Network-based integration of cross-dataset proteomic profiles using fold-change directionality

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ID: 333825
2026
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Abstract
Abstract 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
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Authors Manaka Nishizaki, Norie Araki, Shin Kawano
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Journal Bioinformatics advances
Year 2026
DOI
10.1093/bioadv/vbag321
URL
https://academic.oup.com/bioinformaticsadvances/advance-article-pdf/doi/10.1093/bioadv/vbag321/72452723/vbag321.pdf
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