Unveiling COL4A1 as a Key Driver Gene in scleroderma: Insights from Single-Cell and Bulk RNA Sequencing

Unveiling COL4A1 as a Key Driver Gene in scleroderma: Insights from Single-Cell and Bulk RNA Sequencing | 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 Unveiling COL4A1 as a Key Driver Gene in scleroderma: Insights from Single-Cell and Bulk RNA Sequencing Rui Ma, Chenghao Wu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7376155/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 Keloids and systemic sclerosis (SSc) are fibrotic disorders characterized by abnormal wound healing, leading to excessive collagen deposition and tissue fibrosis. The inflammatory and immune responses play critical roles in their pathogenesis, yet the molecular mechanisms remain elusive. We utilized high-throughput RNA sequencing (GSE130955) and single-cell RNA sequencing (GSE163973) datasets from the GEO database to explore gene expression profiles in scleroderma and keloid tissues. Differentially expressed genes (DEGs) were identified using the limma + voom package. Driver genes were detected through the SJARACNe algorithm, and functional enrichment was analyzed via GO and KEGG. Additionally, cell-cell communication networks were explored using the CellChat package, and gene expression validation was performed using qPCR and Western blot techniques. A total of 823 upregulated and 58 downregulated genes were identified in scleroderma scars, with significant enrichment in pathways related to extracellular matrix organization and immune responses. Key driver genes, including TCF23, PENK, and COL4A1, were identified, showing differential expression between scleroderma patients and controls. Single-cell RNA sequencing revealed key cell types involved in scar formation, particularly fibroblasts and endothelial cells, and highlighted the importance of intercellular signaling through collagen and laminin pathways. COL4A1 overexpression was shown to significantly enhance fibroblast proliferation and invasion in vitro. Our study uncovers novel molecular signatures and cellular interactions involved in the pathogenesis of keloids and scleroderma scars, providing potential therapeutic targets for these fibrotic conditions. Health sciences/Biomarkers Biological sciences/Cell biology Biological sciences/Computational biology and bioinformatics Health sciences/Diseases Biological sciences/Genetics Biological sciences/Immunology Biological sciences/Molecular biology Keloids and systemic sclerosis Single-cell RNA sequencing Driver genes COL4A1 Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 1. Introduction Keloids are benign fibroproliferative tumors in the dermis that arise due to abnormal wound healing, characterized by excessive activation of myofibroblasts and collagen deposition ( 1 ). Unlike normal scars, which typically do not extend beyond the wound and gradually diminish over time, keloids extend beyond the boundaries of the initial injury ( 2 ) often appearing on the chest, back, and earlobes, and are accompanied by pain and itching ( 3 ). This pathological form of healing shares many properties with tumors, such as hyperplasia beyond the wound and invasive growth, significantly affecting patients' psychosomatic health and quality of life. The inflammatory response, involving a variety of immune cells and cytokines, plays a crucial role in keloid formation, though the exact pathological mechanisms remain unclear ( 4 ). Current treatments, including surgical resection, radiotherapy, and local glucocorticoid injections, suffer from high recurrence rates and can exacerbate the condition, complicating management ( 5 – 7 ). Advances in public databases now enable in-depth exploration of the genetic mechanisms and immune microenvironment of keloids, which is essential for developing effective clinical treatments and reducing recurrence rates. Understanding these mechanisms can promote the development of new treatment regimens, improving prognosis and quality of life for keloid patients. Systemic sclerosis (SSc) is an immune-mediated rheumatic disease characterized by skin fibrosis and internal organ involvement, triggered by early vascular lesions and immune system dysfunction, leading to inflammation and tissue fibrosis. Localized scleroderma (LS) is similar to SSc but primarily manifests as localized skin thickening without internal organ involvement ( 8 ). Keloids, on the other hand, are fibrotic proliferative disorders reflecting abnormal wound healing processes, often occurring after trauma, burns, or surgery ( 9 ). Skin fibrotic diseases, including SSc, LS, and keloids, share common features such as fibroblast proliferation and extracellular matrix deposition. Despite current treatment options like methotrexate, mycophenolate mofetil, and local corticosteroid injections having limited efficacy and high recurrence rates, there is an urgent need to understand the underlying mechanisms of these diseases to develop more effective anti-fibrotic therapies. The inflammatory response plays a significant role in these conditions, where downregulation of pro-inflammatory cytokines and upregulation of anti-inflammatory cytokines may reduce scar tissue formation ( 10 ). Overall, controlling the inflammatory process remains a fundamental goal in the prevention and treatment of these fibrotic diseases. Single-cell RNA (scRNA) sequencing is a cutting-edge method whose utility in gene expression analysis has been proven to identify a variety of cell types and subtypes previously associated with diseases( 11 – 12 ). This technique enables researchers to use cell clustering to investigate differences in gene expression and cellular evolution across different populations. Keloids consist of diverse cell types, such as fibroblasts, myofibroblasts, and vascular endothelial cells, making scRNA sequencing an accurate tool for examining the genetic makeup of keloids. In this study, we employed single-cell and somatic bioinformatics analyses to investigate the immune-related genes and their potential regulatory mechanisms in keloids through transcriptomic approaches. This analysis provides an in-depth insight into the cellular interactions and molecular pathways that play a role in keloid pathology, offering potential targets for developing new therapeutic interventions. 2. Materials and Methods 2.1. Data Download and Preprocessing We downloaded a high-throughput RNA sequencing dataset (GSE130955) and a single-cell RNA sequencing dataset (GSE163973) related to skin fibrosis diseases from the GEO database ( https://www.ncbi.nlm.nih.gov/geo/ ). The sequencing platform for the GSE130955 dataset is GPL16791 Illumina HiSeq 2500 (Homo sapiens), containing skin biopsy samples from 48 early diffuse cutaneous systemic sclerosis (dcSSc) patients and 33 healthy controls. The GSE163973 dataset was sequenced on the GPL24676 Illumina NovaSeq 6000 (Homo sapiens) platform and includes dermal fibroblasts from 3 keloid cases and 3 normal scar tissues. All samples were derived from Homo sapiens. For the GSE130955 dataset, raw data were stored in FASTQ format. We performed quality control using the FastQC tool and removed low-quality reads and adapter sequences using the Trimmomatic tool. Subsequently, we aligned the clean reads to the human reference genome (GRCh38) using the STAR software and quantified expression levels using featureCounts software. For the GSE163973 dataset, we processed single-cell data using the Seurat (version 4.0) R package. Initially, we used the emptyDrops function from the DropletUtils package to remove empty droplets and the doubletFinder_v3 function to identify and remove doublets, screening a total of 43,431 cells and 23,753 genes. The data were then normalized (LogNormalize) and highly variable genes were selected (FindVariableFeatures). All genes were subsequently scaled (ScaleData), and dimensionality reduction was performed using Principal Component Analysis (PCA), with significant principal components identified using the ElbowPlot function. Finally, we used the first 35 principal components for Uniform Manifold Approximation and Projection (UMAP) visualization to display different cell subpopulations. 2.2. Differentially Expressed Gene Screening We screened for differentially expressed genes (DEGs) in the scleroderma scar data using the limma + voom package. Volcano plots of DEGs were created using the ggplot2 package, and heatmaps were generated using the pheatmap package to display the differential expression of DEGs. DEGs were identified with p 1.5 after data normalization using the voom function from the limma package. 2.3. Identification of Driver Genes Using the SJARACNe Algorithm We used the SJARACNe algorithm to identify driver genes in the disease process of scleroderma. SJARACNe is a gene network reverse engineering tool based on mutual information (MI) that reconstructs gene regulatory networks from large-scale gene expression data[13]. SJARACNe was run 100 times for bootstrap analysis to enhance network reconstruction robustness and accuracy. Genes with high connectivity (hub genes) in the reconstructed gene regulatory network were identified as potential driver genes. 2.4. Functional Enrichment Analysis Gene Ontology (GO) analysis is a commonly used method for large-scale functional enrichment studies, including biological processes (BP), molecular functions (MF), and cellular components (CC). GO annotation analysis was performed using the clusterProfiler package in R for the differentially expressed genes in the immune intersection, with an FDR threshold of < 0.05 considered statistically significant. We performed pathway enrichment analysis using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database, which is a resource for understanding high-level functions and biological systems (e.g., cells, organs, organisms). KEGG pathway analysis was also conducted using the clusterProfiler package in R, with an FDR threshold of < 0.05 considered statistically significant. We performed pathway enrichment analysis using the Molecular Signatures Database (MSigDB, v7.5.1), a resource for understanding high-level functions and biological systems (e.g., cells, organs, organisms). MSigDB pathway analysis, focusing on Hallmark and C2:KEGG gene sets, was conducted using the clusterProfiler package in R, with an FDR threshold of < 0.05 considered statistically significant. To investigate biological process differences between groups, we performed Gene Set Enrichment Analysis (GSEA) on the gene expression profile dataset of scleroderma patients using MSigDB gene sets. GSEA is a computational method for determining whether a predefined set of genes shows statistically significant differences between two biological states, typically used to estimate pathway and biological process activity changes in expression datasets ( 14 ). A false discovery rate (FDR) < 0.25 was considered significantly enriched. 2.5. Single-Cell Gene Set Scoring We evaluated the performance of multiple gene set scoring methods on single-cell RNA sequencing data using the irGSEA R package ( 15 ). The irGSEA package integrates six different scoring methods based on gene expression ranking, emphasizing relative expression levels rather than absolute values. These methods include AUCell, UCell, Singscore, ssGSEA, JASMINE, and Viper. 2.6. Cell-Cell Communication Analysis The CellChat (version 1.1.3) R package was used to analyze cell-cell communication networks from single-cell transcriptome sequencing data ( 16 ). Using the built-in CellChatDB database, we quantitatively inferred and analyzed cell-cell communication networks from standardized single-cell RNA sequencing data. The subsetCommunication function was used to infer and transfer cell-cell communication information, with edge.weight.max set to max to compare edge weights between different networks. Circle plots were used to display cell group interactions and bubble plots were used to quantify all significant ligand-receptor pairs in cell-cell signal transmission. 2.7. Collection of Specimens from Scar Patients Scar tissue specimens were collected under sterile conditions from patients presenting with hypertrophic scars or keloids at Sir Run Run Shaw Hospital, affiliated with Zhejiang University School of Medicine. Tissue samples were excised from the affected areas using surgical instruments, following established protocols. The excised specimens were immediately placed in RNAlater® (Thermo Fisher Scientific) to stabilize RNA, ensuring the integrity of molecular data for subsequent analyses. All methods were carried out in accordance with relevant guidelines and regulations, including the Declaration of Helsinki. All experimental protocols were approved by the Sir Run Run Shaw Hospital Ethics Committee (Approval No. 20240601-008), ensuring minimal patient discomfort and adherence to ethical standards. Informed patient consent was obtained prior to specimen collection, and all samples were anonymized to protect patient identity. 2.8. Extraction of human primary fibroblasts Clinical samples were obtained from the hospital, rinsed repeatedly in the sterilized 1%-2% PBS solution three times, the connective tissue was removed, and the skin tissue was placed in 0.2% Dispase + 1.0g /L DMEM mixed digestive solution for cold digestion at 4℃ overnight, and the epidermal tissue was peeled off with sterile surgical scissors and tweezers the next day. The tissue was evenly coated in a culture bottle every 2–3 mm, placed in a constant temperature CO2 incubator, placed flat for 1.5h, and then placed vertically for 3.5h to absorb the meat slime that fell from the bottom. Slowly add 3–4 mL mixed culture medium containing DEME low sugar + 10% FBS + 1% double antibody to the bottom, also known as complete culture medium, and then add 1–2 mL culture medium after 3 days of culture. Wait until the cells have crawled out and start changing fluids twice a week. When the cell density reaches more than 90%, it is transmitted to 2 generations. 2.9. qPCR Experiment To quantify the expression of COL4A1 at the mRNA level, total RNA was extracted from fibroblasts using the TRIzol® reagent (Invitrogen) following the manufacturer’s instructions. The extracted RNA was subjected to reverse transcription using the High-Capacity cDNA Reverse Transcription Kit (Applied Biosystems), and the resulting cDNA was used as a template for quantitative polymerase chain reaction (qPCR). The qPCR was performed using SYBR® Green Master Mix (Thermo Fisher Scientific) on an ABI 7500 Real-Time PCR System. Specific primers targeting COL4A1 were employed, and GAPDH served as the endogenous control. Relative expression levels were calculated using the 2^-ΔΔCt method, and all assays were conducted in triplicate to ensure reproducibility of the results. 2.10. Western Blot Western blot analysis was employed to determine the protein expression levels of COL4A1 in fibroblast samples. Cells were lysed in RIPA buffer containing protease and phosphatase inhibitors (Sigma-Aldrich), and the protein concentration was determined using the BCA Protein Assay Kit (Thermo Fisher Scientific). Equal amounts of protein lysates were separated by SDS-PAGE and subsequently transferred to PVDF membranes (Millipore). The membranes were blocked with 5% non-fat milk in TBST and then incubated overnight at 4°C with primary antibodies against COL4A1 (Abcam) and GAPDH (as a loading control). After washing, the membranes were incubated with HRP-conjugated secondary antibodies, and signals were detected using an enhanced chemiluminescence (ECL) system (Amersham). The intensity of the bands was quantified using ImageJ software, and the relative expression levels of COL4A1 were normalized to GAPDH. 2.11. Fibroblast Cloning To assess the role of COL4A1 in fibroblast colony formation, a colony formation assay was conducted. Fibroblasts were seeded into 6-well plates at a density of 1×10³ cells per well. The cells were evenly distributed and cultured in a medium supplemented with 10% foetal bovine serum (FBS) for 10 days. Following the incubation period, the cells were fixed with 4% paraformaldehyde and stained with 0.1% crystal violet. Colonies consisting of 30 or more cells were counted under a light microscope. The experiment was repeated in triplicate to ensure consistency, and the results were analyzed to evaluate the clonogenic potential of fibroblasts influenced by COL4A1 expression. 2.12. Invasion Experiment To evaluate the impact of COL4A1 on fibroblast invasion, a Transwell invasion assay was performed. Fibroblasts were harvested and resuspended in a serum-free medium before being seeded into the upper chamber of a Transwell insert (Corning, NY, USA) coated with Matrigel (BD Biosciences) to simulate the extracellular matrix. The lower chamber was filled with medium containing 10% FBS, serving as a chemoattractant. The cells were allowed to invade through the Matrigel-coated membrane over a 24-hour incubation period at 37°C. After incubation, non-invading cells on the upper surface of the membrane were removed, while the invading cells on the lower surface were fixed with methanol and stained with 0.1% crystal violet. The stained cells were visualized and counted under an Olympus microscope (Tokyo, Japan), and the invasion assay was quantified by counting the average number of invaded cells across four randomly selected fields. This experiment was conducted in triplicate to ensure statistical robustness. 2.13. Statistical Analysis All data calculations and statistical analyses were conducted using R programming ( https://www.r-project.org/ , version 4.0.2). Comparisons of continuous variables between two groups were performed using the independent Student t-test for normally distributed variables and the Mann-Whitney U test (Wilcoxon rank-sum test) for non-normally distributed variables. All statistical P-values were two-sided, with P < 0.05 considered statistically significant. 3. Results 3.1. Differential Gene Screening and Functional Enrichment in Scleroderma Scars Based on the GEO data platform, we summarized the scleroderma scar and normal tissue data from the high-throughput dataset GSE130955. Genes with an expression value of zero were removed, and when encountering multiple molecules with the same name, only the molecule with the highest expression value was retained. After preprocessing the scleroderma scar data, the total number of filtered molecules was 18,532. We extracted differentially expressed genes from the gene expression matrix using R software, as shown in the heatmap and volcano plot (Fig. 1 A-B). With the criteria of P 1.5 or FC < -1.5, we identified 58 downregulated genes and 823 upregulated genes. The GO and KEGG enrichment results for the 58 downregulated genes are shown in the circular heatmap (Fig. 1 C), mainly enriched in the following functions and pathways: glycoprotein complex, heme binding, tetrapyrrole binding, haptoglobin binding, oxidoreductase activity, viral myocarditis, dilated cardiomyopathy, and cortisol synthesis and secretion. The GO and KEGG enrichment results for the 823 upregulated genes are shown in the circular heatmap (Fig. 1 D), mainly enriched in the following functions and pathways: extracellular matrix structural constituent, glycosaminoglycan binding, integrin binding, heparin-binding, cytokine binding, cytokine-cytokine receptor interaction, Staphylococcus aureus infection, complement and coagulation cascades, amoebiasis, and viral protein interaction with cytokine and cytokine receptor. Additionally, we performed Bayesian clustering on the GO analysis of the upregulated genes. The GO enrichment analysis results primarily focused on cellular binding and receptors (Fig. 1 E). Finally, we conducted a Gene Set Enrichment Analysis (GSEA) based on the gene expression profile data from the GSE130955 dataset. GSEA results indicated that the biological processes between scleroderma scars and normal tissues are primarily related to the following biological phenomena (Fig. 1 F). (A) Heatmap: This panel displays the differential gene expression between scleroderma scar tissues and normal samples. Each row represents a gene, and each column represents a sample. The color gradient from brown (high expression) to cyan (low expression) indicates the variation in gene expression levels. (B) Volcano Plot: This panel illustrates the distribution of differentially expressed genes in scleroderma scar tissues. Upregulated genes are shown in red, downregulated genes in blue, and non-significantly changed genes in gray. The significance threshold is P 1.5 or FC < 0.05. (C) Circular Heatmap of Enrichment Analysis: This panel shows the GO and KEGG functional enrichment results for the 58 downregulated genes. (D) Circular Heatmap of Enrichment Analysis: This panel displays the GO and KEGG functional enrichment results for the 823 upregulated genes. (E) Bayesian Clustering Results of GO Enrichment Analysis for MF: This panel shows the Bayesian clustering results of the GO enrichment analysis focusing on molecular function (MF) for the upregulated genes. (F) Gene Set Enrichment Analysis (GSEA) Results: This panel indicates that the primary biological processes distinguishing scleroderma scars from normal tissues are associated with various biological phenomena. 3.2. Screening of Scleroderma Driver Genes To identify driver genes involved in the pathogenesis of scleroderma based on RNA-seq results, we first performed clustering analysis (Figs. 2 A-B) and principal component analysis (PCA) on all patient samples. The Adjusted Rand Index (ARI) demonstrated that scleroderma patients and normal controls could be well distinguished. Subsequently, we calculated driver gene activity (Driver Activity, DA) using the SJARACNe algorithm based on the gene expression data of the patients. We then ranked the top 15 driver genes based on DA and differential gene FC values (Figs. 2 C-D). The identified driver genes are TCF23, PENK, GPR88, EFEMP1, ADAMTS18, SRD5A2, CYP4Z1, SLC1A3, CYP4A22, ALPL, NEDD9, SULF1, CNTNAP2, AKAP12, and COL4A1. 3.3. Enrichment Analysis of Scleroderma Driver Genes First, we visualized the differential expression of the downstream targets of the driver genes (Fig. 3 A). Subsequently, we conducted functional enrichment analysis on the driver genes (Fig. 3 B). The enrichment results indicated that genes associated with scleroderma drivers (TCF23, PENK, GPR88, EFEMP1, ADAMTS18, SRD5A2, CYP4Z1, SLC1A3, CYP4A22, ALPL, NEDD9, SULF1, CNTNAP2, AKAP12, COL4A1) were primarily enriched in the following biological processes: "VECCHI_GASTRIC_CANCER_ADVANCED_VS_EARLY_UP", "TURASHVILI_BREAST_DUCTAL_CARCINOMA_VS_DUCTAL_NORMAL_UP", "HALLMARK_APICAL_SURFACE", "HALLMARK_REACTIVE_OXYGEN_SPECIES_PATHWAY" (Figs. 4 A-B). 3.4. Single-Cell Atlas of Scleroderma Scar Tissue We analyzed the single-cell dataset of scleroderma scar tissue using the Seurat package. Dimensionality reduction of the data was performed using UMAP, displaying the distribution of different cell types (Fig. 5 A and Figure S1 ). The analysis results are as follows: Fibroblasts: 14,096; Endothelial cells: 13,925; Smooth muscle cells: 5,925; Keratinocytes KRT1: 3,702; Keratinocytes KRT5: 2,715; Immune cells: 1,460; Lymphatic endothelial cells: 1,063; Neural cells: 255; Melanocytes: 177; Sweat gland cells: 113. These cell types clustered significantly in the scleroderma scar tissue, suggesting their potential key roles in scar formation. The bubble plot illustrates the differentially expressed genes specific to each cell type. The volcano plot shows the significantly differentially expressed genes between scleroderma scar tissue and normal tissue (Fig. 5 B-C). The bar chart displays the quantity and proportion of different cell types, indicating variability in cell type numbers and proportions among different patients and groups (scleroderma scar group and normal scar group) (Fig. 5 D-F). 3.5. Single-Cell Enrichment Analysis of Scleroderma Scar Tissue GO functional enrichment analysis reveals the specific functions and roles of various cell types in the formation of scleroderma scars. Each cell type shows unique enrichment terms, indicating their involvement in distinct biological processes and molecular functions: Fibroblasts are significantly enriched in GO terms related to the extracellular matrix containing collagen, endoplasmic reticulum lumen, fibroblast, and basement membrane, highlighting their crucial role in scleroderma scar formation through functions associated with the extracellular matrix and basement membrane structures (Fig. 6 A). Endothelial cells are enriched in terms such as endothelial cell membrane, extracellular matrix containing collagen, and platelet granules, suggesting their primary involvement in vascular functions and extracellular matrix maintenance within the scar tissue (Fig. 6 B). Smooth muscle cells show significant enrichment in the basement membrane, contractile fiber, and stress fiber terms, implying their role in scar formation via contractile and stress fiber functions (Fig. 6 C). Keratinocytes KRT1 and KRT5 are enriched in GO terms like a cornified envelope, desmosome, and intermediate filament, indicating their involvement in scleroderma scar formation through cytoskeletal and membrane structure functions (Fig. 6 D-E). Immune cells exhibit enrichment in terms including MHC class I protein complex, endoplasmic reticulum lumen, and endoplasmic reticulum membrane, which suggests their role in antigen presentation and immune responses during scar formation (Fig. 6 F). Lymphatic endothelial cells are enriched in cortical cytoskeleton and endocytic vesicle membrane terms, indicating their function in scleroderma scar formation might be related to endocytosis and cytoskeletal activities (Fig. 6 G). Neural cells show enrichment in terms such as myelin sheath and neuron cell body, suggesting their involvement in scar formation through nerve conduction and myelin-related functions (Fig. 6 H). Melanocytes are significantly enriched in pigment granule membrane and melanosome terms, indicating their role in pigmentation processes within the scar tissue (Fig. 6 I). Sweat gland cells exhibit enrichment in terms like apical plasma membrane, apical plasma membrane region, and apical junction complex, suggesting their role in scar formation through functions related to the apical plasma membrane and cell junctions (Fig. 6 J). 3.6. Differential Expression of Driver Genes Using the GSE130955 dataset, we analyzed the expression levels of driver genes in control (Con) and scleroderma (SSc) patients. Each violin plot illustrates the distribution and median of specific gene expression in both groups. The results indicate significant differences in gene expression between scleroderma patients and controls: TCF23 (Fig. 7 A), PENK (Fig. 7 B), GPR88 (Fig. 7 C), EFEMP1 (Fig. 7 D), ADAMTS18 (Fig. 7 E), SRD5A2 (Fig. 7 F), SLC1A3 (Fig. 7 H), ALPL (Fig. 7 J), NEDD9 (Fig. 7 K), SULF1 (Fig. 7 L), CNTNAP2 (Fig. 7 M), AKAP12 (Fig. 7 N), and COL4A1 (Fig. 7 O) showed higher expression in SSc patients, while CYP4Z1 (Fig. 7 G) and CYP4A22 (Fig. 7 I) showed lower expression. These findings suggest that the expression levels of these driver genes are significantly altered in scleroderma patients compared to controls, indicating their potential roles in the disease's pathology. 3.7. Driver Gene Pathway Activity and Interactions Between Different Cell Types in Scleroderma First, we performed a Protein-Protein Interaction (PPI) analysis on 15 driver genes, and COL4A1 as a Key Driver Gene in Scleroderma (Fig. 8 A). Subsequently, we inferred the activity of these 15 driver genes across different cell types in a single-cell dataset using four methods (AUCell, UCell, singscore, ssgsea). We found that the driver gene activity was predominantly concentrated in fibroblasts (Fig. 8 B). Finally, we analyzed cell-cell interactions using CellChat. The network diagram revealed strong interactions between keratinocytes, smooth muscle cells, endothelial cells, and immune cells. These cell types may play key roles in the pathological process of scleroderma (Fig. 8 C). The heatmap of signaling pathway activity showed significant differences in the activity of COLLAGEN, LAMININ, and CD99 pathways among different cell types. Notably, keratinocytes, smooth muscle cells, and endothelial cells exhibited higher activity in the COLLAGEN and LAMININ pathways, which may be associated with fibrosis and tissue remodeling in scleroderma (Fig. 8 D). These results suggest that different cell types play important roles in the pathogenesis of scleroderma through complex interactions and signaling pathways. These findings provide important clues for further research into the pathological mechanisms and potential therapeutic targets of scleroderma. 3.8. COL4A1 as new Driver Gene in Scleroderma Due to COL4A1 as a Key Driver Gene in Scleroderma. We took COL4A1 as a research subject. To investigate the role played by COL4A1 in scleroderma, we obtained skin tissue samples from scleroderma and volunteers from the clinic. qRT-PCR and western blot results showed that the expression of COL4A1 in tissues of scleroderma patients was significantly higher than that in normal tissues (Fig. 9 A-B). To confirm the role of COL4A1 in scleroderma, we first extracted human primary fibroblasts and elevated the COL4A1 expression level in them with overexpression plasmids (Fig. 9 C). To verify the effect of COL4A1 on fibroblast proliferation, we used cell colony formation, CCK-8, and Ki-67 assays, which showed that COL4A1 overexpression significantly increased fibroblast proliferation (Fig. 9 D-F). To verify the effect of COL4A1 on the invasive ability of fibroblasts, we utilized cell transwell assays, which showed that COL4A1 overexpression significantly increased the invasive ability of fibroblasts (Fig. 9 G). 4. Discussion Wound healing and keloid formation involve a variety of cell types, inflammatory and immune responses, and growth factors. Similarly, scleroderma scar formation is a complex process that lacks a consensus on the most effective treatment with minimal side effects ( 17 ). Emerging evidence highlights the importance of chronic inflammation and changes in the immune microenvironment in keloid and scleroderma scar formation. Persistent inflammation disrupts the balance between ECM synthesis and degradation during wound healing, leading to excessive type I and III collagen secretion by fibroblasts, the main components of ECM, thereby contributing to keloid and scleroderma scar formation ( 18 ). In this study, we assessed the cell types and functional differences in scleroderma scars using scRNA and bulk RNA sequencing data, identifying novel immune-related signatures based on public databases. This approach is crucial for exploring the immune-related mechanisms of scleroderma scar formation and identifying new therapeutic targets. Through dimensionality reduction and annotation of single-cell data, we identified various cell types in scleroderma scars. Pseudotime analysis displayed the clustering results of these cells along the differentiation trajectory. We observed significant differences in the proportion of endothelial cells between the scleroderma scar and control groups. Functional differences were investigated using GO and KEGG enrichment analysis. Although the exact pathogenesis of scleroderma scars remains unclear, their proliferation is supported by corresponding nutritional supplies. The increase in endothelial cells and a new microvascular system in scleroderma scars provide the necessary material basis for their expansion. Previous studies have shown significantly higher blood flow perfusion in hypertrophic scleroderma scars compared to normal skin ( 19 ). Additionally, newly formed blood vessels in the dermal reticular layer of scleroderma scars were significantly increased, with proliferative fibroblasts and newly secreted collagen clustering around neovascularization. These findings suggest that the pathogenesis of scleroderma scars is closely related to angiogenesis and the increase of vascular endothelial cells. Based on transcriptome data, we identified DEGs in scleroderma scars and explored their roles in scar genesis through functional and pathway enrichment analysis. We extracted the GO and KEGG enrichment results for endothelial cells in the disease and control groups, intersecting these with the DEGs from bulk transcriptome data. The results indicated that they participate in immune-related pathways and functions. This suggests that functional differences between the scleroderma scar and control groups in both single-cell and bulk transcriptome data are mediated through immune-related pathways. To identify immunity-related biomarkers, we collected 2,483 immune-related genes from the Import database. The intersections of DEGs of endothelial cells from scRNA sequencing, DEGs from bulk transcriptome data, and immune-related genes were identified as immune-related signatures in scleroderma scars. Furthermore, we performed a Protein-Protein Interaction (PPI) analysis on 15 driver genes, and COL4A1 as a Key Driver Gene in Scleroderma. We found that the driver gene activity was predominantly concentrated in fibroblasts. To validate COL4A1 as the new Driver Gene in Scleroderma, we performed cellular function experiments, and COL4A1 overexpression was shown to significantly enhance fibroblast proliferation and invasion in vitro . Previous studies have demonstrated the crucial role of COL4A1 in inflammation and fibroblast activity, processes central to the progression of fibrotic diseases such as scleroderma ( 20 ).COL4A1 has been shown to regulate the extracellular matrix (ECM), influencing tissue remodeling and fibrosis through its interactions with integrins and other ECM components. For example, in liver fibrosis, COL4A1 promotes fibroblast activation by upregulating integrin-mediated signaling, leading to increased collagen deposition and exacerbation of fibrosis ( 21 ). Similarly, in pulmonary fibrosis, COL4A1 has been found to enhance the secretion of pro-fibrotic cytokines like TGF-β1, which, in turn, stimulates myofibroblast differentiation and perpetuates the fibrotic cycle ( 22 ). These findings underscore the role of COL4A1 in enhancing fibroblast proliferation and ECM remodeling, both critical drivers of fibrosis. Despite the clear role of COL4A1 in fibrosis and inflammation, its involvement in scleroderma-specific mechanisms remains underexplored. While COL4A1 has been studied extensively in other fibrotic diseases like pulmonary fibrosis and liver fibrosis ( 23 ), research into its role in scleroderma, particularly in the regulation of fibroblast activation and inflammation, is still lacking. In summary, while COL4A1 is a well-established regulator of inflammatory and fibroblastic processes in various fibrotic diseases, its precise role in scleroderma remains insufficiently understood. Expanding research on COL4A1 could provide novel insights into the molecular mechanisms underlying scleroderma and open new avenues for therapeutic intervention. The present study has several strengths, including the use of single-cell and bulk RNA sequencing to provide a comprehensive analysis of gene expression in scleroderma tissues. However, the study has limitations. The study relies on in vitro functional assays to validate the role of COL4A1. Further, in vivo studies are necessary to confirm the clinical relevance of our findings. Despite these limitations, our use of advanced bioinformatics tools, such as Seurat for single-cell analysis and WGCNA for network construction, ensures that our findings provide a robust platform for further research. 5. Conclusions This study highlights COL4A1 as a key driver gene in scleroderma, contributing to fibroblast proliferation and invasion. The findings from our single-cell and bulk RNA sequencing analyses reveal potential therapeutic targets for scleroderma and possibly other fibrotic disorders. Targeting COL4A1 could lead to new therapies that mitigate fibrosis in scleroderma and improve patient outcomes. Moreover, the identified immune-related signatures and fibroblast-specific genes provide a roadmap for future therapeutic strategies focused on immune modulation. These results have significant clinical implications and should inspire additional research aimed at validating these targets in clinical settings. Abbreviations Systemic sclerosis (SSc) Differentially expressed genes (DEGs) Localized scleroderma (LS) Single-cell RNA (scRNA) Diffuse cutaneous systemic sclerosis (dcSSc) Principal Component Analysis (PCA) Uniform Manifold Approximation and Projection (UMAP) Kyoto Encyclopedia of Genes and Genomes (KEGG) Cellular components (CC). Biological processes (BP) Molecular functions (MF), Gene Ontology (GO) Gene Set Enrichment Analysis (GSEA) False discovery rate (FDR) Quantitative polymerase chain reaction (qPCR) Enhanced chemiluminescence (ECL) Principal component analysis (PCA) Protein-Protein Interaction (PPI) Declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Availability of data and materials All data supporting the findings of this study are available within the paper and its Supplementary Information. Competing interests The authors declare that they have no competing interests. Funding Not applicable. Authors' contributions Conceptualization, R.M.; methodology, R.M. and C.W.; validation, R.M. and C.W.; formal analysis, R.M. and C.W.; investigation, R.M.; resources, R.M.; data curation, R.M.; writing-original draft preparation, R.M.; writing—review and editing, R.M. and C.W.; visualization, R.M. and C.W.; supervision, R.M.; project administration, R.M. Two authors have read and agreed to the published version of the manuscript. Acknowledgments Not applicable. References Berman, B., Maderal, A. & Raphael, B. Keloids and Hypertrophic Scars. Dermatol. Surg. 43 , S3–18 (2017). Shin, T. M. & Bordeaux, J. S. The Role of Massage in Scar Management: A Literature Review. Dermatol. Surg. 38 , 414–423 (2012). Bayat, A., Arscott, G., Ollier, W. E. R., Ferguson, M. W. J. & Mc Grouther, D. A. Description of site-specific morphology of keloid phenotypes in an Afrocaribbean population. Br. J. Plast. Surg. 57 , 122–133 (2004). Hong, Y-K. et al. Inflammation in Wound Healing and Pathological Scarring. Adv. Wound Care . https://doi.org/10.1089/wound.2021.0161 (2022). Fanous, A. et al. Treatment of Keloid Scars with Botulinum Toxin Type A versus Triamcinolone in an Athymic Nude Mouse Model. Plast. Reconstr. Surg. 143 , 760–767 (2019). Trisliana Perdanasari, A. et al. Recent Developments in the Use of Intralesional Injections Keloid Treatment. Archives Plast. Surg. 41 , 620–629 (2014). Ojeh, N., Bharatha, A., Gaur, U. & Forde, A. L. Keloids: Current and emerging therapies. Scars Burns Healing . 6 , 205951312094049 (2020). Hedlund, E. & Deng, Q. Single-cell RNA sequencing: Technical advancements and biological applications. Mol. Aspects Med. 59 , 36–46 (2018). Haque, A., Engel, J., Teichmann, S. A. & Lönnberg, T. A practical guide to single-cell RNA-sequencing for biomedical research and clinical applications. Genome Med. ; 9 . (2017). Skaug, B. et al. Global skin gene expression analysis of early diffuse cutaneous systemic sclerosis shows a prominent innate and adaptive inflammatory profile. Ann. Rheum. Dis. 79 , 379–386 (2019). Deng, C-C. et al. Single-cell RNA-seq reveals fibroblast heterogeneity and increased mesenchymal fibroblasts in human fibrotic skin diseases. Nat. Commun. 12 , 3709 (2021). Costa-Silva, J., Domingues, D. & Lopes, F. M. RNA-Seq differential expression analysis: An extended review and a software tool. PLOS ONE . 12 , e0190152 (2017). Alireza Khatamian, Paull, E. O., Califano, A. & Yu, J. SJARACNe: a scalable software tool for gene network reverse engineering from big data. ; 35 :2165–2166. (2019). Subramanian, A. et al. Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proceedings of the National Academy of Sciences. ;102:15545–50. (2005). Fan, C. et al. irGSEA: the integration of single-cell rank-based gene set enrichment analysis. Brief. Bioinform. ; 25 . (2024). Jin, S. et al. Inference and analysis of cell-cell communication using CellChat. Nat. Commun. ; 12 . (2021). Zhao, M., Wu, J., Wu, H., Sawalha, A. H. & Lu, Q. Clinical Treatment Options in Scleroderma: Recommendations and Comprehensive Review. Clin. Rev. Allergy Immunol. https://doi.org/10.1007/s12016-020-08831-4 (2021). Ferreli, C. et al. Cutaneous Manifestations of Scleroderma and Scleroderma-Like Disorders: a Comprehensive Review. Clin. Rev. Allergy Immunol. 53 , 306–336 (2017). Jinnin, M. Narrow-sense and broad-sense vascular abnormalities of systemic sclerosis. Immunological Med. 43 , 107–114 (2020). Frommer, M. L. et al. Single-Cell Analysis of ADSC Interactions with Fibroblasts and Endothelial Cells in Scleroderma Skin. Cells 12 , 1784 (2023). Devos, H., Jérôme, Z., Roubelakis, M. G. & Agnieszka Latosinska, V. A. Reviewing the Regulators of COL1A1. Int. J. Mol. Sci. 24 , 10004–10004 (2023). Savin, I. A., Markov, A. V., Zenkova, M. A. & Sen’kova, A. V. Asthma and Post-Asthmatic Fibrosis: A Search for New Promising Molecular Markers of Transition from Acute Inflammation to Pulmonary Fibrosis. Biomedicines 10 , 1017 (2022). Liu, Y. et al. The correlation and role analysis of COL4A1 and COL4A2 in hepatocarcinogenesis. Aging 12 , 204–223 (2020). Additional Declarations No competing interests reported. Supplementary Files FigureS1.pdf Westernnew.pdf Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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1","display":"","copyAsset":false,"role":"figure","size":424847,"visible":true,"origin":"","legend":"\u003cp\u003eDifferential Gene Screening and Functional Enrichment in Scleroderma Scars.\u003c/p\u003e\n\u003cp\u003e(A) Heatmap: This panel displays the differential gene expression between scleroderma scar tissues and normal samples. Each row represents a gene, and each column represents a sample. The color gradient from brown (high expression) to cyan (low expression) indicates the variation in gene expression levels. (B) Volcano Plot: This panel illustrates the distribution of differentially expressed genes in scleroderma scar tissues. Upregulated genes are shown in red, downregulated genes in blue, and non-significantly changed genes in gray. The significance threshold is P \u0026lt; 0.05 with a fold change (FC) \u0026gt; 1.5 or FC \u0026lt; 0.05. (C) Circular Heatmap of Enrichment Analysis: This panel shows the GO and KEGG functional enrichment results for the 58 downregulated genes. (D) Circular Heatmap of Enrichment Analysis: This panel displays the GO and KEGG functional enrichment results for the 823 upregulated genes. (E) Bayesian Clustering Results of GO Enrichment Analysis for MF: This panel shows the Bayesian clustering results of the GO enrichment analysis focusing on molecular function (MF) for the upregulated genes. (F) Gene Set Enrichment Analysis (GSEA) Results: This panel indicates that the primary biological processes distinguishing scleroderma scars from normal tissues are associated with various biological phenomena.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-7376155/v1/ef56a78f3c00ef70eeaffe54.png"},{"id":93238902,"identity":"571fb882-578c-4caf-a470-51c325827915","added_by":"auto","created_at":"2025-10-10 14:36:29","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":405176,"visible":true,"origin":"","legend":"\u003cp\u003eScreening of Scleroderma Driver Genes. (A) Sample Similarity Heatmap: This panel shows the similarity between samples using color coding, with red indicating high similarity and blue indicating low similarity. (B) PCA Analysis: The left plot displays the distribution of samples from the control group (red) and the scleroderma group (blue). The right plot shows the expression of the Adjusted Rand Index (ARI) for the samples. (C) Driver Gene Expression Heatmap: This panel presents the expression levels of the identified driver genes, with red indicating high expression and blue indicating low expression. (D) Heatmap of Differentially Expressed Genes and Driver Genes: This panel shows upregulated genes in red and downregulated genes in blue. The bar plot on the right indicates the logFC values of these genes.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-7376155/v1/40e9312658d3740cb5f53bf8.png"},{"id":93238920,"identity":"a72774f8-0475-4a1c-afe8-7a26097eaa87","added_by":"auto","created_at":"2025-10-10 14:36:30","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":295875,"visible":true,"origin":"","legend":"\u003cp\u003eExpression and Functional Enrichment Analysis of Scleroderma Driver Genes. (A) Differential Expression Analysis of SSc Driver Genes: The upper part of the line graph shows the distribution of t-values for genes between SSc and control (Con) groups, with red indicating genes highly expressed in SSc and blue indicating genes highly expressed in Con. The lower part of the bar chart displays significantly differentially expressed driver genes (DA), with the number of target genes (Target Size) for each gene shown on the left. The heatmap on the right shows the significance p-values for DA genes (DA) and differential expression p-values (DE). (B) Functional Enrichment Analysis of Driver Genes: The bar chart displays significantly enriched biological processes and signaling pathways. The length of each bar represents the p-value, and the associated driver genes are listed on the right side of each bar.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-7376155/v1/addaec7709f6aa17b4b96aaf.png"},{"id":93238927,"identity":"05f87223-4b8d-4c1c-9884-49bb9f6ec4f4","added_by":"auto","created_at":"2025-10-10 14:36:30","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":365976,"visible":true,"origin":"","legend":"\u003cp\u003eEnrichment Analysis of Scleroderma Driver Genes. (A) Heatmap of Driver Gene Functional Enrichment: The X-axis represents significantly enriched biological processes and signaling pathways. The Y-axis shows the names of the driver genes. The deeper the color of the cells, the more significant the association. The right side indicates the p-values of the significantly enriched biological processes and signaling pathways. (B) Bubble Plot of Driver Gene Targets: The Y-axis represents the names of the enriched signaling pathways. The X-axis represents the names of the driver genes. The size of the bubbles indicates the number of target genes. The color of the bubbles represents the significant p-value, with darker colors indicating smaller (more significant) p-values. The bar chart shows the number of target genes for each driver gene, with blue representing all target genes and green representing protein-coding genes.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-7376155/v1/0a39ac6407b38f1c6a2ddb05.png"},{"id":93240316,"identity":"dd07b1f6-428a-4c66-9cd0-7c3345e26e1c","added_by":"auto","created_at":"2025-10-10 14:44:31","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":156207,"visible":true,"origin":"","legend":"\u003cp\u003eSingle-Cell Atlas of Scleroderma Scar Tissue. (A) Single-Cell UMAP Dimensionality Reduction Analysis: UMAP analysis displays the distribution of different cell types in scleroderma scar tissue, including Fibroblasts, Endothelial cells, Smooth muscle cells, Keratinocytes KRT1, Keratinocytes KRT5, Immune cells, Lymphatic endothelial cells, Neural cells, Melanocytes, and Sweat gland cells. (B) Marker Gene Bubble Plot: The bubble plot shows the differentially expressed genes specific to each cell type. The color and size of the bubbles represent the significance and expression level of the genes, respectively. (C) Volcano Plot: The volcano plot illustrates the significantly differentially expressed genes between scleroderma scar tissue and normal tissue. Red dots represent significantly upregulated genes, green dots represent significantly downregulated genes, and black dots represent genes with no significant changes. (D-F) Stacked Bar Charts: The bar charts display the quantity and proportion of different cell types in different patients and groups (scleroderma scar group and normal scar group). Each segment's length in the bar indicates the number or proportion of the corresponding cell type.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-7376155/v1/fbd93c4d981a86f0a8d2b4d9.png"},{"id":93238931,"identity":"34a85411-4a52-407d-bf74-82357741a23e","added_by":"auto","created_at":"2025-10-10 14:36:30","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":253976,"visible":true,"origin":"","legend":"\u003cp\u003eGene Ontology (GO) Functional Enrichment Analysis Results for Different Cell Types. (A-J) GO functional enrichment bar charts are presented for various cell types: (A) Fibroblasts, (B) Endothelial Cells, (C) Smooth Muscle Cells, (D) Keratinocytes KRT1, (E) Keratinocytes KRT5, (F) Immune Cells, (G) Lymphatic Endothelial Cells, (H) Neural Cells, (I) Melanocytes, and (J) Sweat Gland Cells.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-7376155/v1/7944abb7a16d3606d74dc09b.png"},{"id":93238929,"identity":"f45cc742-9f22-424e-82c0-f8cf18ad2931","added_by":"auto","created_at":"2025-10-10 14:36:30","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":205843,"visible":true,"origin":"","legend":"\u003cp\u003eExpression Levels of Differentially Expressed Genes in Scleroderma (SSc) and Control (Con) Groups. Violin plots showing the expression levels of various genes in control and scleroderma patients. The results indicate significant differences in gene expression between scleroderma patients and controls: (A) TCF23, (B) PENK, (C) GPR88, (D) EFEMP1, (E) ADAMTS18, (F) SRD5A2, (H) SLC1A3, (J) ALPL, (K) NEDD9, (L) SULF1, (M) CNTNAP2, (N) AKAP12, and (O) COL4A1 showed higher expression in SSc patients, while (H) CYP4Z1 and (I) CYP4A22 showed lower expression.\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-7376155/v1/607b30610e63ff79c5670bba.png"},{"id":93238941,"identity":"33014129-142a-4f1f-9a1f-2cf488b9cda2","added_by":"auto","created_at":"2025-10-10 14:36:31","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":291445,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of Cell Interactions and Signaling Pathways in Systemic Sclerosis (SSc). (A) PPI Analysis of Driver Genes. (B) Inference of Driver Gene Activity in Single-Cell Datasets Using Four Different Methods (AUCell, UCell, singscore, ssgsea). UMAP plots show changes in cell density in SSc and control groups. The colors represent cell density, with brighter colors indicating higher density values. (C) Network Diagram of Interactions Between Different Cell Types: Nodes represent cell types, and the color and thickness of the edges indicate the quantity and strength of interactions. The size of the nodes represents the number of interactions for that cell type. (D) Heatmap of Activity for Three Major Signaling Pathways (COLLAGEN, LAMININ, CD99) in Different Cell Types. The horizontal axis represents signaling pathways, the vertical axis represents cell types, and the colors represent signaling pathway activity, with deeper colors indicating higher activity.\u003c/p\u003e","description":"","filename":"image8.png","url":"https://assets-eu.researchsquare.com/files/rs-7376155/v1/6f1a014fb112ffee535d2592.png"},{"id":93240319,"identity":"330da25b-ff7d-4ba7-92b2-220f470bc3f1","added_by":"auto","created_at":"2025-10-10 14:44:31","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":94616,"visible":true,"origin":"","legend":"\u003cp\u003eCOL4A1 as a New Driver Gene in Scleroderma. (A-B) The expression of COL4A1 mRNA and protein was measured by qRT-PCR and Western blot in healthy and SSc patients. (C) The expression of COL4A1 was detected by qRT-PCR after COL4A1 overexpression in human fibroblasts. (D-F) The effect of COL4A1 on cell proliferation was measured by colony formation, CCK-8, and Ki-67 assays. (G) The effect of COL4A1 on cell invasion was measured by transwell assay.\u003c/p\u003e","description":"","filename":"image9.png","url":"https://assets-eu.researchsquare.com/files/rs-7376155/v1/25d5ec088c1196659079a1d0.png"},{"id":93243793,"identity":"599cc14b-b23a-4ba0-8505-a106214f489e","added_by":"auto","created_at":"2025-10-10 15:00:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3593981,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7376155/v1/9007ae68-026c-440c-90ec-d5f2236ae016.pdf"},{"id":93238905,"identity":"789c7389-49ba-4639-a20d-dc4554f3cf62","added_by":"auto","created_at":"2025-10-10 14:36:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":5611253,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7376155/v1/1237396ca0eb8cf3e424cdcb.pdf"},{"id":93238910,"identity":"31d5ad5e-1ba9-44bb-bbe2-7678d32d9053","added_by":"auto","created_at":"2025-10-10 14:36:30","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":262903,"visible":true,"origin":"","legend":"","description":"","filename":"Westernnew.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7376155/v1/04fd977464e181c10296f4c2.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Unveiling COL4A1 as a Key Driver Gene in scleroderma: Insights from Single-Cell and Bulk RNA Sequencing","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eKeloids are benign fibroproliferative tumors in the dermis that arise due to abnormal wound healing, characterized by excessive activation of myofibroblasts and collagen deposition (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Unlike normal scars, which typically do not extend beyond the wound and gradually diminish over time, keloids extend beyond the boundaries of the initial injury (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) often appearing on the chest, back, and earlobes, and are accompanied by pain and itching (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). This pathological form of healing shares many properties with tumors, such as hyperplasia beyond the wound and invasive growth, significantly affecting patients' psychosomatic health and quality of life. The inflammatory response, involving a variety of immune cells and cytokines, plays a crucial role in keloid formation, though the exact pathological mechanisms remain unclear (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Current treatments, including surgical resection, radiotherapy, and local glucocorticoid injections, suffer from high recurrence rates and can exacerbate the condition, complicating management (\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Advances in public databases now enable in-depth exploration of the genetic mechanisms and immune microenvironment of keloids, which is essential for developing effective clinical treatments and reducing recurrence rates. Understanding these mechanisms can promote the development of new treatment regimens, improving prognosis and quality of life for keloid patients.\u003c/p\u003e\u003cp\u003eSystemic sclerosis (SSc) is an immune-mediated rheumatic disease characterized by skin fibrosis and internal organ involvement, triggered by early vascular lesions and immune system dysfunction, leading to inflammation and tissue fibrosis. Localized scleroderma (LS) is similar to SSc but primarily manifests as localized skin thickening without internal organ involvement (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Keloids, on the other hand, are fibrotic proliferative disorders reflecting abnormal wound healing processes, often occurring after trauma, burns, or surgery (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Skin fibrotic diseases, including SSc, LS, and keloids, share common features such as fibroblast proliferation and extracellular matrix deposition. Despite current treatment options like methotrexate, mycophenolate mofetil, and local corticosteroid injections having limited efficacy and high recurrence rates, there is an urgent need to understand the underlying mechanisms of these diseases to develop more effective anti-fibrotic therapies. The inflammatory response plays a significant role in these conditions, where downregulation of pro-inflammatory cytokines and upregulation of anti-inflammatory cytokines may reduce scar tissue formation (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Overall, controlling the inflammatory process remains a fundamental goal in the prevention and treatment of these fibrotic diseases.\u003c/p\u003e\u003cp\u003eSingle-cell RNA (scRNA) sequencing is a cutting-edge method whose utility in gene expression analysis has been proven to identify a variety of cell types and subtypes previously associated with diseases(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). This technique enables researchers to use cell clustering to investigate differences in gene expression and cellular evolution across different populations. Keloids consist of diverse cell types, such as fibroblasts, myofibroblasts, and vascular endothelial cells, making scRNA sequencing an accurate tool for examining the genetic makeup of keloids.\u003c/p\u003e\u003cp\u003eIn this study, we employed single-cell and somatic bioinformatics analyses to investigate the immune-related genes and their potential regulatory mechanisms in keloids through transcriptomic approaches. This analysis provides an in-depth insight into the cellular interactions and molecular pathways that play a role in keloid pathology, offering potential targets for developing new therapeutic interventions.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1. Data Download and Preprocessing\u003c/h2\u003e\u003cp\u003eWe downloaded a high-throughput RNA sequencing dataset (GSE130955) and a single-cell RNA sequencing dataset (GSE163973) related to skin fibrosis diseases from the 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). The sequencing platform for the GSE130955 dataset is GPL16791 Illumina HiSeq 2500 (Homo sapiens), containing skin biopsy samples from 48 early diffuse cutaneous systemic sclerosis (dcSSc) patients and 33 healthy controls. The GSE163973 dataset was sequenced on the GPL24676 Illumina NovaSeq 6000 (Homo sapiens) platform and includes dermal fibroblasts from 3 keloid cases and 3 normal scar tissues. All samples were derived from Homo sapiens. For the GSE130955 dataset, raw data were stored in FASTQ format. We performed quality control using the FastQC tool and removed low-quality reads and adapter sequences using the Trimmomatic tool. Subsequently, we aligned the clean reads to the human reference genome (GRCh38) using the STAR software and quantified expression levels using featureCounts software. For the GSE163973 dataset, we processed single-cell data using the Seurat (version 4.0) R package. Initially, we used the emptyDrops function from the DropletUtils package to remove empty droplets and the doubletFinder_v3 function to identify and remove doublets, screening a total of 43,431 cells and 23,753 genes. The data were then normalized (LogNormalize) and highly variable genes were selected (FindVariableFeatures). All genes were subsequently scaled (ScaleData), and dimensionality reduction was performed using Principal Component Analysis (PCA), with significant principal components identified using the ElbowPlot function. Finally, we used the first 35 principal components for Uniform Manifold Approximation and Projection (UMAP) visualization to display different cell subpopulations.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2. Differentially Expressed Gene Screening\u003c/h2\u003e\u003cp\u003eWe screened for differentially expressed genes (DEGs) in the scleroderma scar data using the limma\u0026thinsp;+\u0026thinsp;voom package. Volcano plots of DEGs were created using the ggplot2 package, and heatmaps were generated using the pheatmap package to display the differential expression of DEGs. DEGs were identified with p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and |FC| \u0026gt;1.5 after data normalization using the voom function from the limma package.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3. Identification of Driver Genes Using the SJARACNe Algorithm\u003c/h2\u003e\u003cp\u003eWe used the SJARACNe algorithm to identify driver genes in the disease process of scleroderma. SJARACNe is a gene network reverse engineering tool based on mutual information (MI) that reconstructs gene regulatory networks from large-scale gene expression data[13]. SJARACNe was run 100 times for bootstrap analysis to enhance network reconstruction robustness and accuracy. Genes with high connectivity (hub genes) in the reconstructed gene regulatory network were identified as potential driver genes.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4. Functional Enrichment Analysis\u003c/h2\u003e\u003cp\u003eGene Ontology (GO) analysis is a commonly used method for large-scale functional enrichment studies, including biological processes (BP), molecular functions (MF), and cellular components (CC). GO annotation analysis was performed using the clusterProfiler package in R for the differentially expressed genes in the immune intersection, with an FDR threshold of \u0026lt;\u0026thinsp;0.05 considered statistically significant.\u003c/p\u003e\u003cp\u003eWe performed pathway enrichment analysis using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database, which is a resource for understanding high-level functions and biological systems (e.g., cells, organs, organisms). KEGG pathway analysis was also conducted using the clusterProfiler package in R, with an FDR threshold of \u0026lt;\u0026thinsp;0.05 considered statistically significant.\u003c/p\u003e\u003cp\u003eWe performed pathway enrichment analysis using the Molecular Signatures Database (MSigDB, v7.5.1), a resource for understanding high-level functions and biological systems (e.g., cells, organs, organisms). MSigDB pathway analysis, focusing on Hallmark and C2:KEGG gene sets, was conducted using the clusterProfiler package in R, with an FDR threshold of \u0026lt;\u0026thinsp;0.05 considered statistically significant.\u003c/p\u003e\u003cp\u003eTo investigate biological process differences between groups, we performed Gene Set Enrichment Analysis (GSEA) on the gene expression profile dataset of scleroderma patients using MSigDB gene sets. GSEA is a computational method for determining whether a predefined set of genes shows statistically significant differences between two biological states, typically used to estimate pathway and biological process activity changes in expression datasets (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). A false discovery rate (FDR)\u0026thinsp;\u0026lt;\u0026thinsp;0.25 was considered significantly enriched.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5. Single-Cell Gene Set Scoring\u003c/h2\u003e\u003cp\u003eWe evaluated the performance of multiple gene set scoring methods on single-cell RNA sequencing data using the irGSEA R package (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). The irGSEA package integrates six different scoring methods based on gene expression ranking, emphasizing relative expression levels rather than absolute values. These methods include AUCell, UCell, Singscore, ssGSEA, JASMINE, and Viper.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.6. Cell-Cell Communication Analysis\u003c/h2\u003e\u003cp\u003eThe CellChat (version 1.1.3) R package was used to analyze cell-cell communication networks from single-cell transcriptome sequencing data (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Using the built-in CellChatDB database, we quantitatively inferred and analyzed cell-cell communication networks from standardized single-cell RNA sequencing data. The subsetCommunication function was used to infer and transfer cell-cell communication information, with edge.weight.max set to max to compare edge weights between different networks. Circle plots were used to display cell group interactions and bubble plots were used to quantify all significant ligand-receptor pairs in cell-cell signal transmission.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e2.7. Collection of Specimens from Scar Patients\u003c/h2\u003e\u003cp\u003eScar tissue specimens were collected under sterile conditions from patients presenting with hypertrophic scars or keloids at Sir Run Run Shaw Hospital, affiliated with Zhejiang University School of Medicine. Tissue samples were excised from the affected areas using surgical instruments, following established protocols. The excised specimens were immediately placed in RNAlater\u0026reg; (Thermo Fisher Scientific) to stabilize RNA, ensuring the integrity of molecular data for subsequent analyses. All methods were carried out in accordance with relevant guidelines and regulations, including the Declaration of Helsinki. All experimental protocols were approved by the Sir Run Run Shaw Hospital Ethics Committee (Approval No. 20240601-008), ensuring minimal patient discomfort and adherence to ethical standards. Informed patient consent was obtained prior to specimen collection, and all samples were anonymized to protect patient identity.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e2.8. Extraction of human primary fibroblasts\u003c/h2\u003e\u003cp\u003eClinical samples were obtained from the hospital, rinsed repeatedly in the sterilized 1%-2% PBS solution three times, the connective tissue was removed, and the skin tissue was placed in 0.2% Dispase\u0026thinsp;+\u0026thinsp;1.0g /L DMEM mixed digestive solution for cold digestion at 4℃ overnight, and the epidermal tissue was peeled off with sterile surgical scissors and tweezers the next day. The tissue was evenly coated in a culture bottle every 2\u0026ndash;3 mm, placed in a constant temperature CO2 incubator, placed flat for 1.5h, and then placed vertically for 3.5h to absorb the meat slime that fell from the bottom. Slowly add 3\u0026ndash;4 mL mixed culture medium containing DEME low sugar\u0026thinsp;+\u0026thinsp;10% FBS\u0026thinsp;+\u0026thinsp;1% double antibody to the bottom, also known as complete culture medium, and then add 1\u0026ndash;2 mL culture medium after 3 days of culture. Wait until the cells have crawled out and start changing fluids twice a week. When the cell density reaches more than 90%, it is transmitted to 2 generations.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e2.9. qPCR Experiment\u003c/h2\u003e\u003cp\u003eTo quantify the expression of COL4A1 at the mRNA level, total RNA was extracted from fibroblasts using the TRIzol\u0026reg; reagent (Invitrogen) following the manufacturer\u0026rsquo;s instructions. The extracted RNA was subjected to reverse transcription using the High-Capacity cDNA Reverse Transcription Kit (Applied Biosystems), and the resulting cDNA was used as a template for quantitative polymerase chain reaction (qPCR). The qPCR was performed using SYBR\u0026reg; Green Master Mix (Thermo Fisher Scientific) on an ABI 7500 Real-Time PCR System. Specific primers targeting COL4A1 were employed, and GAPDH served as the endogenous control. Relative expression levels were calculated using the 2^-ΔΔCt method, and all assays were conducted in triplicate to ensure reproducibility of the results.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e2.10. Western Blot\u003c/h2\u003e\u003cp\u003eWestern blot analysis was employed to determine the protein expression levels of COL4A1 in fibroblast samples. Cells were lysed in RIPA buffer containing protease and phosphatase inhibitors (Sigma-Aldrich), and the protein concentration was determined using the BCA Protein Assay Kit (Thermo Fisher Scientific). Equal amounts of protein lysates were separated by SDS-PAGE and subsequently transferred to PVDF membranes (Millipore). The membranes were blocked with 5% non-fat milk in TBST and then incubated overnight at 4\u0026deg;C with primary antibodies against COL4A1 (Abcam) and GAPDH (as a loading control). After washing, the membranes were incubated with HRP-conjugated secondary antibodies, and signals were detected using an enhanced chemiluminescence (ECL) system (Amersham). The intensity of the bands was quantified using ImageJ software, and the relative expression levels of COL4A1 were normalized to GAPDH.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e2.11. Fibroblast Cloning\u003c/h2\u003e\u003cp\u003eTo assess the role of COL4A1 in fibroblast colony formation, a colony formation assay was conducted. Fibroblasts were seeded into 6-well plates at a density of 1\u0026times;10\u0026sup3; cells per well. The cells were evenly distributed and cultured in a medium supplemented with 10% foetal bovine serum (FBS) for 10 days. Following the incubation period, the cells were fixed with 4% paraformaldehyde and stained with 0.1% crystal violet. Colonies consisting of 30 or more cells were counted under a light microscope. The experiment was repeated in triplicate to ensure consistency, and the results were analyzed to evaluate the clonogenic potential of fibroblasts influenced by COL4A1 expression.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e2.12. Invasion Experiment\u003c/h2\u003e\u003cp\u003eTo evaluate the impact of COL4A1 on fibroblast invasion, a Transwell invasion assay was performed. Fibroblasts were harvested and resuspended in a serum-free medium before being seeded into the upper chamber of a Transwell insert (Corning, NY, USA) coated with Matrigel (BD Biosciences) to simulate the extracellular matrix. The lower chamber was filled with medium containing 10% FBS, serving as a chemoattractant. The cells were allowed to invade through the Matrigel-coated membrane over a 24-hour incubation period at 37\u0026deg;C. After incubation, non-invading cells on the upper surface of the membrane were removed, while the invading cells on the lower surface were fixed with methanol and stained with 0.1% crystal violet. The stained cells were visualized and counted under an Olympus microscope (Tokyo, Japan), and the invasion assay was quantified by counting the average number of invaded cells across four randomly selected fields. This experiment was conducted in triplicate to ensure statistical robustness.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e2.13. Statistical Analysis\u003c/h2\u003e\u003cp\u003eAll data calculations and statistical analyses were conducted using R programming (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.r-project.org/\u003c/span\u003e\u003cspan address=\"https://www.r-project.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, version 4.0.2). Comparisons of continuous variables between two groups were performed using the independent Student t-test for normally distributed variables and the Mann-Whitney U test (Wilcoxon rank-sum test) for non-normally distributed variables. All statistical P-values were two-sided, with P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 considered statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e3.1. Differential Gene Screening and Functional Enrichment in Scleroderma Scars\u003c/h2\u003e\u003cp\u003eBased on the GEO data platform, we summarized the scleroderma scar and normal tissue data from the high-throughput dataset GSE130955. Genes with an expression value of zero were removed, and when encountering multiple molecules with the same name, only the molecule with the highest expression value was retained. After preprocessing the scleroderma scar data, the total number of filtered molecules was 18,532. We extracted differentially expressed genes from the gene expression matrix using R software, as shown in the heatmap and volcano plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA-B). With the criteria of P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and FC\u0026thinsp;\u0026gt;\u0026thinsp;1.5 or FC \u0026lt; -1.5, we identified 58 downregulated genes and 823 upregulated genes. The GO and KEGG enrichment results for the 58 downregulated genes are shown in the circular heatmap (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC), mainly enriched in the following functions and pathways: glycoprotein complex, heme binding, tetrapyrrole binding, haptoglobin binding, oxidoreductase activity, viral myocarditis, dilated cardiomyopathy, and cortisol synthesis and secretion. The GO and KEGG enrichment results for the 823 upregulated genes are shown in the circular heatmap (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD), mainly enriched in the following functions and pathways: extracellular matrix structural constituent, glycosaminoglycan binding, integrin binding, heparin-binding, cytokine binding, cytokine-cytokine receptor interaction, Staphylococcus aureus infection, complement and coagulation cascades, amoebiasis, and viral protein interaction with cytokine and cytokine receptor.\u003c/p\u003e\u003cp\u003eAdditionally, we performed Bayesian clustering on the GO analysis of the upregulated genes. The GO enrichment analysis results primarily focused on cellular binding and receptors (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE). Finally, we conducted a Gene Set Enrichment Analysis (GSEA) based on the gene expression profile data from the GSE130955 dataset. GSEA results indicated that the biological processes between scleroderma scars and normal tissues are primarily related to the following biological phenomena (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e(A) Heatmap: This panel displays the differential gene expression between scleroderma scar tissues and normal samples. Each row represents a gene, and each column represents a sample. The color gradient from brown (high expression) to cyan (low expression) indicates the variation in gene expression levels. (B) Volcano Plot: This panel illustrates the distribution of differentially expressed genes in scleroderma scar tissues. Upregulated genes are shown in red, downregulated genes in blue, and non-significantly changed genes in gray. The significance threshold is P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 with a fold change (FC)\u0026thinsp;\u0026gt;\u0026thinsp;1.5 or FC\u0026thinsp;\u0026lt;\u0026thinsp;0.05. (C) Circular Heatmap of Enrichment Analysis: This panel shows the GO and KEGG functional enrichment results for the 58 downregulated genes. (D) Circular Heatmap of Enrichment Analysis: This panel displays the GO and KEGG functional enrichment results for the 823 upregulated genes. (E) Bayesian Clustering Results of GO Enrichment Analysis for MF: This panel shows the Bayesian clustering results of the GO enrichment analysis focusing on molecular function (MF) for the upregulated genes. (F) Gene Set Enrichment Analysis (GSEA) Results: This panel indicates that the primary biological processes distinguishing scleroderma scars from normal tissues are associated with various biological phenomena.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e3.2. Screening of Scleroderma Driver Genes\u003c/h2\u003e\u003cp\u003eTo identify driver genes involved in the pathogenesis of scleroderma based on RNA-seq results, we first performed clustering analysis (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA-B) and principal component analysis (PCA) on all patient samples. The Adjusted Rand Index (ARI) demonstrated that scleroderma patients and normal controls could be well distinguished. Subsequently, we calculated driver gene activity (Driver Activity, DA) using the SJARACNe algorithm based on the gene expression data of the patients. We then ranked the top 15 driver genes based on DA and differential gene FC values (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC-D). The identified driver genes are TCF23, PENK, GPR88, EFEMP1, ADAMTS18, SRD5A2, CYP4Z1, SLC1A3, CYP4A22, ALPL, NEDD9, SULF1, CNTNAP2, AKAP12, and COL4A1.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e3.3. Enrichment Analysis of Scleroderma Driver Genes\u003c/h2\u003e\u003cp\u003eFirst, we visualized the differential expression of the downstream targets of the driver genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Subsequently, we conducted functional enrichment analysis on the driver genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). The enrichment results indicated that genes associated with scleroderma drivers (TCF23, PENK, GPR88, EFEMP1, ADAMTS18, SRD5A2, CYP4Z1, SLC1A3, CYP4A22, ALPL, NEDD9, SULF1, CNTNAP2, AKAP12, COL4A1) were primarily enriched in the following biological processes: \"VECCHI_GASTRIC_CANCER_ADVANCED_VS_EARLY_UP\", \"TURASHVILI_BREAST_DUCTAL_CARCINOMA_VS_DUCTAL_NORMAL_UP\", \"HALLMARK_APICAL_SURFACE\", \"HALLMARK_REACTIVE_OXYGEN_SPECIES_PATHWAY\" (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA-B).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003e3.4. Single-Cell Atlas of Scleroderma Scar Tissue\u003c/h2\u003e\u003cp\u003eWe analyzed the single-cell dataset of scleroderma scar tissue using the Seurat package. Dimensionality reduction of the data was performed using UMAP, displaying the distribution of different cell types (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA and \u003cb\u003eFigure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e). The analysis results are as follows: Fibroblasts: 14,096; Endothelial cells: 13,925; Smooth muscle cells: 5,925; Keratinocytes KRT1: 3,702; Keratinocytes KRT5: 2,715; Immune cells: 1,460; Lymphatic endothelial cells: 1,063; Neural cells: 255; Melanocytes: 177; Sweat gland cells: 113. These cell types clustered significantly in the scleroderma scar tissue, suggesting their potential key roles in scar formation. The bubble plot illustrates the differentially expressed genes specific to each cell type. The volcano plot shows the significantly differentially expressed genes between scleroderma scar tissue and normal tissue (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB-C). The bar chart displays the quantity and proportion of different cell types, indicating variability in cell type numbers and proportions among different patients and groups (scleroderma scar group and normal scar group) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD-F).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003e3.5. Single-Cell Enrichment Analysis of Scleroderma Scar Tissue\u003c/h2\u003e\u003cp\u003eGO functional enrichment analysis reveals the specific functions and roles of various cell types in the formation of scleroderma scars. Each cell type shows unique enrichment terms, indicating their involvement in distinct biological processes and molecular functions:\u003c/p\u003e\u003cp\u003eFibroblasts are significantly enriched in GO terms related to the extracellular matrix containing collagen, endoplasmic reticulum lumen, fibroblast, and basement membrane, highlighting their crucial role in scleroderma scar formation through functions associated with the extracellular matrix and basement membrane structures (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA).\u003c/p\u003e\u003cp\u003eEndothelial cells are enriched in terms such as endothelial cell membrane, extracellular matrix containing collagen, and platelet granules, suggesting their primary involvement in vascular functions and extracellular matrix maintenance within the scar tissue (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB).\u003c/p\u003e\u003cp\u003eSmooth muscle cells show significant enrichment in the basement membrane, contractile fiber, and stress fiber terms, implying their role in scar formation via contractile and stress fiber functions (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC).\u003c/p\u003e\u003cp\u003eKeratinocytes KRT1 and KRT5 are enriched in GO terms like a cornified envelope, desmosome, and intermediate filament, indicating their involvement in scleroderma scar formation through cytoskeletal and membrane structure functions (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD-E).\u003c/p\u003e\u003cp\u003eImmune cells exhibit enrichment in terms including MHC class I protein complex, endoplasmic reticulum lumen, and endoplasmic reticulum membrane, which suggests their role in antigen presentation and immune responses during scar formation (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eF).\u003c/p\u003e\u003cp\u003eLymphatic endothelial cells are enriched in cortical cytoskeleton and endocytic vesicle membrane terms, indicating their function in scleroderma scar formation might be related to endocytosis and cytoskeletal activities (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eG).\u003c/p\u003e\u003cp\u003eNeural cells show enrichment in terms such as myelin sheath and neuron cell body, suggesting their involvement in scar formation through nerve conduction and myelin-related functions (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eH).\u003c/p\u003e\u003cp\u003eMelanocytes are significantly enriched in pigment granule membrane and melanosome terms, indicating their role in pigmentation processes within the scar tissue (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eI).\u003c/p\u003e\u003cp\u003eSweat gland cells exhibit enrichment in terms like apical plasma membrane, apical plasma membrane region, and apical junction complex, suggesting their role in scar formation through functions related to the apical plasma membrane and cell junctions (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eJ).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003e3.6. Differential Expression of Driver Genes\u003c/h2\u003e\u003cp\u003eUsing the GSE130955 dataset, we analyzed the expression levels of driver genes in control (Con) and scleroderma (SSc) patients. Each violin plot illustrates the distribution and median of specific gene expression in both groups. The results indicate significant differences in gene expression between scleroderma patients and controls: TCF23 (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA), PENK (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB), GPR88 (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC), EFEMP1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eD), ADAMTS18 (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eE), SRD5A2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eF), SLC1A3 (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eH), ALPL (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eJ), NEDD9 (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eK), SULF1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eL), CNTNAP2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eM), AKAP12 (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eN), and COL4A1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eO) showed higher expression in SSc patients, while CYP4Z1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eG) and CYP4A22 (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eI) showed lower expression. These findings suggest that the expression levels of these driver genes are significantly altered in scleroderma patients compared to controls, indicating their potential roles in the disease's pathology.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec23\" class=\"Section2\"\u003e\u003ch2\u003e3.7. Driver Gene Pathway Activity and Interactions Between Different Cell Types in Scleroderma\u003c/h2\u003e\u003cp\u003eFirst, we performed a Protein-Protein Interaction (PPI) analysis on 15 driver genes, and COL4A1 as a Key Driver Gene in Scleroderma (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA). Subsequently, we inferred the activity of these 15 driver genes across different cell types in a single-cell dataset using four methods (AUCell, UCell, singscore, ssgsea). We found that the driver gene activity was predominantly concentrated in fibroblasts (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eB). Finally, we analyzed cell-cell interactions using CellChat. The network diagram revealed strong interactions between keratinocytes, smooth muscle cells, endothelial cells, and immune cells. These cell types may play key roles in the pathological process of scleroderma (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eC).\u003c/p\u003e\u003cp\u003eThe heatmap of signaling pathway activity showed significant differences in the activity of COLLAGEN, LAMININ, and CD99 pathways among different cell types. Notably, keratinocytes, smooth muscle cells, and endothelial cells exhibited higher activity in the COLLAGEN and LAMININ pathways, which may be associated with fibrosis and tissue remodeling in scleroderma (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eD). These results suggest that different cell types play important roles in the pathogenesis of scleroderma through complex interactions and signaling pathways. These findings provide important clues for further research into the pathological mechanisms and potential therapeutic targets of scleroderma.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\u003ch2\u003e3.8. COL4A1 as new Driver Gene in Scleroderma\u003c/h2\u003e\u003cp\u003eDue to COL4A1 as a Key Driver Gene in Scleroderma. We took COL4A1 as a research subject. To investigate the role played by COL4A1 in scleroderma, we obtained skin tissue samples from scleroderma and volunteers from the clinic. qRT-PCR and western blot results showed that the expression of COL4A1 in tissues of scleroderma patients was significantly higher than that in normal tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eA-B). To confirm the role of COL4A1 in scleroderma, we first extracted human primary fibroblasts and elevated the COL4A1 expression level in them with overexpression plasmids (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eC). To verify the effect of COL4A1 on fibroblast proliferation, we used cell colony formation, CCK-8, and Ki-67 assays, which showed that COL4A1 overexpression significantly increased fibroblast proliferation (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eD-F). To verify the effect of COL4A1 on the invasive ability of fibroblasts, we utilized cell transwell assays, which showed that COL4A1 overexpression significantly increased the invasive ability of fibroblasts (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eG).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eWound healing and keloid formation involve a variety of cell types, inflammatory and immune responses, and growth factors. Similarly, scleroderma scar formation is a complex process that lacks a consensus on the most effective treatment with minimal side effects (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Emerging evidence highlights the importance of chronic inflammation and changes in the immune microenvironment in keloid and scleroderma scar formation. Persistent inflammation disrupts the balance between ECM synthesis and degradation during wound healing, leading to excessive type I and III collagen secretion by fibroblasts, the main components of ECM, thereby contributing to keloid and scleroderma scar formation (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn this study, we assessed the cell types and functional differences in scleroderma scars using scRNA and bulk RNA sequencing data, identifying novel immune-related signatures based on public databases. This approach is crucial for exploring the immune-related mechanisms of scleroderma scar formation and identifying new therapeutic targets. Through dimensionality reduction and annotation of single-cell data, we identified various cell types in scleroderma scars. Pseudotime analysis displayed the clustering results of these cells along the differentiation trajectory.\u003c/p\u003e\u003cp\u003eWe observed significant differences in the proportion of endothelial cells between the scleroderma scar and control groups. Functional differences were investigated using GO and KEGG enrichment analysis. Although the exact pathogenesis of scleroderma scars remains unclear, their proliferation is supported by corresponding nutritional supplies. The increase in endothelial cells and a new microvascular system in scleroderma scars provide the necessary material basis for their expansion. Previous studies have shown significantly higher blood flow perfusion in hypertrophic scleroderma scars compared to normal skin (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Additionally, newly formed blood vessels in the dermal reticular layer of scleroderma scars were significantly increased, with proliferative fibroblasts and newly secreted collagen clustering around neovascularization. These findings suggest that the pathogenesis of scleroderma scars is closely related to angiogenesis and the increase of vascular endothelial cells.\u003c/p\u003e\u003cp\u003eBased on transcriptome data, we identified DEGs in scleroderma scars and explored their roles in scar genesis through functional and pathway enrichment analysis. We extracted the GO and KEGG enrichment results for endothelial cells in the disease and control groups, intersecting these with the DEGs from bulk transcriptome data. The results indicated that they participate in immune-related pathways and functions. This suggests that functional differences between the scleroderma scar and control groups in both single-cell and bulk transcriptome data are mediated through immune-related pathways.\u003c/p\u003e\u003cp\u003eTo identify immunity-related biomarkers, we collected 2,483 immune-related genes from the Import database. The intersections of DEGs of endothelial cells from scRNA sequencing, DEGs from bulk transcriptome data, and immune-related genes were identified as immune-related signatures in scleroderma scars. Furthermore, we performed a Protein-Protein Interaction (PPI) analysis on 15 driver genes, and COL4A1 as a Key Driver Gene in Scleroderma. We found that the driver gene activity was predominantly concentrated in fibroblasts. To validate COL4A1 as the new Driver Gene in Scleroderma, we performed cellular function experiments, and COL4A1 overexpression was shown to significantly enhance fibroblast proliferation and invasion \u003cem\u003ein vitro\u003c/em\u003e.\u003c/p\u003e\u003cp\u003ePrevious studies have demonstrated the crucial role of COL4A1 in inflammation and fibroblast activity, processes central to the progression of fibrotic diseases such as scleroderma (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e).COL4A1 has been shown to regulate the extracellular matrix (ECM), influencing tissue remodeling and fibrosis through its interactions with integrins and other ECM components. For example, in liver fibrosis, COL4A1 promotes fibroblast activation by upregulating integrin-mediated signaling, leading to increased collagen deposition and exacerbation of fibrosis (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Similarly, in pulmonary fibrosis, COL4A1 has been found to enhance the secretion of pro-fibrotic cytokines like TGF-β1, which, in turn, stimulates myofibroblast differentiation and perpetuates the fibrotic cycle (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). These findings underscore the role of COL4A1 in enhancing fibroblast proliferation and ECM remodeling, both critical drivers of fibrosis. Despite the clear role of COL4A1 in fibrosis and inflammation, its involvement in scleroderma-specific mechanisms remains underexplored. While COL4A1 has been studied extensively in other fibrotic diseases like pulmonary fibrosis and liver fibrosis (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e), research into its role in scleroderma, particularly in the regulation of fibroblast activation and inflammation, is still lacking. In summary, while COL4A1 is a well-established regulator of inflammatory and fibroblastic processes in various fibrotic diseases, its precise role in scleroderma remains insufficiently understood. Expanding research on COL4A1 could provide novel insights into the molecular mechanisms underlying scleroderma and open new avenues for therapeutic intervention.\u003c/p\u003e\u003cp\u003eThe present study has several strengths, including the use of single-cell and bulk RNA sequencing to provide a comprehensive analysis of gene expression in scleroderma tissues. However, the study has limitations. The study relies on in vitro functional assays to validate the role of COL4A1. Further, in vivo studies are necessary to confirm the clinical relevance of our findings. Despite these limitations, our use of advanced bioinformatics tools, such as Seurat for single-cell analysis and WGCNA for network construction, ensures that our findings provide a robust platform for further research.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eThis study highlights COL4A1 as a key driver gene in scleroderma, contributing to fibroblast proliferation and invasion. The findings from our single-cell and bulk RNA sequencing analyses reveal potential therapeutic targets for scleroderma and possibly other fibrotic disorders. Targeting COL4A1 could lead to new therapies that mitigate fibrosis in scleroderma and improve patient outcomes. Moreover, the identified immune-related signatures and fibroblast-specific genes provide a roadmap for future therapeutic strategies focused on immune modulation. These results have significant clinical implications and should inspire additional research aimed at validating these targets in clinical settings.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eSystemic sclerosis (SSc)\u003c/p\u003e\u003cp\u003eDifferentially expressed genes (DEGs)\u003c/p\u003e\u003cp\u003eLocalized scleroderma (LS)\u003c/p\u003e\u003cp\u003eSingle-cell RNA (scRNA)\u003c/p\u003e\u003cp\u003eDiffuse cutaneous systemic sclerosis (dcSSc)\u003c/p\u003e\u003cp\u003ePrincipal Component Analysis (PCA)\u003c/p\u003e\u003cp\u003eUniform Manifold Approximation and Projection (UMAP)\u003c/p\u003e\u003cp\u003eKyoto Encyclopedia of Genes and Genomes (KEGG)\u003c/p\u003e\u003cp\u003eCellular components (CC).\u003c/p\u003e\u003cp\u003eBiological processes (BP)\u003c/p\u003e\u003cp\u003eMolecular functions (MF),\u003c/p\u003e\u003cp\u003eGene Ontology (GO)\u003c/p\u003e\u003cp\u003eGene Set Enrichment Analysis (GSEA)\u003c/p\u003e\u003cp\u003eFalse discovery rate (FDR)\u003c/p\u003e\u003cp\u003eQuantitative polymerase chain reaction (qPCR)\u003c/p\u003e\u003cp\u003eEnhanced chemiluminescence (ECL)\u003c/p\u003e\u003cp\u003ePrincipal component analysis (PCA)\u003c/p\u003e\u003cp\u003eProtein-Protein Interaction (PPI)\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data supporting the findings of this study are available within the paper and its Supplementary Information.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization, R.M.; methodology, R.M. and C.W.; validation, R.M. and C.W.; formal analysis, R.M. and C.W.; investigation, R.M.; resources, R.M.; data curation, R.M.; writing-original draft preparation, R.M.; writing\u0026mdash;review and editing, R.M. and C.W.; visualization, R.M. and C.W.; supervision, R.M.; project administration, R.M. Two authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBerman, B., Maderal, A. \u0026amp; Raphael, B. Keloids and Hypertrophic Scars. \u003cem\u003eDermatol. Surg.\u003c/em\u003e \u003cb\u003e43\u003c/b\u003e, S3\u0026ndash;18 (2017).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShin, T. M. \u0026amp; Bordeaux, J. S. The Role of Massage in Scar Management: A Literature Review. \u003cem\u003eDermatol. Surg.\u003c/em\u003e \u003cb\u003e38\u003c/b\u003e, 414\u0026ndash;423 (2012).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBayat, A., Arscott, G., Ollier, W. E. R., Ferguson, M. W. J. \u0026amp; Mc Grouther, D. A. Description of site-specific morphology of keloid phenotypes in an Afrocaribbean population. \u003cem\u003eBr. J. Plast. Surg.\u003c/em\u003e \u003cb\u003e57\u003c/b\u003e, 122\u0026ndash;133 (2004).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHong, Y-K. et al. Inflammation in Wound Healing and Pathological Scarring. \u003cem\u003eAdv. Wound Care\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1089/wound.2021.0161\u003c/span\u003e\u003cspan address=\"10.1089/wound.2021.0161\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFanous, A. et al. Treatment of Keloid Scars with Botulinum Toxin Type A versus Triamcinolone in an Athymic Nude Mouse Model. \u003cem\u003ePlast. Reconstr. Surg.\u003c/em\u003e \u003cb\u003e143\u003c/b\u003e, 760\u0026ndash;767 (2019).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTrisliana Perdanasari, A. et al. Recent Developments in the Use of Intralesional Injections Keloid Treatment. \u003cem\u003eArchives Plast. Surg.\u003c/em\u003e \u003cb\u003e41\u003c/b\u003e, 620\u0026ndash;629 (2014).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOjeh, N., Bharatha, A., Gaur, U. \u0026amp; Forde, A. L. Keloids: Current and emerging therapies. \u003cem\u003eScars Burns Healing\u003c/em\u003e. \u003cb\u003e6\u003c/b\u003e, 205951312094049 (2020).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHedlund, E. \u0026amp; Deng, Q. Single-cell RNA sequencing: Technical advancements and biological applications. \u003cem\u003eMol. Aspects Med.\u003c/em\u003e \u003cb\u003e59\u003c/b\u003e, 36\u0026ndash;46 (2018).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHaque, A., Engel, J., Teichmann, S. A. \u0026amp; L\u0026ouml;nnberg, T. A practical guide to single-cell RNA-sequencing for biomedical research and clinical applications. \u003cem\u003eGenome Med.\u003c/em\u003e ;\u003cb\u003e9\u003c/b\u003e. (2017).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSkaug, B. et al. Global skin gene expression analysis of early diffuse cutaneous systemic sclerosis shows a prominent innate and adaptive inflammatory profile. \u003cem\u003eAnn. Rheum. Dis.\u003c/em\u003e \u003cb\u003e79\u003c/b\u003e, 379\u0026ndash;386 (2019).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDeng, C-C. et al. Single-cell RNA-seq reveals fibroblast heterogeneity and increased mesenchymal fibroblasts in human fibrotic skin diseases. \u003cem\u003eNat. Commun.\u003c/em\u003e \u003cb\u003e12\u003c/b\u003e, 3709 (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCosta-Silva, J., Domingues, D. \u0026amp; Lopes, F. M. RNA-Seq differential expression analysis: An extended review and a software tool. \u003cem\u003ePLOS ONE\u003c/em\u003e. \u003cb\u003e12\u003c/b\u003e, e0190152 (2017).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAlireza Khatamian, Paull, E. O., Califano, A. \u0026amp; Yu, J. SJARACNe: a scalable software tool for gene network reverse engineering from big data. ;\u003cb\u003e35\u003c/b\u003e:2165\u0026ndash;2166. (2019).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSubramanian, A. et al. Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proceedings of the National Academy of Sciences. ;102:15545\u0026ndash;50. (2005).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFan, C. et al. irGSEA: the integration of single-cell rank-based gene set enrichment analysis. \u003cem\u003eBrief. Bioinform.\u003c/em\u003e ;\u003cb\u003e25\u003c/b\u003e. (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJin, S. et al. Inference and analysis of cell-cell communication using CellChat. \u003cem\u003eNat. Commun.\u003c/em\u003e ;\u003cb\u003e12\u003c/b\u003e. (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhao, M., Wu, J., Wu, H., Sawalha, A. H. \u0026amp; Lu, Q. Clinical Treatment Options in Scleroderma: Recommendations and Comprehensive Review. \u003cem\u003eClin. Rev. Allergy Immunol.\u003c/em\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s12016-020-08831-4\u003c/span\u003e\u003cspan address=\"10.1007/s12016-020-08831-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFerreli, C. et al. Cutaneous Manifestations of Scleroderma and Scleroderma-Like Disorders: a Comprehensive Review. \u003cem\u003eClin. Rev. Allergy Immunol.\u003c/em\u003e \u003cb\u003e53\u003c/b\u003e, 306\u0026ndash;336 (2017).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJinnin, M. Narrow-sense and broad-sense vascular abnormalities of systemic sclerosis. \u003cem\u003eImmunological Med.\u003c/em\u003e \u003cb\u003e43\u003c/b\u003e, 107\u0026ndash;114 (2020).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFrommer, M. L. et al. Single-Cell Analysis of ADSC Interactions with Fibroblasts and Endothelial Cells in Scleroderma Skin. \u003cem\u003eCells\u003c/em\u003e \u003cb\u003e12\u003c/b\u003e, 1784 (2023).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDevos, H., J\u0026eacute;r\u0026ocirc;me, Z., Roubelakis, M. G. \u0026amp; Agnieszka Latosinska, V. A. Reviewing the Regulators of COL1A1. \u003cem\u003eInt. J. Mol. Sci.\u003c/em\u003e \u003cb\u003e24\u003c/b\u003e, 10004\u0026ndash;10004 (2023).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSavin, I. A., Markov, A. V., Zenkova, M. A. \u0026amp; Sen\u0026rsquo;kova, A. V. Asthma and Post-Asthmatic Fibrosis: A Search for New Promising Molecular Markers of Transition from Acute Inflammation to Pulmonary Fibrosis. \u003cem\u003eBiomedicines\u003c/em\u003e \u003cb\u003e10\u003c/b\u003e, 1017 (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu, Y. et al. The correlation and role analysis of COL4A1 and COL4A2 in hepatocarcinogenesis. \u003cem\u003eAging\u003c/em\u003e \u003cb\u003e12\u003c/b\u003e, 204\u0026ndash;223 (2020).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Keloids and systemic sclerosis, Single-cell RNA sequencing, Driver genes, COL4A1","lastPublishedDoi":"10.21203/rs.3.rs-7376155/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7376155/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eKeloids and systemic sclerosis (SSc) are fibrotic disorders characterized by abnormal wound healing, leading to excessive collagen deposition and tissue fibrosis. The inflammatory and immune responses play critical roles in their pathogenesis, yet the molecular mechanisms remain elusive. We utilized high-throughput RNA sequencing (GSE130955) and single-cell RNA sequencing (GSE163973) datasets from the GEO database to explore gene expression profiles in scleroderma and keloid tissues. Differentially expressed genes (DEGs) were identified using the limma\u0026thinsp;+\u0026thinsp;voom package. Driver genes were detected through the SJARACNe algorithm, and functional enrichment was analyzed via GO and KEGG. Additionally, cell-cell communication networks were explored using the CellChat package, and gene expression validation was performed using qPCR and Western blot techniques. A total of 823 upregulated and 58 downregulated genes were identified in scleroderma scars, with significant enrichment in pathways related to extracellular matrix organization and immune responses. Key driver genes, including TCF23, PENK, and COL4A1, were identified, showing differential expression between scleroderma patients and controls. Single-cell RNA sequencing revealed key cell types involved in scar formation, particularly fibroblasts and endothelial cells, and highlighted the importance of intercellular signaling through collagen and laminin pathways. COL4A1 overexpression was shown to significantly enhance fibroblast proliferation and invasion in vitro. Our study uncovers novel molecular signatures and cellular interactions involved in the pathogenesis of keloids and scleroderma scars, providing potential therapeutic targets for these fibrotic conditions.\u003c/p\u003e","manuscriptTitle":"Unveiling COL4A1 as a Key Driver Gene in scleroderma: Insights from Single-Cell and Bulk RNA Sequencing","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-10 14:36:23","doi":"10.21203/rs.3.rs-7376155/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":"7413c16f-41c6-4d59-b69e-efbf16710322","owner":[],"postedDate":"October 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":55797756,"name":"Health sciences/Biomarkers"},{"id":55797757,"name":"Biological sciences/Cell biology"},{"id":55797758,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":55797759,"name":"Health sciences/Diseases"},{"id":55797760,"name":"Biological sciences/Genetics"},{"id":55797761,"name":"Biological sciences/Immunology"},{"id":55797762,"name":"Biological sciences/Molecular biology"}],"tags":[],"updatedAt":"2025-10-10T14:36:26+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-10 14:36:23","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7376155","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7376155","identity":"rs-7376155","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}