基于倍数变化方向性的跨数据集蛋白质组网络整合框架
Network-based integration of cross-dataset proteomic profiles using fold-change directionality
一个基于差异变化方向的蛋白质组整合框架被用于公共蛋白质组数据集，通过倍数变化方向性实现跨数据集比较。研究将两两对比定义为差异蛋白质组谱，用比值比量化上调与下调蛋白的一致性，并将显著谱对可视化为整合网络。在抗癌药物多柔比星处理 vs 对照的 MCF-7 比较成为网络中心枢纽，乳腺癌蛋白质组谱围绕其聚类并与肿瘤分期相关（p = 0.03）。富集分析显示脂质和胆固醇相关通路过度代表。蛋白质组网络整合的源代码已公开。
来源：Bioinformatics Advances · doi.org
Motivation
The rapid expansion of proteomic data has created new opportunities for large-scale integrative analyses. However, substantial variability across platforms, experimental designs, and processing pipelines limits direct quantitative comparisons among studies. Differential proteomic changes between conditions are often considered to be more reproducible than absolute abundances and may therefore provide a robust basis for cross-dataset integration. However, the systematic ability of differential change-based approaches to capture biologically meaningful relationships across heterogeneous datasets remains unclear.
Results
We developed a differential-change framework and applied it to public proteomic datasets. Pairwise contrasts were defined as differential proteomic profiles, and the concordance of up- and down-regulated proteins was quantified using odds ratios. Significant profile pairs were visualized as an integrated network. The treatment of anti-cancer drug doxorubicin vs control (MCF-7) comparison emerged as a central hub, with breast cancer proteome profiles clustering around it and associating with tumor stage (p = 0.03). Enrichment analysis revealed overrepresentation of lipid- and cholesterol-related pathways.
Availability and implementation
The source code for proteome network integration is available at https://github.com/manakanishizaki/proteome-network-integration.git