CXCL8 and JAK2, modulated by apigenin, are two regulators in the pathogenesis of diabetic foot ulcer

CXCL8 and JAK2, modulated by apigenin, are two regulators in the pathogenesis of diabetic foot ulcer | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article CXCL8 and JAK2, modulated by apigenin, are two regulators in the pathogenesis of diabetic foot ulcer Xuan Feng, Zhihai Xu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5754681/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background : Diabetic foot ulcer (DFU) is one of the major chronic complications of diabetes mellitus and a leading cause of disability and death. The aim of this study was to identify immune-related therapeutic targets and drugs for DFU. Methods : Two Gene Expression Omnibus datasets (GSE68183 and GSE80178) were merged, and differentially expressed genes (DEGs) were identified. Immune-related genes (IRGs) were extracted from the Immport database. Then the differentially expressed IRGs (DE-IRGs) were screened. Based on the DE-IRGs, protein-protein interaction network was constructed and hub genes were identified via Cytoscape software. An external dataset GSE134431 was used to verify the expression and diagnostic value of the hub genes. Potential compounds targeting key hub proteins, including C-X-C motif chemokine ligand 8 (CXCL8) and Janus kinase 2 (JAK2), were explored using the HIT2.0 platform and HERB database, and the binding activity between them was verified using molecular docking. Human immortalized keratinocytes (HaCaT cells) induced by high glucose (HG) were used to construct the DFU model in vitro , and cell proliferation, apoptosis and migration were detected by CCK-8, flow cytometry and Transwell assay after apigenin (API) treatment. Reverse transcriptional quantitative polymerase chain reaction and Western blot were used to detect the expression of key hub genes. Results : CXCL8, and JAK2 were identified as hub genes in the pathogenesis of DFU. API, curcumin, quercetin, resveratrol and simvastatin had good binding activity with CXCL8 and JAK2 proteins. Treatment with Apigenin could reverse HG-induced inhibition of HaCaT cell viability and migration, and reduce cell apoptosis. After HaCaT cells were induced by HG, CXCL8 mRNA was significantly up-regulated in DFU, while JAK2 mRNA was significantly down-regulated. API treatment inhibited the expression of CXCL8 and increased the expression of JAK2, p-PI3K, p-AKT and p-mTOR in HG-induced HaCaT cells. Conclusion : CXCL8 and JAK2 may be potential therapeutic targets for DFU. API can reduce HG-induced HaCaT cell injury and is expected to be a potential compound for DFU treatment. Diabetic foot ulcer Immunity Molecular docking Apigenin Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Introduction Diabetic foot (DF), a serious complication of diabetes, is a vascular disease of the lower extremities associated with the risk of infection, amputation, and death [ 1 ]. It is estimated that between 9.1 million and 26.1 million people with diabetes worldwide suffer from foot ulcers each year [ 2 ]. Diabetic foot ulcers (DFU) are one of the leading causes of non-traumatic foot amputations [ 4 ]. Due to chronic hyperglycemia, patients with diabetes are prone to nerve and vascular lesions of lower limbs, resulting in delayed wound healing after injury, and the treatment of DFU is also difficult due to the difficulty of wound repair [ 5 , 6 ]. This are despite advances in conventional treatments, including antibiotics, however, long-term use of these treatments can lead to drug resistance and even delay wound healing [ 7 – 9 ]. Therefore, it is important to study the molecular mechanism of DFU and develop new therapeutic strategies to improve the clinical outcome of DFU patients. Wound healing is an important process initiated after damage to the skin barrier and is usually mediated by growth factors and cytokines released by specific cells [ 10 ]. The healing process of DFU is affected by many intrinsic factors, including vascular problems, fibrosis, immune dysfunction, infection, ischemia, and neuropathy [ 11 ]. Inflammation and immune cell infiltration have been shown to be important factors in the development of DFU [ 12 , 13 ]. The slow onset and delayed regression of inflammatory response is one of the important reasons for the difficulty of wound healing in DFU [ 14 ]. Previous studies have found that the transcription factors FOMI1 and STAT3, which promote the survival of immune cells, are inhibited in DFU, ultimately hindering the wound healing process [ 15 ]. Therefore, exploring new immune-related biomarkers and therapeutic targets is of great significance for improving immune status and accelerating wound healing in chronic diabetes. Here, we aim to explore key immune-related therapeutic targets in DFU through integrated bioinformatics. DF-related gene expression profile datasets were mined from Gene Expression Omnibus (GEO), and hub genes were identified from differentially expressed immune-related genes (IR-DEGs). Next, additional external datasets were used to verify the expression and diagnostic value of hub genes in DFU, and to evaluate the correlation between the expression of key hub genes and immune cell infiltration. In addition, molecular docking analysis was used to verify the binding activity between key hub genes and their potential compounds. Finally, the potential functions and mechanisms of key compounds in treating DFU were explored through in vitro experiments. The flowchart of this study is summarized in Fig. 1 . Materials and methods Collection and processing of datasets Using "diabetic foot" and " homo sapiens " as keyword queries, from GEO database ( https://www.ncbi.nlm.nih.gov/geo/ ), GSE68183, GSE80178 and GSE134431 datasets were downloaded. GSE68183 (GPL16686 platform) contains 3 diabetic foot skin (DFS) tissue samples and 3 non-diabetic foot skin (NDFS) tissue samples. The GSE80178 (GPL16686 platform) contains 9 DFS and 3 NDFS tissue samples. GSE134431 (GPL18573 platform) contains ulcer samples from 13 DF patients and normal skin samples from 8 DF patients. The "inSilicoMerging" R package [ 16 ] was used to merge GSE68183 and GSE80178, and then the batch effect was removed according to the method previously reported [ 17 ]. GSE134431 dataset was used as the external validation. Extraction and differential expression analysis of immune-related genes 1793 immune-related genes (IRGs) were retrieved from the Immport database ( http://www.immport.org ). Differential analysis was performed using the "Limma" R package to identify differentially expressed genes (DEGs) between DFS and NDF ( P 1). Then the genes in the intersection of IRGs and DEGs were collected, and defined as “DE-IRGs”. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways enrichment analyses of DE-IRGs were performed using the "clusterProfiler" R package. Construction of protein-protein interaction (PPI) network and identification of hub genes STRING database ( https://string-db.org/ ) was used to construct PPI network based on the DE-IRGs. The TSV file was downloaded and saved. The TSV file was imported into the Cytoscape 3.9.0 software and the hub genes among DE-IRGs were identified using CytoHubba plugin, based on eight algorithms, including betweenness, closeness, degree, edge percolated component (EPC), maximal clique centrality (MCC), maximum neighborhood component (MNC), radiality, and stress, to assess the PPI network. Then, "UpSet" R package was used to get the intersection of the top 15 genes in the eight algorithms. In addition, GeneMANIA ( http://www.genemania.org/ ) was applied to construct the network of the hub genes. The "ggplot2" R package was used to plot the box pattern of hub gene expression. Comparisons between groups were performed using the Wilcoxon rank-sum test. The receiver operating characteristic (ROC) curve was established using the “pROC” package, the area under the ROC curve (AUC) values were calculated, and visualized using the "ggplot2" package. Immunization landscape assessment Immune cell infiltration analysis was performed using the "CIBERSORT" R package to assess the relative abundance of 22 types of immune cells in each sample in the merged dataset. Wilcoxon rank-sum test was used to compare the differences in the 22 types of immune cells between DFS and NDF. Box plots were used to visualize the immune cell composition of the two groups of samples. In addition, Spearman correlation analysis was used to evaluate the correlation between hub gene expression and immune cell proportion. Collection of hub gene-related compounds and molecular docking Herbal Ingredients' Targets (HIT) 2.0 platform ( http://hit2.badd-cao.net ) and the HERB database ( http://herb.ac.cn/ ) were applied to retrieve and collect hub gene-related compounds [ 18 , 19 ]. The "hub gene-compound" network was constructed using Cytoscape 3.9.0 software. X-ray crystal structures of hub target proteins were obtained from the Protein Data Bank (PDB) database ( https://www.rcsb.org/ ). The PDB file was opened in PyMOL software, and the protein was dehydrated and hydrogenated, and the charge was calculated. At the same time, from the PubChem ( https://www.ncbi.nlm.nih.gov/pccompound/ ), the 3D chemical structure of the chemical was saved in SDF format, and converted into PDB format with OpenBabel 3.1.1 software. The PDB files of the proteins and compounds were converted into PDBQT files with AutoDockTools v1.5.7. Finally, semi-flexible molecular docking was performed using AutoDock Vina v.1.1.2 to calculate binding affinity (kcal/mol). An affinity of less than 0 indicates that the ligand can spontaneously bind to the receptor [ 20 ]. 3D visualization of the docking results was performed using PyMOL software. Mechanism analysis of apigenin (API) in DFU treatment SwissTargetPrediction database ( http://www.‍swisstargetprediction.‍ch) , STITCH (ht tp://http://stitch.embl.de) , Comparative Toxicogenomics Database (CTD) (ht tps://ctdbase.org/; Inference Score ≥ 30) and the HIT 2.0 platform (ht tp://hit2.badd-cao.net/) were applied to jointly predicted API’s targets. In addition, with "diabetic foot ulcer" as the keyword, DFU-related disease targets were retrieved from GeneCards database (ht tps://www.genecards.org/) . The targets in the intersection were defined as API’s targets in DFU treatment. Cell culture Human immortalized keratinocyte cell line HaCaT was purchased from CoBioer (Nanjing, China). The cells were cultivated in Dulbecco's Modified Eagle's Medium (DMEM; Invitrogen, Carlsbad, CA, USA), placed in a humidified incubator at 37℃ in 5% CO 2 . HaCaT cells were randomly divided into control group (5.5 mM D-glucose, Sigma-Aldrich, St. Louis, MO, USA) and high-glucose (HG) group (25 mM D-glucose) [ 21 ]. API (MedChemExpress, Shanghai, China) was dissolved in dimethyl sulfoxide (DMSO; Beyotime, Shanghai, China), and diluted with the medium. To study the effects of API on HaCaT cells alone, the cells were treated with different concentrations of API (0, 1, 5, 10, 50, and 100 µM) for 24 h. To investigate the role of API in the in vitro DFU model, HaCaT cells were pretreated with 50 µM API for 24 h and then cultured with HG for 48 h. Cell viability assay A cell counting kit-8 (CCK-8; Beyotime, Shanghai, China) was applied to detect the viability of HaCaT cells. The cells were inoculated in 96-well plates at a density of 5×10 3 cells/well, and cultivated overnight. Then 10 µL of CCK-8 solution was added to each well and cultured at 37℃ for 2 h. Optical density of each well was measured at 450 nm wavelength with a microplate reader (Dynatech Labs, Chantilly, VA, USA), and the cell viability was calculated. Evaluation of apoptosis An Annexin V-Fluorescein Isothiocyanate (FITC)/propidium iodide (PI) apoptosis detection kit (BD Pharmingen, San Diego, CA, USA) was used to detect apoptosis. All floating and adherent HaCaT cells were collected, washed twice with phosphate buffer saline (PBS), and then re-suspended in the binding buffer. Then, the HaCaT cells were incubated with 5 µL Annexin V-FITC and 5 µL PI at room temperature, away from light for 15 min. The stained cells were analyzed by a FACScan flow cytometer and the results were analyzed by FlowJo v.10 software. Cell migration assay Cell migration assay was measured using transwell inserts (8 µM pore size, Costar, Cambridge, MA, USA). HaCaT cells were collected and suspended in serum-free medium, and the cell density was adjusted to 2×10 5 cells /mL. Then, 100 µL cell suspension was added to the upper chamber of the Transwell chamber, and 600 µL medium containing 10% FBS was added to the lower chamber, and the plates were placed in an incubator (at 37°C in 5% CO 2 ) for 24 h. Following that, the cells remaining in the upper chamber were removed with a cotton swab, and the cells which passed the filter were fixed in 4% paraformaldehyde for 15 min, air-dried at room temperature, and dyed with 0.1% crystal violet for 30 min. After that, 5 fields (×200) were randomly selected under an inverted microscope for observation and counting. Real-time quantitative polymerase chain reaction (RT-qPCR) TRIzol reagent (Invitrogen, Carlsbad, CA, USA) was used to extract total RNA from HaCaT cells. 2 µg RNA was reverse-transcribed into complementary DNA (cDNA) using a PrimeScript™RT kit (Takara, Dalian, China). RT-qPCR was then performed with a 2×SYBR Green RT-qPCR Master Mix kit (Selleckchem, Houston, TX, USA) in CFX Connect Real-Time PCR Detection System (Bio-Rad, Hercules, CA, USA). GAPDH was used as an internal parameter, and the relative gene expression was calculated using 2 −△△CT method. The PCR primers were designed and synthesized by Sangon Biotech (Shanghai, China): C-X-C motif chemokine ligand 8 (CXCL8): forward: 5'-GGTGCAGTTTTGCCAAGGAG-3' and reverse 5'-TGGTCATGAGTACAACAAACTCAC-3'; Janus kinase 2 (JAK2): forward: 5'-CGTTGAGAAGACGGCAAATGTT-3' and reverse: 5'-TGTTGCTGCCACTGCAATAC-3'; GAPDH: forward: 5'-CACTAGGCGCTCACTGTTCT-3' and reverse: 5'-TTCCCGTTCTCAGCCTTCAC-3'. Western blot HaCaT cells were cracked on ice with RIPA lysis buffer containing 1 mM phenyl methyl sulfonyl fluoride (PMSF) (Beyotime, Shanghai, China). The protein samples (20 µg per lane) were separated by electrophoresis and transferred to a polyvinylidene fluoride membrane (Millipore, Billerica, MA, USA). The membranes were blocked with 5% skim milk at room temperature for 2 h. Next, incubation with antibodies was performed. The primary antibody: anti-CXCL8 antibody (ab235584, 1:1000), anti-JAK2 antibody (ab108596, 1:1000), anti-phosphatidylinositol-3-kinase (PI3K) antibody (ab191606, 1:1000), anti-protein kinase B (AKT) antibody (ab179463, 1:1000), anti-mammalian target of rapamycin (mTOR) antibody (ab134903, 1:1000), anti-phospho (p)-AKT antibody (ab38449, 1:1000), anti-p-PI3K antibody (ab278545, 1:1000), anti-p-mTOR antibody (ab131538, 1:1000), and anti-GAPDH antibody (ab9485, 1:1000). Secondary antibody: secondary antibody (ab205718, 1:5000). The antibodies used in this study were purchased from Abcam (Shanghai, China). GAPDH was used an internal reference gene. Protein bands were detected with a BeyoECL Plus kit (Beyotime, Shanghai, China). ImageLab software version 4.1 (Bio-Rad Laboratories, Hercules, CA, USA) was used for image acquisition and density value analysis. Statistical analysis All experiments were conducted independently in triplicate. All data are expressed as mean ± standard deviation (SD). SPSS 21.0 software (IBM Corp., Armonk, NY, USA) was used for statistical analysis of the data. Comparisons between groups were made using Student's t-test or one-way analysis of variance (ANOVA) and Tukey post-hoc test. P < 0.05 was considered statistically significant. Results Identification of DE-IRGs in DFU Before GSE68183 and GSE80178 were merged, it could be observed the sample distribution of the samples in the two datasets before the removal of batch effect was quite different, indicating that there was a batch effect; after removing the batch effect, the data distribution between the two datasets tends to be consistent, indicating that the batch effect is well removed (Fig. 2 A-F). After merging and removing batch effect, an expression matrix containing 12 DFS and 6 NDFS was obtained, and 442 DEGs were identified by consensus, including 324 down-regulated genes and 118 up-regulated genes (Fig. 2 G). To obtain DE-IRGs, 1793 IRGs collected from the Immport database were cross-analyzed with 442 DEGs, and, 46 DE-IRGs were obtained, of which 21 genes were up-regulated and 25 genes were down-regulated (Fig. 2 H). GO analysis showed that these DE-IRGs were associated with 622 GO terms, including 553 biological processes (BP), 15 cell components (CC) and 54 molecular functions (MF). In terms of BP, DE-IRGs were mainly about antimicrobial humoral response, response to lipopolysaccharide, response to molecule of bacterial origin, humoral immune response, and antimicrobial humoral immune response mediated by antimicrobial peptide, etc. (Fig. 3 A); in terms of CC, DE-IRGs were mainly about in collagen-containing extracellular matrix, secretory granule lumen, cytoplasmic vesicle lumen and vesicle lumen and clathrin-coated vesicle membrane, etc. (Fig. 3 B); as for MF, DE-IRGs were mainly about receptor ligand activity, signaling receptor activator activity, growth factor receptor binding, and cytokine receptor binding and cytokine activity, etc. (Fig. 3 C). KEGG analysis showed that these DE-IRGs were enriched in 28 pathways, and the top 10 pathways were the cytokine-cytokine receptor interaction, IL-17 signaling pathway, viral protein interaction with cytokine and cytokine receptor, JAK-STAT signaling pathway, ErbB signaling pathway, PI3K-Akt signaling pathway, Toll-like receptor signaling pathway, Chemokine signaling pathway, neuroactive ligand-receptor interaction, and calcium signaling pathway (Fig. 3 D). Screening hub genes from DE-IRGs A comprehensive network analysis of 46 DE-IRGs was performed using the STRING online platform, and a PPI network with 39 nodes and 100 edges was obtained (Fig. 4 A). Then, through 8 algorithms in CytoHubba plug-in, the top 15 genes in each algorithm were listed respectively (Table 1 ). After cross-analysis, 3 hub genes were identified, including CXCL8, fibroblast growth factor 7 (FGF7), and JAK2 (Fig. 4 B). In addition, based on the GeneMANIA database, the co-expression networks and related functions of these three hub genes were analyzed. These genes displayed a complex network of gene interactions, including 77.64% physical interactions, 8.01% co-expression, 5.37% prediction, 3.63% colocalization, 2.87% genetic interactions, 1.88% pathways, and 0.60% shared protein domains (Fig. 4 C). These genes were involved in the cytokine receptor binding, neutrophil migration, granulocyte chemotaxis, myeloid leukocyte migration, and leukocyte chemotaxis (Fig. 4 C). Based on the merged dataset, the expression of hub gene in DFS and NDFS. Compared with the NDFS group, CXCL8 was significantly up-regulated in the DFS group, while FGF7 and JAK2 were significantly down-regulated (Fig. 5 A). Subsequently, differences in the expression of hub genes between DFS and NDFS were verified in the GSE134431 dataset. Compared with DFS, CXCL8 was significantly overexpressed and JAK2 was significantly lower in DFU (Fig. 5 B). However, no significant difference in FGF7 expression was observed between the two groups (Fig. 5 B). ROC analysis showed that the AUC values of CXCL8, FGF7 and JAK2 in the merged dataset were all greater than 0.8 (ranging from 0.861 to 0.931) (Fig. 5 C). However, in the GSE134431 dataset, only CXCL8 and JAK2 had AUC values greater than 0.75, and FGF7 had AUC values less than 0.6 (Fig. 5 D). These findings suggest that CXCL8 and JAK2 have better diagnostic performance than FGF7. Therefore, CXCL8 and JAK2 were identified as key hub genes. Table 1 The top 15 genes ranked in CytoHubba plug-in. No. Betweenness Closeness Degree EPC MCC MNC Radiality Stress 1 CXCL8 CXCL8 CXCL8 CXCL8 CXCL8 CXCL8 CXCL8 CXCL8 2 FGF7 CXCL10 FGF7 CXCL10 CXCL10 CXCL10 FGF7 FGF7 3 PTGFR FGF7 CXCL10 CXCL9 CXCL9 JAK2 CXCL10 JAK2 4 S100A7 CXCL9 JAK2 FGF7 CXCL11 FGF7 CXCL9 AREG 5 JAK2 JAK2 CXCL9 IL7 JAK2 CXCL9 HBEGF ANGPTL4 6 ANGPTL4 CXCL11 S100A12 JAK2 IL7 S100A12 AREG EREG 7 CYSLTR1 IL7 S100A7 CXCL11 FGF7 CXCL11 CXCL11 HBEGF 8 AREG HBEGF CXCL11 S100A12 SOCS3 IL7 IL7 S100A7 9 HBEGF AREG IL7 SOCS3 S100A12 AREG JAK2 PTGFR 10 EREG S100A7 AREG HBEGF S100A7 SOCS3 EREG IL7 11 CXCL10 S100A12 SOCS3 AREG S100A9 S100A7 SOCS3 CXCL10 12 IL7 SOCS3 HBEGF S100A7 S100A8 HBEGF S100A12 S100A12 13 ADM EREG LIFR SLPI AREG S100A9 S100A7 ADM 14 S100A12 S100A9 S100A9 MMP12 HBEGF EREG SLPI SOCS3 Immunoinfiltration analysis CIBERSORT algorithm was used to analyze the abundance of immune cells in different groups. The distribution of immune cells in various samples is shown in Fig. 6 A. Compared with NDFS group, the proportion of B cells naive, dendritic cells activated and monocytes in DFS group was higher; the proportion of macrophages M1, mast cells resting, T cells gamma delta and T cells regulatory (Tregs) was lower (Fig. 6 B). In addition, the expression levels of CXCL8 are positively correlated with plasma cells (cor = 0.469, p = 0.050), NK cells resting (cor = 0.571, p = 0.013), monocytes (cor = 0.635, p = 0.005), dendritic cells activated (cor = 0.590, p = 0.010) and eosinophils (cor = 0.542, p = 0.020) were positively correlated; and it was negatively correlated with macrophages M2 (cor = -0.490, p = 0.039) and mast cells resting (cor = -0.785, p = 0.000) (Fig. 6 C). The expression levels of JAK2 were strongly negatively correlated with plasma cells (cor = -0.681, p = 0.002), monocytes (cor = -0.573, p = 0.013) and dendritic cells activated (cor = -0.519, p = 0.027); and they were positively correlated with T cells regulatory (Tregs) (cor = 0.655, p = 0.003), macrophages M1 (cor = 0.530, p = 0.024) and mast cells resting (cor = 0.631, p = 0.005) (Fig. 6 C). Drug prediction and molecular docking 7 and 26 compounds associated with CXCL8, and 7 and 11 compounds associated with JAK2, were respectively collected from the HERB database and the HIT2.0 platform. A total of 32 compounds targeting CXCL8 and 17 compounds targeting JAK2 were obtained. The "hub gene-compound" network was then constructed via Cytoscape 3.9.0 software (Fig. 7 A). API, curcumin, quercetin, resveratrol and simvastatin had potential interactions with CXCL8 and JAK2 proteins. Their chemical structures are shown in Fig. 7 B-F. To verify the interactions between these compounds and key hub proteins, molecular docking was performed. API, curcumin, quercetin, resveratrol and simvastatin had binding energies ranging from − 5.9 to -6.6 with CXCL8 protein and from − 8.0 to -8.9 with JAK2 protein (Table 2 ; Fig. 8 A-J). Table 2 The binding affinity between key hub genes and their potential compounds. Compound CAS No. Molecular Weight (g/mol) Binding Affinity (kcal/mol) CXCL8 (PDB ID: 6N2U) JAK2 (PDB ID: 8BXH) Apigenin 520-36-5 270.2 -6.4 -8.9 Curcumin 458-37-7 368.4 -6.5 -8.0 Quercetin 117-39-5 302.2 -6.6 -8.4 Resveratrol 501-36-0 228.2 -6.0 -8.0 Simvastatin 79902-63-9 418.6 -5.9 -8.4 Functional enrichment analysis of API’s targets in DFU treatment The comprehensive binding affinity between API and key hub proteins was strongest. Therefore, the following research focused on exploring the potential therapeutic effect of API on DFU. 103, 50, 338 and 107 potential targets of API were obtained from SwissTaraetPrediction, STITCH, CTD and HIT2.0 databases, respectively, and a total of 463 API-related targets were obtained. The GeneCards database provided 3814 DFU-related disease targets, and after combining DFU-related disease targets with DEGs, a total of 4153 DFU-related targets were obtained, and then a total of 324 API’s therapeutic targets in DFU treatment were obtained (Fig. 9 A). A total of 3749 GO terms were associated with API in DFU treatment, including 3333 BP, 153 CC, and 263 MF. The top 10 BP, CC, and MF terms with the most gene counts were selected for visualization. The top 5 terms in BP were cellular response to chemical stress, response to oxidative stress, response to lipopolysaccharide, response to molecule of bacterial origin, and regulation of apoptotic signaling pathway (Fig. 9 B); the top 5 terms of CC were vesicle lumen, membrane raft, membrane microdomain, membrane region and secretory granule lumen (Fig. 9 B); the top 5 terms of MF were cytokine receptor binding, signaling receptor activator activity, receptor ligand activity, and ubiquitin-like protein ligase binding and ubiquitin protein ligase binding (Fig. 9 B). KEGG analysis showed that 193 KEGG pathways were related to the mechanism of action of API in DFU treatment. The top 15 pathways with the most gene counts were lipid and atherosclerosis, kaposi sarcoma-associated herpesvirus infection, and PI3K-Akt signaling pathway, hepatitis B, Epstein-Barr virus infection, human cytomegalovirus infection, measles, human papillomavirus infection, human T-cell leukemia virus 1 infection, influenza A, MAPK signaling pathway, pathways of neurodegeneration - multiple diseases, shigellosis, hepatitis C and alzheimer disease (Fig. 9 C). API alleviated the injury of HaCaT cells stimulated by HG. To investigate the potential therapeutic effect of API on DFU, HaCaT cells with different concentrations of API (0, 1, 5, 10, 50, and 100 µM) for 24 h. No significant effect on cell viability when HaCaT cells were stimulated by API concentrations below 100 µM (Fig. 10 A). HG stimulation significantly inhibited HaCaT cell proliferation compared to the control group, while API treatment reversed this effect (Fig. 10 B). Flow cytometry showed that HG resulted in increased apoptosis of HaCaT cells, while API treatment attenuated this effect (Fig. 10 C). HG treatment inhibited HaCaT cell migration, while API treatment enhanced HaCaT cell migration (Fig. 10 D). In addition, we analyzed the effects of API treatment on the expression of key hub genes. RT-qPCR and Western blot showed that compared with the control group, the mRNA and protein expression levels of CXCL8 in HG-treated HaCaT cells were significantly increased, while those of JAK2 were significantly decreased; however, after API intervention, a significant decrease in CXCL8 expression was observed, while a significant increase in JAK2 expression was observed (Figs. 10 E &F ). In order to verify whether the mechanism of action of API treatment for DFU was related to the PI3K-Akt signaling pathway, we analyzed the effect of API treatment on the expression of related proteins in the PI3K-Akt signaling pathway under HG treatment. As shown, HG stimulation significantly reduced protein levels of p-PI3K, p-AKT, and p-mTOR in HaCaT cells, while API treatment significantly reversed these effects (Fig. 10 G). Discussion CXCL8, also known as interleukin 8 (IL-8), is a member of the chemokine family that promotes chemotaxis by activating CXCR1 or CXCR2 receptors on target cells [ 22 , 23 ]. During the inflammatory process, CXCL8 recruits white blood cells to the site of infection, ultimately leading to increased neutrophil infiltration [ 24 , 25 ]. Serum CXCL8 is elevated in DFU patients [ 26 ]. Consistent with the results of this study, the expression of CXCL8 was also significantly increased in wound exudates of DFU patients [ 27 ]. JAK2 is a non-receptor tyrosine protein kinase that is involved in intracellular signaling of multiple cytokine receptors, a pathway known as the JAK2-STAT3 pathway. This pathway plays a key role in biological processes such as cell proliferation, differentiation, apoptosis and immune regulation [ 28 , 29 ]. Exosomes derived from platelet-rich plasma have been reported to promote diabetic wound healing by activating the JAK2/STAT3 pathway [ 30 ], which suggesting that JAK2 plays an important role in the pathological process of DFU wound healing. Our study confirmed that CXCL8 and JAK2 were immune-related regulator in DFU pathogenesis. Their expression is related to the infiltrating abundance of plasma cells, monocytes, activated dendritic cells and resting mast cells. Increased abundance of mast cells inhibits the healing process of DFU [ 31 ]. Additionally, it is reported that impaired wound healing in diabetic patients is associated with increased monocyte/macrophage count in the lesion [ 32 ]. Our data suggest that balancing CXCL8 and JAK2 expression is helpful to modulate the immune microenvironment in skin lesion of diabetics. Based on database prediction and molecular docking, it was found that curcumin, quercetin, resveratrol, simvastatin and API could stably bind to CXCL8 and JAK2 proteins through hydrogen bonding. Curcumin has been reported to promote DFU wound healing by inhibiting microRNA-152-3p and activating the FBN1/TGF-β pathway [ 33 ]. Quercetin accelerates diabetic wound healing by reducing inflammatory response and activating the PI3K-Akt signaling pathway [ 34 ]. Resveratrol promotes wound healing in diabetic mice by inhibiting Notch pathway [ 35 ]. Simvastatin is an insoluble oral cholesterol-lowering drug that has been shown to accelerate diabetic wound healing [ 36 ]. API is a natural flavonoid with antioxidant, anti-inflammatory, anti-cancer and neuroprotective effects [ 37 ]. It has been reported that API can promote M2 polarization of macrophages by up-regulating miR-21 expression, thereby accelerating wound healing in diabetic mice [ 38 ]. These findings suggest that these five compounds are beneficial for diabetic wounds and may be potential therapeutic candidates for DFU, which are consistent with the findings of the present study. Wound healing in DFU involves complex regulatory mechanisms, such as cell migration and proliferation, and the secretion of vascular growth factors [ 39 ]. Keratinocytes are the main cell types in the epidermis, and play a key role in promoting wound re-epithelialization and the transition from inflammation to proliferation stage during wound healing [ 40 , 41 ]. However, HG can impair the normal physiological function of keratinocytes, resulting in impaired wound healing [ 42 ]. Therefore, the reduction of HG-induced keratinocyte injury is conducive to DFU healing [ 39 ]. In this study, we observed that the proliferation and migration ability of HaCaT cells were significantly reduced under HG stimulation, while apoptosis was significantly increased, which was consistent with previous studies [ 21 , 43 ]. However, in the present work, it was observed that API treatment significantly reversed these effects. In addition, API could inhibit the mRNA and protein expression levels of CXCL8 in HG-induced HaCaT cells, and up-regulate the mRNA and protein expression levels of JAK2. It is worth noting that KEGG enrichment analysis showed that the targets of API was significantly enriched in the PI3K-Akt pathway. The PI3K-Akt signaling pathway is an indispensable part of the wound healing process in diabetes mellitus, especially in regulating inflammation, cell proliferation and tissue regeneration [ 34 , 44 ]. Plasma endothelial cell-derived extracellular vesicles have been reported to promote diabetic wound healing by regulating the YAP and PI3K/Akt/mTOR pathways [ 45 ]. Insulin promotes phenotypic transformation of macrophages by activating PI3K-Akt and PPAR-γ signaling pathways, thereby improving diabetic wound healing [ 46 ]. This study found that API treatment increased the expression levels of p-PI3K, p-AKT, and p-mTOR in HG-induced HaCaT cells. This suggests that the mechanism of action of API in the treatment of DFU may be related to the activation of PI3K-Akt signaling pathway, providing new explanation to the mechanism by which API promotes the healing of DFU. Inevitably, there are some limitations to the study. Firstly, the clinical data used in this study came from public databases, and the available clinical information for the samples was incomplete, and analysis of the association between hub genes and disease severity was hindered. Secondly, the mechanism of action of API in treating DFU wound healing needs to be validated with in vivo data. Conclusion This study identifies CXCL8 and JAK2 as key therapeutic targets for DFU, and curcumin, quercetin, resveratrol, simvastatin and API are promising candidate drugs. Additionally, we report that API promoted the proliferation and migration of keratinocytes cells by regulating the PI3K-Akt pathway. Collectively, our study provides useful clues for the clinical treatment of DFU. Declarations Ethics approval and consent to participate Not applicable. Consent for publication All authors have given their consent for publication. Competing interests The authors declare that they have no competing interests. Funding This study is financially supported by Guiyang Huimei Qingcheng Medical Beauty Hospital. Author Contribution Conceived and designed the experiments: Z.H.X.; Performed the experiments: X.F. and Z.H.X.; Analyzed the data: X.F.; Wrote the paper: X.F. and Z.H.X.. 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Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5754681","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":398810031,"identity":"c03b52f3-2baa-4826-bcef-574ce6b228c7","order_by":0,"name":"Xuan Feng","email":"","orcid":"","institution":"Guiyang Huimei Qingcheng Medical Beauty Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xuan","middleName":"","lastName":"Feng","suffix":""},{"id":398810033,"identity":"f2ed1f03-7f02-434d-815f-d1b3675c035f","order_by":1,"name":"Zhihai Xu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAElEQVRIie3PsUrDQBzH8f9xEJfDrv9wEX2EfyiUDoG8Sm5JlyiOHRwihesSne9JXE0J2CUPEHAJCE4d6iIZingOThKbbqXcd7nl9znuAFyuIyw2TANQBSPO19stTMVewn6Jv9QpM4BDCHj2SIHqesIF4P6HcbnQbXcbqbxJJm/RHQbUXJfQzZ96iReslmFBqO5NMhtnLyiouUlYUb/2EoFK2xmqBSalzLwfkhFnup+gJf6OIgtVLqdfA4i9X0tBqSpE5Ummh5KAqrE50zx8eETh1xta/feX2Mze/c2uuniuRh9t9xnF5+ssbLt5P/nbVQlQHrC3XeaH7V0ul+v0+wZFVFbEywORdAAAAABJRU5ErkJggg==","orcid":"","institution":"Guiyang Lindong Hospital","correspondingAuthor":true,"prefix":"","firstName":"Zhihai","middleName":"","lastName":"Xu","suffix":""}],"badges":[],"createdAt":"2025-01-03 02:23:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5754681/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5754681/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":73418804,"identity":"acb4cf8f-5648-4778-bb55-1cf4faaa6553","added_by":"auto","created_at":"2025-01-09 17:51:11","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":932535,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eWorkflow flow of the present study\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-5754681/v1/5683fd5143cdcd05ee2a6dee.png"},{"id":73418802,"identity":"f74d29fb-e333-4ffd-901d-03ee20ab1861","added_by":"auto","created_at":"2025-01-09 17:51:11","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":4543152,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eData processing and identification of differentially expressed genes (DEGs)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA\u0026amp;B. Data distribution before (A) and after (B) removal of batch effect.\u003c/p\u003e\n\u003cp\u003eC\u0026amp;D. Density distribution before (C) and after (D) removal of batch effect.\u003c/p\u003e\n\u003cp\u003eE\u0026amp;F. UMAP distribution before (E) and after (F) removal of batch effect.\u003c/p\u003e\n\u003cp\u003eG. Differential expression analysis of the genes in the merged dataset. Red means up-regulated genes, blue means down-regulated genes.\u003c/p\u003e\n\u003cp\u003eH. Venn diagram of immune-related genes (IRGs) and up-regulated/down-regulated DEGs.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-5754681/v1/e6c8017f7d5edd1d6970b58a.png"},{"id":73418816,"identity":"c35235e8-8db4-4a4f-a632-494463e050e3","added_by":"auto","created_at":"2025-01-09 17:51:12","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2978270,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFunctional enrichment analysis of differentially expressed IRGs (DE-IRGs)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA-D. The bubble maps of the enrichment analysis of GO biological process (A), cell component (B), molecular function (C), and KEGG pathway (D) of DE-IRGs. The bubble size represents count, and the bubble color represents the p-value. \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05 was the threshold of significance.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-5754681/v1/579a30fbdf920f4432b24e4b.png"},{"id":73418803,"identity":"004d4208-455d-45e0-b923-7979f0d5d0b6","added_by":"auto","created_at":"2025-01-09 17:51:11","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":17441271,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIdentification of hub genes involved in DFU pathogenesis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA. PPI network of the DE-IRGs. Nodes represent proteins, and edges represent protein-protein interactions.\u003c/p\u003e\n\u003cp\u003eB. Eight topological analysis methods were used to screen hub genes.\u003c/p\u003e\n\u003cp\u003eC. The gene interaction network and potential function of hub genes were analyzed by GeneMANIA.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-5754681/v1/9fad78b34425bf12d54a2ebf.png"},{"id":73419614,"identity":"858717f8-fdaf-42c2-84e1-cfd6f9d6bf72","added_by":"auto","created_at":"2025-01-09 17:59:11","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2662338,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExpression characteristics and diagnostic value of hub genes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA. The expression of hub gene in diabetic foot skin (DFS) and non-diabetic foot skin (NDF) in merged dataset was analyzed by Willcoxon rank-sum test.\u003c/p\u003e\n\u003cp\u003eB. The expression of hub gene in diabetic foot ulcer (DFU) and DFS in GSE134431 dataset was analyzed by Willcoxon rank-sum test.\u003c/p\u003e\n\u003cp\u003eC\u0026amp;D. ROC of hub gene in the merged dataset (C) and GSE134431 dataset (D).\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-5754681/v1/c0f83efe9cd3520412c735e6.png"},{"id":73418800,"identity":"76002eff-1bd4-4658-b648-36b2b593c9e5","added_by":"auto","created_at":"2025-01-09 17:51:11","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":3855635,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eImmunoinfiltration analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA. The proportion of 22 types of immune cell in the DFS group and NDFS group.\u003c/p\u003e\n\u003cp\u003eB. The box plots show the difference in infiltration between DFS and NDFS for 22 types of immune cells. *\u003cem\u003eP\u003c/em\u003e\u0026lt; 0.05, **\u003cem\u003eP\u003c/em\u003e\u0026lt;0.01.\u003c/p\u003e\n\u003cp\u003eC. Spearman correlation between the expression levels of CXCL8 and JAK2 and the abundance of 22 kinds of immune cells. Orange indicates positive correlation and blue indicates negative correlation. *\u003cem\u003eP\u003c/em\u003e\u0026lt; 0.05, **\u003cem\u003eP\u003c/em\u003e\u0026lt;0.01, ***\u003cem\u003eP\u003c/em\u003e\u0026lt;0.001.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-5754681/v1/6be5fdbb1794b6a96d638017.png"},{"id":73418811,"identity":"f236fb2f-0862-4442-a257-a9a2b4244445","added_by":"auto","created_at":"2025-01-09 17:51:12","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":4144801,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConstruction of the \"hub gene-compound\" network\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA. The \"hub gene-compound\" network was constructed using Cytoscape 3.9.0 software. Green circular nodes represent the hub genes CXCL8 and JAK2, and light blue triangular nodes represent compounds.\u003c/p\u003e\n\u003cp\u003eB-F. Chemical structures of Apigenin (API; B), curcumin (C), quercetin (D), resveratrol (E) and simvastatin (F).\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-5754681/v1/5f167b2343b208459065422e.png"},{"id":73418838,"identity":"2cd390b1-cd73-4fd1-b849-07c686a5c11b","added_by":"auto","created_at":"2025-01-09 17:51:13","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":25988161,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eResults of molecular docking\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA-J. Molecular docking diagrams of apigenin (API), curcumin, quercetin, resveratrol and simvastatin with CXCL8 protein (A-E) and JAK2 protein (F-J). Purple indicates amino acid residues surrounding the binding bag, light blue indicates compounds, yelloworange indicates proteins, and yellow dashed lines indicate hydrogen bonding.\u003c/p\u003e","description":"","filename":"Figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-5754681/v1/22f5811482fbabe0d0721316.png"},{"id":73419616,"identity":"ba31ce30-fc18-4d57-b999-edae0bca9c78","added_by":"auto","created_at":"2025-01-09 17:59:12","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":3792559,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eScreening and analysis of DFU targets treated by API\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA. The Venn diagram of the targets of apigenin (API) and DFU-related targets.\u003c/p\u003e\n\u003cp\u003eB. The histogram of GO analysis of the targets of API in DFU treatment. Biological process (BP) is marked by dark cyan, cellular component (CC) is marked by sienna and molecular function (MF) is marked by steel blue.\u003c/p\u003e\n\u003cp\u003eC. Bubble map of KEGG pathway enrichment analysis of the targets of API in DFU treatment. The bubble size represents count, and the bubble color represents the p-value.\u003c/p\u003e","description":"","filename":"Figure9.png","url":"https://assets-eu.researchsquare.com/files/rs-5754681/v1/45b7d596bdbb4f1f1e092c20.png"},{"id":73418822,"identity":"6fedc913-d45c-46e4-86fb-2caedc166987","added_by":"auto","created_at":"2025-01-09 17:51:12","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":12174420,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAPI alleviates HaCaT cell injury induced by high glucose (HG).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA. After the HaCaT cells were treated with different concentrations of API (0, 1, 5, 10, 50 and 100 μM) for 24 h, the cell viability was detected by CCK-8 method.\u003c/p\u003e\n\u003cp\u003eB. HaCaT cells were treated with 100 μM API for 24 h and then stimulated with 30 mM D-glucose for 48 h, and then the cell viability was detected by CCK-8 method.\u003c/p\u003e\n\u003cp\u003eC. The apoptosis level of HaCaT cells was evaluated by flow cytometry.\u003c/p\u003e\n\u003cp\u003eD. Transwell assay was used to detect the migration ability of HaCaT cells.\u003c/p\u003e\n\u003cp\u003eE. The mRNA expression levels of CXCL8 and JAK2 in HaCaT cells were detected by RT-qPCR.\u003c/p\u003e\n\u003cp\u003eF\u0026amp;G. The protein levels of CXCL8, JAK2, p-PI3K, p-AKT and p-mTOR in HaCaT cells were detected by Western blot.\u003c/p\u003e\n\u003cp\u003e* and *** represent \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05 and \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001, respectively.\u003c/p\u003e","description":"","filename":"Figure10.png","url":"https://assets-eu.researchsquare.com/files/rs-5754681/v1/7b10c5ddf11b25170983fbe7.png"},{"id":75518331,"identity":"efb853e6-10b1-40fd-9c99-f527b904834c","added_by":"auto","created_at":"2025-02-05 11:48:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":76061478,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5754681/v1/49f0085f-da0e-4f14-906f-31c4551a4ed9.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"CXCL8 and JAK2, modulated by apigenin, are two regulators in the pathogenesis of diabetic foot ulcer","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDiabetic foot (DF), a serious complication of diabetes, is a vascular disease of the lower extremities associated with the risk of infection, amputation, and death [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. It is estimated that between 9.1\u0026nbsp;million and 26.1\u0026nbsp;million people with diabetes worldwide suffer from foot ulcers each year [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Diabetic foot ulcers (DFU) are one of the leading causes of non-traumatic foot amputations [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Due to chronic hyperglycemia, patients with diabetes are prone to nerve and vascular lesions of lower limbs, resulting in delayed wound healing after injury, and the treatment of DFU is also difficult due to the difficulty of wound repair [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. This are despite advances in conventional treatments, including antibiotics, however, long-term use of these treatments can lead to drug resistance and even delay wound healing [\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Therefore, it is important to study the molecular mechanism of DFU and develop new therapeutic strategies to improve the clinical outcome of DFU patients.\u003c/p\u003e \u003cp\u003eWound healing is an important process initiated after damage to the skin barrier and is usually mediated by growth factors and cytokines released by specific cells [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The healing process of DFU is affected by many intrinsic factors, including vascular problems, fibrosis, immune dysfunction, infection, ischemia, and neuropathy [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Inflammation and immune cell infiltration have been shown to be important factors in the development of DFU [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The slow onset and delayed regression of inflammatory response is one of the important reasons for the difficulty of wound healing in DFU [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Previous studies have found that the transcription factors FOMI1 and STAT3, which promote the survival of immune cells, are inhibited in DFU, ultimately hindering the wound healing process [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Therefore, exploring new immune-related biomarkers and therapeutic targets is of great significance for improving immune status and accelerating wound healing in chronic diabetes.\u003c/p\u003e \u003cp\u003eHere, we aim to explore key immune-related therapeutic targets in DFU through integrated bioinformatics. DF-related gene expression profile datasets were mined from Gene Expression Omnibus (GEO), and hub genes were identified from differentially expressed immune-related genes (IR-DEGs). Next, additional external datasets were used to verify the expression and diagnostic value of hub genes in DFU, and to evaluate the correlation between the expression of key hub genes and immune cell infiltration. In addition, molecular docking analysis was used to verify the binding activity between key hub genes and their potential compounds. Finally, the potential functions and mechanisms of key compounds in treating DFU were explored through \u003cem\u003ein vitro\u003c/em\u003e experiments. The flowchart of this study is summarized in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eCollection and processing of datasets\u003c/h2\u003e \u003cp\u003eUsing \"diabetic foot\" and \"\u003cem\u003ehomo sapiens\u003c/em\u003e\" as keyword queries, from GEO database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/geo/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), GSE68183, GSE80178 and GSE134431 datasets were downloaded. GSE68183 (GPL16686 platform) contains 3 diabetic foot skin (DFS) tissue samples and 3 non-diabetic foot skin (NDFS) tissue samples. The GSE80178 (GPL16686 platform) contains 9 DFS and 3 NDFS tissue samples. GSE134431 (GPL18573 platform) contains ulcer samples from 13 DF patients and normal skin samples from 8 DF patients. The \"inSilicoMerging\" R package [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] was used to merge GSE68183 and GSE80178, and then the batch effect was removed according to the method previously reported [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. GSE134431 dataset was used as the external validation.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eExtraction and differential expression analysis of immune-related genes\u003c/h3\u003e\n\u003cp\u003e1793 immune-related genes (IRGs) were retrieved from the Immport database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.immport.org\u003c/span\u003e\u003cspan address=\"http://www.immport.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Differential analysis was performed using the \"Limma\" R package to identify differentially expressed genes (DEGs) between DFS and NDF (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and |log\u003csub\u003e2\u003c/sub\u003e fold change|\u0026gt;1). Then the genes in the intersection of IRGs and DEGs were collected, and defined as \u0026ldquo;DE-IRGs\u0026rdquo;. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways enrichment analyses of DE-IRGs were performed using the \"clusterProfiler\" R package.\u003c/p\u003e\n\u003ch3\u003eConstruction of protein-protein interaction (PPI) network and identification of hub genes\u003c/h3\u003e\n\u003cp\u003eSTRING database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://string-db.org/\u003c/span\u003e\u003cspan address=\"https://string-db.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used to construct PPI network based on the DE-IRGs. The TSV file was downloaded and saved. The TSV file was imported into the Cytoscape 3.9.0 software and the hub genes among DE-IRGs were identified using CytoHubba plugin, based on eight algorithms, including betweenness, closeness, degree, edge percolated component (EPC), maximal clique centrality (MCC), maximum neighborhood component (MNC), radiality, and stress, to assess the PPI network. Then, \"UpSet\" R package was used to get the intersection of the top 15 genes in the eight algorithms. In addition, GeneMANIA (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.genemania.org/\u003c/span\u003e\u003cspan address=\"http://www.genemania.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was applied to construct the network of the hub genes. The \"ggplot2\" R package was used to plot the box pattern of hub gene expression. Comparisons between groups were performed using the Wilcoxon rank-sum test. The receiver operating characteristic (ROC) curve was established using the \u0026ldquo;pROC\u0026rdquo; package, the area under the ROC curve (AUC) values were calculated, and visualized using the \"ggplot2\" package.\u003c/p\u003e\n\u003ch3\u003eImmunization landscape assessment\u003c/h3\u003e\n\u003cp\u003eImmune cell infiltration analysis was performed using the \"CIBERSORT\" R package to assess the relative abundance of 22 types of immune cells in each sample in the merged dataset. Wilcoxon rank-sum test was used to compare the differences in the 22 types of immune cells between DFS and NDF. Box plots were used to visualize the immune cell composition of the two groups of samples. In addition, Spearman correlation analysis was used to evaluate the correlation between hub gene expression and immune cell proportion.\u003c/p\u003e\n\u003ch3\u003eCollection of hub gene-related compounds and molecular docking\u003c/h3\u003e\n\u003cp\u003eHerbal Ingredients' Targets (HIT) 2.0 platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://hit2.badd-cao.net\u003c/span\u003e\u003cspan address=\"http://hit2.badd-cao.net\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and the HERB database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://herb.ac.cn/\u003c/span\u003e\u003cspan address=\"http://herb.ac.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) were applied to retrieve and collect hub gene-related compounds [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The \"hub gene-compound\" network was constructed using Cytoscape 3.9.0 software. X-ray crystal structures of hub target proteins were obtained from the Protein Data Bank (PDB) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.rcsb.org/\u003c/span\u003e\u003cspan address=\"https://www.rcsb.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The PDB file was opened in PyMOL software, and the protein was dehydrated and hydrogenated, and the charge was calculated. At the same time, from the PubChem (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/pccompound/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/pccompound/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), the 3D chemical structure of the chemical was saved in SDF format, and converted into PDB format with OpenBabel 3.1.1 software. The PDB files of the proteins and compounds were converted into PDBQT files with AutoDockTools v1.5.7. Finally, semi-flexible molecular docking was performed using AutoDock Vina v.1.1.2 to calculate binding affinity (kcal/mol). An affinity of less than 0 indicates that the ligand can spontaneously bind to the receptor [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. 3D visualization of the docking results was performed using PyMOL software.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eMechanism analysis of apigenin (API) in DFU treatment\u003c/h2\u003e \u003cp\u003eSwissTargetPrediction database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.\u0026zwj;swisstargetprediction.\u0026zwj;ch)\u003c/span\u003e\u003cspan address=\"http://www.\u0026zwj;swisstargetprediction.\u0026zwj;ch)\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, STITCH (ht\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003etp://http://stitch.embl.de)\u003c/span\u003e\u003cspan address=\"http://tp://http://stitch.embl.de)\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, Comparative Toxicogenomics Database (CTD) (ht\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003etps://ctdbase.org/;\u003c/span\u003e\u003cspan address=\"http://tps://ctdbase.org/;\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e Inference Score\u0026thinsp;\u0026ge;\u0026thinsp;30) and the HIT 2.0 platform (ht\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003etp://hit2.badd-cao.net/)\u003c/span\u003e\u003cspan address=\"http://tp://hit2.badd-cao.net/)\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e were applied to jointly predicted API\u0026rsquo;s targets. In addition, with \"diabetic foot ulcer\" as the keyword, DFU-related disease targets were retrieved from GeneCards database (ht\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003etps://www.genecards.org/)\u003c/span\u003e\u003cspan address=\"http://tps://www.genecards.org/)\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. The targets in the intersection were defined as API\u0026rsquo;s targets in DFU treatment.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eCell culture\u003c/h3\u003e\n\u003cp\u003eHuman immortalized keratinocyte cell line HaCaT was purchased from CoBioer (Nanjing, China). The cells were cultivated in Dulbecco's Modified Eagle's Medium (DMEM; Invitrogen, Carlsbad, CA, USA), placed in a humidified incubator at 37℃ in 5% CO\u003csub\u003e2\u003c/sub\u003e. HaCaT cells were randomly divided into control group (5.5 mM D-glucose, Sigma-Aldrich, St. Louis, MO, USA) and high-glucose (HG) group (25 mM D-glucose) [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. API (MedChemExpress, Shanghai, China) was dissolved in dimethyl sulfoxide (DMSO; Beyotime, Shanghai, China), and diluted with the medium. To study the effects of API on HaCaT cells alone, the cells were treated with different concentrations of API (0, 1, 5, 10, 50, and 100 \u0026micro;M) for 24 h. To investigate the role of API in the \u003cem\u003ein vitro\u003c/em\u003e DFU model, HaCaT cells were pretreated with 50 \u0026micro;M API for 24 h and then cultured with HG for 48 h.\u003c/p\u003e\n\u003ch3\u003eCell viability assay\u003c/h3\u003e\n\u003cp\u003eA cell counting kit-8 (CCK-8; Beyotime, Shanghai, China) was applied to detect the viability of HaCaT cells. The cells were inoculated in 96-well plates at a density of 5\u0026times;10\u003csup\u003e3\u003c/sup\u003e cells/well, and cultivated overnight. Then 10 \u0026micro;L of CCK-8 solution was added to each well and cultured at 37℃ for 2 h. Optical density of each well was measured at 450 nm wavelength with a microplate reader (Dynatech Labs, Chantilly, VA, USA), and the cell viability was calculated.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eEvaluation of apoptosis\u003c/h2\u003e \u003cp\u003eAn Annexin V-Fluorescein Isothiocyanate (FITC)/propidium iodide (PI) apoptosis detection kit (BD Pharmingen, San Diego, CA, USA) was used to detect apoptosis. All floating and adherent HaCaT cells were collected, washed twice with phosphate buffer saline (PBS), and then re-suspended in the binding buffer. Then, the HaCaT cells were incubated with 5 \u0026micro;L Annexin V-FITC and 5 \u0026micro;L PI at room temperature, away from light for 15 min. The stained cells were analyzed by a FACScan flow cytometer and the results were analyzed by FlowJo v.10 software.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eCell migration assay\u003c/h2\u003e \u003cp\u003eCell migration assay was measured using transwell inserts (8 \u0026micro;M pore size, Costar, Cambridge, MA, USA). HaCaT cells were collected and suspended in serum-free medium, and the cell density was adjusted to 2\u0026times;10\u003csup\u003e5\u003c/sup\u003e cells /mL. Then, 100 \u0026micro;L cell suspension was added to the upper chamber of the Transwell chamber, and 600 \u0026micro;L medium containing 10% FBS was added to the lower chamber, and the plates were placed in an incubator (at 37\u0026deg;C in 5% CO\u003csub\u003e2\u003c/sub\u003e) for 24 h. Following that, the cells remaining in the upper chamber were removed with a cotton swab, and the cells which passed the filter were fixed in 4% paraformaldehyde for 15 min, air-dried at room temperature, and dyed with 0.1% crystal violet for 30 min. After that, 5 fields (\u0026times;200) were randomly selected under an inverted microscope for observation and counting.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eReal-time quantitative polymerase chain reaction (RT-qPCR)\u003c/h2\u003e \u003cp\u003eTRIzol reagent (Invitrogen, Carlsbad, CA, USA) was used to extract total RNA from HaCaT cells. 2 \u0026micro;g RNA was reverse-transcribed into complementary DNA (cDNA) using a PrimeScript\u0026trade;RT kit (Takara, Dalian, China). RT-qPCR was then performed with a 2\u0026times;SYBR Green RT-qPCR Master Mix kit (Selleckchem, Houston, TX, USA) in CFX Connect Real-Time PCR Detection System (Bio-Rad, Hercules, CA, USA). GAPDH was used as an internal parameter, and the relative gene expression was calculated using 2\u003csup\u003e\u0026minus;△△CT\u003c/sup\u003e method. The PCR primers were designed and synthesized by Sangon Biotech (Shanghai, China): C-X-C motif chemokine ligand 8 (CXCL8): forward: 5'-GGTGCAGTTTTGCCAAGGAG-3' and reverse 5'-TGGTCATGAGTACAACAAACTCAC-3'; Janus kinase 2 (JAK2): forward: 5'-CGTTGAGAAGACGGCAAATGTT-3' and reverse: 5'-TGTTGCTGCCACTGCAATAC-3'; GAPDH: forward: 5'-CACTAGGCGCTCACTGTTCT-3' and reverse: 5'-TTCCCGTTCTCAGCCTTCAC-3'.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eWestern blot\u003c/h2\u003e \u003cp\u003eHaCaT cells were cracked on ice with RIPA lysis buffer containing 1 mM phenyl methyl sulfonyl fluoride (PMSF) (Beyotime, Shanghai, China). The protein samples (20 \u0026micro;g per lane) were separated by electrophoresis and transferred to a polyvinylidene fluoride membrane (Millipore, Billerica, MA, USA). The membranes were blocked with 5% skim milk at room temperature for 2 h. Next, incubation with antibodies was performed. The primary antibody: anti-CXCL8 antibody (ab235584, 1:1000), anti-JAK2 antibody (ab108596, 1:1000), anti-phosphatidylinositol-3-kinase (PI3K) antibody (ab191606, 1:1000), anti-protein kinase B (AKT) antibody (ab179463, 1:1000), anti-mammalian target of rapamycin (mTOR) antibody (ab134903, 1:1000), anti-phospho (p)-AKT antibody (ab38449, 1:1000), anti-p-PI3K antibody (ab278545, 1:1000), anti-p-mTOR antibody (ab131538, 1:1000), and anti-GAPDH antibody (ab9485, 1:1000). Secondary antibody: secondary antibody (ab205718, 1:5000). The antibodies used in this study were purchased from Abcam (Shanghai, China). GAPDH was used an internal reference gene. Protein bands were detected with a BeyoECL Plus kit (Beyotime, Shanghai, China). ImageLab software version 4.1 (Bio-Rad Laboratories, Hercules, CA, USA) was used for image acquisition and density value analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eAll experiments were conducted independently in triplicate. All data are expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD). SPSS 21.0 software (IBM Corp., Armonk, NY, USA) was used for statistical analysis of the data. Comparisons between groups were made using Student's t-test or one-way analysis of variance (ANOVA) and Tukey post-hoc test. \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of DE-IRGs in DFU\u003c/h2\u003e \u003cp\u003eBefore GSE68183 and GSE80178 were merged, it could be observed the sample distribution of the samples in the two datasets before the removal of batch effect was quite different, indicating that there was a batch effect; after removing the batch effect, the data distribution between the two datasets tends to be consistent, indicating that the batch effect is well removed (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA-F). After merging and removing batch effect, an expression matrix containing 12 DFS and 6 NDFS was obtained, and 442 DEGs were identified by consensus, including 324 down-regulated genes and 118 up-regulated genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eG). To obtain DE-IRGs, 1793 IRGs collected from the Immport database were cross-analyzed with 442 DEGs, and, 46 DE-IRGs were obtained, of which 21 genes were up-regulated and 25 genes were down-regulated (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eH). GO analysis showed that these DE-IRGs were associated with 622 GO terms, including 553 biological processes (BP), 15 cell components (CC) and 54 molecular functions (MF). In terms of BP, DE-IRGs were mainly about antimicrobial humoral response, response to lipopolysaccharide, response to molecule of bacterial origin, humoral immune response, and antimicrobial humoral immune response mediated by antimicrobial peptide, etc. (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA); in terms of CC, DE-IRGs were mainly about in collagen-containing extracellular matrix, secretory granule lumen, cytoplasmic vesicle lumen and vesicle lumen and clathrin-coated vesicle membrane, etc. (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB); as for MF, DE-IRGs were mainly about receptor ligand activity, signaling receptor activator activity, growth factor receptor binding, and cytokine receptor binding and cytokine activity, etc. (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). KEGG analysis showed that these DE-IRGs were enriched in 28 pathways, and the top 10 pathways were the cytokine-cytokine receptor interaction, IL-17 signaling pathway, viral protein interaction with cytokine and cytokine receptor, JAK-STAT signaling pathway, ErbB signaling pathway, PI3K-Akt signaling pathway, Toll-like receptor signaling pathway, Chemokine signaling pathway, neuroactive ligand-receptor interaction, and calcium signaling pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eScreening hub genes from DE-IRGs\u003c/h2\u003e \u003cp\u003eA comprehensive network analysis of 46 DE-IRGs was performed using the STRING online platform, and a PPI network with 39 nodes and 100 edges was obtained (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Then, through 8 algorithms in CytoHubba plug-in, the top 15 genes in each algorithm were listed respectively (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). After cross-analysis, 3 hub genes were identified, including CXCL8, fibroblast growth factor 7 (FGF7), and JAK2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). In addition, based on the GeneMANIA database, the co-expression networks and related functions of these three hub genes were analyzed. These genes displayed a complex network of gene interactions, including 77.64% physical interactions, 8.01% co-expression, 5.37% prediction, 3.63% colocalization, 2.87% genetic interactions, 1.88% pathways, and 0.60% shared protein domains (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). These genes were involved in the cytokine receptor binding, neutrophil migration, granulocyte chemotaxis, myeloid leukocyte migration, and leukocyte chemotaxis (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). Based on the merged dataset, the expression of hub gene in DFS and NDFS. Compared with the NDFS group, CXCL8 was significantly up-regulated in the DFS group, while FGF7 and JAK2 were significantly down-regulated (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). Subsequently, differences in the expression of hub genes between DFS and NDFS were verified in the GSE134431 dataset. Compared with DFS, CXCL8 was significantly overexpressed and JAK2 was significantly lower in DFU (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). However, no significant difference in FGF7 expression was observed between the two groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). ROC analysis showed that the AUC values of CXCL8, FGF7 and JAK2 in the merged dataset were all greater than 0.8 (ranging from 0.861 to 0.931) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). However, in the GSE134431 dataset, only CXCL8 and JAK2 had AUC values greater than 0.75, and FGF7 had AUC values less than 0.6 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). These findings suggest that CXCL8 and JAK2 have better diagnostic performance than FGF7. Therefore, CXCL8 and JAK2 were identified as key hub genes.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe top 15 genes ranked in CytoHubba plug-in.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBetweenness\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCloseness\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDegree\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEPC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMCC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMNC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRadiality\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eStress\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCXCL8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCXCL8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCXCL8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCXCL8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCXCL8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCXCL8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eCXCL8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eCXCL8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFGF7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCXCL10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFGF7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCXCL10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCXCL10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCXCL10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eFGF7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eFGF7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePTGFR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFGF7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCXCL10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCXCL9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCXCL9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eJAK2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eCXCL10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eJAK2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS100A7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCXCL9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eJAK2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFGF7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCXCL11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFGF7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eCXCL9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eAREG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJAK2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eJAK2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCXCL9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIL7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eJAK2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCXCL9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eHBEGF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eANGPTL4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eANGPTL4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCXCL11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eS100A12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eJAK2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIL7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eS100A12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAREG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eEREG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCYSLTR1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIL7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eS100A7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCXCL11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFGF7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCXCL11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eCXCL11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eHBEGF\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAREG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHBEGF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCXCL11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eS100A12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSOCS3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eIL7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eIL7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eS100A7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHBEGF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAREG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIL7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSOCS3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eS100A12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAREG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eJAK2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003ePTGFR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEREG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS100A7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAREG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHBEGF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eS100A7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSOCS3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eEREG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eIL7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCXCL10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS100A12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSOCS3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAREG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eS100A9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eS100A7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSOCS3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eCXCL10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIL7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSOCS3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHBEGF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eS100A7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eS100A8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHBEGF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eS100A12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eS100A12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eADM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEREG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLIFR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSLPI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAREG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eS100A9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eS100A7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eADM\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS100A12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS100A9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eS100A9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMMP12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHBEGF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eEREG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSLPI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSOCS3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eImmunoinfiltration analysis\u003c/h2\u003e \u003cp\u003eCIBERSORT algorithm was used to analyze the abundance of immune cells in different groups. The distribution of immune cells in various samples is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA. Compared with NDFS group, the proportion of B cells naive, dendritic cells activated and monocytes in DFS group was higher; the proportion of macrophages M1, mast cells resting, T cells gamma delta and T cells regulatory (Tregs) was lower (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). In addition, the expression levels of CXCL8 are positively correlated with plasma cells (cor\u0026thinsp;=\u0026thinsp;0.469, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.050), NK cells resting (cor\u0026thinsp;=\u0026thinsp;0.571, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.013), monocytes (cor\u0026thinsp;=\u0026thinsp;0.635, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005), dendritic cells activated (cor\u0026thinsp;=\u0026thinsp;0.590, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.010) and eosinophils (cor\u0026thinsp;=\u0026thinsp;0.542, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.020) were positively correlated; and it was negatively correlated with macrophages M2 (cor = -0.490, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.039) and mast cells resting (cor = -0.785, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.000) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). The expression levels of JAK2 were strongly negatively correlated with plasma cells (cor = -0.681, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002), monocytes (cor = -0.573, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.013) and dendritic cells activated (cor = -0.519, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.027); and they were positively correlated with T cells regulatory (Tregs) (cor\u0026thinsp;=\u0026thinsp;0.655, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003), macrophages M1 (cor\u0026thinsp;=\u0026thinsp;0.530, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.024) and mast cells resting (cor\u0026thinsp;=\u0026thinsp;0.631, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eDrug prediction and molecular docking\u003c/h2\u003e \u003cp\u003e7 and 26 compounds associated with CXCL8, and 7 and 11 compounds associated with JAK2, were respectively collected from the HERB database and the HIT2.0 platform. A total of 32 compounds targeting CXCL8 and 17 compounds targeting JAK2 were obtained. The \"hub gene-compound\" network was then constructed via Cytoscape 3.9.0 software (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). API, curcumin, quercetin, resveratrol and simvastatin had potential interactions with CXCL8 and JAK2 proteins. Their chemical structures are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB-F. To verify the interactions between these compounds and key hub proteins, molecular docking was performed. API, curcumin, quercetin, resveratrol and simvastatin had binding energies ranging from \u0026minus;\u0026thinsp;5.9 to -6.6 with CXCL8 protein and from \u0026minus;\u0026thinsp;8.0 to -8.9 with JAK2 protein (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA-J).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe binding affinity between key hub genes and their potential compounds.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026minus;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCompound\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCAS No.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMolecular Weight (g/mol)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eBinding Affinity (kcal/mol)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCXCL8 (PDB ID: 6N2U)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eJAK2 (PDB ID: 8BXH)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApigenin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e520-36-5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e270.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-6.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-8.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurcumin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e458-37-7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e368.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-6.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-8.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuercetin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e117-39-5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e302.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-6.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-8.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResveratrol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e501-36-0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e228.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-6.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-8.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSimvastatin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e79902-63-9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e418.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-5.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-8.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eFunctional enrichment analysis of API\u0026rsquo;s targets in DFU treatment\u003c/h2\u003e \u003cp\u003eThe comprehensive binding affinity between API and key hub proteins was strongest. Therefore, the following research focused on exploring the potential therapeutic effect of API on DFU. 103, 50, 338 and 107 potential targets of API were obtained from SwissTaraetPrediction, STITCH, CTD and HIT2.0 databases, respectively, and a total of 463 API-related targets were obtained. The GeneCards database provided 3814 DFU-related disease targets, and after combining DFU-related disease targets with DEGs, a total of 4153 DFU-related targets were obtained, and then a total of 324 API\u0026rsquo;s therapeutic targets in DFU treatment were obtained (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eA). A total of 3749 GO terms were associated with API in DFU treatment, including 3333 BP, 153 CC, and 263 MF. The top 10 BP, CC, and MF terms with the most gene counts were selected for visualization. The top 5 terms in BP were cellular response to chemical stress, response to oxidative stress, response to lipopolysaccharide, response to molecule of bacterial origin, and regulation of apoptotic signaling pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eB); the top 5 terms of CC were vesicle lumen, membrane raft, membrane microdomain, membrane region and secretory granule lumen (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eB); the top 5 terms of MF were cytokine receptor binding, signaling receptor activator activity, receptor ligand activity, and ubiquitin-like protein ligase binding and ubiquitin protein ligase binding (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eB). KEGG analysis showed that 193 KEGG pathways were related to the mechanism of action of API in DFU treatment. The top 15 pathways with the most gene counts were lipid and atherosclerosis, kaposi sarcoma-associated herpesvirus infection, and PI3K-Akt signaling pathway, hepatitis B, Epstein-Barr virus infection, human cytomegalovirus infection, measles, human papillomavirus infection, human T-cell leukemia virus 1 infection, influenza A, MAPK signaling pathway, pathways of neurodegeneration - multiple diseases, shigellosis, hepatitis C and alzheimer disease (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eAPI alleviated the injury of HaCaT cells stimulated by HG.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTo investigate the potential therapeutic effect of API on DFU, HaCaT cells with different concentrations of API (0, 1, 5, 10, 50, and 100 \u0026micro;M) for 24 h. No significant effect on cell viability when HaCaT cells were stimulated by API concentrations below 100 \u0026micro;M (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eA). HG stimulation significantly inhibited HaCaT cell proliferation compared to the control group, while API treatment reversed this effect (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eB). Flow cytometry showed that HG resulted in increased apoptosis of HaCaT cells, while API treatment attenuated this effect (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eC). HG treatment inhibited HaCaT cell migration, while API treatment enhanced HaCaT cell migration (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eD). In addition, we analyzed the effects of API treatment on the expression of key hub genes. RT-qPCR and Western blot showed that compared with the control group, the mRNA and protein expression levels of CXCL8 in HG-treated HaCaT cells were significantly increased, while those of JAK2 were significantly decreased; however, after API intervention, a significant decrease in CXCL8 expression was observed, while a significant increase in JAK2 expression was observed (Figs.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eE\u003cb\u003e\u0026amp;F\u003c/b\u003e). In order to verify whether the mechanism of action of API treatment for DFU was related to the PI3K-Akt signaling pathway, we analyzed the effect of API treatment on the expression of related proteins in the PI3K-Akt signaling pathway under HG treatment. As shown, HG stimulation significantly reduced protein levels of p-PI3K, p-AKT, and p-mTOR in HaCaT cells, while API treatment significantly reversed these effects (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eG).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eCXCL8, also known as interleukin 8 (IL-8), is a member of the chemokine family that promotes chemotaxis by activating CXCR1 or CXCR2 receptors on target cells [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. During the inflammatory process, CXCL8 recruits white blood cells to the site of infection, ultimately leading to increased neutrophil infiltration [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Serum CXCL8 is elevated in DFU patients [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Consistent with the results of this study, the expression of CXCL8 was also significantly increased in wound exudates of DFU patients [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. JAK2 is a non-receptor tyrosine protein kinase that is involved in intracellular signaling of multiple cytokine receptors, a pathway known as the JAK2-STAT3 pathway. This pathway plays a key role in biological processes such as cell proliferation, differentiation, apoptosis and immune regulation [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Exosomes derived from platelet-rich plasma have been reported to promote diabetic wound healing by activating the JAK2/STAT3 pathway [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], which suggesting that JAK2 plays an important role in the pathological process of DFU wound healing. Our study confirmed that CXCL8 and JAK2 were immune-related regulator in DFU pathogenesis. Their expression is related to the infiltrating abundance of plasma cells, monocytes, activated dendritic cells and resting mast cells. Increased abundance of mast cells inhibits the healing process of DFU [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Additionally, it is reported that impaired wound healing in diabetic patients is associated with increased monocyte/macrophage count in the lesion [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Our data suggest that balancing CXCL8 and JAK2 expression is helpful to modulate the immune microenvironment in skin lesion of diabetics.\u003c/p\u003e \u003cp\u003eBased on database prediction and molecular docking, it was found that curcumin, quercetin, resveratrol, simvastatin and API could stably bind to CXCL8 and JAK2 proteins through hydrogen bonding. Curcumin has been reported to promote DFU wound healing by inhibiting microRNA-152-3p and activating the FBN1/TGF-β pathway [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Quercetin accelerates diabetic wound healing by reducing inflammatory response and activating the PI3K-Akt signaling pathway [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Resveratrol promotes wound healing in diabetic mice by inhibiting Notch pathway [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Simvastatin is an insoluble oral cholesterol-lowering drug that has been shown to accelerate diabetic wound healing [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. API is a natural flavonoid with antioxidant, anti-inflammatory, anti-cancer and neuroprotective effects [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. It has been reported that API can promote M2 polarization of macrophages by up-regulating miR-21 expression, thereby accelerating wound healing in diabetic mice [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. These findings suggest that these five compounds are beneficial for diabetic wounds and may be potential therapeutic candidates for DFU, which are consistent with the findings of the present study.\u003c/p\u003e \u003cp\u003eWound healing in DFU involves complex regulatory mechanisms, such as cell migration and proliferation, and the secretion of vascular growth factors [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Keratinocytes are the main cell types in the epidermis, and play a key role in promoting wound re-epithelialization and the transition from inflammation to proliferation stage during wound healing [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. However, HG can impair the normal physiological function of keratinocytes, resulting in impaired wound healing [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Therefore, the reduction of HG-induced keratinocyte injury is conducive to DFU healing [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. In this study, we observed that the proliferation and migration ability of HaCaT cells were significantly reduced under HG stimulation, while apoptosis was significantly increased, which was consistent with previous studies [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. However, in the present work, it was observed that API treatment significantly reversed these effects. In addition, API could inhibit the mRNA and protein expression levels of CXCL8 in HG-induced HaCaT cells, and up-regulate the mRNA and protein expression levels of JAK2. It is worth noting that KEGG enrichment analysis showed that the targets of API was significantly enriched in the PI3K-Akt pathway. The PI3K-Akt signaling pathway is an indispensable part of the wound healing process in diabetes mellitus, especially in regulating inflammation, cell proliferation and tissue regeneration [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Plasma endothelial cell-derived extracellular vesicles have been reported to promote diabetic wound healing by regulating the YAP and PI3K/Akt/mTOR pathways [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Insulin promotes phenotypic transformation of macrophages by activating PI3K-Akt and PPAR-γ signaling pathways, thereby improving diabetic wound healing [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. This study found that API treatment increased the expression levels of p-PI3K, p-AKT, and p-mTOR in HG-induced HaCaT cells. This suggests that the mechanism of action of API in the treatment of DFU may be related to the activation of PI3K-Akt signaling pathway, providing new explanation to the mechanism by which API promotes the healing of DFU.\u003c/p\u003e \u003cp\u003eInevitably, there are some limitations to the study. Firstly, the clinical data used in this study came from public databases, and the available clinical information for the samples was incomplete, and analysis of the association between hub genes and disease severity was hindered. Secondly, the mechanism of action of API in treating DFU wound healing needs to be validated with \u003cem\u003ein vivo\u003c/em\u003e data.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study identifies CXCL8 and JAK2 as key therapeutic targets for DFU, and curcumin, quercetin, resveratrol, simvastatin and API are promising candidate drugs. Additionally, we report that API promoted the proliferation and migration of keratinocytes cells by regulating the PI3K-Akt pathway. Collectively, our study provides useful clues for the clinical treatment of DFU.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eAll authors have given their consent for publication.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis study is financially supported by Guiyang Huimei Qingcheng Medical Beauty Hospital.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceived and designed the experiments: Z.H.X.; Performed the experiments: X.F. and Z.H.X.; Analyzed the data: X.F.; Wrote the paper: X.F. and Z.H.X.. Both authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eNot applicable.\u003c/p\u003e\u003ch2\u003eAvailability of data and materials\u003c/h2\u003e \u003cp\u003eData are available from the corresponding author upon request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003evan Netten JJ, Bus SA, Apelqvist J, Chen P, Chuter V, Fitridge R, Game F, Hinchliffe RJ, Lazzarini PA, Mills J, Monteiro-Soares M, Peters EJG, Raspovic KM, Senneville E, Wukich DK, Schaper NC. International Working Group on the Diabetic Foot. Definitions and criteria for diabetes-related foot disease (IWGDF 2023 update). Diabetes Metab Res Rev. 2024;40(3):e3654.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEdmonds M, Manu C, Vas P. The current burden of diabetic foot disease. 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Diabetes Metab Syndr Obes. 2023;16:4119\u0026ndash;32.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJere SW, Houreld NN, Abrahamse H. Role of the PI3K/AKT (mTOR and GSK3β) signalling pathway and photobiomodulation in diabetic wound healing. Cytokine Growth Factor Rev. 2019;50:52\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWei F, Wang A, Wang Q, Han W, Rong R, Wang L, Liu S, Zhang Y, Dong C, Li Y. Plasma endothelial cells-derived extracellular vesicles promote wound healing in diabetes through YAP and the PI3K/Akt/mTOR pathway. Aging. 2020;12(12):12002\u0026ndash;18.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYu T, Gao M, Yang P, Liu D, Wang D, Song F, Zhang X, Liu Y. Insulin promotes macrophage phenotype transition through PI3K/Akt and PPAR-γ signaling during diabetic wound healing. J Cell Physiol. 2019;234(4):4217\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Diabetic foot ulcer, Immunity, Molecular docking, Apigenin","lastPublishedDoi":"10.21203/rs.3.rs-5754681/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5754681/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e: Diabetic foot ulcer (DFU) is one of the major chronic complications of diabetes mellitus and a leading cause of disability and death. The aim of this study was to identify immune-related therapeutic targets and drugs for DFU.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: Two Gene Expression Omnibus datasets (GSE68183 and GSE80178) were merged, and differentially expressed genes (DEGs) were identified. Immune-related genes (IRGs) were extracted from the Immport database. Then the differentially expressed IRGs (DE-IRGs) were screened. Based on the DE-IRGs, protein-protein interaction network was constructed and hub genes were identified via Cytoscape software. An external dataset GSE134431 was used to verify the expression and diagnostic value of the hub genes. Potential compounds targeting key hub proteins, including C-X-C motif chemokine ligand 8 (CXCL8) and Janus kinase 2 (JAK2), were explored using the HIT2.0 platform and HERB database, and the binding activity between them was verified using molecular docking. Human immortalized keratinocytes (HaCaT cells) induced by high glucose (HG) were used to construct the DFU model \u003cem\u003ein vitro\u003c/em\u003e, and cell proliferation, apoptosis and migration were detected by CCK-8, flow cytometry and Transwell assay after apigenin (API) treatment. Reverse transcriptional quantitative polymerase chain reaction and Western blot were used to detect the expression of key hub genes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: CXCL8, and JAK2 were identified as hub genes in the pathogenesis of DFU. API, curcumin, quercetin, resveratrol and simvastatin had good binding activity with CXCL8 and JAK2 proteins. Treatment with Apigenin could reverse HG-induced inhibition of HaCaT cell viability and migration, and reduce cell apoptosis. After HaCaT cells were induced by HG, CXCL8 mRNA was significantly up-regulated in DFU, while JAK2 mRNA was significantly down-regulated. API treatment inhibited the expression of CXCL8 and increased the expression of JAK2, p-PI3K, p-AKT and p-mTOR in HG-induced HaCaT cells.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e: CXCL8 and JAK2 may be potential therapeutic targets for DFU. API can reduce HG-induced HaCaT cell injury and is expected to be a potential compound for DFU treatment.\u003c/p\u003e","manuscriptTitle":"CXCL8 and JAK2, modulated by apigenin, are two regulators in the pathogenesis of diabetic foot ulcer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-09 17:51:06","doi":"10.21203/rs.3.rs-5754681/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f80d84dd-e728-4016-a5b9-5704ac09bc2d","owner":[],"postedDate":"January 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-02-05T11:38:48+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-09 17:51:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5754681","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5754681","identity":"rs-5754681","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}