Integrating proteomic and phosphoproteomic data for pathway analysis in breast cancer
Integrating proteomic and phosphoproteomic data for pathway analysis in breast cancer
复制标题
整合蛋白质组和磷酸蛋白质组数据进行乳腺癌通路分析
DOI：
10.1186/s12918-018-0646-y
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发表时间：
2018
影响因子：
--
通讯作者：
Jing Li
中科院分区：
文献类型：
--
作者：
Jie Ren;Bo;Jing Li
背景由于蛋白质是细胞功能和生物途径的基本单位，鸟枪蛋白质组学（蛋白质的大规模分析）为我们了解疾病机制做出了巨大贡献。蛋白质组学研究可以检测蛋白质表达和修饰的变化。随着大规模癌症蛋白质组研究的发布，如何将获得的蛋白质组和磷酸蛋白质组数据整合到更全面的通路分析中已成为现实，但仍然具有挑战性。蛋白质组水平的整合途径分析提供了对癌症发展过程中信号网络适应的系统见解。结果在这里，我们整合了蛋白质组和磷酸蛋白质组数据，以对乳腺癌进行途径优先排序。我们从文献中手动收集和整理乳腺癌众所周知的相关通路，作为方法评估中的目标通路（TP）或阳性对照。在整合蛋白质表达和磷酸化方面应用和评估了三种不同的策略，包括基于超几何测试的过度代表性分析、基于柯尔莫哥洛夫-斯米尔诺夫（K-S）测试的基因集分析和基于拓扑的通路分析。相比之下，我们还分别使用蛋白质表达或蛋白质磷酸化的信息评估了该策略的排名性能。与单独使用蛋白质组或磷酸化蛋白质组数据的信息相比，通过数据整合，目标途径的排名更靠前。在路径分析策略的比较中，基于拓扑的方法优于其他方法。乳腺癌的亚型包括 Luminal A、Luminal B、Basal 和 HER2 富集型，其预后差异很大，需要不同的治疗。因此，我们应用基于拓扑的通路分析，整合了四种乳腺癌亚型的蛋白质表达和磷酸化谱。结果表明，TP在所有亚型中均富集，但不同亚型之间的排名存在显着差异。例如，p53通路在Basal样乳腺癌亚型中排名最高，但在HER2富集型乳腺癌中排名不高。 HER2-亚型中粘着斑途径的排名比HER2+亚型中更靠前。结果与之前的一些研究结果一致。结论结果表明，通过将蛋白质组学和磷酸化蛋白质组学相结合，基于网络拓扑的方法在蛋白质组学研究的通路分析中更加强大。这种综合策略还可用于对疾病亚型的特定途径进行排序。
BackgroundAs protein is the basic unit of cell function and biological pathway, shotgun proteomics, the large-scale analysis of proteins, is contributing greatly to our understanding of disease mechanisms. Proteomics study could detect the changes of both protein expression and modification. With the releases of large-scale cancer proteome studies, how to integrate acquired proteomic and phosphoproteomic data in more comprehensive pathway analysis becomes implemented, but remains challenging. Integrative pathway analysis at proteome level provides a systematic insight into the signaling network adaptations in the development of cancer.ResultsHere we integrated proteomic and phosphoproteomic data to perform pathway prioritization in breast cancer. We manually collected and curated breast cancer well-known related pathways from the literature as target pathways (TPs) or positive control in method evaluation. Three different strategies including Hypergeometric test based over-representation analysis, Kolmogorov-Smirnov (K-S) test based gene set analysis and topology-based pathway analysis, were applied and evaluated in integrating protein expression and phosphorylation. In comparison, we also assessed the ranking performance of the strategy using information of protein expression or protein phosphorylation individually. Target pathways were ranked more top with the data integration than using the information from proteomic or phosphoproteomic data individually. In the comparisons of pathway analysis strategies, topology-based method outperformed than the others. The subtypes of breast cancer, which consist of Luminal A, Luminal B, Basal and HER2-enriched, vary greatly in prognosis and require distinct treatment. Therefore we applied topology-based pathway analysis with integrating protein expression and phosphorylation profiles on four subtypes of breast cancer. The results showed that TPs were enriched in all subtypes but their ranks were significantly different among the subtypes. For instance, p53 pathway ranked top in the Basal-like breast cancer subtype, but not in HER2-enriched type. The rank of Focal adhesion pathway was more top in HER2- subtypes than in HER2+ subtypes. The results were consistent with some previous researches.ConclusionsThe results demonstrate that the network topology-based method is more powerful by integrating proteomic and phosphoproteomic in pathway analysis of proteomics study. This integrative strategy can also be used to rank the specific pathways for the disease subtypes.
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影响因子：
11.2
作者：
M. Moynahan;Tracy Y. Cui;M. Jasin
通讯作者：
M. Moynahan;Tracy Y. Cui;M. Jasin
影响因子：
45.3
作者：
Parker, Joel S.;Mullins, Michael;Bernard, Philip S.
通讯作者：
Bernard, Philip S.
影响因子：
4.1
作者：
Nahta R
通讯作者：
Nahta R
影响因子：
64.8
作者：
Wilhelm, Mathias;Schlegl, Judith;Kuster, Bernhard
通讯作者：
Kuster, Bernhard
影响因子：
15.9
作者：
Carracedo, Arkaitz;Ma, Li;Pandolfi, Pier Paolo
通讯作者：
Pandolfi, Pier Paolo