Abstract
Scar physique refers to the abnormal repair of skin injury in some people, which may easily lead to keloid or hypertrophic scar. However, the mechanism of scar physique is still unclear. GSE108110 was obtained from the gene expression omnibus database. Differently expression genes (DEGs) between normal skin tissue of non-scar physique individuals and normal skin tissue of scar physique individuals were screened by R package “limma”. Weighted gene co-expression network analysis was performed to find highly relevant gene modules. Functional annotation of DEGs was made. Protein-protein interaction network was constructed, and the identification and analysis of hub DEGs were performed, including identification of hub DEGs associated with scar diseases, MiRNA of hub DEGs prediction, and functional annotation of miRNA. A total of 1389 up-regulate DEGs and 1672 down-regulate DEGs were screened. weighted gene co-expression network analysis analysis showed that the dendrogram and heatmap were used to quantify module similarity by correlation. The associations between clinic traits and the modules were identified based on the correlation between module and scar physique. Eight common hub genes were obtained. The comparative toxicogenomics database shows common hub genes associated with scar tissue. Gene ontology and Kyoto encyclopedia of genes and genomes analysis were significantly enriched in “fibroblast growth factor receptor signaling pathway”, “epidermal growth factor receptor signaling pathway”, “G1/S transition of mitotic cell cycle”, protein polyubiquitination”, and others. The 8 hub genes might be involved in the development of scarring and used as early diagnosis, prevention and treatment of scar physique.
Keywords: bioinformatic, hub genes, scar physique, skin, weighted gene co-expression network analysis
1. Introduction
Scar physique refers to the abnormal repair of skin injury in some people, which may easily lead to keloid or hypertrophic scar.[1] Patients with scar physique often have abnormal scars due to minor skin injuries, accompanied by abnormal proliferation of local fibroblasts.[2] In patients with scar physique, scars are most commonly located on the chest, shoulder blade and other regions, with pain and itching.[3] What is more, scars may decreased the appearance and functional status of the body, and decreased quality of life in these patients, and even induce depression and other mental diseases.[4,5] However, the mechanism of scar physique, is still unclear. Genetic factors, the mechanisms of transforming growth factor-beta, vascular endothelial growth factor, fibroblast dysplasia, inflammation, and apoptosis may be involved in scar formation.[6,7] Scar physique may have familial hereditary tendency, major histocompatibility complex genes may play an important role in it.[8] Current treatments include surgical resection, scar skin hormone injection and radiotherapy, but the scar can easily recur after excision.[9,10] Therefore, it is of great clinical significance to explore the molecular mechanism and to search for molecular targets that may be used in the early diagnosis and specific treatment of scar formation. Bioinformatics analysis technology is widely used to find molecular changes in the occurrence and development of diseases, and it is a reliable means to find diagnosis and treatment targets.
Bioinformatics analysis technology is widely used to find molecular changes in the occurrence and development of diseases, and it is a reliable means to find diagnosis and treatment targets.[11] Liu et al[12] found that many molecules were abnormally expressed in scar tissue by bioinformatics analysis, and functional analysis showed that SFRP1 may participate in the occurrence and development of scar by regulating Wnt/ β-catenin, suggesting that SFRP1 may be used as a therapeutic target for scar. In fact there are still many molecules related to the mechanism of scar formation which are worth exploring.
This study screened numerous differentially expressed genes (DEGs) between normal skin tissue of non-scar physique individuals and normal skin tissue of scar physique individuals. Simultaneously, gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) analysis were performed. And the research used multiple data algorithm to analyze the role of hub gens on scar physique.
2. Material and Methods
2.1. Data from the gene expression omnibus database
We obtained the transcriptome expression profiles GSE108110 (GPL 570, [HG-U133_Plus_2] Affymetrix Human Genome U133 Plus 2.0 Array) from the gene expression omnibus database (Table 1). The GSE108110 contained 9 scar physique samples and 9 normal samples.
Table 1.
A summary of scar physique microarray datasets from different GEO datasets.
| Series | Platform | Affymetrix GeneChip | Normal | Scar | |
|---|---|---|---|---|---|
| 1 | GSE108110 | GPL 570 | [HG-U133_Plus_2] Affymetrix human genome U133 plus 2.0 array | 9 | 9 |
GEO = gene expression omnibus.
2.2. Screening of DEGs
The DEGs were screened by R package “limma,” the cutoff criteria were P value < .05.
2.3. Weighted gene co-expression network analysis (WGCNA)
WGCNA was an analysis tool, which could describe the patterns of genes between microarray samples and finds highly relevant gene modules.
In our study, WGCNA analysis for DEGs were conducted by R package “WGCNA.”
2.4. Functional annotation of DEGs
GO analysis cis an ontology widely used in bioinformatics analysis, which contain 3 aspects of biology, biological processes, cellular components and molecular functions. KEGG analysis could provide specific pathways and linking genomic information with higher-order functional information. Gene set enrichment analysis (GSEA) (http://software.broadinstitute.org/gsea/index.jsp) is a computational method that could execute GO and KEGG analysis with a given gene list. Metascape (http://metascape.org) is an online analysis tool that could provide a comprehensive gene list annotation and analysis resource. In our study, the GO and KEGG analysis of red model DEGs were performed by GSEA and Metascpe (P < .05).
2.5. Construction and analysis of protein-protein interaction (PPI) network
search tool for the retrieval of interacting genes (http://string-db.org), an online database, could predict and provide the PPI network after importing the red model DEGs. Cytoscape is an analysis tool, which could provide biological network analysis and 2-dimensional visualization for biologists. In our study, the PPI network and hub genes were construct and analyzed by SRTING database and Cytoscape.
2.6. Screening the overlap hub DEGs
Funrich (http://www.funrich.org/) is a biological analysis software. The hub DEGs was screened by 4 algorithms. And then, the Venn plots were used to intersect the 4 groups hub DEGs to obtain the common hub DEGs.
2.7. Identification of hub DEGs associated with diseases
Comparative toxicogenomics database (CTD database, http://ctdbase.org/) is a web-based database. The relationship between gene/protein and disease could be predicted by the CTD database. In our study, the relationships between genes products and diseases were analyzed by this database.
2.8. MiRNA of hub DEGs prediction
TargetScan (www.targetscan.org) is an online database that performs predictive analysis and identifies possible mechanisms for co-regulating the expression of hundreds of genes expressed in different cell types. In our study, TargetScan was used to screen for miRNAs that regulate the hub DEGs.
2.9. Functional annotation of miRNA
DIANA-miRPath v3.0 (http://www.microrna.gr/miRPathv3) was an online analyzes tool suite dedicated to conduct functional and pathway enrichment analysis for miRNA. In our study, GO and KEGG pathway enrichment analysis were performed using miRPath V.3 (P < .05).
2.10. Statistical analysis
The analyses in this study were preform by Perl, R software (version 3.6.1) and SPSS 20.0 (IBM, IBM SPSS Statistics). The hub DEGs and their effect on scar physique based on univariate logistic proportional regression analysis was conducted by SPSS 20.0. Finally, the ROC curves were provided by SPSS 20.0 and MedCalc software.
3. Results
3.1. Identification of DEGs
The merged series contained 1389 up-regulate genes and 1672 down-regulate genes (Fig. 1A).
Figure 1.
Differential expression analysis and WGCNA analysis of the genes in the merged series. (A) Volcano plots of the genes which are different expression (DEGs) between scar physique group and normal group. (B) The cluster of patients with clinical information, red line represents patients with scar physique. (C) The lowest power for which scale independence. (D) Repeated hierarchical clustering tree of the 3061 genes. (E) The dendrogram and heatmap of genes. (F) Interactions between these modules. (G) The associations between clinic traits and the modules. (H) Heatmaps of the 222 DEGs in the Red model. WGCNA = weighted gene co-expression network analysis.
3.2. WGCNA
In our study, WGCNA analysis were conducted by R package “WGCNA.” The cluster of patients was shown in Figure 1B. As shown in Figure 1C, a power value of 8 was the lowest power for which scale independence was below 0.9, and this was selected to produce a hierarchical clustering tree of the 3061 genes (Fig. 1D). In addition, the dendrogram and heatmap were used to quantify module similarity by correlation (Fig. 1E). Interactions between these modules were then analyzed (Fig. 1F). The associations between clinic traits and the modules were identified based on the correlation between module and clinic trait (Fig. 1G).
3.3. Obtain of module DEGs
A heat map of some DEGs expressions in the red model (Fig. 1H).
3.4. Functional and pathway enrichment analysis of DEGs
The enrichment results of GO and KEGG analysis of differently expression genes performed by GSEA were mainly enriched in “regulation_of_multicellular_organismal_development,” “regulation_of_cell_proliferation,” etc. (Fig. 2A and B) (Tables 2 and 3).
Figure 2.
Gene functional enrichment analysis of the red model DEGs by GSEA and Metascape. (A) Gene ontology (GO) analyses by GSEA. (B) Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses of by GSEA. (C) Enrichment_GO-KEGG_ColorByCluster analyses by Metascape. (D) Enrichment_GO-KEGG_ColorByPValue analyses by Metascape. (E) Enrichment_heatmap_HeatmapSelected GO-KEGG analyses by Metascape. DEGs = differently expression genes, GSEA = gene set enrichment analysis.
Table 2.
GO analysis by GSEA.
| TERM | NES | Rank at max | Leading EDGE |
|---|---|---|---|
| Up-regulated | |||
| GO_catabolic_process | −0.92435 | 17 | tags = 13%, list = 8%, signal = 13% |
| GO_intracellular_signal_transduction | −0.82434 | 45 | tags = 35%, list = 20%, signal = 41% |
| GO_system_process | −0.81353 | 48 | tags = 35%, list = 22%, signal = 40% |
| GO_regulation_of_cell_differentiation | −0.4345 | 26 | tags = 12%, list = 12%, signal = 12% |
| GO_regulation_of_cell_proliferation | −0.38836 | 44 | tags = 17%, list = 20%, signal = 19% |
| Down-regulated | |||
| GO_cytoskeleton | 0.966462 | 45 | tags = 36%, list = 20%, signal = 41% |
| GO_tissue_development | 0.888578 | 49 | tags = 40%, list = 22%, signal = 47% |
| GO_response_to_external_stimulus | 0.725989 | 175 | tags = 100%, list = 79%, signal = 430% |
| GO_regulation_of_multicellular_organismal_development | 0.523468 | 59 | tags = 42%, list = 27%, signal = 52% |
| GO_organ_morphogenesis | 0.461641 | 34 | tags = 25%, list = 15%, signal = 27% |
GO = gene ontology, GSEA = gene set enrichment analysis.
Table 3.
KEGG analysis by GSEA.
| TERM | NES | Rank at max | Leading edge |
|---|---|---|---|
| Up-regulated | |||
| KEGG_JAK_stat_signaling_Pathway | −0.66757 | 103 | tags = 100%, list = 46%, signal = 186% |
| KEGG_cytokine_cytokine_receptor_interaction | −0.81086 | 124 | tags = 100%, list = 56%, signal = 223% |
| KEGG_chemokine_signaling_pathway | −0.72824 | 124 | tags = 100%, list = 56%, signal = 224% |
| KEGG_WNT_signaling_pathway | −0.97672 | 72 | tags = 100%, list = 32%, signal = 147% |
| KEGG_histidine_metabolism | −0.94927 | 62 | tags = 100%, list = 28%, signal = 138% |
| Down-regulated | |||
| KEGG_mapk_signaling_pathway | 1.133564 | 37 | tags = 100%, list = 17%, signal = 119% |
| KEGG_cell_cycle | 1.017012 | 42 | tags = 100%, list = 19%, signal = 123% |
| KEGG_cell_adhesion_molecules_CAMS | 0.838378 | 95 | tags = 100%, list = 43%, signal = 174% |
| KEGG_adipocytokine_signaling_pathway | 1.140445 | 46 | tags = 100%, list = 21%, signal = 126% |
| KEGG_PPAR_signaling_pathway | 1.140445 | 46 | tags = 100%, list = 21%, signal = 126% |
GSEA = gene set enrichment analysis, KEGG = Kyoto encyclopedia of genes and genomes.
The enrichment results of GO and KEGG analysis of red model DEGs performed by Metascape were mainly enriched in very-low-density lipoprotein particle assembly,” “transmembrane receptor protein tyrosine phosphatase signaling pathway”, regulation of potassium ion transmembrane transport,” and others (Fig. 2C and E).
3.5. Construction and analysis of protein-protein interaction network
The PPI network of DEGs was constructed by search tool for the retrieval of interacting genes online database and analyzed by Cytoscape software (Fig. 3A). Four different algorithms were employed to identify hub genes and 8 common hub genes were obtained (Fig. 3B). A summary of common hub genes was shown in Table 4. The PPI network of common hub genes was showed in Figure 3C. The heat map of common hub genes (Fig. 3D).
Figure 3.
Relationship between DEGs. (A) Protein-protein interaction (PPI) network, the more the number of connections, the larger of the protein. (B) The common hub genes identified from different algorithm. (C) The common hub genes of protein-protein interaction network. (D) Heat maps of the common hub genes. DEGs = differently expression genes.
Table 4.
A summary of hub genes.
| Symbol | Description | Funtion |
|---|---|---|
| AMPH | Amphiphysin | GO:0048488 synaptic vesicle endocytosis GO:0140238 presynaptic endocytosis GO:0036465 synaptic vesicle recycling |
| VAMP2 | Vesicle associated membrane protein 2 | GO:1903593 regulation of histamine secretion by mast cell GO:0043308 eosinophil degranulation GO:0002553 histamine secretion by mast cell |
| CACNA1D | Calcium voltage-gated channel subunit alpha1 D | GO:0086046 membrane depolarization during SA node cell action potential GO:0099703 induction of synaptic vesicle exocytosis by positive regulation of presynaptic cytosolic calcium ion concentration GO:0086015 SA node cell action potential |
| CALML4 | Calmodulin like 4 | GO:0019722 calcium-mediated signaling GO:0019932 second-messenger-mediated signaling GO:0035556 intracellular signal transduction |
| PTPRD | Protein tyrosine phosphatase receptor type D | GO:0099151 regulation of postsynaptic density assembly GO:0050775 positive regulation of dendrite morphogenesis GO:0099150 regulation of postsynaptic specialization assembly |
| CDKL5 | Cyclin dependent kinase like 5 |
GO:0045773 positive regulation of axon extension GO:0060999 positive regulation of dendritic spine development GO:0050775 positive regulation of dendrite morphogenesis |
| MTSS1 | MTSS I-BAR domain containing 1 | GO:0072160 nephron tubule epithelial cell differentiation GO:2001013 epithelial cell proliferation involved in renal tubule morphogenesis GO:0072102 glomerulus morphogenesis |
| SH3GLB2 | SH3 domain containing GRB2 like, endophilin B2 | GO:0005654 nucleoplasm GO:0031981 nuclear lumen GO:0070013 intracellular organelle lumen |
AMPH = Amphiphysin, CACNA1D = calcium-gated channel subunit alpha1 D, GO = gene ontology, PTPRD = protein tyrosine phosphatase receptor type D.
3.6. Identification of hub genes
The CTD database shows common hub genes associated with scar tissue (Fig. 4).
Figure 4.
Relationship to scar physique group and normal group related to DEGs based on the CTD database. CTD = comparative toxicogenomics database, DEGs = differently expression genes.
3.7. Prediction and functional annotation of miRNA associated with hub genes
The miRNA that regulate the hub genes were screened out with TargetScan (Table 5). GO and KEGG analysis of miRNA were performed by DIANA-miRPath. GO and KEGG analysis were significantly enriched in “fibroblast growth factor receptor signaling pathway,” “epidermal growth factor receptor signaling pathway,” “G1/S transition of mitotic cell cycle”, protein polyubiquitination,” and others (Fig. 5).
Table 5.
A summary of miRNAs that regulate hub genes.
| Gene | Predicted MiR | Gene | Predicted MiR | ||
|---|---|---|---|---|---|
| 1 | AMPH | hsa-miR-425-5p hsa-miR-1248 hsa-miR-6868-3p |
5 | PTPRD | hsa-miR-24-3p hsa-miR-135b-5p hsa-miR-135a-5p |
| 2 | VAMP2 | hsa-miR-328-3p hsa-miR-185-5p hsa-miR-4644 |
6 | CDKL5 | hsa-miR-3649 hsa-miR-3913-5p hsa-miR-3122 |
| 3 | CACNA1D | hsa-miR-489-3p hsa-miR-135b-5p hsa-miR-135a-5p |
7 | MTSS1 | hsa-miR-206 hsa-miR-1-3p hsa-miR-613 |
| 4 | CALML4 | hsa-miR-330-3p hsa-miR-33a-5p hsa-miR-33b-5p |
8 | SH3GLB2 | hsa-miR-182-5p hsa-miR-1271-5p hsa-miR-96-5p |
AMPH = Amphiphysin, CACNA1D = calcium-gated channel subunit alpha1 D, PTPRD = protein tyrosine phosphatase receptor type D.
Figure 5.
Functional and pathway enrichment analysis of miRNAs which could regulate hub genes. (A) BP analyses (B) CC analyses. (C) MF analyses. (D) KEGG analyses of the miRNAs. BP = biological processes, CC = cellular components, KEGG = Kyoto encyclopedia of genes and genomes, MF = molecular functions.
3.8. Statistical analysis
The Roc curves of common hub genes were shown in Figure 6. And the common hub genes and their effect on scar physique based on univariate logistic proportional regression analysis (Table 6).
Figure 6.
ROC curves of hub genes.
Table 6.
The hub genes and their effect on atrial fibrillation based on univariate logistic proportional regression analysis.
| GENE | OR | 95% CI | P value |
|---|---|---|---|
| AMPH | 21.277 | 0.02–0.98 | .049 |
| VAMP2 | 0.997 | 0.912–1.104 | .950 |
| CACNA1D | 62.500 | 0.001–0.296 | .006 |
| CALML4 | 1.000 | 0.156–6.420 | 1.000 |
| PTPRD | 27.778 | 0.003–0.484 | .012 |
| CDKL5 | 0.640 | 0.243–10.031 | .638 |
| MTSS1 | 0.036 | 2.067–379.247 | .012 |
| SH3GLB2 | 0.143 | 0.861–56.895 | .069 |
AMPH = Amphiphysin, CACNA1D = calcium-gated channel subunit alpha1 D, PTPRD = protein tyrosine phosphatase receptor type D.
4. Discussion
Hypertrophic scar and keloid often show abnormal proliferation of fibroblasts and abnormal expression of inflammatory factors, which is an overreaction of the body to skin injury.[13] What is more, slight skin lesions can cause scars in people with physical conditions.[14] Related studies have shown that mesenchymal stromal cells as drivers of inflammation.[15] The cell senescence in the skin microenvironment is related to inflammation. With the occurrence of aging, the body’s ability to resolve inflammation is significantly reduced, leading to an imbalance between pro-inflammatory and anti-inflammatory.[16]
Scarring can affect normal function and appearance, which in turn can affect the patient’s quality of life.[17] In addition, scars can also be life-threatening in some special cases.[18] However, the molecular mechanism of scar development has not been clarified. Carlavan found abnormal changes of inflammatory cells such as T cells and macrophages by comparing the sequencing data and immunohistochemical analysis of scar-prone and non-scar-prone patients. Further analysis suggests relationship exists between the severity of inflammation and the alteration of sebaceous gland structures.[19] What is more, Saint-Jean et al[20] found abnormal expression of IL-2, IL-10, and TLR-4 in normal skin tissues of acne scar patients, suggesting that inflammation and immune response are involved in the occurrence and development of acne scar. However, the treatment and effect of scar are still far from satisfactory. Therefore, it is of great clinical value to find out the molecular mechanism of scar. Through bioinformatics analysis, multiple abnormal genes were found in normal skin tissues of patients with and without scar tissue. We also found a number of hub genes that were significantly correlated with scar physique and scar occurrence. Among them, Amphiphysin (AMPH), calcium-gated channel subunit alpha1 D (CACNA1D), protein tyrosine phosphatase receptor type D (PTPRD), MTSS1 deserves more attention. Compared with the normal skin tissue of non-scar patients, AMPH, CACNA1D, and PTPRD were highly expressed in patients with scar physique, while MTSS1 was low.
AMPH is mainly involved in protein binding, phospholipid binding, endocytosis, synaptic vesicle endocytosis, membrane organization.[21] Abnormal expression of AMPH may be involved in the occurrence and development of a variety of diseases. Pant found that AMPH-1 may affect cell membrane transport by regulating aspartic and glutamic acid, suggesting that AMPH-1 plays an important role in maintaining intracellular circulation.[22] What is more, Chen found that knockout of amph-1 could regulate cell proliferation and apoptosis. Further analysis showed that AMPH-1 might participate in the regulation of cell cycle by activating ERK pathways.[23] Similarly, Yang proved by gene knockout that AMPH might regulate cell proliferation by regulating apoptosis and ERK pathway.[24] In addition, Jiang demonstrated through experiments that AMPH can regulate cell proliferation and apoptosis, and affecting the prognosis of patients, suggesting that AMPH may be used as a disease treatment target.[25] Similarly with the above study, we found that AMPH is highly expressed in people with scar physique. At the same time, a number of algorithms were used to verify the high expression of AMPH in scar people. We speculate that AMPH participates in the occurrence and development of scar by regulating cell cycle, affecting apoptosis, promoting fibroblast proliferation. AMPH may be used as a target for early diagnosis and specific treatment of scar in people with scar physique.
CACNA1D plays a certain role in regulation of voltage-gated calcium channel activity, calcium channel activity, metal ion binding, alpha-actinin binding, calcium ion transport, adenylate cyclase-modulating G protein-coupled receptor signaling pathway.[26] Abnormal expression of CACNA1D may be involved in the progression of a variety of diseases. Villela suggested that changes in the copy number of CACNA1D participate in the pathogenesis of alzheimer disease by regulating Ca2+ homeostasis and related pathways.[27] What is more, by sequencing children with congenital heart disease, Flanagan found that CACNA1D might be involved in the occurrence of severe hypotension and cardiac defects by regulating calcium channel function.[28] In addition, Silva showed multiple inflammation-related genes in children with down syndrome, and further analysis revealed CACNA1D might play an important role in down syndrome pathogenesis by regulating inflammatory response.[29] Similarity, we found that CACNA1D was highly expressed in people with scar physique and a large number of algorithms were used to verify the high expression of CACNA1D in scar people. We speculate that CACNA1D participates in the occurrence and development of scar by regulating calcium channels, affecting intercellular signal transduction and cell cycle, and regulating inflammation. CACNA1D may be used as a target for early diagnosis and specific treatment of people with scar physique.
PTPRD[30,31] mainly participates in protein tyrosine phosphatase activity, transmembrane receptor protein tyrosine phosphatase activity, cell adhesion molecule binding, synaptic membrane adhesion, which has few studies on scar physique. However, the abnormal expression of PTPRD can affect the normal physiological function of the body, and the mechanism of its involvement in the occurrence and development of the disease is mainly studied in the following aspects. Hsu et al[32], through sequencing analysis of patients receiving bevacizumab chemotherapy, found that PTPRD may participate in chemotherapeutic drug resistance by regulating JAK/STAT signal, and then affect the prognosis of patients. In addition, Saskin found that PTPRD copy number variants may be involved in the development of Ewing saroma.[33] Furthermore, Bae proved low expression of PTPRD in patients with gastric cancer by microarray technology and immunohistochemistry. Further analysis showed that PTPRD may affect angiogenesis by regulating CXCL8, and then affect the prognosis of patients with gastric cancer, suggesting that related molecules are potential therapeutic targets for gastric cancer.[34] We found that PTPRD was highly expressed in people with scar physique. And a mass of algorithms was used to verify the high expression of PTPRD in people with scar physique. We speculate that PTPRD participates in the development of scar by regulating intercellular signal transduction, affecting cell cycle, promoting proliferation, keratinization and fibrosis of cells.
MTSS I-BAR domain containing 1 (MTSS1) mainly participate in actin monomer binding, signaling receptor binding, plasma membrane organization, cell adhesion, negative regulation of epithelial cell proliferation.[35] The abnormal expression of MTSS1 may be involved in the development of many diseases. Taylor proved that MTSS1 can affect cell proliferation and migration by regulating cell cycle and protein phosphorylation and affect the prognosis of patients.[36] What is more, Vadakekolathu found that MTSS1 can regulate intercellular adhesion and affect intercellular signal communication, which in turn affects the prognosis of patients, suggesting that related molecules may be used as therapeutic targets.[37] In addition, Liu found that MTSS1 may affect cell proliferation and migration by regulating cell cycle and protein phosphorylation, and then affect disease progression.[38] Similarly, we found low expression of MTSS1 in people with scar physique. At the same time, a large number of algorithms were used to verify the low expression of MTSS1 in people with scar physique. We speculate that MTSS1 can induce scar by affecting cytoduction, regulating cell proliferation and promoting hyperplasia tissue and skin fibrosis. The molecular mechanism of MTSS1 involved in regulating the development of scar remains further explored.
Despite the rigorous bioinformatics analysis in this study, there are still shortcomings. This paper only carries out the big data algorithm verification, a large number of clinical samples and animal experiments should be used for comprehensive verification, so as to better understand the molecule mechanisms of scar.
5. Conclusion
In summary, bioinformatics technology could be a useful tool to explore the mechanism of the occurrence and development of scar. In addition, there are DEGs between normal skin tissues of people with scar physique and non-scar physique. These molecules may be involved in the development of scarring and used as early diagnosis, prevention and treatment of scar physique.
Author contribution
Conceptualization: Shuxian Ma.
Data curation: Xuze Li, Wenhao Wu, Yanjie Yang, Lining Huang.
Formal analysis: Xuze Li, Yanjie Yang, Lining Huang.
Investigation: Shuxian Ma, Wenhao Wu, Lining Huang, Qian Wan.
Methodology: Shuxian Ma, Xuze Li, Wenhao Wu, Yanjie Yang, Qian Wan.
Resources: Wenhao Wu, Pei Zhang, Qian Wan.
Software: Xuze Li, Wenhao Wu, Yanjie Yang.
Validation: Pei Zhang, Qian Wan.
Visualization: Pei Zhang, Qian Wan.
Writing – original draft: Shuxian Ma, Yanjie Yang, Lining Huang, Qian Wan.
Writing – review & editing: Shuxian Ma, Lining Huang.
Abbreviations:
- AMPH
- Amphiphysin
- CACNA1D
- calcium-gated channel subunit alpha1 D
- CTD
- comparative toxicogenomics database
- DEGs
- differently expression genes
- GO
- gene ontology
- GSEA
- gene set enrichment analysis
- KEGG
- Kyoto encyclopedia of genes and genomes
- PPI
- protein-protein interaction
- PTPRD
- protein tyrosine phosphatase receptor type D
- WGCNA
- weighted gene co-expression network analysis
The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.
This study was approved by the ethics committee of the Second Hospital of Hebei Medical University.
The authors have no funding and conflicts of interest to disclose.
How to cite this article: Ma S, Li X, Wu W, Zhang P, Yang Y, Huang L, Wan Q. Screening and identification of hub genes of scar physique via weighted gene co-expression network analysis. Medicine 2023;102:46(e36077).
Contributor Information
Shuxian Ma, Email: shuxianm@163.com.
Xuze Li, Email: doclixuze@163.com.
Wenhao Wu, Email: siwen200801@163.com.
Pei Zhang, Email: zhangpei1984pplive@126.com.
Yanjie Yang, Email: yangyanjie2010@163.com.
Qian Wan, Email: wannnnnnnnnn0827@163.com.
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