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. 2022 May 13;20:208. doi: 10.1186/s12967-022-03412-9

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© The Author(s) 2022

Open AccessThis 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/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

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Fig. 5 — Bioinformatic analysis of differentially expressed miRNA cargoes between iDPSC-EV and DPSC-EV. A The volcano figure showed the differentially expressed miRNAs. Case and con represented iDPSC-EV and DPSC-EV. B Disease analysis of all the upregulated miRNAs’ targets. C Heat map of differentially expressed top 15 up-and down-regulated miRNAs. D Target genes of the top 30 miRNAs were predicted by miRDB and miRWalk. E Functional analysis of the top 30 miRNAs’ targets by GO enrichment