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Identification of Potential Hub Genes Associated with Skin Wound Healing

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Identification of Potential Hub Genes Associated with Skin Wound Healing 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
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