Zhu et al. BMC Surg (2021) 21:303 https://doi.org/10.1186/s12893-021-01298-w

RESEARCH ARTICLE Open Access Identifcation of potential hub genes associated with skin wound healing based on time course bioinformatic analyses Hai‑jun Zhu, Meng Fan and Wei Gao*

Abstract Background: The skin is the largest organ of the body and has multiple functions. Wounds remain a signifcant healthcare problem due to the large number of traumatic and pathophysiological conditions patients sufer. Methods: Gene expression profles of 37 biopsies collected from patients undergoing split-thickness skin grafts at fve diferent time points were downloaded from two datasets (GSE28914 and GSE50425) in the Gene Expression Omnibus (GEO) database. Principal component analysis (PCA) was applied to classify samples into diferent phases. Subsequently, diferentially expressed genes (DEGs) analysis, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway functional enrichment analyses were performed, and protein–protein interaction (PPI) networks created for each phase. Furthermore, based on the results of the PPI, hub genes in each phase were identi‑ fed by molecular complex detection combined with the ClueGO algorithm. Results: Using principal component analysis, the collected samples were divided into four phases, namely intact phase, acute wound phase, infammatory and proliferation phase, and remodeling phase. Intact samples were used as control group. In the acute wound phase, a total of 1 upregulated and 100 downregulated DEGs were identifed. Tyrosinase (TYR), tyrosinase Related Protein 1 (TYRP1) and dopachrome tautomerase (DCT) were considered as hub genes and enriched in tyrosine metabolism which dominate the process of melanogenesis. In the infammatory and proliferation phase, a total of 85 upregulated and 164 downregulated DEGs were identifed. CHEK1, CCNB1 and CDK1 were considered as hub genes and enriched in cell cycle and P53 signaling pathway. In the remodeling phase, a total of 121 upregulated and 49 downregulated DEGs were identifed. COL4A1, COL4A2, and COL6A1 were considered as hub genes and enriched in protein digestion and absorption, and ECM-receptor interaction. Conclusion: This comprehensive bioinformatic re-analysis of GEO data provides new insights into the molecular pathogenesis of wound healing and the potential identifcation of therapeutic targets for the treatment of wounds. Keywords: Bioinformatic analysis, Split-thickness, Skin, Wound healing, Hub genes

Background cases and pathophysiological conditions that clinicians In humans, the skin is the largest, most exposed, and sus- treat [1]. To heal damaged skin, diferent cell types coor- ceptible tissue. Wounds have become a signifcant health- dinate their action at precise stages. Tese complex, care problem due to the increasing number of trauma multi-step stages involve hemostasis, infammation, re- epithelialization following keratinocyte migration and proliferation, and remodeling of the extracellular matrix *Correspondence: [email protected] (ECM), occurring in a temporally overlapping sequence The 4th People’s Hospital of Shenyang, No. 20 Huanghenan Street, [2]. Tus, skin repair is an elaborate process in humans. Huanggu District, Shenyang 110031, China

© The Author(s) 2021. 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://creat​ iveco​ mmons.​ org/​ licen​ ses/​ by/4.​ 0/​ . The Creative Commons Public Domain Dedication waiver (http://creat​ iveco​ ​ mmons.org/​ publi​ cdoma​ in/​ zero/1.​ 0/​ ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. Zhu et al. BMC Surg (2021) 21:303 Page 2 of 13

Numerous experimental studies have been conducted annotated using “GEOquery” [10] and “Bioconductor” that have assisted in establishing a better understanding [11] R packages using the R language platform (version of the wound healing mechanism. However, the cellular 3.5.1). Te genomics platforms used were the GPL570 and molecular mechanisms underpinning tissue repair [HG-U133_Plus_2] Afymetrix Human Genome U133 and their failure to heal remain poorly understood, and Plus 2.0 Array for GSE28914 and GPL10558 Illumina thus current therapies are limited. HumanHT-12 V4.0 expression BeadChip for GSE50425. Partial-thickness skin grafts create a superfcial wound Te GSE28914 dataset represents 25 biopsies, of which at the donor site [3], characterized by a standardized 8 are from samples of intact skin (IS), 6 from acute depth of injury that extends to the epidermis and papil- wounds (AW), 6 from the third-postoperative-day (3rd lary dermis, which are renowned for their prolonged POD), and 5 from the seventh-postoperative-day (7th duration of healing, often resulting in a scar [4, 5]. Tere- POD) from 8 diferent male patients undergoing split- fore, this type of wound is ideal for comparing superf- thickness skin graft harvesting. Te GSE50425 dataset cial wound healing (WH) and scar formation in clinical represents the data from 12 biopsies, including 4 from experimental studies [6]. each of samples of intact skin (IS), the fourteenth-post- Microarray and high-throughput sequencing technolo- operative-day (14th POD), and the twenty-frst-postoper- gies provide genome-wide profling of gene expression, ative-day (21st POD) from four split-thickness skin graft allowing researchers to study WH in both animal models donors. Te IS samples in each array were considered as and humans. It also provides a large choice of gene sets controls. All data are freely accessible and were created with data representing the diferential stages of normal without the involvement of any additional human or ani- WH [7]. In particular, time course studies allow research- mal experiments. ers to study the dynamic behavior of gene expression, and consequently variations in molecular and cellular sta- PCA of gene expression tus over time [8]. A number of recent studies have used In the present study, the quality of each microarray data DNA microarrays to study the physiological and patho- was evaluated by conducting PCA [12] prior to analysis physiological transcriptional response during WH [8, 9]. of the DEGs. Te ‘pca3d’ algorithm in R language was However, the biological information from these studies used to evaluate the PCA of gene expression, resulting has not been fully explored. As bioinformatic technology in a 3-dimensional graph demonstrating which DEGs rapidly advances, numerous data profles from the GEO were considered as variables and showing the diferences database have been reanalyzed by researchers. between IS and wound samples at diferent time points. In the present study, diferentially expressed genes (DEGs) from two GEO datasets (GSE28914 [9] and Identifcation of DEGs GSE50425) in which intact and wounded skin samples Te DEGs between POD and IS samples were identifed over diferent phases of repair were compared, were by adjusting the selection criteria of the DEGs for just reanalyzed. Principal component analysis (PCA) demon- those with P-values (adj-P) ≤ 0.05 and |log2FC|≥ 2. Both strated that the expression of genes in each sample could the normalization of data and the screening of DEGs be categorized into four separate phases. We hypoth- were performed using the linear models of microarray esized that those four phases related to the four emblem- data (limma) package (http://​bioco​nduct​or.​org/​packa​ges/​ atic periods of WH, including intact tissue, acute wound relea​se/​bioc/​html/​limma.​html) within the R language phase, infammation, and the remodeling phase. Fur- environment. Te DEGs scripts of each phase used in the thermore, using bioinformatic methods, the integrated analysis was presented in Additional fle 2 DEGs within each phase were identifed, followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes GO and KEGG pathway enrichment analysis of DEGs and Genomes (KEGG) pathway enrichment analyses. Te role of DEGs in WH was established following the Subsequently, an analysis of PPI and hub genes was also analysis of all DEGs using the DAVID network por- performed. tal (https://​david.​ncifc​rf.​gov/) [13] and their biological attributes extracted, including data about which biologi- Methods cal processes (BP), molecular functions (MF), and cellu- Microarray data information lar components (CC) were involved. Te gene expression profles of patients undergoing ClueGO, a widely used Cytoscape plugin allowing split-thickness skin graft harvesting biopsies (Accession visualization of nonredundant biological terms for large nos. GSE28914 and GSE50425) were obtained from the clusters of genes in a functionally grouped network Gene Expression Omnibus (GEO) database (http://​www.​ [14], was used to decipher functionally grouped KEGG ncbi.​nlm.​nih.​gov/​geo/). Te data were downloaded and pathway annotations, using the ‘fusion’ feature for data Zhu et al. BMC Surg (2021) 21:303 Page 3 of 13

where P < 0.05. Finally, bar graphs and bubble plots of the by STRING of each phase used in the analysis were pre- results of functional enrichment were created using the sented in Additional fle 5. “ggplot2” R package (version 3.2.0; CRAN.R-project.org/ Results package=ggplot2) in the R language environment. Te scripts of KEGG pathway enrichment analysis of each Validation of the datasets phase used in the analysis was presented in Additional Te robust multi-array average (RMA) algorithm was fle 3, and the scripts of GO enrichment analysis of each used to display the distribution of test values after quan- phase used in the analysis was presented in Additional tile normalization, background correction, and quartile fle 4. data normalization of the downloaded data [16]. Te results demonstrated that following normalization, the PPI network and hub gene analysis median of the diferent samples was almost the same A PPI network of all DEGs was plotted using the Search value, indicating a high degree of standardization (Addi- Tool for the Retrieval of Interacting Genes (STRING) tional fle 1A and B). To further validate intra-group data (http://​string-​db.​org/) tool to evaluate all interactional repeatability, PCA was employed to evaluate the data dis- associations of the proteins. Te PPI network was visu- tribution in each sample to guarantee that the data was alized using Cytoscape software (v3.7.2). To identify accurate and reliable. Te evaluation suggested that the high-level genes that play a key role in PPI network, the data from IS and all samples periods up to 21 days post Cytohubba package based on Cytoscape was used to skin injury could be partitioned into four distinct tran- perform the hub gene analysis [24], and the top rank 10 scriptional phases, namely intact phase, acute wound genes in all modules are considered to be the hub genes. phase, infammation, and remodeling phase (Fig. 1A, B). Te degree of all nodes in PPI network was calculated using the Cytoscape plugin MCODE and the most sig- Bioinformatic analysis of the acute wound phase nifcant cluster was selected. ‘Maximum number of Volcano plot analysis was used to present the DEGs in interactors = 0’ and ‘confdence score ≥ 0.4’ were selected AW samples compared with intact samples (Fig. 2A). A as cut-of criteria. To further clarify the identity of the total of 1 upregulated and 100 downregulated genes were genes in most signifcant cluster, KEGG analysis was per- identifed in AW samples compared with IS. formed using ClueGO with the kappa score adjusted to DEG functional annotation was conducted using the refect the relationship between terms where there was DAVID network portal. A threshold of P < 0.001 selected a similarity in related genes [15]. Each gene that repre- for KEGG analysis identifed enrichment in only one sented a link in multiple pathways was identifed as a critical pathway, melanogenesis (Fig. 2B). Bar graphs of key functional gene. Te scripts of PPI network plotted DEGs GO enrichment demonstrate the distribution of

GSE28914 GSE50425 A B 125 100 Acute wound pahase 100 80 Intact phase 75

) ) 60 legend 50 40 Remodeling phase Inflammation phase IS legend

2.7%

1 25 AW 12.7% IS ( 3rd POD 20 14th POD

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-25 -20

-50 Intact phase -40 100 8060 100 -75 40 PC3 (7.4%) 8060 PC3 (9.3%) 20 -60 40 -20 0 20 -40 -20 0 -60 -40 -60

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125 PC1 (24.7%) PC1 (25.1%) Fig. 1 Principal component analysis (PCA) of diferentially expressed genes (DEGs) between intact skin samples and the wound sample at diferent phases. Genes were plotted in 3D visualization, indicating that samples within each group shared more similarity. A The red dots showed the PCA values of 8 intact skin samples (IS), and the green dots indicate the PCA value of 6 acute wounds samples (AW), and the blue dots indicate the PCA value of 6 third-postoperative-day samples (3rd POD), and the yellow dots indicate the PCA value of 5 seventh-postoperative-day samples (7th POD) from GSE28914. B The red dots showed the PCA values of 4 intact skin samples (IS), and the green dots indicate the PCA value of 4 fourteenth-postoperative-day samples (14th POD), and the blue dots indicate the PCA value of 4 twenty frst-postoperative-day samples (21st POD) from GSE50425 Zhu et al. BMC Surg (2021) 21:303 Page 4 of 13

6

123 4 5 6

3.809668

2.223367

11.125 11.150 11.175 11.200 Flod enrichment Fig. 2 Diferentially expressed genes (DEGs) in the acute wound phase, and Gene Ontology (GO) and KEGG pathway analysis of DEGs. A Volcano plot representing DEGs in acute wound samples compared with intact skin samples (control). B The most signifcantly enriched KEGG pathways and numbers of genes involved (P < 0.01); C The most signifcantly enriched (P < 0.01) GO terms and numbers of genes involved in acute wound samples for biological processes, D cellular components, E molecular functions, compared to the control tissue

enriched GO terms in BP, CC, and MF categories. Te which dominate the process of melanogenesis (Fig. 3C). most signifcantly enriched terms for each category is Subsequently, the Hub genes from the PPI network were presented in Fig. 2C–E, for which a threshold of P < 0.001 analyzed by using the MCC algorithm in the CytoHuba was selected. Te results demonstrate that variations plugin, and top 10 Hub genes (Fig. 3D) were ranked in DEGs linked with BP were mostly those enriched in based on the MCC scores, which were PMEL, MLANA, keratinocyte diferentiation, epidermis development, DCT, TYR, TYRP1, GATA Binding Protein 3 (GATA3), and melanin biosynthetic process (Fig. 2C). Variations Fos Proto-Oncogene (FOS), Sciellin (SCEL), Keratin 2 in DEGs linked with CC were signifcantly enriched in (KRT2) and Wnt Family Member 4 (WNT4). melanosome membrane, cornifed envelope, and mela- nosome (Fig. 2D). For MF, DEGs were only signifcantly Bioinformatic analysis of the infammatory enriched in sequence-specifc DNA binding (Fig. 2E). and proliferation phase To explore the biological characteristics of these DEGs, Volcano plot analysis was conducted to display the DEGs a PPI network was created using the STRING database. within the 3rd and 7th POD samples compared with IS in Te PPI network consisted of 59 edges and 98 nodes after GSE28914 (Fig. 4A, B). In total, 360 upregulated and 146 pairs were isolated from the major network (Fig. 3A). Te downregulated genes were identifed in 3rd POD sam- PPI network was additionally analyzed through the use of ples, and 232 upregulated and 94 downregulated genes the MCODE algorithm which indicated that the highest in 7th POD samples, compared with IS. Genes in the 3rd score cluster contained fve key genes, including Melan- and 7th POD samples were chummy in PCA. A total of A (MLANA), tyrosinase (TYR), tyrosinase Related Pro- 249 overlapping DEGs, including 85 upregulated and 164 tein 1 (TYRP1), dopachrome tautomerase (DCT), and downregulated genes were identifed in the two databases premelanosome protein (PMEL) (Fig. 3B). Additional (Fig. 4C). KEGG enrichment analysis of those key genes resulted Using the DAVID portal, functional annotation was in the identifcation of three key functional genes (TYR, performed on overlapping DEGs. A threshold of P < 0.01 TYRP1, and DCT) involved in tyrosine metabolism was selected from which the most signifcant KEGG Zhu et al. BMC Surg (2021) 21:303 Page 5 of 13

A B

Cluster1 score = 5.00

KEGG

Hub genes D Key functionalgenes C

Fig. 3 Hub clusters of DEGs in the acute wound phase analyzed by protein–protein interaction (PPI) analysis and hub genes related to KEGG pathway enrichment. A PPI interaction network. Green nodes represent downregulated genes; red nodes represent upregulated genes. B PPI molecular complex detection method (MCODE) component. The highest scoring module identifed from the PPI network using the MCODE algorithm with a score of 5.00, with fve key genes involved. C Key genes in cluster 1 were annotated in the context of the KEGG database by ClueGO, of which 3 (TYR, TYRP1, and DCT) were enriched in the tyrosine metabolism pathway, and defned as key functional genes. D The top 10 hub genes in the PPI were screened by Cytoscape plugin cytoHubba based on their connectivity degree pathways were determined. Tese DEGs were enriched lineage, rheumatoid arthritis, pertussis, and staphylococ- in cytokine–cytokine receptor interaction, legionellosis, cus aureus infection (Fig. 4D). Te enriched functions of chemokine signaling pathway, amoebiasis, TNF signal- the overlapping DEGs identifed in GO analysis for BP ing pathway, salmonella infection, hematopoietic cell were mainly those of immune response, infammatory Zhu et al. BMC Surg (2021) 21:303 Page 6 of 13

AB3rdPOD samplesvs intact samples 7th POD samplesvs intact samples

10 10

dj-P) dj-P) down-regulated down-regulated

a a not-significant not-significant up-regulated up-regulated 5

og10( og10( 5

-l

-l

0 0 -10 -5 0 5 10 -10 -5 0 5 10 log2(Fold change) log2(Fold change)

CDKEGG TNF signaling pathway -log10(Pvalue) 3rd PODvs Control Staphyococcus aureus infection 7 Salmonella infection 6 Rheumatoid arthritis 5

Pertussis 4 Legionellosis 3 Hematopoietic cell lineage Cytokine-cytokine interaction Gene number Chemokine signaling pathway 10 Amoebiasis 15 7th PODvs Control 5 6789 Fold enrichment

EF Top 10 of biological process enrichment Top 10 of cellular components enrichment G Top 10 of molecular functionsenrichment n nse io po eg es r r y se la ce vit ne r on llu a i u p ce sp act m g r y im res n xtra la ne ase t ry li e llu ra id vi to ce pt acti igna n a mb e e ty ma l s o -log10(Pvalue) xtr me ace -log10(Pvalue) op n vi el e rf d oki i c ivisi 12.5 su 12.5 n m act inflam ell- ll d is sma ll y 10.0 e e e e y c e x 10.0 a ce od ch -log10(Pvalue) c ota 7.5 f pl b 7.5 -typ kin ctivit m t o id e a 10 e y 5.0 n m re 5.0 rin cyto se ng ch wa ne ho a di 8 th 2.5 o c 2.5 se d in to pti b y 6 pa xis mp e x pe se it 4 ng a co kin le o ta v li ot l mp nd cti na m s gra me co e e pro e a ig he xi e so n n llo s ng s c ta int o si ra ta da di ed l m e ti in t cel mo s ro in mb me p r b g ia e s ch k e lex pe in ed ch e d e m o pto d m il roc m mp nd ce in e- ph p nse o co e re r b y n o lic ss e s 0 me e to vit ki utr o ce nd no c8 so n mo e b 0210 030 o la d 0210 0304050 ki cep acti e n ta pro c e N mo re ) ty ch ca tic m ro emo ivi n e Gene count h Gene count h n-1 DPH ct ge th r c R c ki A a 0 5 10 lla yn a u (N se o os cle XC rle e Gene count c bi nu re C te as ucta in d o in ct d n se och du re en et re ric mela d in te p n r k la cu co te he ou -c rric fe Fig. 4 Diferentially expressed genes (DEGs) in the infammatory phase, and Gene Ontology (GO) and KEGG pathway analysis of DEGs. A Volcano plot representing DEGs on the third-postoperative-day (3rd POD) compared with intact skin (control). B Volcano plot representing DEGs on the seventh-postoperative-day (7th POD) compared with control. C Venn diagram displaying overlapping genes that were signifcantly diferentially expressed on the 3rd and 7th POD, compared with the control. D Ten most signifcantly enriched KEGG pathways and numbers of genes involved; E Ten most signifcantly enriched GO terms for biological processes and numbers of genes involved; F Ten most signifcantly enriched GO terms for cellular components and numbers of genes involved gene numbers; G Ten most signifcantly enriched GO terms for molecular function of DEGs and numbers of genes involved, in the signifcantly diferentially expressed overlapping genes on the 3rd and 7th POD compared with the control. P < 0.05 defned as signifcantly enriched

response, chemokine-mediated signaling pathway, chem- After the pairs were isolated from the entire network, otaxis, cell chemotaxis, cell–cell signaling, neutrophil the PPI network of shared DEGs comprised 1504 edges chemotaxis, melanin biosynthetic process, cell division, and 246 nodes (Fig. 5A). Te PPI network of DEGs and collagen catabolic process (Fig. 4E). Te overlapping was further analyzed using the MCODE algorithm, in DEGs linked with CC were signifcantly enriched in extra- which two high-scoring clusters were found. In clus- cellular space, extracellular region, midbody, melano- ter 1, the score as judged by the algorithm was 34.176, some membrane, condensed chromosome kinetochore, which included 35 key genes (Fig. 5B). In cluster 2, the Ndc80 complex, condensed nuclear chromosome outer score judged by the algorithm was 18.6, for which 21 kinetochore, an integral component of the plasma mem- key genes were included (Fig. 5D). Te key genes in brane, kinesin complex, and cell surface (Fig. 4F). In each cluster identifed by KEGG pathway enrichment terms of MF, the overlapping DEGs were signifcantly were re-analyzed using the “ClueGO” and “CluePedia” enriched in chemokine activity, CXCR chemokine recep- plugins for Cytoscape software (P < 0.05). Te results tor binding, metalloendopeptidase activity, serine-type indicated that the key functional genes in cluster 1 endopeptidase activity, cytokine activity, protease bind- were checkpoint kinase 1 (CHEK1), CyclinB1 (CCNB1) ing, interleukin-1 receptor binding, endopeptidase activ- and cyclin-dependent kinases 1 (CDK1), which were ity, ferric-chelate reductase (NADPH) activity, and cupric substantially enriched in cell cycle and P53 signaling reductase activity (Fig. 4G). pathway (Fig. 5C). Te key functional genes in cluster Zhu et al. BMC Surg (2021) 21:303 Page 7 of 13

A B Cluster1 C Key functional genes Score = 34.176 in cluster 1

KEGG

DECluster2 Key functional genes Score = 18.6 Hub genes in cluster 2

F

KEGG

Fig. 5 Hub cluster of DEGs in the infammatory phase analyzed by protein–protein interaction (PPI) and hub genes related to KEGG pathway enrichment. A PPI interaction network. Green nodes represent downregulated genes; red nodes represent upregulated genes. PPI molecular complex detection method (MCODE) component. Two highest-scoring modules identifed in the PPI network using the MCODE algorithm with scores of 34.176 (cluster 1) and 18.6 (cluster 2). B In cluster 1, 35 key genes were involved, and D 21 key genes in cluster 2 were involved. Key genes in cluster 1 (C) and cluster 2 (E) were annotated in the context of the KEGG database. The relationships between these annotated terms were calculated and grouped by ClueGO to create an annotation module network. Genes with connections having more than two terms were defned as key functional genes. F The top 10 hub genes in the PPI were screened by Cytoscape plugin cytoHubba based on their connectivity degree

2 were interleukin (IL) 1B, IL6, C–C Motif Chemokine Bioinformatic analysis of the remodeling phase Ligand (CCL) 4, C-X-C motif chemokine (CXCL) 1, Volcano plot analysis was conducted to display the DEGs CXCL2, CXCL3, CXCL5, CXCL6, and CXCL10, which in the 14th and 21st POD samples compared with IS were considerably enriched in both cytokine–cytokine from GSE50425, respectively. In total, 213 upregulated receptor interaction and IL-17 signaling pathway and 122 downregulated genes were identifed in the 14th (Fig. 5E). Subsequently, the Hub genes from the PPI net- POD samples and 170 upregulated and 96 downregulated work were analyzed by using the MCC algorithm in the genes in the 21st POD samples compared with the IS CytoHuba plugin, and top 10 Hub genes were ranked (Fig. 6A, B). Since gene expression of the 14th and 21st based on the MCC scores (Fig. 5F), which were CDK1, POD samples were clustered when analyzed by PCA, a BUB1 Mitotic Checkpoint Serine/Treonine Kinase B total of 170 overlapping DEGs, including 121 upregulated (BUB1B), BUB1 Mitotic Checkpoint Serine/Treonine and 49 downregulated genes were identifed in the two Kinase (BUB1), NUF2 Component Of NDC80 Kine- databases (Fig. 6C). tochore Complex (NUF2), Cyclin A2 (CCNA2), NDC80 Using the DAVID network portal, functional anno- Kinetochore Complex Component (NDC80), Cell Divi- tation was performed on the overlapping DEGs. Using sion Cycle 20 (CDC20), Aurora Kinase A (AURKA), a threshold of P < 0.01, KEGG pathways were sig- DLG Associated Protein 5 (DLGAP5) and Ubiquitin nifcantly enriched in pathways in cancer, amoebiasis, Conjugating Enzyme E2 C (UBE2C). protein digestion and absorption, ECM-receptor inter- action, focal adhesion, PI3K–Akt signaling pathway and Zhu et al. BMC Surg (2021) 21:303 Page 8 of 13

AB 14thPOD samplesvs intact samples 21st PODsamples vs intact samples

8 8

6 6

)

dj-P down-regulated down-regulated 4 not-significant 4 not-significant

up-regulated 10(adj-P) up-regulated

g10(a

o

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CDKEGG Gene number 7 Pathways in cancer 8 Amoebiasis 9 14thPOD vs Control Protein digestion and absorption 10 11 ECM-receptor interaction

Focal adhesion -log10(Pvalue) 4.5 PI3K-Akt signaling pathway 4.0 Cytokine-cytokine receptor interaction 3.5 21st PODvs Control 3.0 Fold enrichment 2.5 EFG

Top 10 of biological process enrichment cellular components enrichment molecular functionsenrichment

ng on di si on in he gi b ty d re n ivi ll a on ar e io ct e ti ul c m a ity c za l pa -log10(Pvalue) ciu se iv -log10(Pvalue) ni ce r s al a ct 6 a ss ra la x c tid a t rg e xt lu tri p se en x o oc e e a pe a tu 5 tri pr c r m 8 do tid i g a ic is -log10(Pvalue) tra la rix n p nst in m ol s x lu at e pe o d 4 ar b ne e m 6 pe o l c in ul ta e 8 ce r ty nd ra b ity l ca og g 7 ra la en e- e tu rin iv 3 ce n gi in xt lu m 4 in lo c g ct ra e n d 6 e e lu er ta tru te a g xt ag a un 5 ac m s e s in se in e l o tr lu ne m rix a nd co w ly 4 ex u ra at tid bi g to 3 s tic b m p n in e mb u re m r r pe e d ns se eo c e e la lo ag in o as is ac i t m im lu a l r b g sp is es in sm n tr e t co to in e d n e la e en 010320 0 40 ac me c nd rix ge ot p em g tr fa bi 0 5110 5 at io n pr do s la ex th n m g o n ba o w a r an ti e c Gene count ro yc Gene count la f iza g gl lu o n n d eo e on ga o ive ot ac ti or ti er r tr la il iza d p ex gu br n t- re fi ga se le e n or n ate tiv ge e po 0 41812 6 pl ga a an es e co l br y r n or Gene count em at m m nt m e la em nf s c i ba ni ro ch Fig. 6 Diferentially expressed genes (DEGs) in the remodeling phase, and Gene Ontology (GO) and KEGG pathway analysis of DEGs. A Volcano plot representing DEGs on the fourteenth-postoperative-day (14th POD) compared with intact skin (control). B Volcano plot representing DEGs on the twenty frst-postoperative-day (21st POD) compared with the control. C Venn diagram displaying signifcantly diferentially expressed overlapping genes on the 14th and 21st POD compared with the control. D The most signifcantly enriched KEGG pathways and numbers of genes involved; E Ten most signifcantly enriched GO terms for biological processes and numbers of genes involved; F The most signifcantly enriched GO terms for cellular components and numbers of genes involved; G The most signifcantly enriched GO terms for molecular function of DEGs and numbers of genes involved in the overlapping genes that were signifcantly diferentially expressed on the 14th and 21st POD compared with control. P < 0.01 defned as signifcantly enriched

cytokine–cytokine receptor interaction (Fig. 6D). From activity, collagen binding, platelet-derived growth fac- GO analysis of top 10 BPs, the overlapping DEGs were tor binding and proteoglycan binding (Fig. 6G). mainly functionally enriched in cell adhesion, extracel- After the pairs were isolated from the complete net- lular matrix organization, collagen catabolic process, work, the PPI network of overlapping DEGs comprised response to wounding, basement membrane organiza- 201 edges and 168 nodes (Fig. 7A). Te PPI network of tion, angiogenesis, collagen fbril organization, extra- DEGs was further analyzed using the MCODE algorithm, cellular matrix disassembly, and negative regulation of with the highest score cluster containing ten key genes angiogenesis and the chronic infammatory response (Fig. 7B). To elucidate the potential pathway of the key (Fig. 6E). Diferences in the overlapping DEGs linked genes within the cluster, KEGG pathway enrichment was with CC were signifcantly enriched in the extracel- further analyzed using the “ClueGO” and “CluePedia” lular region, extracellular space, extracellular matrix, plugins in Cytoscape software (p < 0.05). Te results indi- proteinaceous extracellular matrix, endoplasmic reticu- cated that seven genes were involved in protein digestion lum lumen, basement membrane and collagen trimer and absorption, and ECM-receptor interaction. Tree (Fig. 6F), while for MF, the overlapping DEGs were sig- key functional genes, COL4A1, COL4A2, and COL6A1, nifcantly enriched in calcium ion binding, serine-type connected those two pathways (Fig. 7C). Subsequently, endopeptidase activity, extracellular matrix structural the Hub genes from the PPI network were analyzed by constituent, integrin binding, metalloendopeptidase using the MCC algorithm in the CytoHuba plugin, and top 10 Hub genes were ranked based on the MCC scores Zhu et al. BMC Surg (2021) 21:303 Page 9 of 13

B Clutser1 Score = 8.889 A

KEGG

C Key functional genes D Hub genes

Fig. 7 Hub clusters of DEGs in the remodeling phase analyzed by protein–protein interactions (PPI) and hub genes related to KEGG pathway enrichment. A PPI interaction network. Green nodes represent downregulated genes; red nodes represent upregulated genes. B PPI molecular complex detection method (MCODE) component. Highest scoring module identifed from the PPI network using the MCODE algorithm with a score of 8.889 (cluster 1), with fve key genes involved. C Key genes in the cluster were annotated in the context of the KEGG database and the relationships between these annotated terms were calculated and grouped using ClueGO to create an annotation module network. Genes with connections to more than two terms were defned as hub genes. D The top 10 hub genes in the PPI were screened by Cytoscape plugin cytoHubba based on their connectivity degree

(Fig. 7D), which were COL5A1, COL5A2, COL4A1, this represents an important area of investigation. Gene COL6A1, COL4A2, Fibronectin 1 (FN1), Nidogen 1 expression microarrays provide a comprehensive analysis (NID1), Nidogen 2 (NID2), Prolyl 4-Hydroxylase Subunit of genome-wide expression profles of clinical samples Alpha 3 (P4HA3) and COL22A1. and have been widely used to explore potential therapeu- tic targets in a variety of diseases [17, 18]. Discussion In 2012, Kristo Nuutila and colleagues used genome- Te normal WH process includes a well-orchestrated and wide expression profling to investigate time-course gene regulated process consisting of a series of events, namely expression in epidermal wounds via the use of 25 split- hemostasis, infammation, proliferation, and ECM thickness skin graft biopsies from 8 healthy adult patients remodeling [2]. Various studies have been conducted (GSE28914) [9]. Te samples were collected from graft on WH, although the specifc molecular mechanisms donor site wounds promptly before and after harvesting, occurring within each phase remain elusive. Terefore, in addition to during the healing process, on the third it is crucial that the pivotal molecules playing critical and seventh day. Furthermore, in 2013, the same team roles in the pathogenesis of WH are identifed so that collected 14 biopsies from patients undergoing split- potential therapeutic targets can be developed, and thus thickness skin graft harvesting (GSE50425), from intact Zhu et al. BMC Surg (2021) 21:303 Page 10 of 13

skin and from their wounds on the 14th and 21st post- belonging to the TYR family. DCT is an important par- operative days. However, in these studies, only the fold alog of TYRP1 [25]. Similarly, TYR, TYRP1, and DCT change in DEGs was reported and no functional analy- in this study were all signifcantly down-regulated in the sis data was evaluated. With the recent development of acute wound samples compared with intact skin. Inter- bioinformatic technology, the comprehensive analysis estingly, combined with Hub genes analysis, we found of microarray data from multiple centers has become a that DCT, TYR, TYRP1 are both Hub and keg functional focus of research attention. Tus, in the present study, genes. Tis possibly suggests that the inhibition of tyros- the gene expression profles from the laboratory of Kristo ine metabolism may play an important role in the initial Nuutila (GSE28914 and GSE50425) were downloaded stage of skin repair. Combined with the KEGG results of and reanalyzed. Using bioinformatic methods, integrated acute wound phase, we speculated that targeting TYRP1 DEGs were identifed, and GO and KEGG pathway or DCT to inhibit the tyrosine metabolic pathway in enrichment analysis performed. Analysis of PPI networks keratinocyte will become a potential therapeutic strategy and hub genes was also subsequently conducted. for alleviating or treating acute wound. Moreover, this Previous studies have reported that the process of speculate has been implemented in the recent studies WH can be categorized into three to fve distinct phases [26, 27], but there is no clinical research. that occur sequentially over time [19, 20]. In the present Te infammatory phase begins with edema due to study, reanalysis of the microarray expression data dem- increased vascular hyperpermeability, generally occur- onstrated, through the use of PCA, that the WH process ring 72 h following skin injury [28]. We analyzed over- could be divided into four phases. We hypothesized that lapping DEGs in the samples harvested on the third and the four phases were intact phase, acute wound phase, seventh day after skin injury and compared them to the infammatory and proliferation phase, and remodeling control group. Interestingly, from a total of 249 DEGs, phase (Fig. 1). DEGs were reanalyzed in each phase from the trend in expression of all overlapping DEGs remained split-thickness skin graft biopsies at diferent healing the same on the 3rd and 7th POD (data not shown). Sub- time points. sequently, the GO terms and KEGG pathways of the Te frst phase of physiological or acute WH is hemo- overlapping DEGs that were signifcantly enriched were stasis and the formation of a provisional wound matrix found to be closely associated with the immune response, occurring promptly after injury and completed within a especially the chemokine signaling pathway, as found in few hours [20]. Our KEGG enrichment results of acute previous studies [2, 29]. wound phase, principally concerning downregulated Interestingly, when the high-scoring clusters in the PPI genes, included those involved in melanogenesis path- network were analyzed using MCODE combined with way when P < 0.01 (Fig. 2B). Although the P-values of the the ClueGO algorithm, we found that the highest scor- signaling pathways regulating pluripotency of stem cells ing cluster was not immune-related, but cell cycle-related and the Hippo signaling pathway were 0.03 and 0.04, not (Fig. 5B–E). Cell proliferation is an essential feature of very much diferent (Additional fle 3). Stem cells play an immune cell activation. Te p53 signaling pathway is a essential role in WH and have been widely studied, they classic cell cycle regulatory pathway applicable in mul- also participate in accelerating the recovery of skin. Addi- tiple cell types. Te results of the present study demon- tionally, no hemostasis-related pathway was identifed in strated that CHEK, CCNB1, and CDK1 play vital roles in the KEGG enrichment analysis, possibly supporting the this phase. Furthermore, combined with Hub genes anal- notion that physiological capillary rupture following skin ysis, only CDK1 was found to be the Hub and key func- injury may not be required [21, 22]. In addition, the GO tional gene. analysis in present study demonstrated that the biologi- In addition, considerable efort has been expended over cal process of keratinocyte diferentiation was initiated recent decades to identify the impact of each cytokine on early in the acute wound phase (Fig. 2C), consistent with various parameters of WH in diverse experimental mod- previous reports that keratinocyte migration is an early els. Te results here reveal that the key functional genes event in wound re-epithelialization [23, 24]. include IL1B, IL-6, CCL4, CXCL1, CXCL2, CXCL3, Subsequent analysis clarifed the proposition that CXCL5, CXCL6, and CXCL10, which can co-express and tyrosine metabolism is the key pathway during the ini- play essential roles in cytokine–cytokine receptor inter- tial stage of WH, with three key functional genes, TYR, actions and the IL-17 signaling pathway. Of these, the TYRP1 and DCT, that are involved (Fig. 3C). TYR, which majority are chemokine receptors, except for IL-1B and converts tyrosine to dopaquinone, is the key enzyme IL-6, which are pro-infammatory cytokines. A previous involved in the rate-limiting step of tyrosine metabo- study found that IL-6 knockout mice sufered delayed lism, and TYRP1 is an important melanosomal enzyme healing. Te results of the present study reveal that Zhu et al. BMC Surg (2021) 21:303 Page 11 of 13

upregulated IL-6 expression is key in skin healing, con- remodeling [36]. Mutations in any of the three genes that sistent with phenotypes observed in IL-6 knockout mice encode the type VI collagen chains (COL6A1, COL6A2, [30]. Another study also found that IL-1B and IL-6 were and COL6A3) can cause disorders that afect muscle and elevated signifcantly during healing and could increase connective tissue, with such clinical features as muscular keratinocyte motility in wounds [31]. Furthermore, a dif- weakness, joint contractures, and laxity [37]. However, ferent study also found that low expression of CXCL1 another study with Col6a1 null mice found that Col6a1 and CXCL5, both of which are chemoattractant for neu- defciency did not result in a visible WH defect, although trophils, inhibited mouse WH [32]. it resulted in decreased tensile strength of the skin and an Te third phase of wound healing is remodeling which altered collagen fbril and basement membrane architec- begins 2 to 3 weeks after the onset of the lesion and can ture [38]. Tus, we speculated that the overexpression of last for a year or more [2]. Te core purpose of this stage COL4A1 and COL4A2 and resulting in type IV collagen is to achieve maximum tensile strength through reorgan- hyperplasia is the cause of scar tissue formation. ization, degradation, and resynthesis of the extracellular In summary, this comprehensive GEO bioinformatic matrix [2]. In the fnal stage of healing, some attempt to data re-analysis provides new insights into the molecu- restore normal tissue structure occurs, with the gradual lar pathogenesis of WH and the identifcation of poten- remodeling of the granulation tissue, resulting in scar tis- tial therapeutic targets for the treatment of each phase of sue that is less cellular and vascular, exhibiting a progres- wounds. Te present study highlights co-expression gene sive increase in the concentration of collagen fbers [33]. networks associated with WH in each phase. However, In the present study, the essential terms from the KEGG further study, such as the analysis of single-cell sequenc- and GO enrichment analysis in this phase were ECM- ing data is required to establish the precise identity of receptor interaction pathway and cell adhesion biological hub genes in the specifc cell types in WH. Moreover, process (Fig. 6), consistent with previous research. Te the main limitation of this study is the lack of experi- BP enrichment further showed that collagen, the most mental verifcation. In the future, it will be of great sig- abundant component of the basement membrane organi- nifcance to conduct a further systematic study on those zation, plays the critical role. Beside, we also found that hub and key functional genes to investigate the detailed cytokine–cytokine receptor interaction continues to play mechanisms. a role, even on the 21st POD, suggesting that infamma- tory still play an important role in this phase. Conclusions To further explore potential targets, we identifed Te present study systematically investigated multiple COL4A1, COL4A2, and COL6A1 as key functional microarray gene expression profles. Te identity of hub genes which co-express both with protein digestion and genes at diferent time points was closely associated with absorption and ECM-receptor interaction (Fig. 6C). the onset, development, and prognosis of WH. However, Together with Hub gene analysis, results showed that future studies are required to elucidate the biological COL4A1, COL4A2, and COL6A1 are both Hub and key function of these genes in WH. functional genes. Te proteins encoded by COL4A1 and COL4A2 are two of the six subunits of type IV collagen. Te protein encoded by COL6A1 is the alpha 1 subunit Abbreviations GEO: Gene Expression Omnibus; PCA: Principal component analysis; DEGs: of type VI collagen. Although 28 types of collagen have Diferentially expressed genes; GO: Gene Ontology; KEGG: Kyoto Encyclopedia been identifed, collagens I and III, comprise approxi- of Genes and Genomes; PPI: Protein–protein interaction; MCC: Maximal Clique mately 90% and 10% of the total collagen in the skin. Centrality; ECM: Extracellular matrix; AW: Acute wounds; POD: Postoperative- day; IS: Intact skin; BP: Biological processes; MF: Molecular functions; CC: Cel‑ However, less prevalent collagen types are also essen- lular components; STRING: Search Tool for the Retrieval of Interacting Genes; tial for normal skin function. Type IV collagen is a type RMA: Robust multi-array average; TYR:​ Tyrosinase; TYRP1: Tyrosinase Related in skin found primarily within the basement membrane Protein 1; DCT: Dopachrome tautomerase; MLANA: Melan-A; PMEL: Premela‑ nosome protein; CHEK1: Checkpoint kinase 1; CDK1: Cyclin-dependent kinases zone. One study demonstrated that type IV collagen 1; IL: Interleukin; CXC: C-X-C motif chemokine; FOS: Fos Proto-Oncogene; SCEL: could be observed through the use of immunolocaliza- Sciellin; KRT2: Keratin 2; WNT4: Wnt Family Member 4; BUB1B: BUB1 Mitotic tion around the cutaneous sensory nerve, blood vessels, Checkpoint Serine/Threonine Kinase B; BUB1: BUB1 Mitotic Checkpoint Serine/ Threonine Kinase; NUF2: NUF2 Component of NDC80 Kinetochore Complex; and sweat glands [34, 35]. Unfortunately, the mechanism CCNA2: Cyclin A2; NDC80: NDC80 Kinetochore Complex Component; CDC20: by which collagen IV achieves wound-healing in the skin Cell Division Cycle 20; AURKA: Aurora Kinase A; DLGAP5: DLG Associated Pro‑ remains elusive [33]. Type VI collagen is a non-fbrillar tein 5; UBE2C: Ubiquitin Conjugating Enzyme E2 C; FN1: Fibronectin 1; NID1: form expressed in many connective tissues and involved Nidogen 1; NID2: Nidogen 2; P4HA3: Prolyl 4-Hydroxylase Subunit Alpha 3. in the organization of matrix, and contributing to tissue Zhu et al. BMC Surg (2021) 21:303 Page 12 of 13

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GEOquery: a bridge between the Gene Expression Omnibus (GEO) and BioConductor. Bioinformatics. 2007;23(14):1846–7. Additional fle 4. GO enrichment analysis of DEGs in each phase. 11. Robinson MD, McCarthy DJ, Smyth GK. edgeR: a Bioconductor package Additional fle 5. STRING data for PPI network in each phase. for diferential expression analysis of digital gene expression data. Bioin‑ formatics. 2010;26(1):139–40. 12. David CC, Jacobs DJ. Principal component analysis: a method for Acknowledgements determining the essential dynamics of proteins. Methods Mol Biol. Not applicable. 2014;1084:193–226. 13. Sherman BT, da Huang W, Tan Q, Guo Y, Bour S, Liu D, Stephens R, Baseler Authors’ contributions MW, Lane HC, Lempicki RA. 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