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. 2021 May 1;14:232. doi: 10.1186/s13071-021-04734-1

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© The Author(s) 2021

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. 3 — Bioinformatics analysis of differentially expressed proteins in serum samples from healthy controls vs those from patients with SHF-F2. a Volcano plot representing the changes in protein abundance (groups B [SHF-F2 patients] vs A [healthy controls]). A total of 66 dysregulated proteins with a fold change of ≥ ± 1.5 and P values < 0.05 were identified. b Hierarchical clustering of the 66 dysregulated proteins (groups B vs A). c Gene ontology (GO) analysis of 66 dysregulated proteins (groups B vs A). The abscissa repressents enriched GO function classifications, which are divided into three major categories: biological process (BP), molecular function (MF) and cellular component (CC). d Analysis of Eukaryotic Orthologous Groups (KOG) of 66 dysregulated proteins (group B vs group A)