Application of individualized differential expression analysis in human cancer proteome
Application of individualized differential expression analysis in human cancer proteome
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DOI：
10.1093/bib/bbac096
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发表时间：
2022-04-02
影响因子：
9.5
通讯作者：
Yu，Rongshan
中科院分区：
文献类型：
--
作者：
Liu，Yachen;Lin，Yalan;Yu，Rongshan
基于液相色谱-质谱联用技术的定量蛋白质组学可以检测生物样品中成千上万种蛋白质的表达，在肿瘤研究中的应用日益广泛。识别肿瘤和正常对照之间的差异表达蛋白（DEPs）通常用于研究癌发生机制。虽然需要个体水平的差异表达分析（DEA）来鉴定患者特异性分子缺陷以更好地对患者分层，但大多数统计学DEP分析方法仅鉴定群体水平的失调蛋白。到目前为止，已经提出了强大的个性化DEA算法的核糖核酸数据，但其性能蛋白质组学数据是underexplored。在此，我们对来自七种癌症类型的癌症蛋白质组数据集上的五种个性化DEA算法进行了系统评估。结果表明，正常组织中蛋白质对的样本内相对表达顺序（REO）具有高度稳定性，为利用REO进行蛋白质个体化DEA提供了基础。此外，个体化DEA算法在检测样品特异性去调节蛋白质方面比群体水平方法实现了更高的精度。为了促进个性化DEA算法在蛋白质组学中用于预后生物标志物发现和个性化医疗，我们提供了个性化DEP分析IDEPAXMBD（XMBD：Xiamen Big Data，中国厦门大学国家健康与医学数据科学研究所的生物医学开放软件计划。（https：//github.com/xmuyulab/IDEPA-XMBD），这是一个用户友好的开源Python工具包，它集成了个性化的DEA算法，用于DEP相关的放松管制模式识别。
Liquid chromatography–mass spectrometry-based quantitative proteomics can measure the expression of thousands of proteins from biological samples and has been increasingly applied in cancer research. Identifying differentially expressed proteins (DEPs) between tumors and normal controls is commonly used to investigate carcinogenesis mechanisms. While differential expression analysis (DEA) at an individual level is desired to identify patient-specific molecular defects for better patient stratification, most statistical DEP analysis methods only identify deregulated proteins at the population level. To date, robust individualized DEA algorithms have been proposed for ribonucleic acid data, but their performance on proteomics data is underexplored. Herein, we performed a systematic evaluation on five individualized DEA algorithms for proteins on cancer proteomic datasets from seven cancer types. Results show that the within-sample relative expression orderings (REOs) of protein pairs in normal tissues were highly stable, providing the basis for individualized DEA for proteins using REOs. Moreover, individualized DEA algorithms achieve higher precision in detecting sample-specific deregulated proteins than population-level methods. To facilitate the utilization of individualized DEA algorithms in proteomics for prognostic biomarker discovery and personalized medicine, we provide Individualized DEP Analysis IDEPAXMBD(XMBD: Xiamen Big Data, a biomedical open software initiative in the National Institute for Data Science in Health and Medicine, Xiamen University, China.) (https://github.com/xmuyulab/IDEPA-XMBD), which is a user-friendly and open-source Python toolkit that integrates individualized DEA algorithms for DEP-associated deregulation pattern recognition.