集合学习模型用于识别癌症中NFκB/TNF信号通路的标志性基因
Yin-Yuan Su1, Yu-Ling Liu1,2, Hsuan-Cheng Huang1
1Institute of Biomedical Informatics, National Yang Ming Chiao Tung University, Taipei, Taiwan.
Journal of translational medicine
|July 20, 2023
概括
合体学习确定了核因子kappa B (NFκB) /瘤坏死因子 (TNF) 途径中的关键基因,这对于癌症至关重要. 这种方法有助于通过揭示特定的基因功能及其对患者生存的影响来发现向的癌症疗法.
科学领域:
- 在瘤学瘤学.
- 生物信息学是一种生物信息学.
- 机器学习 机器学习
背景情况:
- 核因子kappa B (NFκB) 信号传递,下游的瘤亡因子 (TNF),在癌症的发展中至关重要.
- 针对NFκB是具有挑战性的,因为它具有广泛的细胞影响和潜在的非目标效应.
- 合体学习提供了一种强大的方法,可以在复杂的信号通路中确定精确的治疗点.
研究的目的:
- 开发一个集体学习模型,用于识别与各种癌症中NFκB/TNF通路相关的基因.
- 分析已识别的基因的功能作用和预后意义.
- 探索模型在精准医学中的实用性,以三阴性乳腺癌 (TNBC) 为例.
主要方法:
- 在16种癌症类型中使用癌症基因组图谱 (TCGA) 的转录组数据训练了一组学习模型.
- 利用癌症患者作为预测NFκB/TNF通路相关基因的特征.
- 进行功能丰富,生存分析和TNBC病例研究.
主要成果:
- 该模型准确地识别了NFκB调节的基因对TNF的反应,这些基因参与免疫,抗亡和细胞因子反应.
- 这些基因表现出致癌性质,与患者存活率较低相关.
- 一个特定的模块,单核细胞分化,准确地预测了TNBC和其他癌症的低生存率.
结论:
- 集合学习方法有效地在NFκB/TNF通路内发现与癌症相关的基因.
- 将已识别的基因分类为功能组,为开发向癌症治疗提供了洞察力.
- 这种方法有望通过识别特定的分子标来推进精准医学.
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