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一个基于便-微生物-细胞外-囊的代谢学机器学习框架和生物标志物发现,用于预测结肠直肠癌患者.

Fatma Hilal Yagin1, Abedalrhman Alkhateeb2, Cemil Colak1

  • 1Department of Biostatistics and Medical Informatics, Faculty of Medicine, Inonu University, 44280 Malatya, Turkey.

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概括

高通量代谢学确定了五种便代谢物,包括氨基酸,作为结直肠癌 (CRC) 查的潜在生物标志物. 这些代谢物显示出早期诊断和针对性治疗策略在CRC患者的希望.

关键词:
生物标志物发现发现结肠直肠癌是什么意思机器学习是机器学习.代谢组分分析的概况

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科学领域:

  • 代谢学 代谢学 代谢学
  • 在瘤学瘤学.
  • 生物标志物发现发现

背景情况:

  • 大肠直肠癌 (CRC) 是癌症相关死亡的主要原因.
  • 代谢物因其在CRC发育和进展中的作用越来越被认可.
  • 确定可靠的生物标志物对于早期CRC检测和管理至关重要.

研究的目的:

  • 通过使用高通量代谢学来确定结直肠癌 (CRC) 诊断和治疗的潜在便代谢物生物标志物.
  • 评估已识别的代谢物的歧视潜力,并开发CRC查的预测模型.

主要方法:

  • 来自CRC患者和健康对照者的便样本使用高通量代谢学分析.
  • 统计分析包括单变量ROC分析,t测试,折叠变化分析和多变量方法 (SVM,PLS-DA,RF).
  • 用FDR校正的p值<0.05和AUC>0.70的代谢物被选择进行进一步分析.

主要成果:

  • 确定了五种明显差异表达的便代谢物:黄酸,氨基酸,黄油酸,异黄素和素.
  • 氨基酸显示出高的区分潜力 (AUC = 0.806),并且在CRC患者中下调.
  • 使用这五种代谢物的多变量模型在CRC查中达到0.985的高AUC.

结论:

  • 便代谢学可以有效地识别结直肠癌的潜在生物标志物.
  • 氨基酸和其他已识别的代谢物显示出非侵入性CRC检测的前景.
  • 开发的代谢学模型为CRC查提供了一个高度准确的方法.