Single-cell RNA sequencing and transcriptomic analysis reveal the critical signatures involved in nonhealing diabetic foot ulcers

Single-cell RNA sequencing and transcriptomic analysis reveal the critical signatures involved in nonhealing diabetic foot ulcers | 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 Article Single-cell RNA sequencing and transcriptomic analysis reveal the critical signatures involved in nonhealing diabetic foot ulcers Yungang Hu, Lu Yu, Weili Du, Xiaohua Hu, Yuming Shen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4436486/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 a prevalent complication associated with diabetes that is characterised by high morbidity, high disability and high mortality and involves chronic inflammation and infiltration of multiple immune cells. However, the molecular mechanisms underlying DFU remain unclear. Here, we aimed to identify the critical signatures in nonhealing DFUs using single-cell RNA sequencing and transcriptomic analysis. Methods The GSE165816, GSE134431, and GSE143735 datasets were downloaded from the GEO database. First, we preliminarily processed and screened the datasets, removed low-quality data and identified the cell subsets. Each cell subtype was annotated, and the predominant cell types contributing to the disease were analysed. Based on this information, a prediction model was constructed with the training set GSE134431 and testing set GSE143735. Key genes were identified using the LASSO regression algorithm, followed by verification of model accuracy and stability. Additionally, we investigated the molecular mechanisms and changes in signalling pathways associated with this disease using immunoinfiltration analysis, GSEA, and GSVA. Results Through scRNA-seq analysis, we identified 12 distinct cell clusters and determined that the basalKera cell type was important in disease development. A prediction model with high accuracy and stability was constructed incorporating five key genes ( TXN , PHLDA2 , RPLP1 , MT1G , and SDC4 ). Immune cell infiltration analysis, GSEA, and GSVA revealed alterations in immune cells and signalling pathways throughout disease progression, primarily involving CD8 + T cells, T helper cells, the hypoxia-inducible factor signalling pathway, and the interleukin-17 signalling pathway. Conclusions Our study identified six key genes, namely, TXN , PHLDA2 , RPLP1 , MT1G , and SDC4 , which are significantly associated with the development of nonhealing DFU and play a crucial role in immune cell infiltration. The identified genes have the potential to serve as new prevention and treatment strategies for DFU. Health sciences/Endocrinology/Endocrine system and metabolic diseases Health sciences/Diseases/Skin diseases Diabetic foot ulcers single-cell RNA sequencing transcriptomic analysis immune infiltration machine learning Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 Figure 15 1 | INTRODUCTION Diabetic foot ulcers (DFUs), the predominant complication of diabetes, manifest as damage and destruction of the skin and deep tissues of the lower extremities. They are often accompanied by infection and may require amputation in severe cases[ 1 ]. Worldwide, the prevalence of DFU is 6.3%, and recurrence rates within one year and five years reach 42% and 65%, respectively, while the 5-year mortality rate is as high as 30%[ 2 ]. Characterised by its long duration, easy recurrence, high rates of disability, and mortality, DFU has a complex pathogenesis. These manifestations mainly include peripheral neuropathy, peripheral arterial disease, and local tissue infections[ 3 ]. Long-term hyperglycaemia can damage the peripheral nerve, resulting in sensory loss or impairment. Consequently, individuals with diabetes may have difficulty detecting skin damage on their feet, leading to delayed treatment[ 4 ]. Autonomous neuropathy can partially reduce the function of sweat and sebaceous glands in the feet, resulting in dryness and skin damage. The loss of the natural barrier function of the skin provides potential pathways for bacteria and other microorganisms to invade[ 5 ]. Elevated glucose levels can harm the endothelial cells lining blood vessels, leading to microvascular disease and peripheral arterial sclerosis or narrowing and causing foot ischaemia and hypoxia, ultimately resulting in ulceration[ 6 ]. Fifty to sixty percent of DFUs are accompanied by infections, which cause local tissue swelling and further exacerbate tissue ischaemic necrosis; approximately 20% of infections ranging from moderate to severe lead to amputation of the lower limb[ 2 ]. The prevalence of diabetes has increased owing to the continuous improvement in living standards, changes in dietary structure, and the ageing of the population. Consequently, there has been an apparent increase in the incidence of DFU, making it one of the most prominent chronic diseases affecting individuals' health and quality of life[ 7 ]. DFU considerably diminishes patients' quality of life and necessitates prolonged and frequent medical consultations and familial support, imposing substantial strain on both families and the broader society. Currently, the primary treatment for DFU involves local debridement to remove necrotic and hyperkeratotic tissues from the wound and effective blood sugar control to manage the underlying disease[ 8 ]. This approach aims to create favourable conditions for granulation tissue regeneration. Moreover, adjuvant treatments, including pharmacotherapy, wound dressings, and hyperbaric oxygen therapy, are accessible options; however, they do not provide a fundamental solution and are associated with high rates of recurrence and amputation[ 9 ]. For patients, an emphasis on nursing education and self-examination is critical, whereas for clinical doctors, identifying high-risk feet is particularly important[ 10 ]. Therefore, investigating the molecular mechanism of DFU and identifying potential markers for predicting its occurrence and progression may improve the prognosis of patients with DFU, reducing amputation rates, improving patients' quality of life, and decreasing medical expenses. The application of machine learning and bioinformatics to analyse microarray data has become widespread, aiming to identify important genes and offer insights for disease prevention and treatment[ 11 ]. Single-cell RNA sequencing (scRNA-seq) analysis is a neoteric technique that can identify cell types and subtypes associated with various diseases, allowing for the study of intergroup gene expression and differences in cellular development[ 12 , 13 ]. DFU causes multiple cellular functional impairments, including those in macrophages, endothelial cells, keratinocytes, and epidermal cells[ 14 ]. Therefore, scRNA-seq analysis can be used to accurately investigate the pathogenesis of DFU. In our study, scRNA-seq technology was utilised to compare cell subtypes between DFU patients whose ulcers had healed (DFU-Healers) and those whose ulcers did not heal (DFU-Nonhealers) within 12 weeks, and cell-type annotation and screening for genes closely associated with disease progression were performed. A DFU prediction model was further constructed using the machine learning of least absolute shrinkage and selection operation (LASSO) regression method to screen key genes, and the correlations between these key genes and immune cells were analysed via the single-sample gene set enrichment analysis (ssGSEA) algorithm. Additionally, the roles key genes play in the progression of the disease were investigated through gene set enrichment and transcription factor regulatory network analyses. Finally, by establishing a gene coexpression network, the relationships between the key genes and other DFU-related genes involved in disease progression were validated, thereby providing more detailed insight into the pathogenesis of this condition. Our study analysed single-cell data from patients with DFU, revealing crucial cellular types, key genes, immune infiltration patterns, and important regulatory mechanisms within signalling pathways during disease development. Together, our findings provide valuable insights to better understand disease progression and new directions and strategies for prevention and treatment. 2 | MATERIALS AND METHODS 2.1 | Data acquisition All data used in this study were obtained from the Gene Expression Omnibus (GEO) database ( https://www.ncbi.nlm.nih.gov/geo/ )[ 15 ]. The single-cell data file GSE165816, which includes a sample dataset of 22 DFU cases with whole single-cell expression profiles, was used for single-cell analysis[ 16 ]. The dataset GSE134431 was annotated by the GPL18573 platform[ 17 ]. The expression profile data of 14 patients were included, including eight patients with DFU healers as the control group and six with DFU nonhealers as the disease group. The GSE143735 dataset was annotated by the GPL11154 platform[ 18 ]. The expression profile data of eight patients were included, including four patients who were DFU-Healers as the control group and four patients who were DFU-Nonhealers as the disease group. 2.2 | Single-cell analysis First, we processed the expression profile using the Seurat package, during which we filtered out genes that exhibited low expression levels (nFeature_RNA > 200; nCount_RNA < 75000; percent.mt < 20); then, we standardised and normalised the data, performed principal component analysis (PCA), and analysed the data. The best results were observed through ElbowPlot, which was the number of principal components. The spatial relationships among the various clusters were determined using uniform manifold approximation and projection (UMAP) analysis; the clusters were annotated through known cell markers and annotated to some cells that are vital in the progression of the disease. In the final step, by adjusting the logfc threshold parameter of FindAllMarkers to 1, marker genes for each cell subtype were extracted from the single-cell expression profiles. Genes exhibiting a log 2FC > 1 and p_val_adj < 0.05 were identified as unique marker genes for each cell subtype. 2.3 | Contribution of different cell subpopulations to DFUs The contributions of different cell subsets to the disease were characterised by analysing the changes in cell number and gene expression. Initially, characteristic genes for each subset were identified by performing differential gene expression analysis. This process involved screening the top 100 genes most highly expressed in the control group versus the disease group, which were designated characteristic genes for each group. Subsequently, the differential expression levels and the proportions of these genes within each cell subtype were quantified. Ultimately, the square root of FC × PctProp was calculated to determine the contribution of each cell subset to the disease. 2.4 | Construction of the prediction model To select marker genes of key cells, we used the GSE134431 dataset as the training set, while the GSE143735 dataset was used as the testing set. The prediction-related models were constructed using machine learning LASSO regression. Following the integration of the expression value for each gene, a risk score formula was established for each individual and weighted according to the regression coefficient estimated through LASSO regression analysis. The risk score formula was applied to calculate the score for each patient. The precision of the model predictions was assessed by a receiver operating characteristic (ROC) curve. 2.5 | Immunoinfiltration analysis ssGSEA is extensively used to assess the types of immune cells within microenvironments[ 19 ]. This approach identifies 29 immune cell phenotypes, encompassing T cells, B cells, and NK cells, among others. In this study, the ssGSEA algorithm was applied to quantify immune cells within the expression profile, thereby estimating the relative abundances of 29 different types of infiltrating immune cells. 2.6 | Gene set enrichment analysis (GSEA) According to gene expression levels, patients were stratified into high and low gene expression groups. Gene set enrichment analysis (GSEA) was employed to delve deeper into the distinctions in signalling pathways between these two cohorts[ 20 ]. The background gene set was obtained from the Molecular Signatures Database (MsigDB) as an annotated gene set specifically for subtype pathways. A differential expression analysis was conducted to compare pathways across subtypes. Gene sets that were significantly enriched were pinpointed using consistency scores (adjusted p value < 0.05). GSEA is often used in studies that closely combine disease classification with biological relevance. 2.7 | Gene set variation analysis (GSVA) Gene set variation analysis (GSVA) is an unsupervised, nonparametric approach used to evaluate the enrichment of gene sets in transcriptomic data[ 21 ]. GSVA transforms alterations at the gene level into shifts at the pathway level by extensively scoring the gene set of interest, thereby determining the biological function of the sample. In this research, gene sets were sourced from the Molecular Signatures Database (version 7.0), and scores for each gene set were meticulously calculated using the GSVA algorithm. This approach enabled the assessment of possible variations in biological functions among various samples. 2.8 | Regulatory network analysis of key genes The ‘RcisTarget’ R package was utilised to predict transcription factors, with all computations reliant on motifs. The normalised enrichment score (NES) for a motif is influenced by the aggregate count of motifs in the database. In addition to motifs annotated from the original data, further annotations were generated through motif similarity and gene sequencing analyses. To begin estimating motif overexpression within a gene set, the area under the curve (AUC) for each motif-motif set pairing was calculated. This calculation was grounded in the recovery curve analysis, which compared the gene set against the ordering of motifs. The NES for each motif was then derived from the AUC distribution across all motifs within the gene set. For the rankings database of gene motifs, this study employed RcisTarget.hg19.motifDBs.cisbpOnly.500 bp. 2.9 | Statistical analysis All the statistical analyses in this study were conducted using the R programming language (version 4.2), with p < 0.05 indicating significance. 3 | RESULTS 3.1 | Single-cell analysis For this analysis, single-cell data were obtained from the GSE165816 dataset, which comprises 22 samples. Initially, the data samples were screened based on nFeature_RNA and nCount_RNA parameters (nFeature_RNA > 200; nCount_RNA < 75000; percent.mt < 20) (Fig. 1 A, B). The ten genes with the highest standard deviations are displayed in Fig. 1 C. The data underwent a series of processing steps, including homogenisation, standardisation, PCA, and, subsequently, harmony analysis in the specified sequence (Fig. 1 D–F). Finally, 12 subgroups were identified by UMAP analysis (Fig. 2 A). 3.2 | Annotation of cells This study provided further annotations for each cell subtype, identifying 12 clusters. These genes were annotated to 10 cell categories: smooth muscle cells (SMCs), fibroblasts, endothelial cells, basalKera cells, M1 macrophages, natural killer T (NKT) cells, plasma cells, cycling cells, lymphatic endothelial cells, and mast cells (Fig. 2 B). A bubble chart and cell proportion histogram of the classic markers of these 10 cell lines are shown in Fig. 2 C and D, respectively. 3.3 | Contribution of different cell subpopulations to DFUs By screening the top 100 genes exhibiting high expression in the control and disease groups (total sample), we quantified the differential expression levels and the expression proportions of these genes within each cell subtype. To assess their contribution to the disease, we ultimately used the square root of FC × PctProp as the metric for disease contribution. BasalKera cells were found to have the greatest contribution (Fig. 3 A); therefore, they were utilised as key cells for subsequent analysis. Eighty-five genes were identified after screening for genes with a log2FC > 1 and p_val_adj < 0.05. We also analysed the differences in the metabolic pathways of basalKera cells between the two groups and detected differences in amino acid metabolism-related signatures, C3-specific metabolism signatures, and other pathways (Fig. 3 B). The developmental trajectories of the basalKera cell subtypes are displayed in Fig. 4 A–C. 3.4 | Construction of the prediction model The training set consisted of the GSE134431 dataset, and the GSE143735 dataset was utilised as the testing set. We identified the marker genes for 85 basalKera cells and applied LASSO regression for feature selection. Through LASSO regression, five genes were pinpointed as characteristic of DFU, leading to the construction of a predictive model (Fig. 5 A, B). The model formula was as follows: risk score = syndecan 4 ( SDC4 ) x (-0. 234035697775474) + metallothionein 1G ( MT1G ) x (-0. 0390163864465646) + ribosomal protein P1 ( RPLP1x ) (-0. 006312810810358) + pleckstrin homology-like domain, family A, member 2 ( PHLDA2 ) x 0. 0796966886025995 + thioredoxin ( TXN ) × 0. 110990164644402 (Fig. 5 C). The findings indicated that the predictive model developed with these five genes demonstrated strong diagnostic efficacy, with an AUC of 1 (Fig. 5 D). We used the GSE143735 dataset as a test set. Validation of the diagnostic model with an external dataset demonstrated its good stability, as indicated by an AUC value of 0.8125 (Fig. 5 E). Five key genes were identified: TXN , PHLDA2 , RPLP1 , MT1G , and SDC4 . 3.5 | Immune cell infiltration analysis The microenvironment, which predominantly consists of immune cells, the extracellular matrix, and a range of growth and inflammatory factors, along with unique physical and chemical properties, substantially affects disease diagnosis, prevention, and treatment. By examining the association between key genes and immune infiltration within the diabetic foot dataset, we investigated the potential mechanisms through which these key genes may impact diabetic foot progression. This study revealed the percentage of immune cells present in each patient and the interrelations between different forms of immune cells (Fig. 6 A, B). Furthermore, notable differences in CD8 + T cell, dendritic cell (DC), human leukocyte antigen (HLA), major histocompatibility complex (MHC) class I, T helper cell, Th2 cell, and tumour-infiltrating lymphocyte (TIL) counts were detected between the two groups (Fig. 6 C). Additionally, this study investigated the associations between key genes and immune cells and revealed a strong correlation between key genes and immune cells. Among these genes, MT1G had a markedly positive correlation with Tfh, PHLDA2 had a markedly negative correlation with HLA, RPLP1 had a markedly positive correlation with MHC_class_I, SDC4 had a markedly positive correlation with DCs, and TXN had a markedly negative correlation with DCs (Fig. 6 D). 3.6 | Gene set enrichment analysis (GSEA) This study examined the signalling pathways related to five key genes and investigated the potential mechanisms through which these key genes influence disease development. GSEA revealed that the pathways enriched by MT1G mainly included the interleukin (IL)-17 signalling pathway and oxytocin signalling pathway (Fig. 7 A, B); the pathways enriched by PHLDA2 mainly included the hypoxia-inducible factor (HIF)-1 signalling pathway and IL-17 signalling pathway (Fig. 7 C, D); the pathways enriched by RPLP1 mainly included the cytosolic DNA-sensing pathway and mRNA surveillance pathway (Fig. 7 E, F); the pathways enriched by SDC4 mainly included the IL-17 signalling pathway and the intestinal immune network for IgA production (Fig. 7 G, H); and the pathways enriched by TXN mainly included the HIF-1 signalling pathway and IL-17 signalling pathway (Fig. 7 I, J). 3.7 | Gene set variation analysis (GSVA) GSVA revealed that high expression of MT1G can enrich signalling pathways such as FATTY_ACID_METABOLISM and G2M_CHECKPOINT (Fig. 8 A); high expression of PHLDA2 can enrich signalling pathways such as MTORC1_SIGNALLING and REACTIVE_OXYGEN_SPECIES_PATHWAY (Fig. 8 B); high expression of RPLP1 can enrich TGF_BETA_SIGNALLING, HEDGEHOG_SIGNALLING, and other signalling pathways (Fig. 8 C); high expression of SDC4 can enrich signalling pathways such as INTERFERON_GAMMA_RESPONSE and FATTY_ACID_METABOLISM (Fig. 8 D); and high expression of TXN can enrich signalling pathways such as MTORC1_SIGNALLING and REACTIVE_OXYGEN_SPECIES_PATHWAY (Fig. 8 E). 3.8 | Regulatory network analysis of key genes The five key genes constituted the gene set for analysis. The results revealed that these genes are governed by multiple transcription factors. Therefore, an analysis of the enrichment of transcription factors was conducted using cumulative distribution curves. The annotation and enrichment analysis for motif-TFs of significant genes revealed that the motif cisbp__M4010 had the highest normalised enrichment score (NES) of 6.29. All enriched motifs, alongside their corresponding transcription factors for the key genes, are shown in Fig. 9 A, B). Furthermore, the five key genes were predicted by utilising the miRcode database, which yielded 74 mRNA‒miRNA relationship pairs and 53 miRNAs that were graphically represented in Cytoscape (Fig. 9 C). 3.9 | Expression of key genes in single cells The expression levels of key genes were analysed in single cells: SMCs, fibroblasts, endothelial cells, basalKera cells, M1 macrophages, NKT cells, plasma cells, cycling cells, lymphatic endothelial cells, and mast cells (Fig. 10 A, B). Exhaustion factor scores and cytokine levels were obtained using the GeneCards database ( https://www.genecards.org/ ), and correlation analysis was conducted using the five key genes. The results are presented in Figs. 11 and 12 . Finally, genes associated with the progression of DFU, specifically vascular endothelial growth factor A (VEGFA), platelet-derived growth factor subunit B (PDGFB), and HIF1A, were obtained from the GeneCards database ( https://www.genecards.org/ ). Figures 13 , 14 and 15 showed the coexpression of HIF1A, PDGFB, VEGFA with the five key genes was visualised across 10 cell types, respectively. 4 | DISCUSSION Refractory healing of DFUs is the primary factor contributing to continuous ulceration and infection, with infection further exacerbating the condition and ultimately leading to amputation. Wound healing occurs through a complex and highly coordinated sequence that encompasses four dynamic, overlapping, and distinct phases: haemostasis, inflammation, proliferation, and remodelling[ 22 ]. This complex phase is tightly regulated by multiple cell types and involves cellular migration and proliferation, deposition of the extracellular matrix (ECM), and tissue remodelling. We observed distinct alterations in the cellular composition of ulcer tissue in patients with DFU compared to that in normal individuals. Specifically, there was a significant increase in M1 macrophages, accompanied by a notable decrease in endothelial, fibroblast, and basalKera cells. Macrophages, a crucial component of innate immunity, play an essential role in all stages of wound healing[ 23 ]. In DFU wounds, macrophages exhibit an overproduction of inflammatory cytokines and a skewed M1/M2 ratio, characterised by a predominance of the proinflammatory M1 phenotype and a deficiency in the proregenerative M2 phenotype[ 24 ]. This imbalance leads to the overexpression of interleukin-1 beta (IL-1β) and tumour necrosis factor-alpha (TNF-α)[ 25 ]. Inflammatory cells that accumulate in chronic wounds persistently produce reactive oxygen species, resulting in impaired endothelial cell function, poor angiogenesis, and hindered tissue granulation[ 26 ]. Additionally, the high-glucose environment and the accumulation of advanced glycation end products (AGEs) impair fibroblast and basalKera functions, resulting in decreased ECM deposition and delayed re-epithelialisation[ 27 ]. Consequently, the prolonged inflammatory period, shortened proliferative period, and irregular remodelling in DFU wounds create a wound environment characterised by heightened inflammation, increased oxidative stress, and reduced oxygen availability[ 28 ]. Collectively, these factors contribute to the refractory healing of DFUs. To pinpoint the key cells involved in nonhealing DFU, we screened for the top 100 highly expressed genes and determined their differential expression levels and proportions within each cell subtype as contributors to the disease. These results indicated that basalKera cells made the most significant contribution, indicating that basalKera cells are located in the basal layer of keratinocytes, which are located in the deepest layer of the epidermis and are connected to the dermis by a thin basement membrane[ 16 ]. This cell type exhibits an active proliferative capacity and plays a crucial role in wound epithelialisation. In DFUs, AGE-induced upregulation of matrix metalloproteinase-9 significantly affects keratinocyte migration and proliferation, thereby inhibiting wound healing[ 29 ]. The LASSO regression algorithm is widely employed in machine learning to effectively process high-dimensional latitudinal data, conduct feature selection to mitigate model overfitting, and is suitable for screening feature genes and constructing prediction models[ 30 ]. The present study involved the selection of 85 marker genes specific to basalKera cells. By means of LASSO regression analysis, the TXN , PHLDA2 , RPLP1 , MT1G , and SDC4 genes were identified and incorporated into a predictive model that exhibited robust diagnostic efficacy and remarkable stability. Xu et al. utilised transcriptome sequencing datasets of DFU for analysis and revealed that TXN is the hub gene associated with the wound-healing process of DFU[ 31 ]. This finding aligns with the outcomes of our study. However, little is known about the roles of PHLDA2 , RPLP1 , MT1G , and SDC4 in the progression of DFU. In this study, for the first time, it was reported that the expression of PHLDA2 was upregulated, while that of RPLP1 , MT1G , and SDC4 was downregulated in the skin ulcers of patients with DFU and controls. Visual analysis of the coexpression patterns of key genes associated with DFU revealed strong correlations between MT1G and PDGFB , between PHLDA2 and VEGFA , between RPLP1 and HIF1A , between SDC4 and PDGFB , and between TXN and HIF1A . Consequently, these genes may play crucial roles in DFU progression by modulating inflammation, hypoxia, and angiogenesis. Due to excessive inflammation, ischaemia, and hypoxia in DFUs, the wound immune microenvironment undergoes significant alterations, leading to an imbalance in the regulation of immune cells and cytokines during DFU wound healing[ 32 ]. The analysis of immune infiltration revealed large differences in the presence of CD8 + T cells, DCs, HLA, MHC class I T helper cells, Th2 cells, and TILs between patients with DFU and controls. Additionally, a pronounced correlation was observed between key genes and immune cell populations. The delicate equilibrium between reactivity and tolerance to inflammation is primarily regulated by T cells[ 33 ]. The immune response mediated by CD8 + T cells is critically important for the dual regulation of wound healing after injury to organs and tissues[ 34 ]. In DFUs, regulatory T cells exhibit excessive activation, while CD8 + T cells are depleted, resulting in a diminished immune response at the ulcer site and impaired wound healing[ 35 ]. Moreover, DFUs are characterised by an elevated proportion of Th1 cells and an increased Th1/Th2 ratio[ 36 ]. These two subtypes are integral in regulating proinflammatory and anti-inflammatory factors, playing pivotal roles in the immune response. Normally, the early inflammatory response is dominated by Th1, followed by an enhanced Th2 response to prevent the damage caused by excessive Th1 activity[ 37 ]. However, an imbalance between the two immune responses may lead to persistent inflammation, hindering wound healing[ 38 ]. Chronic inflammation and weakened immunity are the main characteristics of the DFU wound microenvironment. Anticipated to become a new therapeutic approach for DFU, targeted immunotherapy focusing on diminishing the infiltration of inflammatory cells holds promise. To obtain a more comprehensive understanding of the molecular mechanism underlying the progression of DFU, we conducted a GSEA pathway enrichment analysis and observed that the key genes are primarily involved in the HIF − 1 signalling pathway and IL − 17 signalling pathway. HIF-1 serves as an adaptive regulatory factor in hypoxia, exerting its influence on angiogenesis through the regulation of downstream heme oxygenase and VEGF. In DFUs, a chronic inflammatory response leads to vascular endothelial cell injury, microvascular contraction, and capillary wall thickening, thereby reducing the local oxygen supply and blood supply. This leaves the wound in a state of ischemia and hypoxia, significantly decreasing HIF-1α expression with insufficient angiogenesis, thus affecting wound healing. IL-17 is an inflammatory cytokine that is mainly generated by activated T cells. Its main function involves promoting the activation of T cells and inducing the production of various cytokines, such as IL-6, IL-8, and granulocyte-macrophage stimulating factor (GM-CSF). Ultimately, this leads to the initiation of inflammation[ 39 ]. Aberrant activation of IL-17 results in excessive proliferation and abnormal differentiation of keratinocytes while downregulating the expression of molecules associated with keratinocyte differentiation, ultimately compromising the integrity of the skin barrier[ 40 ]. Several studies have revealed the participation of the IL-17 signalling pathway in the progression of DFU[ 41 , 42 ]. Zhang et al. reported that inhibition of the IL-17 signalling pathway accelerates diabetic wound healing[ 43 ]. In summary, the main factors contributing to the challenging healing of DFUs are chronic inflammation, local hypoxia, and inadequate angiogenesis. In contrast to previous studies, this study investigated DFU-Healers and DFU-Nonhealers to better reveal the mechanism underlying the difficulty of healing DFU. We used single-cell sequencing to screen key nonhealing DFU cells and combined transcriptome analysis and machine learning to construct a DFU prognostic model to identify key genes. To further characterise the critical signatures in nonhealing DFUs, a systematic analysis was performed using immunoinfiltration analysis, GSEA, GSVA, and ssGSVA. This study has several limitations. First, we opted for the LASSO regression machine learning algorithm to construct the prediction model. However, employing a single algorithm may introduce data bias. Hence, incorporating multiple machine learning algorithms is expected to enhance both the accuracy and stability of predictive models. Additionally, although we identified key genes and signalling pathways that contribute to DFU, further studies are required to validate their precise roles and molecular mechanisms in the pathogenesis of DFU. This will be the primary focus of our future studies. 5 | CONCLUSION Through single-cell RNA sequencing and transcriptomic analysis, a set of five genes ( TXN , PHLDA2 , RPLP1 , MT1G , and SDC4 ) were identified as pivotal contributors to the onset and progression of nonhealing DFU. Additionally, a prediction model was constructed using the LASSO regression algorithm. Finally, analysis of immune infiltration and signalling pathways revealed the potential mechanisms underlying DFU development. The aforementioned findings offer innovative perspectives and avenues for forthcoming clinical investigations and therapeutic approaches. Declarations Funding This study was supported by the National Key Research and Development Program of China (2022YFC2403004) and the Jiangxi Provincial Natural Science Foundation(20232BAB216055). Author Contribution YGH wrote the main manuscript text. LY and WLD assisted with the writing process and provided suggestions.XHH and YMS made significant contributions to the manuscript revision. All authors reviewed the manuscript Acknowledgement The assistance provided by the members of the Department of Burns and Plastic Surgery at Beijing Jishuitan Hospital, Capital Medical University, is greatly appreciated. Data Availability The study utilized publicly available datasets for analysis. 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Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A. 2005;102(43):15545-50. Zhang J, Gu J, Guo S, Huang W, Zheng Y, Wang X, et al. Establishing and validating a pathway prognostic signature in pancreatic cancer based on miRNA and mRNA sets using GSVA. Aging (Albany NY). 2020;12(22):22840-58. Visan I. Wound healing. Nat Immunol. 2019;20(9):1089. Hassanshahi A, Moradzad M, Ghalamkari S, Fadaei M, Cowin AJ, Hassanshahi M. Macrophage-Mediated Inflammation in Skin Wound Healing. Cells. 2022;11(19):2953. Al Sadoun H. Macrophage Phenotypes in Normal and Diabetic Wound Healing and Therapeutic Interventions. Cells. 2022;11(15):2430. Geng K, Ma X, Jiang Z, Huang W, Gao C, Pu Y, et al. Innate Immunity in Diabetic Wound Healing: Focus on the Mastermind Hidden in Chronic Inflammatory. Front Pharmacol. 2021;12:653940. Polaka S, Katare P, Pawar B, Vasdev N, Gupta T, Rajpoot K, et al. Emerging ROS-Modulating Technologies for Augmentation of the Wound Healing Process. ACS Omega. 2022;7(35):30657-72. Schmidt BM, Holmes CM, Najarian K, Gallagher K, Haus JM, Shadiow J, et al. On diabetic foot ulcer knowledge gaps, innovation, evaluation, prediction markers, and clinical needs. J Diabetes Complications. 2022;36(11):108317. Dmitriyeva M, Kozhakhmetova Z, Urazova S, Kozhakhmetov S, Turebayev D, Toleubayev M. Inflammatory Biomarkers as Predictors of Infected Diabetic Foot Ulcer. Curr Diabetes Rev. 2022;18(6):e280921196867. Zhang J, Yang C, Wang C, Liu D, Lao G, Liang Y, et al. AGE-induced keratinocyte MMP-9 expression is linked to TET2-mediated CpG demethylation. Wound Repair Regen. 2016;24(3):489-500. Liang Y, Chen S, Xie J, Yan G, Guo T, Li T, et al. Establishment of a prognostic model based on m(6)A regulatory factors and stemness of hepatocellular carcinoma using RNA-seq data and scRNA-seq data. J Cancer Res Clin Oncol. 2023;149(14):12881-96. Xu F, Rui SL, Luo PQ, Chen Y, Ma Y, Deng WQ. [Bioinformatics Analysis of Hub Genes of Diabetic Foot Ulcer and Their Biofunctions]. Sichuan Da Xue Xue Bao Yi Xue Ban. 2022;53(6):961-8. Zhao Y, Wang D, Qian T, Zhang J, Li Z, Gong Q, et al. Biomimetic Nanozyme-Decorated Hydrogels with H(2)O(2)-Activated Oxygenation for Modulating Immune Microenvironment in Diabetic Wound. ACS Nano. 2023;17(17):16854-69. Sabat R, Wolk K, Loyal L, Döcke WD, Ghoreschi K. T cell pathology in skin inflammation. Semin Immunopathol. 2019;41(3):359-77. Damo M, Hornick NI, Venkat A, William I, Clulo K, Venkatesan S, et al. PD-1 maintains CD8 T cell tolerance towards cutaneous neoantigens. Nature. 2023;619(7968):151-9. Cheng Y, Ren L, Niyazi A, Sheng L, Zhao Y. Identification of potential immunologic resilience in the healing process of diabetic foot ulcers. Int Wound J. 2023;21(3):e14465. Sun XJ, Chen JA, Li G, Wang L, Wang TY, Wang AP. Maggot debridement therapy stimulates wound healing by altering macrophage activation. Int Wound J. 2023;21(3):e14477. Piazza S, Fumagalli M, Martinelli G, Pozzoli C, Maranta N, Angarano M, et al. Hydrolyzable Tannins in the Management of Th1, Th2 and Th17 Inflammatory-Related Diseases. Molecules. 2022;27(21):7593. Umapathy D, Dornadula S, Rajagopalan A, Murthy N, Mariappanadar V, Kesavan R, et al. Potential of circulatory procalcitonin as a biomarker reflecting inflammation among South Indian diabetic foot ulcers. J Vasc Surg. 2018;67(4):1283-91.e2. Mills K. IL-17 and IL-17-producing cells in protection versus pathology. Nat Rev Immunol. 2023;23(1):38-54. Gupta RK, Gracias DT, Figueroa DS, Miki H, Miller J, Fung K, et al. TWEAK functions with TNF and IL-17 on keratinocytes and is a potential target for psoriasis therapy. Sci Immunol. 2021;6(65):eabi8823. Wang X, Jiang G, Zong J, Lv D, Lu M, Qu X, et al. Revealing the novel ferroptosis-related therapeutic targets for diabetic foot ulcer based on the machine learning. Front Genet. 2022;13:944425. Geng J, Zhou G, Guo S, Ma C, Ma J. Underlying Mechanism of Traditional Herbal Formula Chuang-Ling-Ye in the Treatment of Diabetic Foot Ulcer through Network Pharmacology and Molecular Docking. Curr Pharm Des. 2024. Zhang JJ, Zhou R, Deng LJ, Cao GZ, Zhang Y, Xu H, et al. Huangbai liniment and berberine promoted wound healing in high-fat diet/Streptozotocin-induced diabetic rats. Biomed Pharmacother. 2022;150:112948. 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. 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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-4436486","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":308810466,"identity":"04692419-ede1-41e6-9fea-edfa3e2164a0","order_by":0,"name":"Yungang Hu","email":"","orcid":"","institution":"Beijing Jishuitan Hospital, Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yungang","middleName":"","lastName":"Hu","suffix":""},{"id":308810467,"identity":"b177a38c-b2e6-4048-b526-cab70536f0f3","order_by":1,"name":"Lu Yu","email":"","orcid":"","institution":"Beijing Jishuitan Hospital, Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Lu","middleName":"","lastName":"Yu","suffix":""},{"id":308810468,"identity":"2e38cca3-f8e2-48da-a0a4-d3011ce78962","order_by":2,"name":"Weili Du","email":"","orcid":"","institution":"Beijing Jishuitan Hospital, Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Weili","middleName":"","lastName":"Du","suffix":""},{"id":308810469,"identity":"065120ec-d5ce-4241-b54d-098ba7c046a9","order_by":3,"name":"Xiaohua Hu","email":"","orcid":"","institution":"Beijing Jishuitan Hospital, Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiaohua","middleName":"","lastName":"Hu","suffix":""},{"id":308810470,"identity":"8e89570f-1dee-4698-92d2-7ac7ce930e4b","order_by":4,"name":"Yuming Shen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA70lEQVRIiWNgGAWjYDACCQjFA8QJDAwVEnL8pGk5cMbCWLKBSC0QcLCtInEDIS3ys5uPPeZt2yZjzr/g8eeP8yQYNzAwP3x0A48WgzvH0o15227zWM54kCZxcJsEszkDm7FxDj4tEjlm0iAtBjcOpDEAtbBZNvCwSePTIj8j/xtMS/KHg3MkeAwOENDCcCOHDaLlfEOCxMEGCQmCWgxupJlJzjkHsoUhTeLMMQkDyWYCfpGfkfxM4k3ZbXuD82eSP1TU1NX3szc/fIzXYUDABIpHBomcBAiXmYByEGD8ASL5jx8gQu0oGAWjYBSMRAAAIKxO/TUmhv4AAAAASUVORK5CYII=","orcid":"","institution":"Beijing Jishuitan Hospital, Capital Medical University","correspondingAuthor":true,"prefix":"","firstName":"Yuming","middleName":"","lastName":"Shen","suffix":""}],"badges":[],"createdAt":"2024-05-17 11:40:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4436486/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4436486/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":57492948,"identity":"c3033d16-dd16-478b-b9a2-e70c1c305e21","added_by":"auto","created_at":"2024-05-31 11:54:29","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2754974,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eSingle-cell analysis of diabetic foot ulcers. \u003c/strong\u003e\u003c/em\u003e\u003cem\u003e(A) Quality control for single-cell data involved the presentation of metrics such as cell count, gene count, and sequencing depth for each sample. (B) The correlation between sequencing depth and mitochondrial content (left) and the correlation between sequencing depth and the number of genes (right); both are positively correlated. (C) Gene feature variance plot showing significant differences between cells. (D) Variance ranking chart for each PC. (E, F) Visualisation of principal component analysis and distribution of PCs; colours stand for samples, and points stand for cells.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-4436486/v1/fd9f586353d0c7790f6704e1.png"},{"id":57492944,"identity":"b5820fb2-a934-425e-8aeb-efb00d5d90e8","added_by":"auto","created_at":"2024-05-31 11:54:28","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1168442,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eAnnotation of cells. \u003c/strong\u003e\u003c/em\u003e\u003cem\u003e(A) Based on the significant components available in principal component analysis, the cells were partitioned into 12 clusters utilising the UMAP algorithm. (B) Annotation of 12 clusters of cells. (C) Doplot bubble plot of 10 cell‒cell signalling molecules. (D) Differences in the percentages of 10 cell types between the two sample groups.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-4436486/v1/cbf4d2057f42a36c0ea3e733.png"},{"id":57492950,"identity":"efe6c98f-d78b-49a7-896b-5569d430323b","added_by":"auto","created_at":"2024-05-31 11:54:29","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2193891,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eAnalysis of the importance of cells and metabolic pathways. \u003c/strong\u003e\u003c/em\u003e\u003cem\u003e(A) The importance of 10 types of cells, from left to right, increases successively. (B) Heatmap illustrating the correlation between two sample groups and metabolic pathways, with pink representing the control group and green representing the diabetic foot ulcer group.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-4436486/v1/e356fb35c410dc67a353968d.png"},{"id":57492947,"identity":"b0a47e5c-f8bc-49fd-b57f-6ee2d08ae3c5","added_by":"auto","created_at":"2024-05-31 11:54:29","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1417009,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eCell development trajectory. \u003c/strong\u003e\u003c/em\u003e\u003cem\u003e(A, B) Cell pseudotime analysis and developmental trajectory. (C) The gene expression dynamics of each pigment cell branch.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-4436486/v1/98b8d88e006f88ef0f683bec.png"},{"id":57493478,"identity":"1abd4c47-813c-48ca-a5d7-fa0b62419bb5","added_by":"auto","created_at":"2024-05-31 12:02:28","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":562267,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eConstruction of the prediction model. \u003c/strong\u003e\u003c/em\u003e\u003cem\u003e(A) Tenfold cross-validation was performed using the LASSO model to ascertain the minimum lambda value for parameter tuning. (B) The distribution of LASSO coefficients and the gene combinations at the minimum lambda value were examined. (C) The coefficients of the LASSO genes were analysed. (D, E) Receiver operating characteristic curves of the model were generated.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-4436486/v1/8c068074ca39d3d4a58f9147.png"},{"id":57493479,"identity":"9fa8cf40-e2a3-425e-bef8-35b352720c61","added_by":"auto","created_at":"2024-05-31 12:02:29","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":2143407,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eImmune cell infiltration analysis. \u003c/strong\u003e\u003c/em\u003e\u003cem\u003e(A) Relative percentages of 29 immune cell subgroups. (B) Pearson correlation coefficients for 29 types of immune cells, where blue signifies a negative correlation and red signifies a positive correlation. (C) Variations in immune cell composition between the two samples. (D) Correlations between key genes and immune cells.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Fig6.png","url":"https://assets-eu.researchsquare.com/files/rs-4436486/v1/68ef3f7804155689d0104e58.png"},{"id":57492951,"identity":"122a0ab7-ef25-43ed-bf78-9f9d03236b25","added_by":"auto","created_at":"2024-05-31 11:54:29","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":3583126,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eGene set enrichment analysis of key genes.\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e(A–J) Key genes involved in the Kyoto Encyclopedia of Genes and Genomes signalling pathway, pathway regulation and related genes.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Fig7.png","url":"https://assets-eu.researchsquare.com/files/rs-4436486/v1/63cede7ccbfa996adf97537b.png"},{"id":57492949,"identity":"4c734365-9998-4762-8cb2-87426a2f561a","added_by":"auto","created_at":"2024-05-31 11:54:29","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":1848145,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eGSVA of key genes. \u003c/strong\u003e\u003c/em\u003e\u003cem\u003e(A–E) GSVA of key genes. Blue\u003c/em\u003e denotes the signalling pathways associated with high gene expression, and green indicates those linked to low gene expression.\u003c/p\u003e","description":"","filename":"Fig8.png","url":"https://assets-eu.researchsquare.com/files/rs-4436486/v1/779f495b2890f7a961eba9f8.png"},{"id":57492952,"identity":"b98b25cf-1170-415a-a198-2475946f4350","added_by":"auto","created_at":"2024-05-31 11:54:29","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":1447189,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eKey gene-related transcriptional regulation and miRNA networks. \u003c/strong\u003e\u003c/em\u003e\u003cem\u003e(A) Transcriptional regulatory networks of key genes, with red representing key genes and green representing transcription factors. (B) All the enriched motifs, together with transcription factors associated with key genes. (C) miRNA networks of key genes, with red representing mRNAs and blue representing miRNAs.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Fig9.png","url":"https://assets-eu.researchsquare.com/files/rs-4436486/v1/d8c1b1a7235af4f7990c7674.png"},{"id":57492954,"identity":"32abe76e-de5c-4ece-808d-bd5c2cd29316","added_by":"auto","created_at":"2024-05-31 11:54:29","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":763030,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eExpression of single cells. \u003c/strong\u003e\u003c/em\u003e\u003cem\u003e(A, B) Expression of key genes in single cells.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Fig10.png","url":"https://assets-eu.researchsquare.com/files/rs-4436486/v1/a8e1b7d58cdceb4cce4eafb5.png"},{"id":57493480,"identity":"152f6015-23df-49ef-827c-86cde09c9dd3","added_by":"auto","created_at":"2024-05-31 12:02:29","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":1478183,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eDifferences in the expression of key genes and cytokine factor scores.\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e(A–E) Differences in the expression of key genes (MT1G,PHLDA2,RPLP1,SDC4,TXN) and cytokine scores, with blue representing high expression and yellow representing low expression.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Fig11.png","url":"https://assets-eu.researchsquare.com/files/rs-4436486/v1/f39c594393e3b9635b8e8c6f.png"},{"id":57492955,"identity":"ccc6f7f2-d712-4ae2-8583-635b1da89588","added_by":"auto","created_at":"2024-05-31 11:54:29","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":1702741,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eDifferences in the expression of key genes and exhaustion factor scores.\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e(A–E) Differences in the expression of key genes(MT1G,PHLDA2,RPLP1,SDC4,TXN) and depletion factor scores, with blue representing high expression and yellow representing low expression.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Fig12.png","url":"https://assets-eu.researchsquare.com/files/rs-4436486/v1/3f3a5086dbcde2c0802b46e4.png"},{"id":57492959,"identity":"10be859b-388d-437f-95b1-a48c1962890e","added_by":"auto","created_at":"2024-05-31 11:54:30","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":1724660,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eAnalysis of the coexpression of key genes and HIF1A in single cells. \u003c/strong\u003e\u003c/em\u003e\u003cem\u003e(A–E) The coexpression and correlation of each key gene with HIF1A.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Fig13.png","url":"https://assets-eu.researchsquare.com/files/rs-4436486/v1/05d6f503004a024530819905.png"},{"id":57492958,"identity":"79d58384-a2a4-458d-8aea-46745d7de211","added_by":"auto","created_at":"2024-05-31 11:54:29","extension":"png","order_by":14,"title":"Figure 14","display":"","copyAsset":false,"role":"figure","size":1570532,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eAnalysis of the coexpression of key genes and PDGFB in single cells. \u003c/strong\u003e\u003c/em\u003e\u003cem\u003e(A–E) The coexpression and correlation of each key gene with PDGFB.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Fig14.png","url":"https://assets-eu.researchsquare.com/files/rs-4436486/v1/202bf5403888bfad007b6d1d.png"},{"id":57492957,"identity":"fcb153f9-ccac-4c89-a5c3-6bc79ee52b48","added_by":"auto","created_at":"2024-05-31 11:54:29","extension":"png","order_by":15,"title":"Figure 15","display":"","copyAsset":false,"role":"figure","size":1617849,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eAnalysis of the coexpression of key genes and VEGFA in single cells. \u003c/strong\u003e\u003c/em\u003e\u003cem\u003e(A–E) The coexpression and correlation of each key gene with VEGFA.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Fig15.png","url":"https://assets-eu.researchsquare.com/files/rs-4436486/v1/5d4ae8f986d8da54ddc916b8.png"},{"id":61079571,"identity":"2b390305-84da-4a8d-8f5d-f8d1d51b7ecd","added_by":"auto","created_at":"2024-07-25 10:25:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":22527387,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4436486/v1/a5707267-dcc2-4c39-8228-468c6786bd63.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Single-cell RNA sequencing and transcriptomic analysis reveal the critical signatures involved in nonhealing diabetic foot ulcers","fulltext":[{"header":"1 | INTRODUCTION","content":"\u003cp\u003eDiabetic foot ulcers (DFUs), the predominant complication of diabetes, manifest as damage and destruction of the skin and deep tissues of the lower extremities. They are often accompanied by infection and may require amputation in severe cases[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Worldwide, the prevalence of DFU is 6.3%, and recurrence rates within one year and five years reach 42% and 65%, respectively, while the 5-year mortality rate is as high as 30%[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Characterised by its long duration, easy recurrence, high rates of disability, and mortality, DFU has a complex pathogenesis. These manifestations mainly include peripheral neuropathy, peripheral arterial disease, and local tissue infections[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Long-term hyperglycaemia can damage the peripheral nerve, resulting in sensory loss or impairment. Consequently, individuals with diabetes may have difficulty detecting skin damage on their feet, leading to delayed treatment[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Autonomous neuropathy can partially reduce the function of sweat and sebaceous glands in the feet, resulting in dryness and skin damage. The loss of the natural barrier function of the skin provides potential pathways for bacteria and other microorganisms to invade[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Elevated glucose levels can harm the endothelial cells lining blood vessels, leading to microvascular disease and peripheral arterial sclerosis or narrowing and causing foot ischaemia and hypoxia, ultimately resulting in ulceration[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Fifty to sixty percent of DFUs are accompanied by infections, which cause local tissue swelling and further exacerbate tissue ischaemic necrosis; approximately 20% of infections ranging from moderate to severe lead to amputation of the lower limb[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The prevalence of diabetes has increased owing to the continuous improvement in living standards, changes in dietary structure, and the ageing of the population. Consequently, there has been an apparent increase in the incidence of DFU, making it one of the most prominent chronic diseases affecting individuals' health and quality of life[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. DFU considerably diminishes patients' quality of life and necessitates prolonged and frequent medical consultations and familial support, imposing substantial strain on both families and the broader society.\u003c/p\u003e \u003cp\u003eCurrently, the primary treatment for DFU involves local debridement to remove necrotic and hyperkeratotic tissues from the wound and effective blood sugar control to manage the underlying disease[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. This approach aims to create favourable conditions for granulation tissue regeneration. Moreover, adjuvant treatments, including pharmacotherapy, wound dressings, and hyperbaric oxygen therapy, are accessible options; however, they do not provide a fundamental solution and are associated with high rates of recurrence and amputation[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. For patients, an emphasis on nursing education and self-examination is critical, whereas for clinical doctors, identifying high-risk feet is particularly important[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Therefore, investigating the molecular mechanism of DFU and identifying potential markers for predicting its occurrence and progression may improve the prognosis of patients with DFU, reducing amputation rates, improving patients' quality of life, and decreasing medical expenses. The application of machine learning and bioinformatics to analyse microarray data has become widespread, aiming to identify important genes and offer insights for disease prevention and treatment[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Single-cell RNA sequencing (scRNA-seq) analysis is a neoteric technique that can identify cell types and subtypes associated with various diseases, allowing for the study of intergroup gene expression and differences in cellular development[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. DFU causes multiple cellular functional impairments, including those in macrophages, endothelial cells, keratinocytes, and epidermal cells[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Therefore, scRNA-seq analysis can be used to accurately investigate the pathogenesis of DFU.\u003c/p\u003e \u003cp\u003eIn our study, scRNA-seq technology was utilised to compare cell subtypes between DFU patients whose ulcers had healed (DFU-Healers) and those whose ulcers did not heal (DFU-Nonhealers) within 12 weeks, and cell-type annotation and screening for genes closely associated with disease progression were performed. A DFU prediction model was further constructed using the machine learning of least absolute shrinkage and selection operation (LASSO) regression method to screen key genes, and the correlations between these key genes and immune cells were analysed via the single-sample gene set enrichment analysis (ssGSEA) algorithm. Additionally, the roles key genes play in the progression of the disease were investigated through gene set enrichment and transcription factor regulatory network analyses. Finally, by establishing a gene coexpression network, the relationships between the key genes and other DFU-related genes involved in disease progression were validated, thereby providing more detailed insight into the pathogenesis of this condition. Our study analysed single-cell data from patients with DFU, revealing crucial cellular types, key genes, immune infiltration patterns, and important regulatory mechanisms within signalling pathways during disease development. Together, our findings provide valuable insights to better understand disease progression and new directions and strategies for prevention and treatment.\u003c/p\u003e"},{"header":"2 | MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 | Data acquisition\u003c/h2\u003e \u003cp\u003eAll data used in this study were obtained from the Gene Expression Omnibus (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)[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The single-cell data file GSE165816, which includes a sample dataset of 22 DFU cases with whole single-cell expression profiles, was used for single-cell analysis[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The dataset GSE134431 was annotated by the GPL18573 platform[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The expression profile data of 14 patients were included, including eight patients with DFU healers as the control group and six with DFU nonhealers as the disease group. The GSE143735 dataset was annotated by the GPL11154 platform[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The expression profile data of eight patients were included, including four patients who were DFU-Healers as the control group and four patients who were DFU-Nonhealers as the disease group.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 | Single-cell analysis\u003c/h2\u003e \u003cp\u003eFirst, we processed the expression profile using the Seurat package, during which we filtered out genes that exhibited low expression levels (nFeature_RNA\u0026thinsp;\u0026gt;\u0026thinsp;200; nCount_RNA\u0026thinsp;\u0026lt;\u0026thinsp;75000; percent.mt\u0026thinsp;\u0026lt;\u0026thinsp;20); then, we standardised and normalised the data, performed principal component analysis (PCA), and analysed the data. The best results were observed through ElbowPlot, which was the number of principal components. The spatial relationships among the various clusters were determined using uniform manifold approximation and projection (UMAP) analysis; the clusters were annotated through known cell markers and annotated to some cells that are vital in the progression of the disease. In the final step, by adjusting the logfc threshold parameter of FindAllMarkers to 1, marker genes for each cell subtype were extracted from the single-cell expression profiles. Genes exhibiting a log\u003csup\u003e2FC\u003c/sup\u003e\u0026thinsp;\u0026gt;\u0026thinsp;1 and p_val_adj\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were identified as unique marker genes for each cell subtype.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 | Contribution of different cell subpopulations to DFUs\u003c/h2\u003e \u003cp\u003eThe contributions of different cell subsets to the disease were characterised by analysing the changes in cell number and gene expression. Initially, characteristic genes for each subset were identified by performing differential gene expression analysis. This process involved screening the top 100 genes most highly expressed in the control group versus the disease group, which were designated characteristic genes for each group. Subsequently, the differential expression levels and the proportions of these genes within each cell subtype were quantified. Ultimately, the square root of FC \u0026times; PctProp was calculated to determine the contribution of each cell subset to the disease.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 | Construction of the prediction model\u003c/h2\u003e \u003cp\u003eTo select marker genes of key cells, we used the GSE134431 dataset as the training set, while the GSE143735 dataset was used as the testing set. The prediction-related models were constructed using machine learning LASSO regression. Following the integration of the expression value for each gene, a risk score formula was established for each individual and weighted according to the regression coefficient estimated through LASSO regression analysis. The risk score formula was applied to calculate the score for each patient. The precision of the model predictions was assessed by a receiver operating characteristic (ROC) curve.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 | Immunoinfiltration analysis\u003c/h2\u003e \u003cp\u003essGSEA is extensively used to assess the types of immune cells within microenvironments[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. This approach identifies 29 immune cell phenotypes, encompassing T cells, B cells, and NK cells, among others. In this study, the ssGSEA algorithm was applied to quantify immune cells within the expression profile, thereby estimating the relative abundances of 29 different types of infiltrating immune cells.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 | Gene set enrichment analysis (GSEA)\u003c/h2\u003e \u003cp\u003eAccording to gene expression levels, patients were stratified into high and low gene expression groups. Gene set enrichment analysis (GSEA) was employed to delve deeper into the distinctions in signalling pathways between these two cohorts[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The background gene set was obtained from the Molecular Signatures Database (MsigDB) as an annotated gene set specifically for subtype pathways. A differential expression analysis was conducted to compare pathways across subtypes. Gene sets that were significantly enriched were pinpointed using consistency scores (adjusted p value\u0026thinsp;\u0026lt;\u0026thinsp;0.05). GSEA is often used in studies that closely combine disease classification with biological relevance.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 | Gene set variation analysis (GSVA)\u003c/h2\u003e \u003cp\u003eGene set variation analysis (GSVA) is an unsupervised, nonparametric approach used to evaluate the enrichment of gene sets in transcriptomic data[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. GSVA transforms alterations at the gene level into shifts at the pathway level by extensively scoring the gene set of interest, thereby determining the biological function of the sample. In this research, gene sets were sourced from the Molecular Signatures Database (version 7.0), and scores for each gene set were meticulously calculated using the GSVA algorithm. This approach enabled the assessment of possible variations in biological functions among various samples.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8 | Regulatory network analysis of key genes\u003c/h2\u003e \u003cp\u003eThe \u0026lsquo;RcisTarget\u0026rsquo; R package was utilised to predict transcription factors, with all computations reliant on motifs. The normalised enrichment score (NES) for a motif is influenced by the aggregate count of motifs in the database. In addition to motifs annotated from the original data, further annotations were generated through motif similarity and gene sequencing analyses. To begin estimating motif overexpression within a gene set, the area under the curve (AUC) for each motif-motif set pairing was calculated. This calculation was grounded in the recovery curve analysis, which compared the gene set against the ordering of motifs. The NES for each motif was then derived from the AUC distribution across all motifs within the gene set. For the rankings database of gene motifs, this study employed RcisTarget.hg19.motifDBs.cisbpOnly.500 bp.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.9 | Statistical analysis\u003c/h2\u003e \u003cp\u003eAll the statistical analyses in this study were conducted using the R programming language (version 4.2), with p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicating significance.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 | RESULTS","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.1 | Single-cell analysis\u003c/h2\u003e \u003cp\u003eFor this analysis, single-cell data were obtained from the GSE165816 dataset, which comprises 22 samples. Initially, the data samples were screened based on nFeature_RNA and nCount_RNA parameters (nFeature_RNA\u0026thinsp;\u0026gt;\u0026thinsp;200; nCount_RNA\u0026thinsp;\u0026lt;\u0026thinsp;75000; percent.mt\u0026thinsp;\u0026lt;\u0026thinsp;20) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA, B). The ten genes with the highest standard deviations are displayed in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC. The data underwent a series of processing steps, including homogenisation, standardisation, PCA, and, subsequently, harmony analysis in the specified sequence (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD\u0026ndash;F). Finally, 12 subgroups were identified by UMAP analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.2 | Annotation of cells\u003c/h2\u003e \u003cp\u003eThis study provided further annotations for each cell subtype, identifying 12 clusters. These genes were annotated to 10 cell categories: smooth muscle cells (SMCs), fibroblasts, endothelial cells, basalKera cells, M1 macrophages, natural killer T (NKT) cells, plasma cells, cycling cells, lymphatic endothelial cells, and mast cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). A bubble chart and cell proportion histogram of the classic markers of these 10 cell lines are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC and D, respectively.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.3 | Contribution of different cell subpopulations to DFUs\u003c/h2\u003e \u003cp\u003eBy screening the top 100 genes exhibiting high expression in the control and disease groups (total sample), we quantified the differential expression levels and the expression proportions of these genes within each cell subtype. To assess their contribution to the disease, we ultimately used the square root of FC \u0026times; PctProp as the metric for disease contribution. BasalKera cells were found to have the greatest contribution (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA); therefore, they were utilised as key cells for subsequent analysis. Eighty-five genes were identified after screening for genes with a log2FC\u0026thinsp;\u0026gt;\u0026thinsp;1 and p_val_adj\u0026thinsp;\u0026lt;\u0026thinsp;0.05. We also analysed the differences in the metabolic pathways of basalKera cells between the two groups and detected differences in amino acid metabolism-related signatures, C3-specific metabolism signatures, and other pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). The developmental trajectories of the basalKera cell subtypes are displayed in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA\u0026ndash;C.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.4 | Construction of the prediction model\u003c/h2\u003e \u003cp\u003eThe training set consisted of the GSE134431 dataset, and the GSE143735 dataset was utilised as the testing set. We identified the marker genes for 85 basalKera cells and applied LASSO regression for feature selection. Through LASSO regression, five genes were pinpointed as characteristic of DFU, leading to the construction of a predictive model (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA, B). The model formula was as follows: risk score\u0026thinsp;=\u0026thinsp;syndecan 4 (\u003cem\u003eSDC4\u003c/em\u003e) x (-0. 234035697775474)\u0026thinsp;+\u0026thinsp;metallothionein 1G (\u003cem\u003eMT1G\u003c/em\u003e) x (-0. 0390163864465646)\u0026thinsp;+\u0026thinsp;ribosomal protein P1 (\u003cem\u003eRPLP1x\u003c/em\u003e) (-0. 006312810810358)\u0026thinsp;+\u0026thinsp;pleckstrin homology-like domain, family A, member 2 (\u003cem\u003ePHLDA2\u003c/em\u003e) x 0. 0796966886025995\u0026thinsp;+\u0026thinsp;thioredoxin (\u003cem\u003eTXN\u003c/em\u003e) \u0026times; 0. 110990164644402 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). The findings indicated that the predictive model developed with these five genes demonstrated strong diagnostic efficacy, with an AUC of 1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). We used the GSE143735 dataset as a test set. Validation of the diagnostic model with an external dataset demonstrated its good stability, as indicated by an AUC value of 0.8125 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE). Five key genes were identified: \u003cem\u003eTXN\u003c/em\u003e, \u003cem\u003ePHLDA2\u003c/em\u003e, \u003cem\u003eRPLP1\u003c/em\u003e, \u003cem\u003eMT1G\u003c/em\u003e, and \u003cem\u003eSDC4\u003c/em\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.5 | Immune cell infiltration analysis\u003c/h2\u003e \u003cp\u003eThe microenvironment, which predominantly consists of immune cells, the extracellular matrix, and a range of growth and inflammatory factors, along with unique physical and chemical properties, substantially affects disease diagnosis, prevention, and treatment. By examining the association between key genes and immune infiltration within the diabetic foot dataset, we investigated the potential mechanisms through which these key genes may impact diabetic foot progression. This study revealed the percentage of immune cells present in each patient and the interrelations between different forms of immune cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA, B). Furthermore, notable differences in CD8\u0026thinsp;+\u0026thinsp;T cell, dendritic cell (DC), human leukocyte antigen (HLA), major histocompatibility complex (MHC) class I, T helper cell, Th2 cell, and tumour-infiltrating lymphocyte (TIL) counts were detected between the two groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). Additionally, this study investigated the associations between key genes and immune cells and revealed a strong correlation between key genes and immune cells. Among these genes, \u003cem\u003eMT1G\u003c/em\u003e had a markedly positive correlation with Tfh, \u003cem\u003ePHLDA2\u003c/em\u003e had a markedly negative correlation with HLA, \u003cem\u003eRPLP1\u003c/em\u003e had a markedly positive correlation with MHC_class_I, \u003cem\u003eSDC4\u003c/em\u003e had a markedly positive correlation with DCs, and \u003cem\u003eTXN\u003c/em\u003e had a markedly negative correlation with DCs (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.6 | Gene set enrichment analysis (GSEA)\u003c/h2\u003e \u003cp\u003eThis study examined the signalling pathways related to five key genes and investigated the potential mechanisms through which these key genes influence disease development. GSEA revealed that the pathways enriched by \u003cem\u003eMT1G\u003c/em\u003e mainly included the interleukin (IL)-17 signalling pathway and oxytocin signalling pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA, B); the pathways enriched by \u003cem\u003ePHLDA2\u003c/em\u003e mainly included the hypoxia-inducible factor (HIF)-1 signalling pathway and IL-17 signalling pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC, D); the pathways enriched by \u003cem\u003eRPLP1\u003c/em\u003e mainly included the cytosolic DNA-sensing pathway and mRNA surveillance pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eE, F); the pathways enriched by \u003cem\u003eSDC4\u003c/em\u003e mainly included the IL-17 signalling pathway and the intestinal immune network for IgA production (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eG, H); and the pathways enriched by \u003cem\u003eTXN\u003c/em\u003e mainly included the HIF-1 signalling pathway and IL-17 signalling pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eI, J).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.7 | Gene set variation analysis (GSVA)\u003c/h2\u003e \u003cp\u003eGSVA revealed that high expression of \u003cem\u003eMT1G\u003c/em\u003e can enrich signalling pathways such as FATTY_ACID_METABOLISM and G2M_CHECKPOINT (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA); high expression of \u003cem\u003ePHLDA2\u003c/em\u003e can enrich signalling pathways such as MTORC1_SIGNALLING and REACTIVE_OXYGEN_SPECIES_PATHWAY (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eB); high expression of \u003cem\u003eRPLP1\u003c/em\u003e can enrich TGF_BETA_SIGNALLING, HEDGEHOG_SIGNALLING, and other signalling pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eC); high expression of \u003cem\u003eSDC4\u003c/em\u003e can enrich signalling pathways such as INTERFERON_GAMMA_RESPONSE and FATTY_ACID_METABOLISM (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eD); and high expression of \u003cem\u003eTXN\u003c/em\u003e can enrich signalling pathways such as MTORC1_SIGNALLING and REACTIVE_OXYGEN_SPECIES_PATHWAY (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eE).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.8 | Regulatory network analysis of key genes\u003c/h2\u003e \u003cp\u003eThe five key genes constituted the gene set for analysis. The results revealed that these genes are governed by multiple transcription factors. Therefore, an analysis of the enrichment of transcription factors was conducted using cumulative distribution curves. The annotation and enrichment analysis for motif-TFs of significant genes revealed that the motif cisbp__M4010 had the highest normalised enrichment score (NES) of 6.29. All enriched motifs, alongside their corresponding transcription factors for the key genes, are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eA, B). Furthermore, the five key genes were predicted by utilising the miRcode database, which yielded 74 mRNA‒miRNA relationship pairs and 53 miRNAs that were graphically represented in Cytoscape (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e3.9 | Expression of key genes in single cells\u003c/h2\u003e \u003cp\u003eThe expression levels of key genes were analysed in single cells: SMCs, fibroblasts, endothelial cells, basalKera cells, M1 macrophages, NKT cells, plasma cells, cycling cells, lymphatic endothelial cells, and mast cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eA, B). Exhaustion factor scores and cytokine levels were obtained using the GeneCards database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.genecards.org/\u003c/span\u003e\u003cspan address=\"https://www.genecards.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and correlation analysis was conducted using the five key genes. The results are presented in Figs.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e and \u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003e. Finally, genes associated with the progression of DFU, specifically vascular endothelial growth factor A (VEGFA), platelet-derived growth factor subunit B (PDGFB), and HIF1A, were obtained from the GeneCards database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.genecards.org/\u003c/span\u003e\u003cspan address=\"https://www.genecards.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Figures\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003e,\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e14\u003c/span\u003e and \u003cspan refid=\"Fig15\" class=\"InternalRef\"\u003e15\u003c/span\u003e showed the coexpression of HIF1A, PDGFB, VEGFA with the five key genes was visualised across 10 cell types, respectively.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4 | DISCUSSION","content":"\u003cp\u003eRefractory healing of DFUs is the primary factor contributing to continuous ulceration and infection, with infection further exacerbating the condition and ultimately leading to amputation. Wound healing occurs through a complex and highly coordinated sequence that encompasses four dynamic, overlapping, and distinct phases: haemostasis, inflammation, proliferation, and remodelling[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. This complex phase is tightly regulated by multiple cell types and involves cellular migration and proliferation, deposition of the extracellular matrix (ECM), and tissue remodelling. We observed distinct alterations in the cellular composition of ulcer tissue in patients with DFU compared to that in normal individuals. Specifically, there was a significant increase in M1 macrophages, accompanied by a notable decrease in endothelial, fibroblast, and basalKera cells. Macrophages, a crucial component of innate immunity, play an essential role in all stages of wound healing[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. In DFU wounds, macrophages exhibit an overproduction of inflammatory cytokines and a skewed M1/M2 ratio, characterised by a predominance of the proinflammatory M1 phenotype and a deficiency in the proregenerative M2 phenotype[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. This imbalance leads to the overexpression of interleukin-1 beta (IL-1β) and tumour necrosis factor-alpha (TNF-α)[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Inflammatory cells that accumulate in chronic wounds persistently produce reactive oxygen species, resulting in impaired endothelial cell function, poor angiogenesis, and hindered tissue granulation[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Additionally, the high-glucose environment and the accumulation of advanced glycation end products (AGEs) impair fibroblast and basalKera functions, resulting in decreased ECM deposition and delayed re-epithelialisation[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Consequently, the prolonged inflammatory period, shortened proliferative period, and irregular remodelling in DFU wounds create a wound environment characterised by heightened inflammation, increased oxidative stress, and reduced oxygen availability[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Collectively, these factors contribute to the refractory healing of DFUs.\u003c/p\u003e \u003cp\u003eTo pinpoint the key cells involved in nonhealing DFU, we screened for the top 100 highly expressed genes and determined their differential expression levels and proportions within each cell subtype as contributors to the disease. These results indicated that basalKera cells made the most significant contribution, indicating that basalKera cells are located in the basal layer of keratinocytes, which are located in the deepest layer of the epidermis and are connected to the dermis by a thin basement membrane[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. This cell type exhibits an active proliferative capacity and plays a crucial role in wound epithelialisation. In DFUs, AGE-induced upregulation of matrix metalloproteinase-9 significantly affects keratinocyte migration and proliferation, thereby inhibiting wound healing[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. The LASSO regression algorithm is widely employed in machine learning to effectively process high-dimensional latitudinal data, conduct feature selection to mitigate model overfitting, and is suitable for screening feature genes and constructing prediction models[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. The present study involved the selection of 85 marker genes specific to basalKera cells. By means of LASSO regression analysis, the \u003cem\u003eTXN\u003c/em\u003e, \u003cem\u003ePHLDA2\u003c/em\u003e, \u003cem\u003eRPLP1\u003c/em\u003e, \u003cem\u003eMT1G\u003c/em\u003e, and \u003cem\u003eSDC4\u003c/em\u003e genes were identified and incorporated into a predictive model that exhibited robust diagnostic efficacy and remarkable stability. Xu et al. utilised transcriptome sequencing datasets of DFU for analysis and revealed that \u003cem\u003eTXN\u003c/em\u003e is the hub gene associated with the wound-healing process of DFU[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. This finding aligns with the outcomes of our study. However, little is known about the roles of \u003cem\u003ePHLDA2\u003c/em\u003e, \u003cem\u003eRPLP1\u003c/em\u003e, \u003cem\u003eMT1G\u003c/em\u003e, and \u003cem\u003eSDC4\u003c/em\u003e in the progression of DFU. In this study, for the first time, it was reported that the expression of \u003cem\u003ePHLDA2\u003c/em\u003e was upregulated, while that of \u003cem\u003eRPLP1\u003c/em\u003e, \u003cem\u003eMT1G\u003c/em\u003e, and \u003cem\u003eSDC4\u003c/em\u003e was downregulated in the skin ulcers of patients with DFU and controls. Visual analysis of the coexpression patterns of key genes associated with DFU revealed strong correlations between \u003cem\u003eMT1G\u003c/em\u003e and \u003cem\u003ePDGFB\u003c/em\u003e, between \u003cem\u003ePHLDA2\u003c/em\u003e and \u003cem\u003eVEGFA\u003c/em\u003e, between \u003cem\u003eRPLP1\u003c/em\u003e and \u003cem\u003eHIF1A\u003c/em\u003e, between \u003cem\u003eSDC4\u003c/em\u003e and \u003cem\u003ePDGFB\u003c/em\u003e, and between \u003cem\u003eTXN\u003c/em\u003e and \u003cem\u003eHIF1A\u003c/em\u003e. Consequently, these genes may play crucial roles in DFU progression by modulating inflammation, hypoxia, and angiogenesis.\u003c/p\u003e \u003cp\u003eDue to excessive inflammation, ischaemia, and hypoxia in DFUs, the wound immune microenvironment undergoes significant alterations, leading to an imbalance in the regulation of immune cells and cytokines during DFU wound healing[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. The analysis of immune infiltration revealed large differences in the presence of CD8\u003csup\u003e+\u003c/sup\u003e T cells, DCs, HLA, MHC class I T helper cells, Th2 cells, and TILs between patients with DFU and controls. Additionally, a pronounced correlation was observed between key genes and immune cell populations. The delicate equilibrium between reactivity and tolerance to inflammation is primarily regulated by T cells[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. The immune response mediated by CD8\u0026thinsp;+\u0026thinsp;T cells is critically important for the dual regulation of wound healing after injury to organs and tissues[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. In DFUs, regulatory T cells exhibit excessive activation, while CD8\u003csup\u003e+\u003c/sup\u003e T cells are depleted, resulting in a diminished immune response at the ulcer site and impaired wound healing[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Moreover, DFUs are characterised by an elevated proportion of Th1 cells and an increased Th1/Th2 ratio[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. These two subtypes are integral in regulating proinflammatory and anti-inflammatory factors, playing pivotal roles in the immune response. Normally, the early inflammatory response is dominated by Th1, followed by an enhanced Th2 response to prevent the damage caused by excessive Th1 activity[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. However, an imbalance between the two immune responses may lead to persistent inflammation, hindering wound healing[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Chronic inflammation and weakened immunity are the main characteristics of the DFU wound microenvironment. Anticipated to become a new therapeutic approach for DFU, targeted immunotherapy focusing on diminishing the infiltration of inflammatory cells holds promise.\u003c/p\u003e \u003cp\u003eTo obtain a more comprehensive understanding of the molecular mechanism underlying the progression of DFU, we conducted a GSEA pathway enrichment analysis and observed that the key genes are primarily involved in the HIF\u0026thinsp;\u0026minus;\u0026thinsp;1 signalling pathway and IL\u0026thinsp;\u0026minus;\u0026thinsp;17 signalling pathway. HIF-1 serves as an adaptive regulatory factor in hypoxia, exerting its influence on angiogenesis through the regulation of downstream heme oxygenase and VEGF. In DFUs, a chronic inflammatory response leads to vascular endothelial cell injury, microvascular contraction, and capillary wall thickening, thereby reducing the local oxygen supply and blood supply. This leaves the wound in a state of ischemia and hypoxia, significantly decreasing HIF-1α expression with insufficient angiogenesis, thus affecting wound healing. IL-17 is an inflammatory cytokine that is mainly generated by activated T cells. Its main function involves promoting the activation of T cells and inducing the production of various cytokines, such as IL-6, IL-8, and granulocyte-macrophage stimulating factor (GM-CSF). Ultimately, this leads to the initiation of inflammation[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Aberrant activation of IL-17 results in excessive proliferation and abnormal differentiation of keratinocytes while downregulating the expression of molecules associated with keratinocyte differentiation, ultimately compromising the integrity of the skin barrier[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Several studies have revealed the participation of the IL-17 signalling pathway in the progression of DFU[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Zhang et al. reported that inhibition of the IL-17 signalling pathway accelerates diabetic wound healing[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. In summary, the main factors contributing to the challenging healing of DFUs are chronic inflammation, local hypoxia, and inadequate angiogenesis.\u003c/p\u003e \u003cp\u003eIn contrast to previous studies, this study investigated DFU-Healers and DFU-Nonhealers to better reveal the mechanism underlying the difficulty of healing DFU. We used single-cell sequencing to screen key nonhealing DFU cells and combined transcriptome analysis and machine learning to construct a DFU prognostic model to identify key genes. To further characterise the critical signatures in nonhealing DFUs, a systematic analysis was performed using immunoinfiltration analysis, GSEA, GSVA, and ssGSVA.\u003c/p\u003e \u003cp\u003eThis study has several limitations. First, we opted for the LASSO regression machine learning algorithm to construct the prediction model. However, employing a single algorithm may introduce data bias. Hence, incorporating multiple machine learning algorithms is expected to enhance both the accuracy and stability of predictive models. Additionally, although we identified key genes and signalling pathways that contribute to DFU, further studies are required to validate their precise roles and molecular mechanisms in the pathogenesis of DFU. This will be the primary focus of our future studies.\u003c/p\u003e"},{"header":"5 | CONCLUSION","content":"\u003cp\u003eThrough single-cell RNA sequencing and transcriptomic analysis, a set of five genes (\u003cem\u003eTXN\u003c/em\u003e, \u003cem\u003ePHLDA2\u003c/em\u003e, \u003cem\u003eRPLP1\u003c/em\u003e, \u003cem\u003eMT1G\u003c/em\u003e, and \u003cem\u003eSDC4\u003c/em\u003e) were identified as pivotal contributors to the onset and progression of nonhealing DFU. Additionally, a prediction model was constructed using the LASSO regression algorithm. Finally, analysis of immune infiltration and signalling pathways revealed the potential mechanisms underlying DFU development. The aforementioned findings offer innovative perspectives and avenues for forthcoming clinical investigations and therapeutic approaches.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis study was supported by the National Key Research and Development Program of China (2022YFC2403004) and the Jiangxi Provincial Natural Science Foundation(20232BAB216055).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eYGH wrote the main manuscript text. LY and WLD assisted with the writing process and provided suggestions.XHH and YMS made significant contributions to the manuscript revision. All authors reviewed the manuscript\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe assistance provided by the members of the Department of Burns and Plastic Surgery at Beijing Jishuitan Hospital, Capital Medical University, is greatly appreciated.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe study utilized publicly available datasets for analysis. Specifically, the single-cell databases GSE165816,as well as transcriptome data from GSEGSE165816 and GSE143735, were sourced from the GEO platform(https://www.ncbi.nlm.nih.gov/geo/).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003e\u003cem\u003eMonteiro-Soares M, Boyko EJ, Jeffcoate W, Mills JL, Russell D, Morbach S, et al. Diabetic foot ulcer classifications: A critical review. Diabetes Metab Res Rev. 2020;36 Suppl 1:e3272.\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003e\u003cem\u003eArmstrong DG, Tan TW, Boulton A, Bus SA. 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Biomed Pharmacother. 2022;150:112948.\u003c/em\u003e\u003c/li\u003e\n\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 ulcers, single-cell RNA sequencing, transcriptomic analysis, immune infiltration, machine learning","lastPublishedDoi":"10.21203/rs.3.rs-4436486/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4436486/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e\u003c/p\u003e \u003cp\u003eDiabetic foot ulcer (DFU) is a prevalent complication associated with diabetes that is characterised by high morbidity, high disability and high mortality and involves chronic inflammation and infiltration of multiple immune cells. However, the molecular mechanisms underlying DFU remain unclear. Here, we aimed to identify the critical signatures in nonhealing DFUs using single-cell RNA sequencing and transcriptomic analysis.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe GSE165816, GSE134431, and GSE143735 datasets were downloaded from the GEO database. First, we preliminarily processed and screened the datasets, removed low-quality data and identified the cell subsets. Each cell subtype was annotated, and the predominant cell types contributing to the disease were analysed. Based on this information, a prediction model was constructed with the training set GSE134431 and testing set GSE143735. Key genes were identified using the LASSO regression algorithm, followed by verification of model accuracy and stability. Additionally, we investigated the molecular mechanisms and changes in signalling pathways associated with this disease using immunoinfiltration analysis, GSEA, and GSVA.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThrough scRNA-seq analysis, we identified 12 distinct cell clusters and determined that the basalKera cell type was important in disease development. A prediction model with high accuracy and stability was constructed incorporating five key genes (\u003cem\u003eTXN\u003c/em\u003e, \u003cem\u003ePHLDA2\u003c/em\u003e, \u003cem\u003eRPLP1\u003c/em\u003e, \u003cem\u003eMT1G\u003c/em\u003e, and \u003cem\u003eSDC4\u003c/em\u003e). Immune cell infiltration analysis, GSEA, and GSVA revealed alterations in immune cells and signalling pathways throughout disease progression, primarily involving CD8\u003csup\u003e+\u003c/sup\u003e T cells, T helper cells, the hypoxia-inducible factor signalling pathway, and the interleukin-17 signalling pathway.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusions\u003c/b\u003e\u003c/p\u003e \u003cp\u003eOur study identified six key genes, namely, \u003cem\u003eTXN\u003c/em\u003e, \u003cem\u003ePHLDA2\u003c/em\u003e, \u003cem\u003eRPLP1\u003c/em\u003e, \u003cem\u003eMT1G\u003c/em\u003e, and \u003cem\u003eSDC4\u003c/em\u003e, which are significantly associated with the development of nonhealing DFU and play a crucial role in immune cell infiltration. The identified genes have the potential to serve as new prevention and treatment strategies for DFU.\u003c/p\u003e","manuscriptTitle":"Single-cell RNA sequencing and transcriptomic analysis reveal the critical signatures involved in nonhealing diabetic foot ulcers","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-31 11:54:23","doi":"10.21203/rs.3.rs-4436486/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":"7b0709cb-ee7e-450f-8099-63542316a041","owner":[],"postedDate":"May 31st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":32627399,"name":"Health sciences/Endocrinology/Endocrine system and metabolic diseases"},{"id":32627400,"name":"Health sciences/Diseases/Skin diseases"}],"tags":[],"updatedAt":"2024-08-19T01:41:57+00:00","versionOfRecord":[],"versionCreatedAt":"2024-05-31 11:54:23","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4436486","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4436486","identity":"rs-4436486","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}