Identified BIRC5 and HDAC1 as Novel Diagnostic Biomarkers Linked to Centrosome-Immune Crosstalk for Cutaneous Squamous Cell Carcinoma via Machine Learning-Based Multi-omics Analysis

Identified BIRC5 and HDAC1 as Novel Diagnostic Biomarkers Linked to Centrosome-Immune Crosstalk for Cutaneous Squamous Cell Carcinoma via Machine Learning-Based Multi-omics Analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Identified BIRC5 and HDAC1 as Novel Diagnostic Biomarkers Linked to Centrosome-Immune Crosstalk for Cutaneous Squamous Cell Carcinoma via Machine Learning-Based Multi-omics Analysis Yaqi Fan, Chi Zhang, Yuanjing Zhang, Ruixue Chang, Jiajia Xie, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7496212/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 15 You are reading this latest preprint version Abstract Background Cutaneous squamous cell carcinoma (cSCC) is a common skin cancer where immune dysregulation plays a critical role in its progression and resistance to therapy. Centrosomal amplification (CA), a marker of genomic instability, contributes to cancer development by affecting cell division, immune cell activation, antigen presentation, and cytokine signaling. Further research into the interaction between centrosomal alterations and immune regulation could reveal new therapeutic targets and improve diagnosis and treatment strategies for cSCC. Methods Centrosomal- and immune-related signature biomarkers of cSCC were screened from public databases using 101 combinatorial models based on 10 machine learning algorithms, followed by RT-qPCR for validation and artificial neural networks (ANN) to assess the diagnostic efficacy of these biomarkers. Functional mechanisms were explored by enrichment analysis, immune infiltration profiling, and single-cell RNA sequencing. Results Two biomarkers, BIRC5 and HDAC1, were identified. They were mainly expressed in epithelial cells and both showed high diagnostic value for cSCC. These biomarkers were significantly related to the cell cycle and immune checkpoints, and were especially correlated with CD276. Single-cell RNA sequencing identified eight cell types, with BIRC5 and HDAC1 showing the highest expression levels in epithelial cells, suggesting their potential role in cSCC pathogenesis by modulating epithelial cell function during tumor initiation and progression. Conclusion This study identified and validated two centrosome-immune crosstalk biomarkers, BIRC5 and HDAC1, that may serve as novel targets for precise diagnostic and therapeutic strategies in cSCC management. cSCC Centrosomal Immune single-cell RNA sequencing Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction Cutaneous squamous cell carcinoma (cSCC) is a cutaneous malignancy that originates from squamous cells and is the second most common non-melanoma cutaneous tumor[ 1 ]. The incidence of cSCC is increasing worldwide, with an expected annual increase of 2%-4%[ 2 ]. The disease is more pronounced in high-risk groups, including the elderly, immunosuppressed patients, and those with chronic exposure to ultraviolet radiation[ 2 ]. The risk of cSCC metastasis ranges from 0.1% to 9.9% with a mortality rate of 2.8%. More than two-thirds of patients with metastases die from focal skin or lymph node metastasis[ 3 ] For early cSCC, and curative resection is one of the most effective therapies with radiotherapy used as an adjuvant. Radiotherapy, targeted therapy, and immunotherapy are typically used[ 3 ]. However, the overall response rates to chemotherapy and targeted therapy are unsatisfactory, and the safety and efficacy of immunotherapy in immunosuppressed patients requires further investigation[ 4 , 5 ]. Therefore, research on the key genes involved in cSCC and the development of new therapeutic targets are crucial for the prevention and treatment of cSCC[ 6 ]. Immunosuppression is a risk factor for cSCC[ 7 ]. Immune system dysregulation in cSCC manifests in multiple ways, including a significant increase in regulatory T cells and myeloid-derived suppressor cells, T cell exhaustion caused by high expression of immune checkpoint molecules[ 6 ], weakened anti-tumor immunity dominated by CD8 + T cells[ 8 ], as well as inhibited Th1-type antitumor immune responses, and enhanced Th2-type immune responses. Collectively, these findings highlight the pivotal involvement of the immune mechanisms in the pathogenesis and clinical evolution of cSCC. A large volume of evidence has shown a close association between cSCC risk and age-related immune senescence, further emphasizing the role of cSCC as an immune-related disease[ 5 ]. Immune checkpoint blockade therapy has shown promising results in advanced cSCC[ 7 ]. Beyond immune dysregulation, genomic instability represents another critical hallmark of cancer that plays a pivotal role in cSCC development and progression, with centrosomal abnormalities serving as the key drivers of genomic instability. Centrosomes are important intracellular organelles containing centrioles, proteinaceous materials, and auxiliary structures. They are mainly responsible for the formation and organization of spindle microtubules during cell division to ensure correct separation of chromosomes[ 9 ]. Centrosome amplification is associated with multiple human diseases, such as infections with oncogenic viruses, type 2 diabetes, environmental pollution poisoning, and inflammatory diseases[ 10 ].Besides, CA occurs in almost all types of cancer[ 11 ]. An cSCC abnormality in the number of centrosomes is a common and important pathological feature of cSCC. Studies have found that cSCC tumor cells exhibit significant centrosome number disorders, including cells containing one centrosome (CTRB¹⁺) and cells containing two centrosomes (CTRB²⁺), with some cells having ≥ 3 centrosomes. This phenomenon reflects severe dysregulation of centrosome replication and separation process[ 12 ]. CA leads to the formation of multipolar spindles, triggering chromosome separation errors, which in turn causes chromosome instability and aneuploidy, ultimately promoting malignant progression of tumors.[ 10 , 13 , 14 ]. More importantly, there is a complex interrelationship between CA and immune system function. On the one hand, CA can induce cellular senescence, and senescent cells release inflammatory factors through the senescence-associated secretory phenotype, thereby affecting the recruitment and activity of immune cells; on the other hand, cytokines secreted by immune cells may further exacerbate CA, forming a vicious cycle that promotes tumorigenesis[ 15 ]. At the molecular level, CA can activate the NF-κB signaling pathway by forming a NEMO-PIDDosome complex (comprising PIDD1, RIPK1, and NEMO), triggering a sterile inflammatory response and promoting the secretion of pro-inflammatory cytokines (IL-6 and CCL2) and chemokines, thereby further recruiting immune cells and regulating the tumor microenvironment[ 16 ]. Therefore, exploring immune and centrosome-associated biomarkers in cSCC will provide important evidence for the formulation of clinical treatment strategies.In this study, we screened biomarkers related to cSCC, centrosomes, and immunity using machine learning methods based on public transcriptomic datasets. Gene set enrichment analysis (GSEA), gene set variation analysis (GSVA), functional enrichment, immune microenvironment, regulatory network, drug prediction, and other analyses were performed. Additionally, key cells were identified for pseudotime series and cell communication analyses based on the expression distribution of biomarkers in single-cell data, to explore new insights and targets for the diagnosis and treatment of cSCC. 2. Materials and methods 2.1Source of data The GSE108008, GSE45164, and GSE144236 datasets were acquired from the GEO database ( https://www.ncbi.nlm.nih.gov/geo/ ). GSE108008 (platform GPL16686) was a training set that included skin samples from 10 healthy individuals (control group) and 10 cSCC samples (cSCC group) of tumor[ 17 ]. GSE45164 (platform GPL571) was a validation set that included 3 healthy control individual samples of skin and 10 cSCC samples of the tumor[ 18 ]. GSE144236 (platform GPL20301) was a single-cell RNA sequencing (sc-RAN seq) dataset comprising 10 healthy control individuals’ skin samples and 10 cSCC tumor [ 19 ]. In addition, a total of 727 centrosome-related genes (CRGs) and 1,793 immune-related genes (IRGs) were obtained from the MSigDB ( https://www.gsea-msigdb.org/gsea/msigdb ) and the ImmPort database ( https://immport.org/shared/home ) [ 20 ]. 2.2 Identification of candidate biomarkers and construction of protein-protein interaction (PPI) networks Differential expression analysis of cSCC and control samples in GSE108008 was performed to obtain differentially expressed genes (DEGs) via "limma" R package (v 3.54.0) [ 21 ] (|log 2 Fold Change (FC)| >0.5 and p < 0.05). The top 10 up- and down-regulated DEGs were visualized by Volcano map with "ggplot2" R package (v 3.4.1) [ 22 ]. The top 50 up- and down-regulated DEGs were selected according to log 2 FC ranking, and these were visualized by heat map with "ComplexHeatmap" R package (v 2.14.0)[ 23 ]. The intersection of DEGs, CRGs, and IRGs were recorded as candidate biomarkers via "ggvenn" R package (v 0.1.9) [ 24 ]. Candidate biomarkers interactions at the protein level were analyzed using STRING ( http://www/string-db.org/ ) (confidence scores ≥ 0.15) and the PPI network was visualized using Cytoscape software (version 3.8.2) [ 25 ]. 2.3 Screening and validation of biomarkers Within the GSE108008 and GSE45164 datasets, a robust predictive framework was constructed by integrating 10 distinct machine learning algorithms to create 101 potential algorithmic combinations. To ascertain the reliability and generalizability of each combination model, the area under the curve (AUC) value corresponding to the model was determined for both GSE108008 and GSE45164 datasets. Subsequently, the combination of models in the two datasets that satisfied the maximum AUC value (AUC > 0.7) was identified and was referred to as the optimal model combination. Candidate biomarkers for the optimal model combination were selected as signature biomarkers for subsequent analysis. Subsequently, the expression of signature biomarkers was analyzed in both GSE108008 and GSE45164 datasets. Signature biomarkers with significant differences and consistent trends between groups in the two datasets were used as biomarkers. The receiver operating characteristic (ROC) curve was plotted utilizing "pROC" R package (v 1.18.0) [ 26 ], and the diagnostic value of the biomarkers for cSCC samples was evaluated, utilizing the AUC with a threshold greater than 0.7. To further evaluate the predictive accuracy of biomarkers for cSCC, the expression data of biomarkers were converted into gene scores using min-max normalization, and an artificial neural network (ANN) was constructed by "NeuralNetTools" (v 1.5.3) [ 27 ] and "neuralnet" R packages (v 1.44.2) [ 28 ]. To accurately appraise the predictive performance of the ANN model, both the confusion matrix and ROC curve were generated via "pROC" R package (v 1.18.0). Meanwhile, within the GSE108008 dataset, building upon the expression profiles of these biomarkers, a predictive nomogram was created utilizing the "rms" R package (v 6.8.1) (PMID: 28951289). This nomogram was designed to optimize the forecasting potential of the biomarkers in predicting cSCC, whereby each gene's expression was assigned a distinct point value that, when summed, delineated the total point index for cSCC likelihood. A calibration curve was generated utilizing the "rms" R package (v 6.8.1) to verify the nomogram's forecasting accuracy. Additionally, the "pROC" R package (v 1.18.0) was utilized to construct ROC curve, thereby evaluating the predictive effectiveness of biomarkers and nomogram through the AUC value (AUC > 0.70). 2.4 Gene set variation analysis To investigate the differential pathways of biomarkers, cSCC samples in GSE108008 were stratified into high- and low-expression groups based on median biomarker expression levels. Then the "GSVA" R package (v 1.5.3) [ 28 ] was applied to calculate the GSVA scores of all samples, and then the "limma" R package (v. 1.5.3) was used to analyze the differences in GSVA scores between the high- and low-expression groups. A background gene set (c2.cp.kegg.v2023.1. Hs.symbols.gmt) was downloaded from MSigDB, and the pathways were visualized through "ggplot2" R package (v 3.4.1)[ 22 ] (p < 0.05). 2.5 Gene set enrichment analysis To clarify the potential biological pathways of the biomarkers, the Spearman correlation coefficients of the biomarkers and all genes were analyzed and ranked. The ‘c2.cp.kegg.v2023.1. Hs.symbols.gmt’ was selected as the reference gene set and GSEA was performed (adjusted p < 0.05). The pathways ranked in the top three in the NES were visualized using the enrichment plot (v 1.18.4) package[ 29 ]. 2.6 Immune infiltration analysis Further examination focused on comparative evaluation of immune cell infiltration levels in cSCC and controls. In GSE108008, the expression levels of 48 immune checkpoints were compared between cSCC and control groups [ 30 ]. The association between immune checkpoints and biomarkers was analyzed using the Spearman’s correlation analysis. The single sample GSEA (ssGSEA) scores of 28 immune cells [ 31 ] in 2 groups were estimated using the ssGSEA algorithm of "GSVA" R package (v 1.42.0) [ 32 ]. Subsequently, the Wilcoxon test (p < 0.05) was performed to investigate the difference in immune cell abundance of 2 groups, and the results were visualized by plotting box plots with "ggplot2" R package (v 3.4.1). Finally, Spearman correlation analysis was performed by "psych" R package (v 2.1.6) [ 33 ] to calculate the correlation (cor) between differential immune cells and between differential immune cells and biomarkers. 2.7 Regulatory network construction, potential drug prediction and molecular docking In order to delve into the protential molecular regulatory mechanisms governing biomarkers, the TargetScan ( https://www.targetscan.org/vert_80/ ) and miRWalk ( http://mirwalk.umm.uni-heidelberg.de/ ) databases were utilized to predict the miRNAs regulated by biomarkers, and the common predictions from both databases being considered as the key miRNAs. Then, the lncRNAs of key miRNAs (clipExpNum > 20) were predicted in the StarBase ( http://starbase.sysu.edu.cn/ ) database, and the lncRNA-key miRNA-mRNA network was visualized via "ggalluvial" R package (v 0.12.5) [ 34 ]. To further explore potential drugs interacting with biomarkers, the CTD ( https://ctdbase.org/ ) was searched for drugs related to biomarkers (reference count > 3). The drug-biomarker network was visualized using Cytoscape software (v. 3.8.2). The 3D structures of the active ingredients were obtained from PubChem database ( https://pubchem.ncbi.nlm.nih.gov/ ). In addition, we obtained the protein crystal structures of the biomarkers from the AlphaFold database ( https://alphafold.ebi.ac.uk ). The drug active ingredients and protein crystal structures of each biomarker were preprocessed and molecularly docked using the CB-Dock2 tool ( http://clab.labshare.cn/cb-dock/php/blinddock.php ). The PyMOL (v 2.5) software[ 35 ] was used to visualize the results. 2.8 Analysis of the scRNA-seq data Further analysis was conducted to explore the cellular mechanisms underlying cSCC and examine the unique expression patterns of biomarkers across various cell types. The 10x single-cell transcriptome sequencing data of GSE144236 was created as seurat objects by "Seurat" R package (v 4.3.0) [ 36 ], while cells with less than 200 genes and genes with less than 3 cells covered were excluded from subsequent analysis. The criteria for quality control were set to 500 < nFeature_RNA < 5,000, nCount_RNA < 20,000, and percentage mt < 5%. The gene expression of cells was normalized by "NormalizeData" function in "Seurat" R package (v 4.3.0)[ 37 ], and the highly variable genes were selected by ANOVA. the top 1500 highly variable genes were selected for further analyses. Subsequently, the principal components (PCs) were obtained through principal component analysis (PCA) by "Seurat" R package (v 4.3.0). UMAP cell clustering analysis was used to identify cell clusters (resolution = 0.1). The cellular annotation of different cell clusters was performed based on the literature [ 38 ] via "SingleR" package (v 1.0.6) [ 39 ]. The distribution of biomarkers in different cell types was shown by UMAP plots and Doplot plots, and selected highly expressed cells as key cell clusters were analyzed for functional enrichment by "ReactomeGSA" package (v 1.12.0) [ 40 ]. 2.9 Cellular communication analysis and pseudo-time trajectory analyses To further decipher the molecular dialogues and infer the intricate interactions between annotated cell types, the "CellChat" R package (v 1.6.1) [ 41 ] was used for communication analysis within these annotated cell types. Moreover, to investigate the developmental differentiation trajectory and evolution of key cells, pseudo-time analysis was carried out using the "monocle" R package (v 2.26.0) [ 42 ]. Additionally, changes in biomarker expression over time across the different states were observed. 2.10 Experimental verification RT-qPCR was performed on clinical samples from patients with cSCC and healthy controls. Five patient samples with a confirmed diagnosis and five samples from healthy controls were obtained from the First Affiliated Hospital of the University of Science and Technology of China(USTC). Informed consent was obtained from all the participants. The study was approved by the Ethics Committee of the First Affiliated Hospital of the USTC. Total RNA was extracted using the TRIzol reagent. Complementary DNA (cDNA) was generated by reverse transcription. Quantitative PCR was conducted using cDNA as the template with the primers listed in Table S1 , and gene expression levels were measured using the 2^-ΔΔCt method. GraphPad Prism (v 8.0)[ 43 ] was used to visualize the results. 2.11 Statistical analysis All data analyses were conducted using R software (v. 4.2.2). Wilcoxon test was used to analyze the differences between the cSCC and control groups. Statistical significance was set at P < 0.05. 3. Results 3.1 Identification of 7 candidate biomarkers In GSE108008, 1,036 DEGs were screened, comprising 709 genes with increased expression and 327 with decreased expression (Fig. 1 A-B). Subsequently, the 1,036 DEGs, 727 CRGs, and 1,793 IRGs were intersected, resulting in the identification of 7 candidate biomarkers (HDAC1, SOCS1, HBEGF, PML, NDRG1, BIRC5, and PAK2) (Fig. 1 C) The PPI network was contained 7 nodes and 8 edges, which suggested that these 7 candidate biomarkers had interactions (Fig. 1 D). a Volcano plot of differentially expressed genes (DEGs) between human cSCC tissues and healthy individual skin tissues from the GSE108008 dataset (n = 10). b Heat map of DEGs in the GSE108008 dataset. c Venn diagram of the intersection of 1036 DEGs, 727 centrosome-related genes (CRGs), and 1,793 immune-related genes (IRGs) in cSCC. d Protein–protein interaction (PPI) network of seven candidate genes. 3.2 Screening and validation of BIRC5 and HDAC1 In GSE108008 and GSE45164, the combination RF and glmBoost + NaiveBayes had the highest AUC values (AUC = 1), which were referred to as the optimal model combination (Fig. 2 A-B). The candidate biomarkers obtained using the RF model were HDAC1, NDRG1, HBEGF, PML, and SOCS1. The candidate biomarkers resulting from the glmBoost + Naive Bayes model were HDAC1, SOCS1, HBEGF, PML, NDRG1, and BIRC5. Finally, concatenated biomarkers of the RF and glmBoost + Naive Bayes combination were selected as signature biomarkers (HDAC1, SOCS1, HBEGF, PML, NDRG1, and BIRC5). In GSE108008 and GSE45164, the expression of six signature biomarkers was analyzed in both groups, and genes exhibiting significant differences and consistent trends were identified as biomarkers. In conclusion, two biomarkers (BIRC5 and HDAC1) were identified, which showed notably higher expression levels in the cSCC group samples (Fig. 2 C) and were also validated in clinical samples (Fig. 2 C), indicating that these biomarkers had good diagnostic capability in clinical samples. Meanwhile, the AUC of the biomarkers was greater than 0.9, indicating that BIRC5 and HDAC1 had a goo d diagnostic value for cSCC (Fig. 2 D). To further evaluate the predictive performance of BIRC5 and HDAC1 for cSCC, an ANN model was constructed in GSE108008 and exhibited a high degree of predictive efficacy for cSCC (AUC = 0.9) (Fig. 2 E-G). These findings suggest that these biomarkers may play key roles in the development and pathogenesis of cSCC, highlighting their potential as therapeutic targets. Furthermore, a nomogram was constructed in the GSE108008 dataset, translating each biomarker's contribution into a score to maximally assess the risk of cSCC (Fig. 2 H). The calibration curve suggested an excellent model fit and confirming the nomogram's precision in predictive performance (Fig. 2 I). Meanwhile, the ROC curve emphasized that the nomogram had good predictive performance, with an AUC value of 0.87 in the GSE108008 dataset (Fig. 2 J), suggesting that the nomogram had a strong capability to assess the occurrence probability of cSCC patients. These results highlight the nomogram's robust ability to assess occurrence probability of cSCC occurrence, although further validation in larger clinical samples was required. a AUC values of the first 40 models in the training (GSE108008) and validation (GSE45164) datasets. b ROC curve of the RF and glmBoost + naive Bayes models for both training and validation sets. c Expression of the six signature biomarkers (HDAC1, SOCS1, HBEGF, PML, NDRG1, and BIRC5) in both the control and cSCC groups in GSE45164 and GSE108008; expression of BIRC5 and HDAC1 was also detected in clinical samples. d ROC curves of BIRC5 and HDAC1 in the training and validation sets. e-g ANN model for GSE108008. h Nomogram on the basis of the diagnostic genes was constructed for clinical utilize. i Calibration curve of the nomogram. j ROC curve of the nomogram. 3.3 Enrichment analysis of BIRC5 and HDAC1 GSVA results showed that BIRC5 was enriched in a total of 37 pathways, of which 15 pathways were activated in the high-expression group (e.g., DNA replication, homologous recombination, and cell cycle), and 22 pathways were activated in the low-expression group (e.g., primary bile acid biosynthesis, allograft rejection, and drug metabolism cytochrome P450) (Fig. 3 A). Furthermore, HDAC1 was enriched in 43 pathways, of which 34 pathways were activated in the high-expression group (e.g., ubiquitin-mediated proteolysis, citrate cycle TCA cycle, and Parkinson’s disease), and nine pathways were activated in the low-expression group (e.g., asthma, ECM receptor interaction, and neuroactive ligand receptor interaction) (Fig. 3 B). The GSEA results showed that BIRC5 was enriched in 38 KEGG pathways, and that HDAC1 was enriched in 58 pathways (Fig. 3 C-D). The neuroactive ligand-receptor interaction pathways, spliceosome, and the cell cycle were significantly enriched by both BIRC5 and HDAC1. These enrichment results deepen our understanding of the biological significance of these biomarkers and their potential implications in cSCC mechanisms. a,b Gene set enrichment analysis (GSEA) results of BIRC5 and HDAC1. c,d Gene set variation analysis (GSVA) results for BIRC5 and HDAC1. 3.4 Immune infiltration analysis of BIRC5 and HDAC1 Immune checkpoint analysis showed that among the 48 immune checkpoints, the expression levels of 11 immune checkpoints were significantly different (p value < 0.05). CD200R1 was highly expressed in the control group, whereas BTLA, CD27, CD274, CD276, CD80, CD86, CTLA4, ICOS, PDCD1LG2, and TIGIT were highly expressed in the cSCC group (Fig. 4 A). Spearman’s correlation analysis revealed that between immune checkpoints (CD27, CD274, CD276, CD80, CTLA4, ICOS, PDCD1LG2, and TIGIT) and the two biomarkers showed significant positive correlations (cor > 0.3, p < 0.05) (Fig. 4 B, Table 1 ). Among these, CD276 showed the strongest correlation with BIRC5 (cor = 0.67, p < 0.05) and HDAC1(cor = 0.70, p < 0.05). Subsequently, a heatmap was plotted for the ssGSEA scores of the 28 infiltrating immune cells in both the groups (Fig. 4 C). Wilcoxon test revealed significant disparities in the abundance of 12 immune cells (p < 0.05) (e.g., activated B cells, activated CD4 T cells, and activated CD8 T cells) (Fig. 4 D). The control group showed significant increases in effector memory CD4 + T cells and helper T-cell type 17. Spearman’s correlation analysis revealed that differential immune cells mostly showed significant positive correlations, in which natural killer T cells had the strongest correlation with activated dendritic cell (DC) (cor = 0.9, p 0.30, p 0.30, P < 0.05) (Fig. 4 F). These findings suggest that modulating the expression of these biomarkers or their associated immune-infiltrating cells may be a promising therapeutic strategy. Table 1 The Spearman correlation analysis of immune checkpoints and biomarkers Gene Immune checkpoint Cor P value HDAC1 BIRC5 HDAC1 BIRC5 CD276 0.700752 0.673684 0.000579 0.001128 CD27 0.511278 0.401504 0.021221 0.079317 CD86 0.458647 0.169925 0.041954 0.473849 PDCD1LG2 0.517293 0.46015 0.019501 0.041201 CD274 0.673684 0.566917 0.001128 0.009146 CD200R1 -0.19549 -0.31128 0.408819 0.181576 CD80 0.693233 0.484211 0.000701 0.030508 TIGIT 0.557895 0.485714 0.010585 0.029921 CTLA4 0.590977 0.455639 0.00607 0.043493 ICOS 0.577444 0.46015 0.007673 0.041201 BTLA 0.266165 0.067669 0.256671 0.776821 a Analysis of immune checkpoints in the cSCC and control groups. b Spearman correlation analysis between immune checkpoints and biomarkers. c,d Heat map of immune cell infiltration in the cSCC and control groups. Twelve immune cell types exhibited significantly different infiltration scores (p < 0.05) between the two groups. e Heat map of correlations between differentially expressed immune cell types. f Correlation between differentially expressed immune cells and biomarkers. 3.5 Regulation network construction and potential drug prediction of BIRC5 and HDAC1 A total of 31 key miRNAs for BIRC5 and 12 key miRNAs for HDAC1 were predicted using TargetScan and miRWalk databases. The lncRNA-key miRNA-mRNA regulatory network showed that LINC00910 and AL356488.2 were regulated by hsa-miR-877-5p and hsa-miR-3940-3p in BIRC5; AC245014.3 was regulated by hsa-miR-3614-5p in HDAC1 (Fig. 5 A). In addition, 20 target drugs (such as bisphenol A, doxorubicin, and resveratrol) acting on BIRC5 and 4 target drugs (like valproic acid, vorinostat, bisphenol A, and trichostatin A) acting on HDAC1 were predicted based on the CTD database (Fig. 5 B). Remarkably, BPA is associated with both BIRC5 and HDAC1. The results of the molecular docking experiment indicated that resveratrol had a stronger binding capacity to BIRC5 (-8.7 kcal/mol) and that curcumin had a stronger binding capacity to HDAC1 (-8.2 kcal/mol) (Fig. 5 C). a The lncRNA-miRNA-mRNA regulatory network of BIRC5 and HDAC1. b Network of drugs and biomarkers. c Molecular docking of biomarkers with drug components. 3.6 Identification of epithelial cells as key cells First, we filtered the ineligible cells and yielded 48,164 cells and 24,544 genes for subsequent analysis (Fig. 6 A). After quality control, 37,320 cells and 24,544 genes were identified (Fig. 6 B). Analysis of variance (ANOVA) of genes was performed on the cells and found 1500 highly variable genes were identified. The top 10 highly variable genes were labeled (Fig. 6 C). Meanwhile, we selected the top 30 PCs (p < 0.05) for subsequent analysis (Fig. 6 D). Fifteen different cell clusters were identified using UMAP cluster analysis (Fig. 6 E). Different cell clusters were annotated, resulting in eight cell clusters: epithelial cells, myeloid cells, T cells, fibroblasts, melanocytes, monocytes, B/plasma cells, and endothelial cells (Fig. 6 F). Myeloid cells were annotated as Langerhans cells, macrophages, myeloid-derived suppressor cells (MDSCs), CLEC9A DCs, and AS DCs (Fig. 6 G). BIRC5 and HDCA1 showed the highest expression levels in epithelial cells; therefore, epithelial cells were designated as key cell clusters for subsequent analysis (Fig. 6 H-I). Additionally, functional enrichment analysis was performed on epithelial cells; for example, sterols are 12-hydroxylated by CYP8B1, activation of Na-permeable kainate receptors, and acetylcholine inhibits contraction of outer hair cells (Fig. 6 J). a before and b after quality control of single-cell analysis of GSE144236 dataset. c Top ten highly variable genes. d The dResults of the principal component analysis. e t-SNE plot of cell clustering, f and further annotated into eight cell clusters. g Myeloid cell type h UMAP plot of biomarker expression. i Differential expression of biomarkers among distinct cell populations. j Functional enrichment analysis of epithelial cells. 3.7 Cellular communication analysis and pseudo-time trajectory analyses The number and weight of interactions between different cell types were shown in the cell communication network ( Fig. 7 A ) . Epithelial cells communicate with several other cells and the strongest communication occured with fibroblasts. We simulated and analyzed the cell track differentiation of all epithelial cells, which differentiated into 13 states: the darker the blue color, the earlier the cell differentiation ( Fig. 7 B ) . Moreover, HDAC1 expression decreased with differentiation time ( Fig. 7 C ) . a Interactions between different cell types. b Heterochrony of cell differentiation. c Temporal expression of biomarkers. 4. Discussion Currently, cSCC is primarily treated with surgery. However, for advanced metastatic cSCC, identifying effective systemic therapeutic targets can significantly improve patient prognosis. Previous studies have confirmed that the centrosome amplification phenomenon observed in cSCC could interfere with the mitotic process, leading to abnormal chromosome separation and accelerating tumor progression[ 44 ]. CA releases pro-invasive factors such as IL-8 and ANGPTL4 through additional centriole-related secretory phenotypes, recruiting Th2 cells and M2 macrophages to form a tumor immune microenvironment. Meanwhile, aneuploid tumors reduce MHC class I antigen presentation and CD8⁺ T cell infiltration due to centriole abnormalities, thereby evading immune surveillance[ 15 ] Therefore, we used a total of 101 machine learning modelsto identify six biomarkers that may be involved in centrosome duplication and tumor immunity in cSCC, and narrowed them to two biomarkers through expression verification and ROC analysis. The ANN models and ROC curve suggested that BIRC5 and HDAC1 have a high diagnostic value for cSCC. GSEA revealed the most significantly enriched pathways for BIRC5 and HDAC1 expression. Immune infiltration profiling revealed a significant association between the two biomarkers and key immune cells. lncRNA-key miRNA-mRNA networks of BIRC5 and HDAC1 were constructed to better understand the key miRNAs and lncRNAs in cSCC. In addition, we predicted potential drugs that target BIRC5 and HDAC1. Using single-cell RNA-seq datasets, we found that BIRC5 and HDAC1 were highly expressed in epithelial cells, supporting the key role of epithelial cells in cSCC. Thus, our findings may provide a potential valuable reference for exploring the diagnosis and clinical management of patients, and offer a theoretical foundation for further research on cSCC pathogenesis. BIRC5 is a key regulator of cell division and inhibition of apoptosis within the inhibitor of apoptosis protein (IAP) family[ 45 ]. Previous studies have found that BIRC5 is highly expressed in various SCC (including oral, esophageal, and laryngeal SCC) and is associated with poor prognosis[ 46 – 48 ]. Interestingly, these types of SCC commonly exhibit centrosome duplication[ 49 – 51 ], suggesting that high expression of BIRC5 may be closely associated with centrosome abnormalities characteristic of SCC. As a core component of the chromosome passenger complex,[ 45 ] the abnormal expression of BIRC5 may serve as a key molecular basis for centrosome amplification and chromosomal instability in SCC, providing important clues for understanding the critical role of BIRC5 in centrosome-immune interactions in cSCC. In the current study, we demonstrated that BIRC5 expression is significantly upregulated in cSCC and has high predictive value for patient prognosis. Our study also found a strong correlation between BIRC5 and CD276 expression levels. Previous studies have suggested that CD276 plays a key role in immune escape in cSCC[ 52 ]. CD276 can also activate the JAK2/STAT3 pathway by affecting STAT phosphorylation, thereby upregulating BIRC5 gene[ 53 ]. So CD276 might regulate the expression of BIRC5 and they coorperate to act on immune escape in cSCC.Meanwhile, a previous study indicated that BIRC5 blockade could inhibit tumorigenesis, metastasis, and recurrence of cSCC [ 54 ], suggesting that BIRC5 is not only a prognostic predictor of cSCC, but also plays a role in tumorigenesis and therapy resistance of cSCC. These findings suggest that BIRC5 is a promising novel therapeutic target for cSCC treatment. BIRC5 expression and function can be inhibited through multiple mechanisms[ 45 , 46 ]. Molecular docking analysis in this study revealed that resveratrol exhibits a potentially high binding affinity to the BIRC5 protein, suggesting its role as a putative modulator of BIRC5 activity. Resveratrol is a natural phytoalexin that exhibits anticancer effects in multiple tumors [ 55 ]. Notably, resveratrol can inhibit the NF-κB pathway to prevent the photocarcinogenesis of ultraviolet light, which is the main pathogenesis of skin SCC[ 56 ]. Resveratrol has also been shown to be effective in cancer treatment, both in vitro and in vivo. Previous research has shown that resveratrol can downregulate the Wnt/β-catenin pathway mediated by NEAT-1, thereby inhibiting BIRC5 expression[ 57 ]. Whether such an effect exists in SCC requires further investigation. The histone deacetylase (HDAC) enzyme family catalyzes lysine deacetylation of histones and non-histones. HDAC1 is an important family member that is mainly located in the cell nucleus[ 58 ]. Traditionally, HDAC1 has been recognized for its ability to transduce extracellular and environmental signals by fine-tuning pivotal endothelial activities, notably angiogenesis, inflammation, redox equilibrium, and NO-dependent signaling pathways.[ 58 , 59 ]. Recent studies have indicated that HDAC1 is upregulated in multiple cancers, including myeloma, glioblastoma, colon cancer, breast cancer, ovarian cancer, and gastric cancer, where it promotes tumor progression by enhancing cell migration, invasion, and epithelial-mesenchymal transition (EMT)[ 58 , 60 – 64 ]. HDAC1 inhibition suppresses EMT in glioblastoma, induces apoptosis and cell cycle arrest in colon cancer, and impairs proliferation of other malignancies[ 61 – 64 ]. In cSCC, HDAC1 primarily involves the formation of an active transcriptional repression complex with δNp63α, thereby maintaining the survival of cSCC cells by inhibiting the expression of proapoptotic Bcl-2 family member genes[ 65 ]. Vorinostat, an HDAC inhibitor, reduced HDAC1 expression in epidermoid carcinoma A431 cells while increasing histone H3 and p53 acetylation. This process impaired tumor cell proliferation, manifested by downregulation of the expression of proliferating cell nuclear antigen and cyclins D1, D2, E, and A. It also induces apoptosis through inhibition of mTOR signaling, accompanied by reduced activity of the AKT and extracellular signal-regulated kinase (ERK) signaling pathways associated with cell survival. This leads to the inhibition of human xenograft tumor growth, resulting in tumors with well-differentiated features and extensive necrotic areas, thus providing a mechanistic basis for the treatment of cSCC[ 66 ]. These studies demonstrate that epigenetic therapeutic strategies targeting HDAC1 hold significant promise for cutaneous squamous cell carcinoma (cSCC), offering novel insights into combination-targeted therapies. GSEA revealed that the enriched HDAC1 pathways in cSCC were mainly concentrated in ubiquitin-mediated proteolysis. Research has indicated that the ubiquitin-proteasome system plays an important role in the development of skin cancer by regulating the NF-κB pathway. NF-κB, a key regulator of the immune and inflammatory pathways, has dual tumor-promoting and tumor-suppressing functions in cell proliferation and differentiation[ 67 ]. This finding provides important clues regarding the role of HDAC1 in cSCC as it regulates transcriptional control through protein degradation. Given the critical role of the ubiquitin-proteasome system in cSCC, proteasome inhibitors (such as bortezomib, ixazomib, and carfilzomib) and ubiquitin E1 enzyme inhibitor MLN7243 have been shown to selectively kill cSCC cells, providing experimental evidence for the use of proteasome and ubiquitin E1 inhibitors in cSCC therapy[ 68 ]. Using ssGSEA, we found that, among all immune cells, MDSC infiltration had the highest enrichment. Correlation analysis suggested a strong positive correlation with T Cells. These two cell types exert immunosuppressive functions by inhibiting effector T cell proliferation and reducing cytokine production, thereby jointly promoting tumor progression and resistance to immunotherapy[ 69 , 70 ]. Further studies have confirmed that increased numbers of MDSCs exacerbate the tumor's immunosuppressive microenvironment, thus promoting cSCC development[ 71 ], indicating the critical role of immunosuppressive cell subpopulations in cSCC immune escape. Based on the above findings, the present study demonstrated that HDAC1 is closely associated with tumor autophagy and immune regulation, which supports the identification of HDAC1 as a potential molecular target for cSCC therapy, with its inhibitors potentially benefiting advanced patients in particular. Several HDAC1 inhibitors have shown promising therapeutic potentials. MS275, a selective HDAC1/3 inhibitor, has demonstrated promising efficacy in Phase I clinical trials for advanced refractory solid tumors/lymphomas [ 58 , 72 ]. RGFP109 has also been reported to specifically inhibit the activity of HDAC1[ 61 ], offering a significant advantage in terms of selectivity. Molecular docking analysis indicated that the traditional Chinese medicinal component curcumin exhibited a high affinity for HDAC1. Experimental studies have found that curcumin first transiently upregulates HDAC1 expression in Raji cells (within 24 h) before downregulating HDAC1 expression in a concentration-dependent manner [ 73 ], suggesting that it may exert its anti-cSCC effects through the dose-dependent inhibition of HDAC1. Such a biphasic regulatory mechanism may provide an important reference for optimizing the therapeutic doses and administration regimens. Given the central role of HDAC1 in the pathogenesis of cSCC and the good safety and efficacy of existing inhibitors, HDAC1-targeted therapy holds promise as an important strategy for personalized treatment of cSCC, particularly for advanced patients with limited response to conventional treatment regimens.Interestingly, based on the CTD database, bisphenol A(BPA)is predicted to interact with BIRC5 and HDAC1. BPA is an endocrine disruptor commonly used as an ingredient in food containers[ 58 ]. BPA is widely present in freshwater and the atmosphere, and is associated with a variety of tumors[ 59 ]. BPA affects BIRC5 mRNA levels in fish spermatocytes [ 60 ]. In human cervical cancer studies, BIRC5 was found to be a BPA response gene that may be involved in the carcinogenic effect of BPA on cervical cancer[ 61 ]. These findings indicate that BPA is unlikely to be a therapeutic drug for SCC but instead promotes the occurrence and development of skin SCC by interacting with BIRC5. 5. Conclusion In conclusion, our study systematically screened biomarkers associated with cSCC, centrosome duplication, and immune regulation, using machine learning models. We identified BIRC5 and HDAC1 as pivotal diagnostic signatures, and further explored their roles in modulating cSCC progression and immune responses. These findings provide novel insights into potential therapeutic strategies for cSCC. However, this study also has some limitations. First, the sample size of the dataset used was small, and the clinical validation only involved 5 pairs of clinical samples, which may affect the universality of the diagnostic value of BIRC5 and HDAC1. Secondly, the functional mechanism exploration of BIRC5 and HDAC1 in this study is mainly based on bioinformatics analysis. Although the difference of gene expression was verified by RT-qPCR, the functional verification of cell experiment and animal model was lacking, and the specific molecular pathway of the two regulatory participation in centrosome-immune interaction was not directly revealed. Third, the drug prediction part is only based on the CTD database screening, and its clinical transformation value and practical application prospects still need further evaluation by subsequent clinical trials or clinical data support. In the future, we will collect large samples from multiple centers for verification. Gene knockout and gene over-expression experiments will be conducted to detect changes in tumor proliferation, apoptosis and immune-related molecules at the in vivo and in vitro levels to clarify the functional mechanism of BIRC5/HDAC1. In addition, the inhibitory efficiency of the drug on BIRC5/HDAC1 and the killing effect on cSCC cells were verified by in vitro experiments, providing experimental basis for clinical drug application. Abbreviations ANN,artificial neural network; AUC,area under the curve; cSCC,cutaneous squamous cell carcinoma; CA,centrosome abnormalities; cDNA, complementary DNA; CRGs, centrosome-related genes; DEGs, differentially expressed genes; EMT, epithelial-mesenchymal transition; HDAC, histone deacetylase; IAP, inhibitor of apoptosis protein; IRGs, immune-related genes; MDSCs, myeloid-derived suppressor cells; PC, principal components; PCA, principal component analysis; PPI, protein-protein interaction; GSEA,gene set enrichment analysis; GSVA, gene set variation analysis; ROC, receiver operating characteristic; USTC, University of Science and Technology of China. Declarations Conflict of interest The authors declare no conflicts of interest in this work. Funding This work was supported by the National Natural Science Foundation of China (grant number: 82303737). Author contributions YF, JT and YZ designed the experiments. YF, RC and FJ performed the bioinformatics analysis. CZ and SZ collected clinical specimens and performed PCR validation. YF, JX and YZ wrote the manuscript. Data availability statement The datasets generated in the current study are available in the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/). GSE108008: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE108008 GSE45164: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE45164 GSE144236: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE144236 Ethical approval This study was conducted in accordance with the principles of the Declaration of Helsinki. The study was approved by the Ethics Committee of the First Affiliated Hospital of the USTC (Approval Number : 2025KY No.386). 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Venn diagram of the intersection of 1036 DEGs, 727 centrosome-related genes (CRGs), and 1,793 immune-related genes (IRGs) in cSCC.\u003cstrong\u003e d\u003c/strong\u003e Protein–protein interaction (PPI) network of seven candidate genes.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7496212/v1/d50bf2e5c2e9b482ae339ff3.png"},{"id":95751179,"identity":"11d3ae9b-84b8-4133-8c3b-e7baedaa3c4d","added_by":"auto","created_at":"2025-11-12 15:45:23","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":413285,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eScreening and validation of BIRC5 and HDAC1\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea \u003c/strong\u003eAUC values of the first 40 models in the training (GSE108008) and validation (GSE45164) datasets.\u003cstrong\u003e b \u003c/strong\u003eROC curve of the RF and glmBoost + naive Bayes models for both training and validation sets.\u003cstrong\u003e c \u003c/strong\u003eExpression of the six signature biomarkers (HDAC1, SOCS1, HBEGF, PML, NDRG1, and BIRC5) in both the control and cSCC groups in GSE45164 and GSE108008; expression of BIRC5 and HDAC1 was also detected in clinical samples. \u003cstrong\u003ed\u003c/strong\u003e ROC curves of BIRC5 and HDAC1 in the training and validation sets. \u003cstrong\u003ee-g\u003c/strong\u003e ANN model for GSE108008. \u003cstrong\u003eh \u003c/strong\u003eNomogram on the basis of the diagnostic genes was constructed for clinical utilize. \u003cstrong\u003ei \u003c/strong\u003eCalibration curve of the nomogram.\u003cstrong\u003e j \u003c/strong\u003eROC curve of the nomogram.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7496212/v1/490b12d6ece6c98cd15e6474.png"},{"id":95801658,"identity":"a2162e25-9a8f-485e-9a9d-091c4d37238b","added_by":"auto","created_at":"2025-11-13 08:25:50","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":281805,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEnrichment analysis of BIRC5 and HDAC1\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea,b \u003c/strong\u003eGene set enrichment analysis (GSEA) results of BIRC5 and HDAC1. \u003cstrong\u003ec,d \u003c/strong\u003eGene set variation analysis (GSVA) results for BIRC5 and HDAC1.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7496212/v1/fc5eb4487164105e51ce5cef.png"},{"id":95751185,"identity":"2c0cca8a-4de3-419e-8192-5042996f171e","added_by":"auto","created_at":"2025-11-12 15:45:23","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":64018,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eImmune infiltration analysis of BIRC5 and HDAC1\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea\u003c/strong\u003eAnalysis of immune checkpoints in the cSCC and control groups. \u003cstrong\u003eb\u003c/strong\u003eSpearman correlation analysis between immune checkpoints and biomarkers.\u003cstrong\u003e c,d\u003c/strong\u003eHeat map of immune cell infiltration in the cSCC and control groups. Twelve immune cell types exhibited significantly different infiltration scores (p\u0026lt;0.05) between the two groups. \u003cstrong\u003ee\u003c/strong\u003e Heat map of correlations between differentially expressed immune cell types. \u003cstrong\u003ef\u003c/strong\u003e Correlation between differentially expressed immune cells and biomarkers.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7496212/v1/a397653b2b63b8691031e5c1.png"},{"id":95751188,"identity":"093a0196-279e-42ff-903d-08ba7d01f1c3","added_by":"auto","created_at":"2025-11-12 15:45:23","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":440860,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRegulation network construction and potential drug prediction of BIRC5 and HDAC1\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea\u003c/strong\u003eThe lncRNA-miRNA-mRNA regulatory network of BIRC5 and HDAC1. \u003cstrong\u003eb\u003c/strong\u003e Network of drugs and biomarkers. \u003cstrong\u003ec \u003c/strong\u003eMolecular docking of biomarkers with drug components.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7496212/v1/fd797a24c8ad19bf61ab9f7e.png"},{"id":95751184,"identity":"5f53a7d4-57ed-4e87-bf7e-d06cce1c30f4","added_by":"auto","created_at":"2025-11-12 15:45:23","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":367422,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIdentification of epithelial cells as key cells\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea \u003c/strong\u003ebefore and \u003cstrong\u003eb\u003c/strong\u003e after quality control of single-cell analysis of GSE144236 dataset.\u003cstrong\u003e c \u003c/strong\u003eTop ten highly variable genes. \u003cstrong\u003ed\u003c/strong\u003e The dResults of the principal component analysis.\u003cstrong\u003e e\u003c/strong\u003e t-SNE plot of cell clustering, \u003cstrong\u003ef \u003c/strong\u003eand further annotated into eight cell clusters. \u003cstrong\u003eg \u003c/strong\u003eMyeloid cell type \u003cstrong\u003eh\u003c/strong\u003eUMAP plot of biomarker expression.\u003cstrong\u003e i \u003c/strong\u003eDifferential expression of biomarkers among distinct cell populations. \u003cstrong\u003ej\u003c/strong\u003e Functional enrichment analysis of epithelial cells.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7496212/v1/351315142dc8cca6d8bcbbff.png"},{"id":95751191,"identity":"996a80be-6d57-4eaf-8e10-6abf2939abef","added_by":"auto","created_at":"2025-11-12 15:45:23","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":415958,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCellular communication analysis and pseudo-time trajectory analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea\u003c/strong\u003eInteractions between different cell types. \u003cstrong\u003eb\u003c/strong\u003e Heterochrony of cell differentiation. \u003cstrong\u003ec\u003c/strong\u003e Temporal expression of biomarkers.\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-7496212/v1/9524b8a2609941cdd7ecadd0.png"},{"id":96238969,"identity":"fbb51b65-3125-44e9-96fc-5881889f3bf6","added_by":"auto","created_at":"2025-11-19 06:58:58","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3461709,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7496212/v1/ef258819-a977-4da1-ac00-969284b5e024.pdf"},{"id":95751180,"identity":"ccc94232-ea7a-45e9-b158-78d36046ba00","added_by":"auto","created_at":"2025-11-12 15:45:23","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":14498,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7496212/v1/f82c76e51383599a5a3496a1.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Identified BIRC5 and HDAC1 as Novel Diagnostic Biomarkers Linked to Centrosome-Immune Crosstalk for Cutaneous Squamous Cell Carcinoma via Machine Learning-Based Multi-omics Analysis","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eCutaneous squamous cell carcinoma (cSCC) is a cutaneous malignancy that originates from squamous cells and is the second most common non-melanoma cutaneous tumor[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The incidence of cSCC is increasing worldwide, with an expected annual increase of 2%-4%[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The disease is more pronounced in high-risk groups, including the elderly, immunosuppressed patients, and those with chronic exposure to ultraviolet radiation[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The risk of cSCC metastasis ranges from 0.1% to 9.9% with a mortality rate of 2.8%. More than two-thirds of patients with metastases die from focal skin or lymph node metastasis[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] For early cSCC, and curative resection is one of the most effective therapies with radiotherapy used as an adjuvant. Radiotherapy, targeted therapy, and immunotherapy are typically used[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. However, the overall response rates to chemotherapy and targeted therapy are unsatisfactory, and the safety and efficacy of immunotherapy in immunosuppressed patients requires further investigation[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Therefore, research on the key genes involved in cSCC and the development of new therapeutic targets are crucial for the prevention and treatment of cSCC[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eImmunosuppression is a risk factor for cSCC[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Immune system dysregulation in cSCC manifests in multiple ways, including a significant increase in regulatory T cells and myeloid-derived suppressor cells, T cell exhaustion caused by high expression of immune checkpoint molecules[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], weakened anti-tumor immunity dominated by CD8\u0026thinsp;+\u0026thinsp;T cells[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], as well as inhibited Th1-type antitumor immune responses, and enhanced Th2-type immune responses. Collectively, these findings highlight the pivotal involvement of the immune mechanisms in the pathogenesis and clinical evolution of cSCC. A large volume of evidence has shown a close association between cSCC risk and age-related immune senescence, further emphasizing the role of cSCC as an immune-related disease[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Immune checkpoint blockade therapy has shown promising results in advanced cSCC[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Beyond immune dysregulation, genomic instability represents another critical hallmark of cancer that plays a pivotal role in cSCC development and progression, with centrosomal abnormalities serving as the key drivers of genomic instability.\u003c/p\u003e\u003cp\u003eCentrosomes are important intracellular organelles containing centrioles, proteinaceous materials, and auxiliary structures. They are mainly responsible for the formation and organization of spindle microtubules during cell division to ensure correct separation of chromosomes[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Centrosome amplification is associated with multiple human diseases, such as infections with oncogenic viruses, type 2 diabetes, environmental pollution poisoning, and inflammatory diseases[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].Besides, CA occurs in almost all types of cancer[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. An cSCC abnormality in the number of centrosomes is a common and important pathological feature of cSCC. Studies have found that cSCC tumor cells exhibit significant centrosome number disorders, including cells containing one centrosome (CTRB\u0026sup1;⁺) and cells containing two centrosomes (CTRB\u0026sup2;⁺), with some cells having\u0026thinsp;\u0026ge;\u0026thinsp;3 centrosomes. This phenomenon reflects severe dysregulation of centrosome replication and separation process[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. CA leads to the formation of multipolar spindles, triggering chromosome separation errors, which in turn causes chromosome instability and aneuploidy, ultimately promoting malignant progression of tumors.[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eMore importantly, there is a complex interrelationship between CA and immune system function. On the one hand, CA can induce cellular senescence, and senescent cells release inflammatory factors through the senescence-associated secretory phenotype, thereby affecting the recruitment and activity of immune cells; on the other hand, cytokines secreted by immune cells may further exacerbate CA, forming a vicious cycle that promotes tumorigenesis[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. At the molecular level, CA can activate the NF-κB signaling pathway by forming a NEMO-PIDDosome complex (comprising PIDD1, RIPK1, and NEMO), triggering a sterile inflammatory response and promoting the secretion of pro-inflammatory cytokines (IL-6 and CCL2) and chemokines, thereby further recruiting immune cells and regulating the tumor microenvironment[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Therefore, exploring immune and centrosome-associated biomarkers in cSCC will provide important evidence for the formulation of clinical treatment strategies.In this study, we screened biomarkers related to cSCC, centrosomes, and immunity using machine learning methods based on public transcriptomic datasets. Gene set enrichment analysis (GSEA), gene set variation analysis (GSVA), functional enrichment, immune microenvironment, regulatory network, drug prediction, and other analyses were performed. Additionally, key cells were identified for pseudotime series and cell communication analyses based on the expression distribution of biomarkers in single-cell data, to explore new insights and targets for the diagnosis and treatment of cSCC.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1Source of data\u003c/h2\u003e\u003cp\u003eThe GSE108008, GSE45164, and GSE144236 datasets were acquired 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\u003cb\u003e).\u003c/b\u003e GSE108008 (platform GPL16686) was a training set that included skin samples from 10 healthy individuals (control group) and 10 cSCC samples (cSCC group) of tumor[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. GSE45164 (platform GPL571) was a validation set that included 3 healthy control individual samples of skin and 10 cSCC samples of the tumor[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. GSE144236 (platform GPL20301) was a single-cell RNA sequencing (sc-RAN seq) dataset comprising 10 healthy control individuals\u0026rsquo; skin samples and 10 cSCC tumor [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. In addition, a total of 727 centrosome-related genes (CRGs) and 1,793 immune-related genes (IRGs) were obtained from the MSigDB (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.gsea-msigdb.org/gsea/msigdb\u003c/span\u003e\u003cspan address=\"https://www.gsea-msigdb.org/gsea/msigdb\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e and the ImmPort database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://immport.org/shared/home\u003c/span\u003e\u003cspan address=\"https://immport.org/shared/home\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Identification of candidate biomarkers and construction of protein-protein interaction (PPI) networks\u003c/h2\u003e\u003cp\u003eDifferential expression analysis of cSCC and control samples in GSE108008 was performed to obtain differentially expressed genes (DEGs) via \"limma\" R package (v 3.54.0) [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] (|log\u003csub\u003e2\u003c/sub\u003eFold Change (FC)| \u0026gt;0.5 and p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The top 10 up- and down-regulated DEGs were visualized by Volcano map with \"ggplot2\" R package (v 3.4.1) [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The top 50 up- and down-regulated DEGs were selected according to log\u003csub\u003e2\u003c/sub\u003eFC ranking, and these were visualized by heat map with \"ComplexHeatmap\" R package (v 2.14.0)[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The intersection of DEGs, CRGs, and IRGs were recorded as candidate biomarkers via \"ggvenn\" R package (v 0.1.9) [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Candidate biomarkers interactions at the protein level were analyzed using STRING (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www/string-db.org/\u003c/span\u003e\u003cspan address=\"http://www/string-db.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e (confidence scores\u0026thinsp;\u0026ge;\u0026thinsp;0.15) and the PPI network was visualized using Cytoscape software (version 3.8.2) [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Screening and validation of biomarkers\u003c/h2\u003e\u003cp\u003eWithin the GSE108008 and GSE45164 datasets, a robust predictive framework was constructed by integrating 10 distinct machine learning algorithms to create 101 potential algorithmic combinations. To ascertain the reliability and generalizability of each combination model, the area under the curve (AUC) value corresponding to the model was determined for both GSE108008 and GSE45164 datasets. Subsequently, the combination of models in the two datasets that satisfied the maximum AUC value (AUC\u0026thinsp;\u0026gt;\u0026thinsp;0.7) was identified and was referred to as the optimal model combination. Candidate biomarkers for the optimal model combination were selected as signature biomarkers for subsequent analysis. Subsequently, the expression of signature biomarkers was analyzed in both GSE108008 and GSE45164 datasets. Signature biomarkers with significant differences and consistent trends between groups in the two datasets were used as biomarkers. The receiver operating characteristic (ROC) curve was plotted utilizing \"pROC\" R package (v 1.18.0) [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], and the diagnostic value of the biomarkers for cSCC samples was evaluated, utilizing the AUC with a threshold greater than 0.7. To further evaluate the predictive accuracy of biomarkers for cSCC, the expression data of biomarkers were converted into gene scores using min-max normalization, and an artificial neural network (ANN) was constructed by \"NeuralNetTools\" (v 1.5.3) [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] and \"neuralnet\" R packages (v 1.44.2) [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. To accurately appraise the predictive performance of the ANN model, both the confusion matrix and ROC curve were generated via \"pROC\" R package (v 1.18.0).\u003c/p\u003e\u003cp\u003eMeanwhile, within the GSE108008 dataset, building upon the expression profiles of these biomarkers, a predictive nomogram was created utilizing the \"rms\" R package (v 6.8.1) (PMID: 28951289). This nomogram was designed to optimize the forecasting potential of the biomarkers in predicting cSCC, whereby each gene's expression was assigned a distinct point value that, when summed, delineated the total point index for cSCC likelihood. A calibration curve was generated utilizing the \"rms\" R package (v 6.8.1) to verify the nomogram's forecasting accuracy. Additionally, the \"pROC\" R package (v 1.18.0) was utilized to construct ROC curve, thereby evaluating the predictive effectiveness of biomarkers and nomogram through the AUC value (AUC\u0026thinsp;\u0026gt;\u0026thinsp;0.70).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Gene set variation analysis\u003c/h2\u003e\u003cp\u003eTo investigate the differential pathways of biomarkers, cSCC samples in GSE108008 were stratified into high- and low-expression groups based on median biomarker expression levels. Then the \"GSVA\" R package (v 1.5.3) [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] was applied to calculate the GSVA scores of all samples, and then the \"limma\" R package (v. 1.5.3) was used to analyze the differences in GSVA scores between the high- and low-expression groups. A background gene set (c2.cp.kegg.v2023.1. Hs.symbols.gmt) was downloaded from MSigDB, and the pathways were visualized through \"ggplot2\" R package (v 3.4.1)[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Gene set enrichment analysis\u003c/h2\u003e\u003cp\u003eTo clarify the potential biological pathways of the biomarkers, the Spearman correlation coefficients of the biomarkers and all genes were analyzed and ranked. The \u0026lsquo;c2.cp.kegg.v2023.1. Hs.symbols.gmt\u0026rsquo; was selected as the reference gene set and GSEA was performed (adjusted p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The pathways ranked in the top three in the NES were visualized using the enrichment plot (v 1.18.4) package[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.6 Immune infiltration analysis\u003c/h2\u003e\u003cp\u003eFurther examination focused on comparative evaluation of immune cell infiltration levels in cSCC and controls. In GSE108008, the expression levels of 48 immune checkpoints were compared between cSCC and control groups [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. The association between immune checkpoints and biomarkers was analyzed using the Spearman\u0026rsquo;s correlation analysis. The single sample GSEA (ssGSEA) scores of 28 immune cells [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] in 2 groups were estimated using the ssGSEA algorithm of \"GSVA\" R package (v 1.42.0) [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Subsequently, the Wilcoxon test (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) was performed to investigate the difference in immune cell abundance of 2 groups, and the results were visualized by plotting box plots with \"ggplot2\" R package (v 3.4.1). Finally, Spearman correlation analysis was performed by \"psych\" R package (v 2.1.6) [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] to calculate the correlation (cor) between differential immune cells and between differential immune cells and biomarkers.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e2.7 Regulatory network construction, potential drug prediction and molecular docking\u003c/h2\u003e\u003cp\u003eIn order to delve into the protential molecular regulatory mechanisms governing biomarkers, the TargetScan (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.targetscan.org/vert_80/\u003c/span\u003e\u003cspan address=\"https://www.targetscan.org/vert_80/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e and miRWalk (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://mirwalk.umm.uni-heidelberg.de/\u003c/span\u003e\u003cspan address=\"http://mirwalk.umm.uni-heidelberg.de/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e databases were utilized to predict the miRNAs regulated by biomarkers, and the common predictions from both databases being considered as the key miRNAs. Then, the lncRNAs of key miRNAs (clipExpNum\u0026thinsp;\u0026gt;\u0026thinsp;20) were predicted in the StarBase (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://starbase.sysu.edu.cn/\u003c/span\u003e\u003cspan address=\"http://starbase.sysu.edu.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e database, and the lncRNA-key miRNA-mRNA network was visualized via \"ggalluvial\" R package (v 0.12.5) [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. To further explore potential drugs interacting with biomarkers, the CTD (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ctdbase.org/\u003c/span\u003e\u003cspan address=\"https://ctdbase.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e was searched for drugs related to biomarkers (reference count\u0026thinsp;\u0026gt;\u0026thinsp;3). The drug-biomarker network was visualized using Cytoscape software (v. 3.8.2). The 3D structures of the active ingredients were obtained from PubChem database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubchem.ncbi.nlm.nih.gov/\u003c/span\u003e\u003cspan address=\"https://pubchem.ncbi.nlm.nih.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e In addition, we obtained the protein crystal structures of the biomarkers from the AlphaFold database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://alphafold.ebi.ac.uk\u003c/span\u003e\u003cspan address=\"https://alphafold.ebi.ac.uk\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e The drug active ingredients and protein crystal structures of each biomarker were preprocessed and molecularly docked using the CB-Dock2 tool (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://clab.labshare.cn/cb-dock/php/blinddock.php\u003c/span\u003e\u003cspan address=\"http://clab.labshare.cn/cb-dock/php/blinddock.php\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e The PyMOL (v 2.5) software[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] was used to visualize the results.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e2.8 Analysis of the scRNA-seq data\u003c/h2\u003e\u003cp\u003eFurther analysis was conducted to explore the cellular mechanisms underlying cSCC and examine the unique expression patterns of biomarkers across various cell types. The 10x single-cell transcriptome sequencing data of GSE144236 was created as seurat objects by \"Seurat\" R package (v 4.3.0) [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], while cells with less than 200 genes and genes with less than 3 cells covered were excluded from subsequent analysis. The criteria for quality control were set to 500\u0026thinsp;\u0026lt;\u0026thinsp;nFeature_RNA\u0026thinsp;\u0026lt;\u0026thinsp;5,000, nCount_RNA\u0026thinsp;\u0026lt;\u0026thinsp;20,000, and percentage mt\u0026thinsp;\u0026lt;\u0026thinsp;5%. The gene expression of cells was normalized by \"NormalizeData\" function in \"Seurat\" R package (v 4.3.0)[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], and the highly variable genes were selected by ANOVA. the top 1500 highly variable genes were selected for further analyses. Subsequently, the principal components (PCs) were obtained through principal component analysis (PCA) by \"Seurat\" R package (v 4.3.0). UMAP cell clustering analysis was used to identify cell clusters (resolution\u0026thinsp;=\u0026thinsp;0.1). The cellular annotation of different cell clusters was performed based on the literature [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] via \"SingleR\" package (v 1.0.6) [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. The distribution of biomarkers in different cell types was shown by UMAP plots and Doplot plots, and selected highly expressed cells as key cell clusters were analyzed for functional enrichment by \"ReactomeGSA\" package (v 1.12.0) [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e2.9 Cellular communication analysis and pseudo-time trajectory analyses\u003c/h2\u003e\u003cp\u003eTo further decipher the molecular dialogues and infer the intricate interactions between annotated cell types, the \"CellChat\" R package (v 1.6.1) [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e] was used for communication analysis within these annotated cell types. Moreover, to investigate the developmental differentiation trajectory and evolution of key cells, pseudo-time analysis was carried out using the \"monocle\" R package (v 2.26.0) [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Additionally, changes in biomarker expression over time across the different states were observed.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e2.10 Experimental verification\u003c/h2\u003e\u003cp\u003eRT-qPCR was performed on clinical samples from patients with cSCC and healthy controls. Five patient samples with a confirmed diagnosis and five samples from healthy controls were obtained from the First Affiliated Hospital of the University of Science and Technology of China(USTC). Informed consent was obtained from all the participants. The study was approved by the Ethics Committee of the First Affiliated Hospital of the USTC. Total RNA was extracted using the TRIzol reagent. Complementary DNA (cDNA) was generated by reverse transcription. Quantitative PCR was conducted using cDNA as the template with the primers listed in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e, and gene expression levels were measured using the 2^-ΔΔCt method. GraphPad Prism (v 8.0)[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e] was used to visualize the results.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e2.11 Statistical analysis\u003c/h2\u003e\u003cp\u003eAll data analyses were conducted using R software (v. 4.2.2). Wilcoxon test was used to analyze the differences between the cSCC and control groups. Statistical significance was set at P\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Identification of 7 candidate biomarkers\u003c/h2\u003e\u003cp\u003eIn GSE108008, 1,036 DEGs were screened, comprising 709 genes with increased expression and 327 with decreased expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA-B). Subsequently, the 1,036 DEGs, 727 CRGs, and 1,793 IRGs were intersected, resulting in the identification of 7 candidate biomarkers (HDAC1, SOCS1, HBEGF, PML, NDRG1, BIRC5, and PAK2) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC) The PPI network was contained 7 nodes and 8 edges, which suggested that these 7 candidate biomarkers had interactions (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003ea\u003c/b\u003e Volcano plot of differentially expressed genes (DEGs) between human cSCC tissues and healthy individual skin tissues from the GSE108008 dataset (n\u0026thinsp;=\u0026thinsp;10). \u003cb\u003eb\u003c/b\u003e Heat map of DEGs in the GSE108008 dataset. \u003cb\u003ec\u003c/b\u003e Venn diagram of the intersection of 1036 DEGs, 727 centrosome-related genes (CRGs), and 1,793 immune-related genes (IRGs) in cSCC. \u003cb\u003ed\u003c/b\u003e Protein\u0026ndash;protein interaction (PPI) network of seven candidate genes.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Screening and validation of BIRC5 and HDAC1\u003c/h2\u003e\u003cp\u003eIn GSE108008 and GSE45164, the combination RF and glmBoost\u0026thinsp;+\u0026thinsp;NaiveBayes had the highest AUC values (AUC\u0026thinsp;=\u0026thinsp;1), which were referred to as the optimal model combination (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA-B). The candidate biomarkers obtained using the RF model were HDAC1, NDRG1, HBEGF, PML, and SOCS1. The candidate biomarkers resulting from the glmBoost\u0026thinsp;+\u0026thinsp;Naive Bayes model were HDAC1, SOCS1, HBEGF, PML, NDRG1, and BIRC5. Finally, concatenated biomarkers of the RF and glmBoost\u0026thinsp;+\u0026thinsp;Naive Bayes combination were selected as signature biomarkers (HDAC1, SOCS1, HBEGF, PML, NDRG1, and BIRC5). In GSE108008 and GSE45164, the expression of six signature biomarkers was analyzed in both groups, and genes exhibiting significant differences and consistent trends were identified as biomarkers. In conclusion, two biomarkers (BIRC5 and HDAC1) were identified, which showed notably higher expression levels in the cSCC group samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC) and were also validated in clinical samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC), indicating that these biomarkers had good diagnostic capability in clinical samples. Meanwhile, the AUC of the biomarkers was greater than 0.9, indicating that BIRC5 and HDAC1 had a goo\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ed\u003c/span\u003e diagnostic value for cSCC (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). To further evaluate the predictive performance of BIRC5 and HDAC1 for cSCC, an ANN model was constructed in GSE108008 and exhibited a high degree of predictive efficacy for cSCC (AUC\u0026thinsp;=\u0026thinsp;0.9) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE-G). These findings suggest that these biomarkers may play key roles in the development and pathogenesis of cSCC, highlighting their potential as therapeutic targets.\u003c/p\u003e\u003cp\u003eFurthermore, a nomogram was constructed in the GSE108008 dataset, translating each biomarker's contribution into a score to maximally assess the risk of cSCC (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eH). The calibration curve suggested an excellent model fit and confirming the nomogram's precision in predictive performance (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eI). Meanwhile, the ROC curve emphasized that the nomogram had good predictive performance, with an AUC value of 0.87 in the GSE108008 dataset (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eJ), suggesting that the nomogram had a strong capability to assess the occurrence probability of cSCC patients. These results highlight the nomogram's robust ability to assess occurrence probability of cSCC occurrence, although further validation in larger clinical samples was required.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003ea\u003c/b\u003e AUC values of the first 40 models in the training (GSE108008) and validation (GSE45164) datasets. \u003cb\u003eb\u003c/b\u003e ROC curve of the RF and glmBoost\u0026thinsp;+\u0026thinsp;naive Bayes models for both training and validation sets. \u003cb\u003ec\u003c/b\u003e Expression of the six signature biomarkers (HDAC1, SOCS1, HBEGF, PML, NDRG1, and BIRC5) in both the control and cSCC groups in GSE45164 and GSE108008; expression of BIRC5 and HDAC1 was also detected in clinical samples. \u003cb\u003ed\u003c/b\u003e ROC curves of BIRC5 and HDAC1 in the training and validation sets. \u003cb\u003ee-g\u003c/b\u003e ANN model for GSE108008. \u003cb\u003eh\u003c/b\u003e Nomogram on the basis of the diagnostic genes was constructed for clinical utilize. \u003cb\u003ei\u003c/b\u003e Calibration curve of the nomogram. \u003cb\u003ej\u003c/b\u003e ROC curve of the nomogram.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Enrichment analysis of BIRC5 and HDAC1\u003c/h2\u003e\u003cp\u003eGSVA results showed that BIRC5 was enriched in a total of 37 pathways, of which 15 pathways were activated in the high-expression group (e.g., DNA replication, homologous recombination, and cell cycle), and 22 pathways were activated in the low-expression group (e.g., primary bile acid biosynthesis, allograft rejection, and drug metabolism cytochrome P450) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Furthermore, HDAC1 was enriched in 43 pathways, of which 34 pathways were activated in the high-expression group (e.g., ubiquitin-mediated proteolysis, citrate cycle TCA cycle, and Parkinson\u0026rsquo;s disease), and nine pathways were activated in the low-expression group (e.g., asthma, ECM receptor interaction, and neuroactive ligand receptor interaction) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). The GSEA results showed that BIRC5 was enriched in 38 KEGG pathways, and that HDAC1 was enriched in 58 pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC-D). The neuroactive ligand-receptor interaction pathways, spliceosome, and the cell cycle were significantly enriched by both BIRC5 and HDAC1. These enrichment results deepen our understanding of the biological significance of these biomarkers and their potential implications in cSCC mechanisms.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003ea,b\u003c/b\u003e Gene set enrichment analysis (GSEA) results of BIRC5 and HDAC1. \u003cb\u003ec,d\u003c/b\u003e Gene set variation analysis (GSVA) results for BIRC5 and HDAC1.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Immune infiltration analysis of BIRC5 and HDAC1\u003c/h2\u003e\u003cp\u003eImmune checkpoint analysis showed that among the 48 immune checkpoints, the expression levels of 11 immune checkpoints were significantly different (p value\u0026thinsp;\u0026lt;\u0026thinsp;0.05). CD200R1 was highly expressed in the control group, whereas BTLA, CD27, CD274, CD276, CD80, CD86, CTLA4, ICOS, PDCD1LG2, and TIGIT were highly expressed in the cSCC group (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Spearman\u0026rsquo;s correlation analysis revealed that between immune checkpoints (CD27, CD274, CD276, CD80, CTLA4, ICOS, PDCD1LG2, and TIGIT) and the two biomarkers showed significant positive correlations (cor\u0026thinsp;\u0026gt;\u0026thinsp;0.3, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Among these, CD276 showed the strongest correlation with BIRC5 (cor\u0026thinsp;=\u0026thinsp;0.67, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and HDAC1(cor\u0026thinsp;=\u0026thinsp;0.70, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Subsequently, a heatmap was plotted for the ssGSEA scores of the 28 infiltrating immune cells in both the groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). Wilcoxon test revealed significant disparities in the abundance of 12 immune cells (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (e.g., activated B cells, activated CD4 T cells, and activated CD8 T cells) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). The control group showed significant increases in effector memory CD4\u0026thinsp;+\u0026thinsp;T cells and helper T-cell type 17. Spearman\u0026rsquo;s correlation analysis revealed that differential immune cells mostly showed significant positive correlations, in which natural killer T cells had the strongest correlation with activated dendritic cell (DC) (cor\u0026thinsp;=\u0026thinsp;0.9, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE). BIRC5 and HDAC1 levels were positively correlated with CD56 dim NK cells, activated CD4 T cells, and memory B cells (|cor| \u0026gt;0.30, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). These were negatively correlated with T cell type 17 and effector memory CD4 T cells (|cor| \u0026gt;0.30, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF). These findings suggest that modulating the expression of these biomarkers or their associated immune-infiltrating cells may be a promising therapeutic strategy.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe Spearman correlation analysis of immune checkpoints and biomarkers\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e\u003cp\u003eGene\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eImmune checkpoint\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eCor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHDAC1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBIRC5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHDAC1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eBIRC5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCD276\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.700752\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.673684\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.000579\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.001128\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCD27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.511278\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.401504\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.021221\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.079317\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCD86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.458647\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.169925\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.041954\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.473849\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePDCD1LG2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.517293\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.46015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.019501\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.041201\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCD274\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.673684\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.566917\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.001128\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.009146\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCD200R1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.19549\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.31128\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.408819\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.181576\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCD80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.693233\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.484211\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.000701\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.030508\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTIGIT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.557895\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.485714\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.010585\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.029921\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCTLA4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.590977\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.455639\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.00607\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.043493\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eICOS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.577444\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.46015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.007673\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.041201\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBTLA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.266165\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.067669\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.256671\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.776821\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003ea\u003c/b\u003e Analysis of immune checkpoints in the cSCC and control groups. \u003cb\u003eb\u003c/b\u003e Spearman correlation analysis between immune checkpoints and biomarkers. \u003cb\u003ec,d\u003c/b\u003e Heat map of immune cell infiltration in the cSCC and control groups. Twelve immune cell types exhibited significantly different infiltration scores (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) between the two groups. \u003cb\u003ee\u003c/b\u003e Heat map of correlations between differentially expressed immune cell types. \u003cb\u003ef\u003c/b\u003e Correlation between differentially expressed immune cells and biomarkers.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e3.5 Regulation network construction and potential drug prediction of BIRC5 and HDAC1\u003c/h2\u003e\u003cp\u003eA total of 31 key miRNAs for BIRC5 and 12 key miRNAs for HDAC1 were predicted using TargetScan and miRWalk databases. The lncRNA-key miRNA-mRNA regulatory network showed that LINC00910 and AL356488.2 were regulated by hsa-miR-877-5p and hsa-miR-3940-3p in BIRC5; AC245014.3 was regulated by hsa-miR-3614-5p in HDAC1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). In addition, 20 target drugs (such as bisphenol A, doxorubicin, and resveratrol) acting on BIRC5 and 4 target drugs (like valproic acid, vorinostat, bisphenol A, and trichostatin A) acting on HDAC1 were predicted based on the CTD database (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). Remarkably, BPA is associated with both BIRC5 and HDAC1. The results of the molecular docking experiment indicated that resveratrol had a stronger binding capacity to BIRC5 (-8.7 kcal/mol) and that curcumin had a stronger binding capacity to HDAC1 (-8.2 kcal/mol) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003ea\u003c/b\u003e The lncRNA-miRNA-mRNA regulatory network of BIRC5 and HDAC1. \u003cb\u003eb\u003c/b\u003e Network of drugs and biomarkers. \u003cb\u003ec\u003c/b\u003e Molecular docking of biomarkers with drug components.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003e3.6 Identification of epithelial cells as key cells\u003c/h2\u003e\u003cp\u003eFirst, we filtered the ineligible cells and yielded 48,164 cells and 24,544 genes for subsequent analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). After quality control, 37,320 cells and 24,544 genes were identified (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). Analysis of variance (ANOVA) of genes was performed on the cells and found 1500 highly variable genes were identified. The top 10 highly variable genes were labeled (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). Meanwhile, we selected the top 30 PCs (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) for subsequent analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD). Fifteen different cell clusters were identified using UMAP cluster analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE). Different cell clusters were annotated, resulting in eight cell clusters: epithelial cells, myeloid cells, T cells, fibroblasts, melanocytes, monocytes, B/plasma cells, and endothelial cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eF). Myeloid cells were annotated as Langerhans cells, macrophages, myeloid-derived suppressor cells (MDSCs), CLEC9A DCs, and AS DCs (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eG). BIRC5 and HDCA1 showed the highest expression levels in epithelial cells; therefore, epithelial cells were designated as key cell clusters for subsequent analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eH-I). Additionally, functional enrichment analysis was performed on epithelial cells; for example, sterols are 12-hydroxylated by CYP8B1, activation of Na-permeable kainate receptors, and acetylcholine inhibits contraction of outer hair cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eJ).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003ea\u003c/b\u003e before and \u003cb\u003eb\u003c/b\u003e after quality control of single-cell analysis of GSE144236 dataset. \u003cb\u003ec\u003c/b\u003e Top ten highly variable genes. \u003cb\u003ed\u003c/b\u003e The dResults of the principal component analysis. \u003cb\u003ee\u003c/b\u003e t-SNE plot of cell clustering, \u003cb\u003ef\u003c/b\u003e and further annotated into eight cell clusters. \u003cb\u003eg\u003c/b\u003e Myeloid cell type \u003cb\u003eh\u003c/b\u003e UMAP plot of biomarker expression. \u003cb\u003ei\u003c/b\u003e Differential expression of biomarkers among distinct cell populations. \u003cb\u003ej\u003c/b\u003e Functional enrichment analysis of epithelial cells.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003e3.7 Cellular communication analysis and pseudo-time trajectory analyses\u003c/h2\u003e\u003cp\u003eThe number and weight of interactions between different cell types were shown in the cell communication network \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA\u003cb\u003e)\u003c/b\u003e. Epithelial cells communicate with several other cells and the strongest communication occured with fibroblasts. We simulated and analyzed the cell track differentiation of all epithelial cells, which differentiated into 13 states: the darker the blue color, the earlier the cell differentiation \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB\u003cb\u003e)\u003c/b\u003e. Moreover, HDAC1 expression decreased with differentiation time \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003ea\u003c/b\u003e Interactions between different cell types. \u003cb\u003eb\u003c/b\u003e Heterochrony of cell differentiation. \u003cb\u003ec\u003c/b\u003e Temporal expression of biomarkers.\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eCurrently, cSCC is primarily treated with surgery. However, for advanced metastatic cSCC, identifying effective systemic therapeutic targets can significantly improve patient prognosis. Previous studies have confirmed that the centrosome amplification phenomenon observed in cSCC could interfere with the mitotic process, leading to abnormal chromosome separation and accelerating tumor progression[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. CA releases pro-invasive factors such as IL-8 and ANGPTL4 through additional centriole-related secretory phenotypes, recruiting Th2 cells and M2 macrophages to form a tumor immune microenvironment. Meanwhile, aneuploid tumors reduce MHC class I antigen presentation and CD8⁺ T cell infiltration due to centriole abnormalities, thereby evading immune surveillance[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] Therefore, we used a total of 101 machine learning modelsto identify six biomarkers that may be involved in centrosome duplication and tumor immunity in cSCC, and narrowed them to two biomarkers through expression verification and ROC analysis. The ANN models and ROC curve suggested that BIRC5 and HDAC1 have a high diagnostic value for cSCC. GSEA revealed the most significantly enriched pathways for BIRC5 and HDAC1 expression. Immune infiltration profiling revealed a significant association between the two biomarkers and key immune cells. lncRNA-key miRNA-mRNA networks of BIRC5 and HDAC1 were constructed to better understand the key miRNAs and lncRNAs in cSCC. In addition, we predicted potential drugs that target BIRC5 and HDAC1. Using single-cell RNA-seq datasets, we found that BIRC5 and HDAC1 were highly expressed in epithelial cells, supporting the key role of epithelial cells in cSCC. Thus, our findings may provide a potential valuable reference for exploring the diagnosis and clinical management of patients, and offer a theoretical foundation for further research on cSCC pathogenesis.\u003c/p\u003e\u003cp\u003eBIRC5 is a key regulator of cell division and inhibition of apoptosis within the inhibitor of apoptosis protein (IAP) family[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Previous studies have found that BIRC5 is highly expressed in various SCC (including oral, esophageal, and laryngeal SCC) and is associated with poor prognosis[\u003cspan additionalcitationids=\"CR47\" citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Interestingly, these types of SCC commonly exhibit centrosome duplication[\u003cspan additionalcitationids=\"CR50\" citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e], suggesting that high expression of BIRC5 may be closely associated with centrosome abnormalities characteristic of SCC. As a core component of the chromosome passenger complex,[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e] the abnormal expression of BIRC5 may serve as a key molecular basis for centrosome amplification and chromosomal instability in SCC, providing important clues for understanding the critical role of BIRC5 in centrosome-immune interactions in cSCC. In the current study, we demonstrated that BIRC5 expression is significantly upregulated in cSCC and has high predictive value for patient prognosis. Our study also found a strong correlation between BIRC5 and CD276 expression levels. Previous studies have suggested that CD276 plays a key role in immune escape in cSCC[\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. CD276 can also activate the JAK2/STAT3 pathway by affecting STAT phosphorylation, thereby upregulating BIRC5 gene[\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. So CD276 might regulate the expression of BIRC5 and they coorperate to act on immune escape in cSCC.Meanwhile, a previous study indicated that BIRC5 blockade could inhibit tumorigenesis, metastasis, and recurrence of cSCC [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e], suggesting that BIRC5 is not only a prognostic predictor of cSCC, but also plays a role in tumorigenesis and therapy resistance of cSCC. These findings suggest that BIRC5 is a promising novel therapeutic target for cSCC treatment. BIRC5 expression and function can be inhibited through multiple mechanisms[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Molecular docking analysis in this study revealed that resveratrol exhibits a potentially high binding affinity to the BIRC5 protein, suggesting its role as a putative modulator of BIRC5 activity. Resveratrol is a natural phytoalexin that exhibits anticancer effects in multiple tumors [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. Notably, resveratrol can inhibit the NF-κB pathway to prevent the photocarcinogenesis of ultraviolet light, which is the main pathogenesis of skin SCC[\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. Resveratrol has also been shown to be effective in cancer treatment, both in vitro and in vivo. Previous research has shown that resveratrol can downregulate the Wnt/β-catenin pathway mediated by NEAT-1, thereby inhibiting BIRC5 expression[\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. Whether such an effect exists in SCC requires further investigation.\u003c/p\u003e\u003cp\u003eThe histone deacetylase (HDAC) enzyme family catalyzes lysine deacetylation of histones and non-histones. HDAC1 is an important family member that is mainly located in the cell nucleus[\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. Traditionally, HDAC1 has been recognized for its ability to transduce extracellular and environmental signals by fine-tuning pivotal endothelial activities, notably angiogenesis, inflammation, redox equilibrium, and NO-dependent signaling pathways.[\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. Recent studies have indicated that HDAC1 is upregulated in multiple cancers, including myeloma, glioblastoma, colon cancer, breast cancer, ovarian cancer, and gastric cancer, where it promotes tumor progression by enhancing cell migration, invasion, and epithelial-mesenchymal transition (EMT)[\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e, \u003cspan additionalcitationids=\"CR61 CR62 CR63\" citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. HDAC1 inhibition suppresses EMT in glioblastoma, induces apoptosis and cell cycle arrest in colon cancer, and impairs proliferation of other malignancies[\u003cspan additionalcitationids=\"CR62 CR63\" citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn cSCC, HDAC1 primarily involves the formation of an active transcriptional repression complex with δNp63α, thereby maintaining the survival of cSCC cells by inhibiting the expression of proapoptotic Bcl-2 family member genes[\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. Vorinostat, an HDAC inhibitor, reduced HDAC1 expression in epidermoid carcinoma A431 cells while increasing histone H3 and p53 acetylation. This process impaired tumor cell proliferation, manifested by downregulation of the expression of proliferating cell nuclear antigen and cyclins D1, D2, E, and A. It also induces apoptosis through inhibition of mTOR signaling, accompanied by reduced activity of the AKT and extracellular signal-regulated kinase (ERK) signaling pathways associated with cell survival. This leads to the inhibition of human xenograft tumor growth, resulting in tumors with well-differentiated features and extensive necrotic areas, thus providing a mechanistic basis for the treatment of cSCC[\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. These studies demonstrate that epigenetic therapeutic strategies targeting HDAC1 hold significant promise for cutaneous squamous cell carcinoma (cSCC), offering novel insights into combination-targeted therapies.\u003c/p\u003e\u003cp\u003eGSEA revealed that the enriched HDAC1 pathways in cSCC were mainly concentrated in ubiquitin-mediated proteolysis. Research has indicated that the ubiquitin-proteasome system plays an important role in the development of skin cancer by regulating the NF-κB pathway. NF-κB, a key regulator of the immune and inflammatory pathways, has dual tumor-promoting and tumor-suppressing functions in cell proliferation and differentiation[\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]. This finding provides important clues regarding the role of HDAC1 in cSCC as it regulates transcriptional control through protein degradation. Given the critical role of the ubiquitin-proteasome system in cSCC, proteasome inhibitors (such as bortezomib, ixazomib, and carfilzomib) and ubiquitin E1 enzyme inhibitor MLN7243 have been shown to selectively kill cSCC cells, providing experimental evidence for the use of proteasome and ubiquitin E1 inhibitors in cSCC therapy[\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]. Using ssGSEA, we found that, among all immune cells, MDSC infiltration had the highest enrichment. Correlation analysis suggested a strong positive correlation with T Cells. These two cell types exert immunosuppressive functions by inhibiting effector T cell proliferation and reducing cytokine production, thereby jointly promoting tumor progression and resistance to immunotherapy[\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]. Further studies have confirmed that increased numbers of MDSCs exacerbate the tumor's immunosuppressive microenvironment, thus promoting cSCC development[\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e], indicating the critical role of immunosuppressive cell subpopulations in cSCC immune escape.\u003c/p\u003e\u003cp\u003eBased on the above findings, the present study demonstrated that HDAC1 is closely associated with tumor autophagy and immune regulation, which supports the identification of HDAC1 as a potential molecular target for cSCC therapy, with its inhibitors potentially benefiting advanced patients in particular. Several HDAC1 inhibitors have shown promising therapeutic potentials. MS275, a selective HDAC1/3 inhibitor, has demonstrated promising efficacy in Phase I clinical trials for advanced refractory solid tumors/lymphomas [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e]. RGFP109 has also been reported to specifically inhibit the activity of HDAC1[\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e], offering a significant advantage in terms of selectivity. Molecular docking analysis indicated that the traditional Chinese medicinal component curcumin exhibited a high affinity for HDAC1. Experimental studies have found that curcumin first transiently upregulates HDAC1 expression in Raji cells (within 24 h) before downregulating HDAC1 expression in a concentration-dependent manner [\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e], suggesting that it may exert its anti-cSCC effects through the dose-dependent inhibition of HDAC1. Such a biphasic regulatory mechanism may provide an important reference for optimizing the therapeutic doses and administration regimens. Given the central role of HDAC1 in the pathogenesis of cSCC and the good safety and efficacy of existing inhibitors, HDAC1-targeted therapy holds promise as an important strategy for personalized treatment of cSCC, particularly for advanced patients with limited response to conventional treatment regimens.Interestingly, based on the CTD database, bisphenol A(BPA)is predicted to interact with BIRC5 and HDAC1. BPA is an endocrine disruptor commonly used as an ingredient in food containers[\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. BPA is widely present in freshwater and the atmosphere, and is associated with a variety of tumors[\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. BPA affects BIRC5 mRNA levels in fish spermatocytes [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. In human cervical cancer studies, BIRC5 was found to be a BPA response gene that may be involved in the carcinogenic effect of BPA on cervical cancer[\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. These findings indicate that BPA is unlikely to be a therapeutic drug for SCC but instead promotes the occurrence and development of skin SCC by interacting with BIRC5.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn conclusion, our study systematically screened biomarkers associated with cSCC, centrosome duplication, and immune regulation, using machine learning models. We identified BIRC5 and HDAC1 as pivotal diagnostic signatures, and further explored their roles in modulating cSCC progression and immune responses. These findings provide novel insights into potential therapeutic strategies for cSCC.\u003c/p\u003e\u003cp\u003eHowever, this study also has some limitations. First, the sample size of the dataset used was small, and the clinical validation only involved 5 pairs of clinical samples, which may affect the universality of the diagnostic value of BIRC5 and HDAC1. Secondly, the functional mechanism exploration of BIRC5 and HDAC1 in this study is mainly based on bioinformatics analysis. Although the difference of gene expression was verified by RT-qPCR, the functional verification of cell experiment and animal model was lacking, and the specific molecular pathway of the two regulatory participation in centrosome-immune interaction was not directly revealed. Third, the drug prediction part is only based on the CTD database screening, and its clinical transformation value and practical application prospects still need further evaluation by subsequent clinical trials or clinical data support. In the future, we will collect large samples from multiple centers for verification. Gene knockout and gene over-expression experiments will be conducted to detect changes in tumor proliferation, apoptosis and immune-related molecules at the in vivo and in vitro levels to clarify the functional mechanism of BIRC5/HDAC1. In addition, the inhibitory efficiency of the drug on BIRC5/HDAC1 and the killing effect on cSCC cells were verified by in vitro experiments, providing experimental basis for clinical drug application.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eANN,artificial neural network; AUC,area under the curve; \u0026nbsp;cSCC,cutaneous squamous cell carcinoma; CA,centrosome abnormalities; cDNA, complementary DNA; CRGs, centrosome-related genes; DEGs, differentially expressed genes; EMT, epithelial-mesenchymal transition; HDAC, histone deacetylase; IAP, inhibitor of apoptosis protein; IRGs, immune-related genes; MDSCs, myeloid-derived suppressor cells; PC, principal components; PCA, principal component analysis; PPI, protein-protein interaction; GSEA,gene set enrichment analysis; GSVA, gene set variation analysis; ROC, receiver operating characteristic; USTC, University of Science and Technology of China.\u003c/p\u003e\n"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e \u003c/p\u003e\n\u003cp\u003eThe authors declare no conflicts of interest in this work. \u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Natural Science Foundation of China (grant number: 82303737).\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e \u003c/p\u003e\n\u003cp\u003eYF, JT and YZ designed the experiments. YF, RC and FJ performed the bioinformatics analysis. CZ and SZ collected clinical specimens and performed PCR validation. YF, JX and YZ wrote the manuscript.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated in the current study are available in the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/). GSE108008: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE108008\u003c/p\u003e\n\u003cp\u003eGSE45164: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE45164\u003c/p\u003e\n\u003cp\u003eGSE144236: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE144236\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eEthical approval \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with the principles of the Declaration of Helsinki. The study was approved by the Ethics Committee of the First Affiliated Hospital of the USTC (Approval Number : 2025KY No.386).\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed consent was obtained from all individual participants included in the study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eVenables ZC, Autier P, Nijsten T, et al (2019) Nationwide Incidence of Metastatic Cutaneous Squamous Cell Carcinoma in England. JAMA Dermatol 155:298\u0026ndash;306. https://doi.org/10.1001/jamadermatol.2018.4219\u003c/li\u003e\n\u003cli\u003ede Jong E, Lammerts MUPA, Genders RE, Bouwes Bavinck JN (2022) Update of advanced cutaneous squamous cell carcinoma. 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Anticancer Res 33:3527\u0026ndash;3541\u003c/li\u003e\n\u003cli\u003eAlshetaiwi H, Pervolarakis N, McIntyre LL, et al (2020) Defining the emergence of myeloid-derived suppressor cells in breast cancer using single-cell transcriptomics. Sci Immunol 5:eaay6017. https://doi.org/10.1126/sciimmunol.aay6017\u003c/li\u003e\n\u003cli\u003eThornton AM, Shevach EM (1998) CD4+CD25+ immunoregulatory T cells suppress polyclonal T cell activation in vitro by inhibiting interleukin 2 production. J Exp Med 188:287\u0026ndash;296. https://doi.org/10.1084/jem.188.2.287\u003c/li\u003e\n\u003cli\u003eBai X, Shan F, Qu N, et al (2021) Regulatory role of methionine enkephalin in myeloid-derived suppressor cells and macrophages in human cutaneous squamous cell carcinoma. Int Immunopharmacol 99:107996. https://doi.org/10.1016/j.intimp.2021.107996\u003c/li\u003e\n\u003cli\u003eRyan QC, Headlee D, Acharya M, et al (2005) Phase I and pharmacokinetic study of MS-275, a histone deacetylase inhibitor, in patients with advanced and refractory solid tumors or lymphoma. J Clin Oncol Off J Am Soc Clin Oncol 23:3912\u0026ndash;3922. https://doi.org/10.1200/JCO.2005.02.188\u003c/li\u003e\n\u003cli\u003eWu Q, Chen Y, Li X (2006) HDAC1 expression and effect of curcumin on proliferation of Raji cells. J Huazhong Univ Sci Technol Med Sci Hua Zhong Ke Ji Xue Xue Bao Yi Xue Ying Wen Ban Huazhong Keji Daxue Xuebao Yixue Yingdewen Ban 26:199\u0026ndash;201, 210. https://doi.org/10.1007/BF02895815.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dion","sideBox":"Learn more about [Discover Oncology](https://www.springer.com/12672)","snPcode":"","submissionUrl":"","title":"Discover Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"cSCC, Centrosomal, Immune, single-cell RNA sequencing","lastPublishedDoi":"10.21203/rs.3.rs-7496212/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7496212/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eCutaneous squamous cell carcinoma (cSCC) is a common skin cancer where immune dysregulation plays a critical role in its progression and resistance to therapy. Centrosomal amplification (CA), a marker of genomic instability, contributes to cancer development by affecting cell division, immune cell activation, antigen presentation, and cytokine signaling. Further research into the interaction between centrosomal alterations and immune regulation could reveal new therapeutic targets and improve diagnosis and treatment strategies for cSCC.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eCentrosomal- and immune-related signature biomarkers of cSCC were screened from public databases using 101 combinatorial models based on 10 machine learning algorithms, followed by RT-qPCR for validation and artificial neural networks (ANN) to assess the diagnostic efficacy of these biomarkers. Functional mechanisms were explored by enrichment analysis, immune infiltration profiling, and single-cell RNA sequencing.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eTwo biomarkers, BIRC5 and HDAC1, were identified. They were mainly expressed in epithelial cells and both showed high diagnostic value for cSCC. These biomarkers were significantly related to the cell cycle and immune checkpoints, and were especially correlated with CD276. Single-cell RNA sequencing identified eight cell types, with BIRC5 and HDAC1 showing the highest expression levels in epithelial cells, suggesting their potential role in cSCC pathogenesis by modulating epithelial cell function during tumor initiation and progression.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eThis study identified and validated two centrosome-immune crosstalk biomarkers, BIRC5 and HDAC1, that may serve as novel targets for precise diagnostic and therapeutic strategies in cSCC management.\u003c/p\u003e","manuscriptTitle":"Identified BIRC5 and HDAC1 as Novel Diagnostic Biomarkers Linked to Centrosome-Immune Crosstalk for Cutaneous Squamous Cell Carcinoma via Machine Learning-Based Multi-omics Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-12 15:45:18","doi":"10.21203/rs.3.rs-7496212/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-28T15:24:05+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-24T19:01:00+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-23T04:23:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"224233280551256188616290137803626719912","date":"2025-11-17T18:49:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"85715828003231319883080115433576320873","date":"2025-11-12T11:25:18+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-08T13:13:24+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-07T14:05:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"90928337102021945326400139838688419237","date":"2025-11-03T08:08:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"259709131920325457619056640825989182803","date":"2025-11-03T07:12:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"249543989194912670484873318650439622674","date":"2025-11-03T05:37:22+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-03T05:35:41+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-11-01T13:52:24+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-30T09:40:28+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-30T02:46:00+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Oncology","date":"2025-10-30T02:42:32+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dion","sideBox":"Learn more about [Discover Oncology](https://www.springer.com/12672)","snPcode":"","submissionUrl":"","title":"Discover Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c3d32be7-e914-4b33-b3bf-7c91e8b3bdf3","owner":[],"postedDate":"November 12th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-01-27T10:18:45+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-12 15:45:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7496212","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7496212","identity":"rs-7496212","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}