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. 2024 Nov 5;14:26811. doi: 10.1038/s41598-024-78171-2

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© The Author(s) 2024

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

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Fig. 3 — Bioinformatic analysis of in-house datasets (decellularised, secretome, iTRAQ). (a) Bioinformatic pipeline of proteomic data processing, and filtering results by HR-M3 metastatic status on the Homo sapiens MatrisomeDB 2.0 background with P < 0.05 (in bold are protein hits after each analysis step); (b) Venn diagrams reflecting the overlap of proteins from each dataset (i) 197 genes used for ORA, (ii) 76 genes upregulated in HR FC ≥ 1.5; (c) Proportion of proteins from 76 genes classified by matrisome nomenclature for combined datasets (All) and separately (decellularised, secretome, iTRAQ) - sub-categories include core matrisome ECM glycoproteins, collagens, proteoglycans and matrisome-associated secreted factors, ECM-affiliated proteins, ECM regulators; & (d) ORA pathways showing (i) GO pathways from MF classification, (ii) KEGG pathways^22–24.