探索与TranNet的瘤正常交叉对话:环境在瘤进展中的作用
Bayarbaatar Amgalan1, Chi-Ping Day2, Teresa M Przytycka1
1National Center for Biotechnology Information/National Library of Medicine, National Institutes of Health, Bethesda, Maryland, United States of America.
PLoS computational biology
|September 18, 2023
概括
瘤邻近的组织不是健康的对照;它们受到瘤微环境的影响. 一种新的计算方法TranNet识别了将这些组织与瘤联系起来的基因表达预测因子,为癌症进展和生物标志物提供了洞察力.
科学领域:
- 计算生物学是一种计算生物学.
- 癌症研究 癌症研究
- 基因组学就是基因组学.
背景情况:
- 在癌症研究中作为对照使用的瘤邻近组织并不真正健康,而是受到瘤微环境的影响.
- 了解这些组织和瘤之间的基因表达差异对于识别癌症生物标志物和了解瘤启动和进展至关重要.
研究的目的:
- 开发和验证一个计算方法,TranNet,分析匹配的瘤和相邻的对照样本之间的基因表达关系.
- 为了确定从邻近的控制组织到瘤状态的过渡的潜在调节者 (预测者).
主要方法:
- TranNet从匹配的对照和瘤样本的基因表达特征中推断出一个稀疏加权的双部分图.
- 该方法应用于多种癌症类型的癌症基因组图谱 (TCGA) 数据.
主要成果:
- TranNet确定了与瘤微环境调节相关的途径中丰富的预测因子,包括G蛋白结合受体信号传递,免疫过程和细胞粘附.
- 这些预测因子的目标涉及组织重塑 (例如,上皮细胞-介质细胞过渡),免疫反应和细胞增殖.
- 已识别的预测因子可以作为瘤进展的标志物和促进者.
结论:
- "TranNet"提供了对瘤邻近组织,瘤和瘤微环境之间的复杂关系的新见解.
- 通过TranNet识别的预测因素代表了未来癌症研究,生物标志物发现和治疗标识的宝贵资源.
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