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同表达的患者特异分析,以测量个体的生物网络重新连接

Lanying Wei¹, Yucui Xin², Mengchen Pu²

¹Beijing StoneWise Technology Co Ltd, Danling SOHO, Beijing, China weilanying@stonewise.cn.

Life science alliance

|November 17, 2023

概括

"Cosinet"是一种用于差异共同表达 (DCE) 网络分析的新工具,可识别患者个体基因表达的差异. 这种方法通过将患者特异性网络与生存等临床结果联系起来,推进了精准医学.

科学领域:

生物信息学是一种生物信息学.
计算生物学计算生物学
基因组学就是基因组学.

背景情况:

差异性共同表达 (DCE) 网络分析对于了解疾病机制和开发个性化疗法至关重要.
目前的DCE方法平均跨样本的网络,忽略了个别患者特定的疾病病因.

研究的目的:

引入Cosinet,这是一个基于DCE分析的单样网络重新布线程度量化的新工具.
通过使患者特定的网络分析,解决当前DCE方法的局限性.

主要方法:

开发了Cosinet,这是一个基于DCE的工具,用于量化单个样本网络重新布线.
将Cosinet应用于两个乳腺癌数据集,以分析基因共同表达模式.

主要成果:

科西内特在乳腺癌患者之间发现了基因共同表达模式的显著差异.
产生了与整体存活率和无复发间隔显著相关的患者特异性得分.
这些关联在调整临床风险因素 (年龄,瘤大小,HER2,PAM50) 后仍然显著.

结论:

科西内特使患者特定的DCE网络分析成为可能,超越了平均网络测量.
这种工具增强了用于精准医学应用的DCE分析的潜力.
科西内特 (Cosinet) 便于识别与临床结果相关的个体患者个人资料.

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Protein Networks

Protein Networks

An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
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