  • 确定可靠的生物标志物用于GBC启动和进展至关重要,但具有挑战性.

研究的目的:

  • 通过机器学习和生物信息学方法识别GBC的新生物标志物.
  • 评估已识别的基因标记物的诊断和预后潜力.

主要方法:

  • 两个微阵列数据集 (GSE100363,GSE139682) 的基因表达差异分析.
  • 基因本体学和通路丰富分析.
  • 蛋白质与蛋白质相互作用网络的构建和枢纽基因的识别.
  • 机器学习模型培训 (SVM,NB,RF) 和生物标志物预测的验证.
  • 使用GEPIA数据库进行外部验证.

主要成果:

  • 在GBC中鉴定出146个差异表达基因 (DEGs),包括39个上调和107个下调.
  • 发现了11个枢纽基因,其中SLIT3,COL7A1和CLDN4与GBC有很强的相关性.
  • 机器学习模型证实了这些关键基因的诊断潜力.
  • 突出了11个基因 (NTRK2, COL14A1, SCN4B, ATP1A2, SLC17A7, SLIT3, COL7A1, CLDN4, CLEC3B, ADCYAP1R1, MFAP4) 的组,这些基因对GBC至关重要.

结论:

  • SLIT3,COL7A1和CLDN4被确定为GBC的高度预测性的生物标志物.
  • 这些发现支持改善GBC的早期诊断和预后.
  • 鉴定的生物标志物可以帮助胆囊癌患者的临床决策.