Single-cell transcriptomic analyses reveal heterogeneity and key subsets associated with survival and response to PD-1 blockade in cervical squamous cell carcinoma

Single-cell transcriptomic analyses reveal heterogeneity and key subsets associated with survival and response to PD-1 blockade in cervical squamous cell carcinoma | 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 Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Single-cell transcriptomic analyses reveal heterogeneity and key subsets associated with survival and response to PD-1 blockade in cervical squamous cell carcinoma Qitai zhao, Xia Li, Zhao Zhao, Yanmei Cheng, Jiaqin Yan, Fang Ren, and 10 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4589423/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Understanding the intricate tumor microenvironment (TME) is crucial for elucidating the mechanisms underlying the progression of cervical squamous cell carcinoma (CSCC) and its response to anti-PD-1 therapy. In this study, we characterized 50,649 cells obtained from CSCC for single-cell RNA sequencing and integrated bulk sequencing data from The Cancer Genome Atlas (TCGA) and clinical specimens to explore cell composition, metabolic processes, signaling pathways, specific transcription factors, lineage tracking and response to immunotherapy. We identified 31 subsets of stromal and immune cells in the tumor microenvironment (TME) and observed distinct patterns in the metabolic processes and signaling pathways of these cells between tumor and normal tissues. Collagen signaling was found to be crucial for the interaction between stromal and immune cells. Furthermore, PCLAF-TAEpis were negatively correlated with CXCL13 + CD8 + tumor-reactive T cells, overall survival, and the response to anti-PD-1therapy in patients with CSCC. In vivo experiments demonstrated that PCLAF-TAEpis promoted tumor growth and hindered the therapeutic efficacy of anti-PD-1 treatment by inhibiting the infiltration and function of T cells. Collectively, our findings illuminate the heterogeneity of the complex TME in CSCC and offer evidence supporting PCLAF-TAEpis as a promising therapeutic target. Cervical squamous cell carcinoma Single-cell RNA sequencing Heterogeneity Tumor-associated epithelial cells Immunotherapy response Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Cervical cancer (CC) is one of the most prevalent female malignancies globally in low- and middle-income regions, accounting for 7.5% of all female cancer deaths( 1 , 2 ). The most prevalent type of CC is cervical squamous cell carcinoma (CSCC), especially in patients with human papillomavirus (HPV) infection, which was considered as the leading risk factor for CC( 3 ). Currently, the efficacy of surgery, chemotherapy, and radiotherapy for early-stage and low-risk CC is satisfactory( 4 , 5 ). However, survival for metastatic CC is still poor( 6 ). Recent immunotherapies that block immune checkpoints, such as programmed cell death-1 (PD-1) and programmed cell death ligand-1 (PD-L1), have exhibited substantial anti-tumor activity and a good biosafety profile in clinical trials for the treatment of recurrent CC or metastatic CC( 7 , 8 ). However, these treatments have not achieved better results in most patients because of the complexity and heterogeneity of the tumor microenvironment (TME). TME is a dynamic system sculpted by tumor cells, surrounding cells, and molecules, including stromal cells, immune cells, extracellular matrix, chemokines, and cytokines( 9 ). Accumulating evidence suggests that TME components are linked to the progression of patients with CC( 10 ). Therefore, understanding the complexity of TME is essential for tumor treatment. In our previous work, we performed a comprehensive characterization of immune infiltration through bulk sequencing data, we found that tumor cells highly expressed Keratin, type I cytoskeletal 23 (KRT23) to inhibit the accumulation of CD8 + T cells( 11 ). Recent studies have utilized single-cell RNA sequencing (scRNA-seq) to investigate the heterogeneity of TME in various tumor types, such as hepatocellular carcinoma, colon cancer, lung cancer, and ovary cancer( 12 – 14 )at single-cell resolution. Some studies have performed scRNA-seq to uncover the TME of CC. Keqin Hua et al. found intra-tumoral heterogeneity and transcriptional activities of endothelial cells and constructed a cell landscape during CC pregression( 15 , 16 ). However, the precise composition and function of the CSCC cell landscape are still unclear. Herein, we profile the transcriptome of 50649 cells derived from six tumor tissues and two adjacent normal tissues of CSCC through scRNA-seq. We displayed a comprehensive cell landscape of the TME and investigated its function and lineage tracking. Of note, we found that PCLAF-TAEpis were negatively correlated with tumor-specific CXCL13 + CD8 + T cells and infiltration of these two subsets of cells was correlated with tumor progression. Furthermore, we used in vivo experiments to explore the role of PCLAF-TAEpis in limiting the efficiency of anti-PD-1 treatment.Our analysis sheds light on the heterogeneity of CSCC and demonstrates that PCLAF- TAEpis are a viable therapeutic target. Materials and methods Human specimens Six tumor tissues and two adjacent normal tissues were promptly obtained post-surgical resection from treatment-naive CSCC patients for scRNA-seq. Tissues for immunohistochemistry (IHC) and multi-color immunofluorescence (IF) were procured during surgery and fixed in formalin for 48 hours. This study included fifty patients pathologically diagnosed with CSCC to assess the correlation between CD24 and PD-1 expression and survival. Furthermore, thirty-seven patients who underwent radiotherapy and anti-PD-1 therapy were enrolled to investigate the link between CD24 expression and clinical response. The clinical response for each target lesion was evaluated based on RECIST v.1.1 criteria. Cell lines and regents U14 cell cervical cancer cell line were purchased from Hefei Wanwu Biotechnology Co., LTD. U14 cells were cultured in Dulbecco’s Modified Eagle’s Medium (DMEM) (Gibco) with 10% fetal bovine serum (Gibco) and 1×Penicillin-Streptomycin Solution (Gibco) at 37℃ and 5% CO2. Murine PD-1(clone2.43) antibody were purchased from BioXCell. Animal experiments All animal protocols were conducted in accordance with the National Institutes of Health (NIH) Guidelines for the Care and Use of Laboratory Animals and were approved by the institutional animal care committee. Female C57BL/6J mice aged 6–8 weeks were procured from Charles River. For the animal experiments, 5 x 10^6 U14 cells were initially implanted subcutaneously into the right flank of the mice in 100 µL of PBS. Upon reaching a tumor volume of 1000 mm^3, the tumor, adjacent skin tissue, and cervical tissue of the mice were surgically excised. The tissues were then sectioned into small pieces and enzymatic digestion was carried out using the tumor dissociation kit (Miltenyi Biotec) for one hour at 37°C. Afterward, single cells were isolated using flow cytometry (BD FACSaria III). The cells were tagged with APC anti-mouse EpCAM (#118213, BioLegend) and FITC anti-mouse CD24 (#101805, BioLegend) for the isolation of PCLAF-tumor-associated epithelial (TAEpis) and normal epithelial cells. The epithelial cells were then cultured and expanded using a specific culture medium for epithelial cells obtained from MINGZHOUBIO. Subsequently, 5 x 10^6 U14 cells and either TAEpis or normal epithelial cells were subcutaneously implanted into the right flank of mice in 100 µL of PBS. Anti-PD-1 antibody (200 µg per mouse) was administered intraperitoneally every two days for a total of three doses.Tumor volume was calculated every two days using the formula: (length x width^2)/2. The survival of tumor-bearing mice was assessed daily. If the experimental endpoints were reached or the tumor volume reached 2000 mm^3, all mice were humanely euthanized following the NIH guidelines. Isolation of single cell Tumor and normal tissues were washed thrice with PBS and subsequently sliced into 1–3 mm^3 pieces. These tissue sections were then placed in a 10-mL digestion medium comprising 0.2% collagenase I/II, DNAse I (Sigma), and 25 units dispase in DMEM. The samples were incubated on an orbital shaker at 37°C and 250 RPM for 15 minutes. Following this, approximately 20 mL of ice-cold PBS containing 5% fetal bovine serum was introduced, and the mixture was filtered through a 40-µm cell strainer. After centrifugation at 1500 RPM for 5 minutes at 4°C, 5 mL of red blood cell lysis buffer was added and mixed for 15 minutes. The samples were subsequently centrifuged; the resulting single cells were suspended in a sorting buffer and quantified using an automatic cell counter. Single-cell sequencing, filtering, and normalization Single-cell data library preparation was conducted using the Chromium Single Cell 3’ Library, Gel Bead & Multiplex Kit, and Chip Kit (10x Genomics) following the manufacturer’s guidelines. The raw data was processed into unique molecular identifier (UMI) counts using Cellranger 3.0.2 (10x Genomics) with the GRCh38 reference genome. Further analyses were carried out utilizing Seruat (version 4.2.1) in R (version 4.0.1). Each sample underwent filtering based on gene numbers, gene counts, mitochondrial gene fraction, and hemoglobin genes as detailed in Additional file1: Table S1 . The filtered UMI counts were log-transformed for normalization, and 2000 variable genes were identified using the variance stabilizing transformation method for subsequent cell analysis. The data were then scaled using the ScaleData function, and a principal component analysis (PCA) was conducted with default parameters. Unsupervised cell clustering and cell type annotation Canonical correlation analysis (CCA) was utilized to mitigate batch effects among the samples, integrating all samples into a comprehensive data matrix. Following CCA, clusters were discerned at a resolution of 0.3, revealing ten primary cell types characterized by canonical marker gene expression: epithelial cells, fibroblasts, endothelial cells, myeloid cells, B cells, mast cells, CD4 + and CD8 + T cells, NK cells, and plasma cells. Subsequently, epithelial cells, fibroblasts, myeloid cells, and T cells were selected using the subset function for a repeated PCA analysis, excluding B cells and endothelial cells due to their limited numbers. The identified major cell types underwent a re-clustering process, with marker genes pinpointed using the findAllMarkers function based on log2 fold change > 0.5 and P-value < 0.05, and visualization was achieved with the DotPlot function. SCENIC analysis SCENIC (version 1.0.0.3) was used to analyze TF activity across a subsets of each major cell type. Row count matrix was used as input; the regulons and TF activity for each subsets of major cell types were calculated using the pySCENIC (version 0.8.9) pipeline with motif collection version mc9nr. The differentially activated TFs of each subcluster were identified using the Wilcoxon rank sum test against TFs with log-fold-change > 0.5, and p -value < 0.05 were considered as significantly upregulated. Trajectory analysis R package “Slingshot” was used to infer lineage tracking of epithelial cells, ECs, fibroblasts, neutrophils, CD4 + and CD8 + T cells, respectively.Integrated data were transformed into the format of SingleCellExperiment. The analysis were performed using default parameters. Estimate the activity of the metabolic and signaling pathways The metabolic and signaling pathways were selected from the KEGG database ( https://www.kegg.jp/ ) using the R package “KEGGREST”; seven metabolic pathways were used as previously described dataset( 17 ). The GSVA package was used to calculate the pathway score of these terms. A t -value was used to compare the difference in score between the tumor and normal tissue. Heatmap was used to visualize the average expression of score across each subcluster. Cell communication analysis The Cellchat package was used to investigate communication across cells by integrating gene expression with prior knowledge of the interactions between signaling ligands, receptors, and their cofactors( 18 ). Normalized matrix and Metadata were used as input. The CellchatDB database, which contains 2021 validated molecular interactions, was used to analyze the receptor-ligand interaction. Significant interactions were calculated using default parameters. Identification and enrichment analysis of DEGs To identify the DEGs between the two subsets of cells, we first transformed UMI counts to transcripts per million (TPM) using hg19 genome references. The “Limma” package was used to identify DEGs with logFC > 1, p-value < 0.05, and then DEGs were utilized to perform Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analysis through “clusterProfiler” and “org.Hs.eg.db” packages. TCGA analysis To explore the effect of cell types on the survival of CSCC, maker genes of each subsets were selected (log2 fold change > 0.5, p -value < 0.05), and housekeeping genes were used as previously identified( 19 ). TCGA data of CSCC were downloaded from the UCSC Xena database ( http://xena.ucsc.edu/ ) with the formation of log2(x + 1) transformed RSEM normalized count. Subsequently, GSVA was used to calculate the cell score and housekeeping gene score, and the cell score was normalized with the housekeeping gene score. The survival cut-off point value of the risk score was calculated by maximally selected rank statistics using “survminer” and visualized using the “survival” package. Immunohistochemistry and multi-color immunofluorescence The solid tumor specimens were cut into 5-mm sections and affixed to glass slides. Following this, the slides underwent deparaffinization and antigen retrieval using standard procedures. Subsequently, H2O2 was applied to block the slides for 20 minutes in the dark, followed by a 15-minute PBS (pH 7.4) wash. Blocking of nonspecific antigens was carried out using 10% goat serum for 30 minutes. The slides were then rinsed with PBS and exposed to primary antibodies CD24 (#ab290730, Abcam, Cambridge, MA, USA), PD-1 (#ab52857, Abcam), and CD8 (#ab237709, Abcam) overnight at 4°C. After another PBS wash, the slides were incubated with an HRP-labeled secondary antibody for 50 minutes at 25°C. Subsequently, CY3 and CY5-TSA were applied, incubated for 15 minutes at 25°C, and followed by antigen retrieval. A mixture of PD-1 and CD8 was added to the slides and left overnight at 4°C. Mixed secondary antibodies were applied and incubated for 50 minutes. Finally, the images were examined using confocal microscopy (IX71, Olympus). Survival analysis of PD-1 and CD24 The immunohistochemistry (IHC) score for PD-1 and CD24 was determined as previously outlined( 20 ). Each sample underwent assessment for both nucleic and membrane staining intensity (blank = 0, light yellow = 1, yellow = 2, brown = 3) and the percentage of stained cells (0% = 0, 1–24% = 1, 25–49% = 2, > 50% = 3). An IHC score of ≥ 4 indicated high expression, while an IHC score of < 4 indicated low expression. The relationship between PD-1 and CD24 expression and the overall survival of CSCC was analyzed using the log-rank test. Flow cytometry analysis Single cells were obtained from tumor tissues as described above. Cells were labeled with Zombie dye, Percp/Cyanine 5.5 anti-mouse CD45(#157208, BioLegend), APC anti-mouse CD3 (#100236,BioLegend), FITC anti-mouse CD4(#100406, BioLegend), PE/Cyanine7 anti-mouse CD8 (#100722, BioLegend), PE anti-mouse CD11b (#101208, BioLegend), Brilliant Violet 421 anti-mouse B220(#103251, BioLegend), APC/Cyanine7 anti-mouse NK1.1(#156510, BioLegend), PE anti-mouse IFN-γ(#505808, BioLegend), APC/Cyanine7 anti-mouse(#506344,BioLegend) for 15 minute at 4 ℃. For intracellular cytokine staining, cells were activated with Cell Activation Cocktail (#423303, Biolegend) for 6h and then fixed and permeabilized. Flow cytometry were performed by CytoFLEX(/Beckman Coulter). Statistical analyses Statistical analyses were conducted using GraphPad Prism (version 8.0) and R (version 4.0.1). The Wilcoxon test was utilized to compare group differences, ANOVA was employed for comparisons involving more than two groups, and the log-rank test was applied to examine survival disparities between the two groups. A significance level of p < 0.05 was deemed statistically significant. Results A single-cell transcriptomic atlas of CSCC To investigate the cellular composition of TME in CSCC, we collected six tumor tissues and two adjacent normal tissues from six treatment-naïve CSCC patients for scRNA-seq (Fig. 1 A). The clinical details of the patients are provided in Table 1 . Following quality filtering, a total of 50,649 cells, with an average of 2,282 genes per cell, were analyzed. This dataset comprised 31,232 cells from tumor tissues and 19,437 cells from normal tissues (Additional file1: Fig. S1 A and 1B, Table S2 ). Unsupervised clustering using uniform manifold approximation and projection (UMAP) demonstrated distinct clustering based on tissue origin, mitigating batch effects from the samples (Fig. 1 B, Additional file1:Fig. S2 A). Ten major cell types were identified based on canonical markers, comprising three stromal cell types (epithelial cells, fibroblasts, and endothelial cells (ECs)), and seven immune cell types (myeloid cells, mast cells, B cells, plasma cells, NK cells, CD4 + T cells, and CD8 + T cells) (Fig. 1 C, Additional file1:Table S3 ). Cell type ratios exhibited variations between tumor and normal tissues (Fig. 1 D). Normal tissues showed abundance in ECs and fibroblasts, while B cells and mast cells were prevalent in tumor tissues, indicating an immune activation state within TME (Fig. 1 E). Additionally, the cellular compositions differed significantly between tumor and normal tissues. Tumor tissues displayed a higher proportion of epithelial cells followed by immune cells, whereas normal tissues had higher proportions of fibroblasts and endothelial cells (Fig. 1 F). Survival analysis indicated that high infiltration of CD4 + T cells, CD8 + T cells, NK cells, plasma cells, and B cells correlated with favorable overall survival, suggesting a positive association between an activated immune response and tumor control; however, no correlation was observed between stromal cells and survival (Fig. 1 G, Additional file1:Fig. S2 B). Analysis of cell communication and correlations revealed complex interactions within the TME, with most cells showing positive correlations indicative of cooperation in the TME (Fig. 1 H, Additional file1:Fig. S2 C and 2D). In conclusion, this study provides a comprehensive overview of the major cell types in CSCC. Table 1 Clinical information of included patients Patients Samples Age Stage Differentiation Pathological type Patient1 PT1 68 IIA Low CSCC Patient1 PN1 68 IIA Low CSCC Patient2 PT2 39 IIIA Moderate CSCC Patient3 PT3 57 IIB Low CSCC Patient4 PT4 47 IIB High CSCC Patient5 PT5 39 IB Low CSCC Patient5 PN5 39 IB Low CSCC Patient6 PT6 42 IB Moderate CSCC Intratumoral heterogeneity of epithelial cells in CSCC To delve deeper into understanding the phenotype and function of epithelial cells within TME dominated by CSCC, we performed subsetting and re-clustering of these cells. Through unsupervised clustering using UMAP, we delineated eight distinct clusters of epithelial cells. The distribution of these clusters varied depending on the sample source and tissue origin (Fig. 2 A and 2 B; Additional file1:Fig. S3 A).Cluster 3 (C3), defined by its high expression of carcinoembryonic antigen (CEACAM5, CEACAM6) and mucin (MUC20) genes, exhibited characteristics typically enriched in tumor cells, specifically excluding those found in normal tissues. Clusters 4 (C4) and 6 (C6), designated as C4-KRTDAP and C6-TFF3, respectively, showed some shared marker genes with C3 but displayed a lower level of carcinoembryonic antigen expression. Notably, these two clusters demonstrated activation of immune-related pathways such as antigen processing and presentation, leukocyte chemotaxis, and antimicrobial function, categorizing them as immune-associated epithelial cells (IAEpis) (Additional file1:Fig. S3 B).Cluster 5 (C5), denoted as C5-PCLAF, was predominantly present in tumor tissues and exhibited high expression of cell cycle-related genes (PCNA, CLSPN), signifying tumor-associated epithelial cells (TAEpis). Conversely, clusters 7 (C7) and 8 (C8) identified as C7-CENPF and C8-NEURL1B - tended to aggregate in tumor tissues, displaying similar patterns of metabolic pathways and signal transduction as C5. This suggested a transitional state of TAEpis within these clusters. Cluster 2 (C2), labeled as C2-DST, showcased a high expression of stromal genes (CCN1, COL17A1) associated with wound healing and matrix remodeling, predominantly found in normal tissues, representing normal epithelial cells.Cluster 1 (C1) did not exhibit distinct markers, indicating a transitional state that was less well-defined (Fig. 2 C and 2 D, Additional file1:Table S4 ). In examining the functions of epithelial cells, gene set enrichment analysis (GSVA) was employed to quantify the activity levels of metabolic and signaling pathways. The findings revealed heightened metabolic activity in epithelial cells within tumor tissues, particularly evident in the increased activity of sphingolipid metabolism, the pentose phosphate pathway, and glycerophospholipid metabolism. Conversely, tryptophan metabolism, taurine and hypotaurine metabolism, and fatty acid biosynthesis exhibited higher scores in normal epithelial cells (Fig. 2 E; Additional file1:Fig. S3 C).Further analysis showed that intratumoral epithelial cells were characterized by the activation of signaling pathways such as HIF-1, mTOR, notch, and VEGF, while displaying a reduction in immune-related signaling (Fig. 2 F). Examination of specific clusters highlighted that C5-PCLAF and C8-NEURL1B demonstrated similar metabolic profiles, with heightened activation in TME. In contrast, C2-DST appeared to be in a state of metabolic equilibrium (Fig. 2 G; Additional file1:Fig. S3 D).Interestingly, distinct signaling patterns were observed between C2 and C8, with C2 predominately activating calcium and cytokine-cytokine receptor signaling pathways (Fig. 2 H). Transcription factor (TF) analysis using SCENIC identified specific TFs associated with each cluster, although C1 did not exhibit any unique TFs (Fig. 2 I).Survival analysis indicated that a greater abundance of C8 correlated with a more favorable overall prognosis, suggestive of an “immune-hot” TME in this subtype of CSCC due to the activation of the NF-κB and TNF signaling pathways (Fig. 2 J; Additional file1:Fig. S3 E). In summary, these results elucidate the intratumoral diversity and plasticity of epithelial cells in CSCC. The interconversion of inflammatory and cancer-associated fibroblasts correlates with the survival outcome of CSCC patients Reclustering of fibroblasts identified five distinct clusters, with these subsets displaying universal distribution across samples but showing disparities between tumor and adjacent normal tissues (Fig. 3 A and 3 B, Additional file1:Fig. S4 A). Cluster 1 (C1), labeled as C1-SPER4, exhibited elevated expression of SPER4 along with several chemokines such as CXCL1, CXCL14, and PDGFRA. This fibroblast cluster, prevalent in normal tissues, was classified as inflammatory-associated fibroblasts (IAFs). Cluster 2 (C2), known as C2-MMP11, showed high expression of matrix metallopeptidase genes like MMP11, MMP2, and MMP14, a range of collagens, and other extracellular matrix components including COL1A1, COL3A1, COL1A2, COL5A2, and COL12A1. Moreover, the presence of FAP, a characteristic gene of cancer-associated fibroblasts (CAFs), designated this cluster as CAFs. Functional analysis indicated that CAFs were associated with the activation of extracellular matrix organization and collagen fibril organization (Additional file1:Fig. S4 B).Clusters 3 (C3) and 5 (C5) were identified as subsets of myofibroblasts, both expressing typical myofibroblast marker genes such as ACTA2 and genes involved in myogenesis like MYH11, MUSTN1, and DES. However, while C3 exhibited moderate expression of these genes, suggesting an immature state, C5-MYH11 was predominant in normal tissues, whereas C3-MUSTN1 showed higher levels in tumor tissues. Cluster 4 (C4), identified as C4-RGS5, comprised pericytes characterized by the expression of the pericyte marker RGS5 (Fig. 3 C and 3 D, Additional file1:Table S5 ). Metabolic analysis unveiled heightened activity in intratumoral fibroblasts, showcasing increased metabolic pathways like glycolysis, gluconeogenesis, and pyrimidine metabolism, alongside reduced cholesterol metabolism and steroid biosynthesis levels (Fig. 3 E, Additional file1:Fig. S4 C). Observation of the fibroblasts within TME demonstrated concurrent activation of oncogenic and immune-related signaling, emphasizing the diverse nature of fibroblasts within the TME (Fig. 3 F).Further exploration of each cluster revealed that C2-MMP11-CAFs exhibited the most vigorous metabolic processes, in contrast to a shared metabolic pattern between C3-MUST1 and C5-DES, while C1-SPER4 displayed a more quiescent metabolic profile (Fig. 3 G, Additional file1:Fig. S4 D). Consistent with these findings, C2-MMP11 showed activation across various oncogenic signaling pathways, C3 activated the calcium signaling pathway, and C5 activated the phosphatidylinositol signaling system (Fig. 3 H).TF analysis identified distinct TFs specific to each cluster. The similar TF expression between the two subsets of cancer-associated fibroblasts (CAFs) suggested the potential for mutual transformation (Fig. 3 I). Trajectory analysis revealed a differentiation pathway from C1-SPER4-IAFs to C2-MMP11-CAFs (Fig. 3 J). Moreover, survival analysis indicated that a high infiltration of IAFs correlated with favorable survival outcomes in CSCC patients, while the presence of CAFs displayed an opposing trend. Conversely, the other subsets showed no significant correlation with patient survival (Fig. 3 K, Additional file1:Fig. S4 E).In summary, these findings illuminate the intricate landscape of fibroblasts in CSCC. Myeloid cell heterogeneity in CSCC correlates with tumor progression Myeloid cells encompass significant immune cell populations within TME that exert anti-tumor effects. Sub-clustering of myeloid cells post-batch correction revealed eight distinctive subsets (Fig. 4 A). While non-unique subsets were evident across samples, notable differences were observed between tumor and adjacent normal tissues (Fig. 4 B, Additional file1:Fig. S5 A). Specifically, C1-C1QA was characterized as macrophages (C1QA, C1QB), C4-CD163 identified as tumor-associated tumor-associated macrophages (TAMs) expressing key metabolic genes like SLC40A1 and FOLR2, linked to TAMs proliferation and polarization. Dendritic cell subsets were also identified, with C5-CD1C representing conventional type 2 dendritic cells (cDC2) expressing CD1C, FCER1A, and HLA-DQB1, and C8-LAMP3 denoted as mature dendritic cells, or cDC3, with high expression of LAMP3 and FCN1 enabling migration to tumors via elevated CCR7 expression.Four subsets of neutrophils were clustered in CSCC, with C2-S100A8 exhibiting high expression of pro-inflammatory genes like IL1B, indicative of conventional neutrophils. On the other hand, C3-CXCR4 and C6-CXCL8 displayed high expression of CXCL8 and CSF3R predominantly in tumor tissues, representing a subset of tumor-associated neutrophils (TANs). C7-ISG15 exhibited high expression of interferon-induced and stimulated genes (IFIT2, IFIT3, ISG15, and ISG20), characterizing a type I interferon-producing neutrophil phenotype, both displaying high expression of the neutrophil-specific antigen CD16B (encoded by FCGR3B) (Fig. 4 C and 4 D, Additional file1:Fig. S5 B, Table S6 ).Further characterization of the C6 and C7 neutrophil subsets involved differential gene expression and pathway enrichment analysis. Results showed distinct transcription profiles between the two clusters, with C6-CXCL8 activating oncogenic signaling pathways like MAPK and NF-κB, while C7-ISG15 enhancing immune-related pathways such as chemokine signaling, Toll-like receptor signaling, and antigen processing and presentation (Additional file1:Fig. S5 C and 5D). Unlike stromal cells, the metabolism and signal transduction of myeloid cells in tumor tissues are significantly suppressed (Fig. 4 E and 4 F, Additional file1:Fig. S5 E). Upon detailed analysis of each cluster, it was evident that macrophages and dendritic cells displayed enhanced metabolic activity and signaling capabilities compared to neutrophils, underscoring their crucial role in TME (Fig. 4 G and 4 H, Additional file1:Fig. S5 F). While unique TFs were identified in each cluster, C6-CXCL8-TANs and C7-ISG15-Neutrophils exhibited similar TF expression levels, suggesting a potential interconversion between these two neutrophil subsets (Fig. 4 I). Trajectory analysis of the four neutrophil subsets revealed three distinct differentiation paths originating from C2-S100A8 conventional neutrophils (Fig. 4 J). Consistent with the aforementioned findings, survival analysis of these clusters indicated that increased infiltration of C6-CXCL8-TANs was associated with unfavorable overall survival, while the trend was reversed for C7. Additionally, high infiltration of the two dendritic cell subsets predicted a more favorable overall survival outcome (Fig. 4 K, Additional file1:Fig. S5 G). Characterization of T cells in CSCC T cells play a pivotal role in immune responses. Re-clustering of T and NK cells unveiled four clusters of CD8 + T cells, five clusters of CD4 + T cells, one cluster of double-positive cells, and one cluster of NK cells (Fig. 5 A). While no specific clusters were consistent across patients, distinct tissue origins were evident in some clusters (Fig. 5 B, Additional file1:Fig. S6 A). Noteworthy CD8 clusters included CD8-C1-ZNF683, characterized by high expression of ZNF683 and T cell-related chemokines and cytokines like CCL4, CCL5, GZMB, and IFNG, labeled as tissue-resident memory T cells (Trm). CD8-C3-GZMK displayed heightened GZMK expression and TNFSF9 co-stimulatory molecules, being newly recognized as a transitional state denoted as effector memory T cells (Tem). CD8-C6-CXCL13, with a pronounced expression of inhibitory genes like HAVCR2, LAG3, and PDCD1, denoted a state of exhaustion (Tex). Notably, this cluster showed high levels of cytokines GZMB, PRF1, and IFNG, underscoring its anti-tumor function, identified as tumor-specific T cells expressing CD39 (ENTPD1-encoded) and CD103 (ITGAE-encoded). Consistently, CD8-C6-CXCL13 T cells were exclusively observed in tumor tissues (Additional file1:Fig. S6 B). CD8-C8-GPR183 were classified as central memory T cells (Tcm). Regulatory T cell (Treg) subsets, CD4-C4 and CD4-C9, expressed Treg markers FOXP3 and IL2RA (CD25), with CD4-C4 exhibiting higher gene expression and a tumor-exclusive presence, indicating an enhanced suppressive nature. CD4-C2-IL7R, notably expressing homing receptor CCR7, was categorized as central memory T cells (Tcm). CD4-C7-CD40LG notably expressed TNF, defining it as effector T cells (Teff). CD4-C10-CXCL13 exhibited elevated CXCL13 and BHLHE40 expression, recently classified as Th1-like cells (Th1). NK cells were distinguished by the expression of NKG7. The CD4 and CD8 double-positive T cell subset, DP-C11-HIST1H1B, displayed high expression of cell-cycle-related genes like MKI67, STMN1, and TOP2A, indicating proliferative potential (Fig. 5 C and 5 D, Additional file1:Table S7 ). Metabolic analysis unveiled a metabolic response of T cells to hypoxia in TME. Both CD4 + and CD8 + T cells upregulated oxidative phosphorylation, glycolysis, gluconeogenesis, and fatty acid metabolism (Fig. 5 E). Furthermore, T cells within the tumor tissue exhibited heightened activity in seven metabolic processes (Additional file1:Fig. S6 C). Consistent with these findings, activation of HIF-1 signaling in T cells was observed (Fig. 5 F). Subsequent analysis of each cluster revealed that CD4-C9-FOXP3low, CD4-C10-CXCL13, and CD8-C6-CXCL13 displayed robust metabolic activity (Fig. 5 G, Additional file1:Fig. S6 D). Interestingly, both CXCL13 + CD8 + and CD4 + T cells displayed activation of apoptosis signaling pathways, including ferroptosis, necroptosis, and general apoptosis signaling pathways (Fig. 5 H).SCENIC analysis revealed a novel TF, nuclear factor interleukin-3-regulated (NFIL3), was specific to CD8-C6-CXCL13 and is recognized as a pivotal immune regulator. NFIL3 overexpression inhibits Tregs function and regulates cytokine expression in Th2 cells ( 21 , 22 ). Conversely, the role of NFIL3 in the formation or function of the CD8-C6-CXCL13 cluster remains largely unexplored (Fig. 5 I). Trajectory analysis indicated that CD8-C1-ZNF683 and CD4-C2-IL7R represent the initial states of CD8 + and CD4 + T cells, respectively. Furthermore, CD8-C3-GZMK, CD8-C6-CXCL13, and CD8-C8-GPR183 delineate distinct differentiation pathways from CD8-C1-ZNF683. Additionally, a potential association between CD4-C10-CXCL13 and Tregs was observed (Fig. 5 J). Survival analysis revealed a positive correlation between high T cell infiltration levels and improved survival outcomes (Fig. 5 K, Additional file1:Fig. S6 E). In conclusion, our findings suggest an enhanced activity of T cells within the TME. The presence of PCLAF + TAEpis showed a negative correlation with the abundance of CXCL13 + CD8 + T cells. To investigate the cellular interactions within TME of CSCC, we conducted Cellchat analysis of the identified clusters. Our findings demonstrated extensive interactions among most clusters, particularly between epithelial cells and fibroblasts (Fig. 6 A, Additional file1:Table S8 ). Specifically, we observed that the collagen signaling pathway played a significant role in mediating these interactions (Fig. 6 B). Further scrutiny revealed fibroblasts and epithelial cells as the primary cells engaged in interactions (Fig. 6 C). Subsequent receptor-ligand analysis unveiled interactions between collagen-related genes like COL1A1 with ITGA1 or ITGB1, predominantly occurring in T cells and stromal cells (Additional file1:Fig S7 A and 7B). Correlation analysis further supported the interrelationships among these cell types. Moreover, a negative correlation was noted between C5-PCLAF TAEpis and C6-CD8 CXCL13 T cells, identified as novel tumor-reactive T cells (Fig. 6 D) ( 23 ).To characterize C5-PCLAF TAEpis, we initially conducted an intersection analysis of the marker genes of C5-PCLAF TAEpis with epithelial cells, identifying CD24 as a specific membrane marker for C5-PCLAF TAEpis suitable for immunofluorescence labeling (Additional file1: Figure S7 C, Table S9 ). Subsequently, utilizing multi-immunofluorescence, we labeled CD24, CD8A, and PD-1 for two distinct cell types. The findings revealed a negative correlation between CD24 expression and CD8 + PDCD1 + T cells, implying the formation of a physical barrier (Fig. 6 E). Consistent with these observations, Immunohistochemistry (IHC) analysis indicated a negative correlation between CD24 and PD-1 expression (Additional file1:Fig. S7 D and 7E). Survival analysis elucidated that increased CD24 expression correlated with poorer survival outcomes, while elevated PD-1 expression was associated with a more favorable prognosis (Fig. 6 F). Furthermore, high PD-1 expression was linked to greater tumor cell differentiation, while CD24 showed no significant correlation (Tables 2 and 3 ). Subsequently, we assessed the expression of CD24 in response to anti-PD-1 therapy among CSCC patients who underwent radiotherapy combined with anti-PD-1 blockade, a detailed listing of patient characteristics in Table 4 . The analysis revealed higher CD24 expression levels in tumor tissues of non-responsive patients (Fig. 6 G and 6 H). Additionally, CD24 expression was identified as a significant predictor of the response to anti-PD-1 therapy with enhanced specificity and sensitivity (AUC:0.768). These collective findings suggest a potential pro-tumor role of PCLAF + TAEpis in suppressing tumor-specific CD8 + T cells. Table 2 Correlation of PCNA expression and clinical parameters Total (n = 50) High (n = 25) Low (n = 25) p Age 50.08 ± 11.32 52.16 ± 9.84 46.68 ± 12.97 0.124 Stage, n (%) 0.919 I 24( 48 ) 14 ( 45 ) 10 ( 53 ) II 20( 40 ) 13 ( 42 ) 7 ( 37 ) III 6( 12 ) 4 ( 13 ) 2 ( 11 ) Stage2,n(%) 0.825 I 24( 48 ) 14 ( 45 ) 10 ( 53 ) II + III 26( 52 ) 17 (55) 9 ( 47 ) Differentiation,n(%) 0.273 L 17 ( 34 ) 8 ( 26 ) 9 ( 47 ) M 18 ( 36 ) 12 ( 39 ) 6 ( 32 ) H 15 ( 30 ) 11 ( 35 ) 4 ( 21 ) Differentiation2,n(%) 0.445 L + M 35 (70) 20 (65) 15 (79) H 15 ( 30 ) 11 ( 35 ) 4 ( 21 ) Table 3 Correlation of PD-1 expression and clinical parameters Total (n = 50) High (n = 25) Low (n = 25) p Age 50.08 ± 11.32 49.4 ± 10.36 50.76 ± 12.39 0.676 Stage, n (%) 0.571 I 24( 48 ) 14(56) 10( 40 ) II 20( 40 ) 9( 36 ) 11( 44 ) III 6( 12 ) 2( 8 ) 4( 16 ) Stage2,n(%) 0.396 I 24( 48 ) 14(56) 10( 40 ) II + III 26( 52 ) 11( 44 ) 15(60) Differentiation,n(%) < 0.001 L 17 ( 34 ) 10 ( 40 ) 7 ( 28 ) M 18 ( 36 ) 0 (0) 18 (72) H 15 ( 30 ) 15 (60) 0 (0) Differentiation2,n(%) < 0.001 L + M 35 (70) 10 ( 40 ) 25 (100) H 15 ( 30 ) 15 (60) 0 (0) Table 4 characteristics of CESC patients treated with radiotherapy and anti-PD-1 blockade Response(CR + PR) (n = 25) Non-response(PD + SD) (n = 12) Age 50.04 ± 14.19 48.08 ± 9.811 Histological grade , n (%) Well or moderately differentiated 18(72%) 2(17%) Poorly differentiated 4(16%) 8(66%) Unknown 3(12%) 2(17%) Expression of PD-L1 < 1% 7(28%) 5(42%) ≥ 1–49% 8(32%) 3(25%) ≥ 50% 8(32%) 3(25%) Unknown 2(8%) 1(8%) Surgery Yes 9(36%) 2(17%) No 16(64%) 10(83%) Tumor(T) 0 1(4%) 0 1 2(8%) 0 2 9(36%) 1(8%) 3 11(44%) 7(58%) 4 2(8%) 4(34%) Node(N) 0 7(28%) 2(16%) 1 12(48%) 5(42%) 2 6(24%) 5(42%) Metastasis(M) 0 20(80%) 8(66%) 1 5(20%) 4(34%) PCLAF + TAEpis inhibit infiltration and function of T cells To elucidate the role of PCLAF + TAEpis, we initially isolated these cell types from tumor and adjacent tissues in tumor-bearing mice based on the membrane markers EpCAM and CD24 using flow cytometry, defining them as PCLAF + TAEpis (TAEpis). Concurrently, we isolated EpCAM + epithelial cells from cervical tissue of tumor-free mice, referred to as normal epithelial cells (NAEpis). Subsequently, U14 mouse cervical tumor cells were co-implanted with TAEpis or NAEpis into mice (Fig. 7 A). The baseline tumor volumes were similar among the groups before anti-PD-1 treatment (Fig. 7 B). Notably, TAEpis promoted tumor growth and attenuated the efficacy of anti-PD-1 treatment, while NAEpis had no impact on tumor progression (Fig. 7 C and 7 D). Consistent with these findings, TAEpis reduced the survival of tumor-bearing mice following anti-PD-1 treatment compared to NAEpis (Fig. 7 E). Furthermore, we examined the tumor-infiltrating immune subsets within five groups (Additional file1:Fig. S8 ), revealing a significant decline in T cells, including CD3, CD4, and CD8 + T cells, in the TAEpis plus U14 group. Although PD-1 treatment partially restored the T cell ratios, a sustained decrease was observed in the TAEpis plus U14 group compared to the NAEpis plus U14 group under PD-1 treatment (Fig. 7 F-K). Notably, the ratios of CD4 + and CD8 + T cells remained consistent across these groups (Fig. 7 L-M). Additionally, functional analysis indicated that co-implantation with TAEpis resulted in reduced secretion of IFN-γ in both CD4 and CD8 + T cells, while TNF-α levels exhibited a slight decrease in the TAEpis group (Fig. 7 N-Q). Discussion In this study, we performed scRNA-seq to profile 55469 cells from six tumor samples and two adjacent normal samples. Some major cell types were identified: three stromal cells (epithelial cells, endothelial cells, and fibroblasts) and five immune cells (myeloid cells, T cells, NK cells, B cells, and mast cells). No specific cell types were found in CSCC, and this is consistent with recent studies described in esophageal squamous cell carcinoma and lung cancer( 24 , 25 ). However, fractions of these cells varied between tumor and normal tissues, and the same subsets of these cells were correlated with the progression of CSCC. Our study provided a deep characterization of single cells in the TME of CSCC. By comparing the fractions of major cell types between tumor and normal tissues, we observed that B cells tend to accumulate in tumor tissues. B cells are a major component of adaptive immune cells( 26 ). Recent studies have revealed that B cells are involved in the construction of tertiary lymphoid structures (TLS), which were correlated with the response to immunotherapy( 27 , 28 ). The interaction of antigen-specific T cells and B cells in TLS is crucial for T cell-based tumor control and the presence of TLS was correlated with the survival of patients with ovarian cancer, endometrial cancer, and head and neck squamous cell carcinoma( 29 – 31 ). Furthermore, long-lived B cells also expressed genes (MHC class II, CD80, and CD86) associated with antigen presentation( 29 ). Notably, the role of B cells in TME is controversial. For several cancer types, increased infiltration of B cells was linked to increased invasiveness in bladder cancer and reduced survival of patients with renal cell carcinoma( 32 , 33 ). In mouse models, depletion of B cells increased the responsiveness to oxaliplatin treatment, whereas adoptive transfer of B cells promoted tumor growth in prostate cancer( 34 , 35 ). These B cells are also identified as “regulatory B cells” with immunosuppressive function. Recent studies have identified subsets of B cells through scRNA-seq. In breast cancer, B cells can be subdivided into naive B cells, memory B cells, plasma cells, and germinal cells( 36 ). In human papillomavirus (HPV)-positive head and neck cancer, three subsets of B cells were observed, including activated B cells, germinal center B cells, and HPV-specific antibody-secreting cells( 37 ). We observed a small fraction of B cells in the TME of CSCC, and this is in line with the findings of a previous study( 38 ). Therefore, no subsets of B cells were further explored. Epithelial cells were most abundant in tumor tissues, and re-clustering of epithelial cells identified eight subsets. Although previous studies have performed scRNA-seq in CC, the characterization of epithelial cells has not been explored( 15 , 16 , 38 ). Most human cancers are derived from epithelial cells, including the cervix( 39 ). We found that epithelial cells in tumor tissue exhibited more activated metabolic pathways and signal transduction. Furthermore, we observed three subsets of tumor-associated epithelial cells. These subsets of epithelial cells exhibited higher metabolic activity and signal transduction than tumor cells, indicating the important role of these subsets. Previous studies have focused on the plasticity of epithelial cells, specifically in epithelial-mesenchymal transition, in promoting metastasis of tumor cells( 40 ). However, little is known about tumor-associated epithelial cells. In TME, the cytokines and other molecules secreted by tumor cells interact with normal epithelial cells, leading to the change of this group of cells that has the function of promoting tumor progression. We observed that C5-PCLAF TAEpis were negatively correlated with CXCL13 + CD8 + T cells. CXCL13 + CD8 + T cells highly expressed some inhibitory molecules, such as PDCD1, HAVCR2 (TIM-3), CTLA4, and TIGIT, demonstrated a phenotype of “exhausted-like” and are consistent with the findings of previous studies( 41 ). Recent studies have demonstrated that this subtype of CD8 + T cells were tumor-specific T cells and respond to anti-PD-1 and PD-L1 immunotherapy in lung cancer and breast cancer( 42 , 43 ). Multi-color immunofluorescence revealed that C5-PCLAF TAEpis were negatively correlated with CXCL13 + CD8 + T cells, and high infiltration of C5-PCLAF TAEpis were associated with poor survival. Moreover, our in vivo experiments demonstrated that PCLAF TAEpis limited the anti-PD-1 treatment by inhibiting the infiltration and function of T cells. Myeloid cells and T cells are the major components of immune cells. In the present study, we identified eight myeloid clusters: four CD8 + T clusters and five CD4 + T clusters. Most of these clusters were well defined by previous studies( 41 , 44 , 45 ). Notably, four clusters of neutrophils were found. Neutrophils are short-lived and predominantly compromise approximately 50–70% of total white blood cells( 46 ). Neutrophils have long been known to have an essential role both in acute and chronic inflammation( 47 ). We observed two subsets of neutrophils linked to inflammation, C2-S100A8 highly expressed IL-1β; C7-ISG15 highly expressed genes involved in type I interferon. Recent studies have proved that increased infiltration of neutrophils was found in various tumors, and these neutrophils were defined as TANs( 48 ). In TME, secretion of growth factors (GM-CSF and G-CSF) and inflammatory cytokines (IL-6) by tumor cells and other stromal cells altered the maturation stage of neutrophils and polarized it to TANs( 49 ). Neutrophils secreted neutrophil elastase, reactive oxygen species (ROS), and reactive nitrogen species (RNS) to promote tumor initiation and metastasis( 50 ). Currently, few studies have explored the phenotype and function of neutrophils using scRNA-seq. In liver cancer, a total of 34,307 neutrophils were divided into 11 subsets, of which six subsets were TANs. Of these TANs, CCL4 + TANs expressed high levels of CCL4 and CCL3 to recruit macrophages, and PD-L1 + TANs can suppress T cell function( 51 ). In another study, four subsets of TANs were identified in non-small cell lung cancer, and tissue-resistant neutrophil signatures can predict the response to immunotherapy( 52 ). We observed two subsets of TANs in this study: C3-CXCR4 and C6-CXCL8. These two subsets of TANs demonstrated a higher alanine aspartate and glutamate metabolism as well as taurine and hypotaurine metabolism compared with other myeloid cells. The role of glutamate and taurine metabolism in tumor progression has been reported; however, its effect on the formation and function of TANs remains unclear( 53 , 54 ). In summary, our scRNA-seq analysis of single cells in tumor tissue and corresponding normal tissues revealed heterogeneity of the composition in the TME of CSCC. Meanwhile, a comprehensive characterization of epithelial cells and neutrophils perfects the cell atlas of CSCC. The interactions of these cells and their function provide novel potential therapeutic targets. There are some limitations in this study. First, many adjacent normal tissues were small because of the challenge of obtaining samples. Second, some findings require in vitro and in vivo experiment validation. Overall, our study performed a comprehensive characterization of the cell atlas in CSCC and provided evidence that PCLAF TAEpis is a potential therapeutic target. In conclusion,this study performed a single cell transcriptomic analysis of TME in CSCC, and identified total of 31 subsets of immune and stromal cells.We observed that PCLAF-TAEpis were correlated with resistance to anti-PD-1treatment through inhibiting the infiltration and function of T cells.This work provides a deep understanding of the complexity of TME in CSCC and evidence of PCLAF-TAEpis as a therapeutic target. Declarations Acknowledgments We thank team of TCGA for providing available RNA-sequencing data and clinical information of patients with CSCC. Author Contributions XL and ZZ conceived the study, analyzed data, write paper and palatially support the study; YMC and JQY collected tumor sample and performed IHC, multi-color IF , analyzed the data and performed in vivo experiments; FR,YYJ, JHL, BHW and JQL performed parts of experiments and constructed parts of figures;CYW, MMG, HG and MLF checked the sample pathology and performed parts of IHC and IF imaging; MJ and HRS provided tumor samples for scRNA-seq,designed study and revised the paper; QTZ analyzed scRNA-data, write paper and support study.All authors reviewed and approved the final manuscript. Funding This study was supported by National funded postdoctoral researcher program(GZC20232435),the Henan Medical Science and Technology Project (LHGJ2090116) . Data availability statement Raw single cell RNA sequencing data are available in GEO database with accession number GSE224327. Bulk RNA-seq data from online website UCSCxena (http://xena.ucsc.edu/). 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Also discoverable on Platform About Our Team In Review Editorial Policies Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4589423","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":328315779,"identity":"1bb89374-ccca-4b41-a86b-8bbd2a3dcd83","order_by":0,"name":"Qitai 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11:02:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4589423/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4589423/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":62655233,"identity":"79f21a11-6b97-4f86-8843-e0c99d80d39a","added_by":"auto","created_at":"2024-08-17 01:33:18","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":350573,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCharacterization of CSCC with scRNA-seq. (A)\u003c/strong\u003e Schematic representation of the single-cell RNA sequencing (scRNA-seq) study design illustrating the application of scRNA-seq on cells obtained from six tumor and two adjacent tumor samples. \u003cstrong\u003e(B)\u003c/strong\u003e UMAP projection displaying 50,649 cells from six CSCC patients, revealing ten distinct cell types.\u003cstrong\u003e(C)\u003c/strong\u003e The UMAP plot depicts the segregation of the ten major cell types based on their tissue origins. \u003cstrong\u003e(D)\u003c/strong\u003e Dot plot illustrating the specific gene markers for each cell type categorized by tissue origin. \u003cstrong\u003e(E)\u003c/strong\u003e Heatmap demonstrating the Odds Ratios (ORs) of the ten major cell types in tumor and normal tissues, where ORs \u0026gt; 1.5 indicate a preference for distribution in the respective tissue. \u003cstrong\u003e(F) \u003c/strong\u003eBox plot showcasing the distribution of the ten major cell types in normal (upper panel) and tumor tissues (lower panel). \u003cstrong\u003e(G)\u003c/strong\u003e Kaplan-Meier survival curves investigating the correlation between CD4\u003csup\u003e+\u003c/sup\u003e and CD8\u003csup\u003e+\u003c/sup\u003e T cells and the overall survival of patients with CSCC. \u003cstrong\u003e(H)\u003c/strong\u003e Circular plot presenting the interconnections among the ten major cell types.\u003c/p\u003e","description":"","filename":"fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-4589423/v1/38bfd0e25996cbeb38f4da04.png"},{"id":62655641,"identity":"79bf934c-ae1f-4c36-9f71-9dd8d1bc2b0b","added_by":"auto","created_at":"2024-08-17 01:41:19","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":155947,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRe-clustering of epithelial cells in CSCC. (A)\u003c/strong\u003e The UMAP plot showing eight subsets of epithelial cells. \u003cstrong\u003e(B)\u003c/strong\u003eThe UMAP plot illustrating the separation of epithelial cell subsets based on tissue origin. \u003cstrong\u003e(C)\u003c/strong\u003e Dot plot exhibiting specific gene markers for each epithelial cell subset. \u003cstrong\u003e(D)\u003c/strong\u003e Heatmap demonstrating the ORs of eight epithelial cell subsets in tumor and normal tissues; ORs \u0026gt;1.5 indicate a preference for the cell type to be distributed in the corresponding tissue.\u003cstrong\u003e(E) \u003c/strong\u003eBar plot showcasing the GSVA score of metabolic pathways in tumor and normal tissues within epithelial cells. \u003cstrong\u003e(F)\u003c/strong\u003e Bar plot presenting the GSVA score of signaling pathways in tumor and normal tissues within epithelial cells. \u003cstrong\u003e(G)\u003c/strong\u003e Heatmap displaying the GSVA score of metabolic pathways in each epithelial cell subset.\u003cstrong\u003e (H)\u003c/strong\u003e Heatmap illustrating the GSVA score of signaling pathways in each epithelial cell subset. \u003cstrong\u003e(I)\u003c/strong\u003e Kaplan-Meier survival curves demonstrating the correlation between C8-NEURL1B-TAEpis and the overall survival of patients with CSCC.\u003c/p\u003e","description":"","filename":"fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-4589423/v1/e250e101d4bb267b857e9db5.png"},{"id":62655237,"identity":"7696085d-8643-41ef-a1a9-537649ee5175","added_by":"auto","created_at":"2024-08-17 01:33:18","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":166109,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eProfiling of fibroblasts in CSCC.(A)\u003c/strong\u003e The UMAP plot illustrating five subsets of fibroblasts. \u003cstrong\u003e(B)\u003c/strong\u003eThe UMAP plot displaying the separation of fibroblast subsets based on tissue origin. \u003cstrong\u003e(C) \u003c/strong\u003eDot plot exhibiting specific gene markers for each subset of fibroblasts.\u003cstrong\u003e (D)\u003c/strong\u003e Heatmap presenting odds ratios (ORs) of the five fibroblast subsets in tumor and normal tissues; ORs \u0026gt; 1.5 suggest a preference for these cells to be distributed in the corresponding tissue.\u003cstrong\u003e (E) \u003c/strong\u003eBar plot demonstrating the GSVA score of metabolic pathways in tumor and normal tissues within fibroblasts. \u003cstrong\u003e(F) \u003c/strong\u003eBar plot showcasing the GSVA score of signaling pathways in tumor and normal tissues in fibroblasts. \u003cstrong\u003e(G) \u003c/strong\u003eHeatmap illustrating the GSVA score of metabolic pathways in each subset of fibroblasts. \u003cstrong\u003e(H)\u003c/strong\u003e Heatmap presenting the GSVA score of signaling pathways in each fibroblast subset. \u003cstrong\u003e(I)\u003c/strong\u003e Heatmap showing the expression of TFs in each subset of fibroblasts. \u003cstrong\u003e(J) \u003c/strong\u003eThe UMAP plot exhibiting the lineage tracking line of the five subsets of fibroblasts. \u003cstrong\u003e(K)\u003c/strong\u003e Kaplan-Meier survival curves demonstrating the correlation of C1-SFPR4-IAFs, C2-MMP11-CAFs, and the overall survival of patients with CSCC.\u003c/p\u003e","description":"","filename":"fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-4589423/v1/1d9ef956555bc0a2d29b9bcf.png"},{"id":62655640,"identity":"ba788555-a677-48d3-8ece-e1a5c54690cd","added_by":"auto","created_at":"2024-08-17 01:41:19","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":175292,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDissection of myeloid cells in CSCC. (A) \u003c/strong\u003eThe UMAP plot illustrating eight subsets of myeloid cells. \u003cstrong\u003e(B)\u003c/strong\u003eThe UMAP plot displaying myeloid cell subsets segregated by tissue origin. \u003cstrong\u003e(C)\u003c/strong\u003eDot plot exhibiting specific gene markers for each subset of myeloid cells. \u003cstrong\u003e(D)\u003c/strong\u003eHeatmap presenting ORs of the eight myeloid cell subsets in tumor and normal tissues; ORs \u0026gt; 1.5 suggest a preference for these cell types to be distributed in the corresponding tissue. \u003cstrong\u003e(E)\u003c/strong\u003e Bar plot demonstrating the GSVA score of metabolic pathways in tumor and normal tissues within myeloid cells.\u003cstrong\u003e (F) \u003c/strong\u003eBar plot showcasing the GSVA score of signaling pathways in tumor and normal tissues in myeloid cells. \u003cstrong\u003e(G)\u003c/strong\u003e Heatmap illustrating the GSVA scores of metabolic pathways in each myeloid cell subset. \u003cstrong\u003e(H)\u003c/strong\u003e Heatmap displaying the GSVA scores of signaling pathways in each myeloid cell subset. \u003cstrong\u003e(I)\u003c/strong\u003eHeatmap showing the expression of TFs in each myeloid cell subset. \u003cstrong\u003e(J) \u003c/strong\u003eThe UMAP plot presenting the lineage tracking line of four subsets of neutrophils. \u003cstrong\u003e(K)\u003c/strong\u003eKaplan-Meier survival curves revealing the correlation of C6-CXCL8-TANs, C7-ISG15-Neus, and overall survival of patients with CSCC.\u003c/p\u003e","description":"","filename":"fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-4589423/v1/7dac30d0160dc21d1863f9a0.png"},{"id":62655642,"identity":"78a347ae-f99b-417a-8ff2-04914f697e3d","added_by":"auto","created_at":"2024-08-17 01:41:19","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":192047,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCharacterization of T and NK cells in CSCC. (A)\u003c/strong\u003e The UMAP plot illustrating ten subsets of T and NK cells. \u003cstrong\u003e(B) \u003c/strong\u003eThe UMAP plot displaying T and NK cell subsets segregated by tissue origin. \u003cstrong\u003e(C)\u003c/strong\u003e Dot plot exhibiting specific gene markers for each subset of T and NK cells. \u003cstrong\u003e(D)\u003c/strong\u003e Heatmap presenting the ORs of the ten T and NK cell subsets in tumor and normal tissues; ORs \u0026gt; 1.5 suggest a preference for these cell types to be distributed in the corresponding tissue. \u003cstrong\u003e(E)\u003c/strong\u003e Bar plot demonstrating the GSVA score of metabolic pathways in tumor and normal tissues within T and NK cells. \u003cstrong\u003e(F)\u003c/strong\u003e Bar plot showcasing the GSVA score of signaling pathways in tumor and normal tissues in T and NK cells. \u003cstrong\u003e(G)\u003c/strong\u003e Heatmap illustrating the GSVA scores of metabolic pathways in each T and NK cell subset. \u003cstrong\u003e(H)\u003c/strong\u003e Heatmap displaying the GSVA scores of signaling pathways in each T and NK cell subset. \u003cstrong\u003e(I)\u003c/strong\u003e Heatmap showing the expression of TFs in each T and NK cell subset. \u003cstrong\u003e(J)\u003c/strong\u003e The UMAP plot presenting the lineage tracking line of CD4\u003csup\u003e+\u003c/sup\u003e and CD8\u003csup\u003e+\u003c/sup\u003e T cells. \u003cstrong\u003e(K)\u003c/strong\u003e Kaplan-Meier survival curves demonstrating the correlation of CD8-C6-CXCL13-Tex, CD4-C10-CXCL13-Th1, and overall survival of patients with CSCC.\u003c/p\u003e","description":"","filename":"fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-4589423/v1/1fdd7256bdaf4e279273ad94.png"},{"id":62655241,"identity":"33374158-b719-4a28-a8b2-613b6c72a30e","added_by":"auto","created_at":"2024-08-17 01:33:19","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1393180,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eC5-PCLAF TAEpis were negatively correlated with CXCL13\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003eCD8\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003eT cells. (A)\u003c/strong\u003e Circos plot depicting interconnections of cell subsets in CSCC. \u003cstrong\u003e(B)\u003c/strong\u003e Heatmap illustrating cellular relationships mediated by collagen signaling in CSCC. (C) Dot plot highlighting key signal senders and receivers in CSCC. \u003cstrong\u003e(D)\u003c/strong\u003e Heatmap displaying correlations among all cell subsets. \u003cstrong\u003e(E)\u003c/strong\u003e Multi-color Immunofluorescence (IF) image revealing the expression patterns of CD24, PD-1, and CD8. \u003cstrong\u003e(F) \u003c/strong\u003eKaplan-Meier survival curves demonstrating the relationship between CD24 and PD-1 expression and overall survival in CSCC patients. \u003cstrong\u003e(G) \u003c/strong\u003eRepresentative Magnetic Resonance Imaging (MRI) images of the cervix and lungs from CESC patients responsive or non-responsive to PD-1 antibody treatment. \u003cstrong\u003e(H)\u003c/strong\u003e Box plot presenting the Immunohistochemistry (IHC) score of CD24 in tumor tissues of responsive and non-responsive patients. \u003cstrong\u003e(I) \u003c/strong\u003eReceiver Operating Characteristic (ROC) curve showing the Area Under the Curve of CD24 for predicting response to anti-PD-1 treatment.\u003c/p\u003e","description":"","filename":"fig6.png","url":"https://assets-eu.researchsquare.com/files/rs-4589423/v1/f22e783084b95ba23bd716ed.png"},{"id":62655255,"identity":"0154bd75-0cb8-436e-a160-da10b59b867b","added_by":"auto","created_at":"2024-08-17 01:33:19","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":144294,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eC5-PCLAF TAEpis inhibited anti-PD-1 efficiency in mouse model. (A) \u003c/strong\u003eOverview of the in vivo study design. \u003cstrong\u003e(B)\u003c/strong\u003e Baseline tumor volumes across five groups. \u003cstrong\u003e(C)\u003c/strong\u003e Growth curves of U14 tumors co-implanted with TAEpis or NAEpis under anti-PD-1 treatment. \u003cstrong\u003e(D)\u003c/strong\u003e Growth curves of individual mice in each group. \u003cstrong\u003e(E) \u003c/strong\u003eKaplan-Meier curves illustrating the survival rates of mice in each group. \u003cstrong\u003e(F-K)\u003c/strong\u003e Bar charts presenting the proportions of CD3\u003csup\u003e+\u003c/sup\u003e T cells, CD4\u003csup\u003e+\u003c/sup\u003e T cells, CD8\u003csup\u003e+\u003c/sup\u003e T cells, CD11b\u003csup\u003e+\u003c/sup\u003e myeloid cells, B220\u003csup\u003e+\u003c/sup\u003e B cells, and NK1.1\u003csup\u003e+\u003c/sup\u003e NK cells within CD45\u003csup\u003e+\u003c/sup\u003e immune cells.\u003cstrong\u003e(L and M) \u003c/strong\u003eBar charts displaying the ratios of CD4\u003csup\u003e+\u003c/sup\u003e and CD8\u003csup\u003e+ \u003c/sup\u003eT cells within CD3\u003csup\u003e+\u003c/sup\u003e T cells.\u003cstrong\u003e (N-Q) \u003c/strong\u003eBar charts indicating the proportions of IFN-γ\u003csup\u003e+\u003c/sup\u003e and TNF-α\u003csup\u003e+\u003c/sup\u003e cells within CD8\u003csup\u003e+\u003c/sup\u003e and CD4\u003csup\u003e+\u003c/sup\u003e T cells.\u003c/p\u003e","description":"","filename":"fig7.png","url":"https://assets-eu.researchsquare.com/files/rs-4589423/v1/9e218b3f5d619ed1c2d6f507.png"},{"id":64321280,"identity":"08e86f58-e5a4-44a8-8764-c02305991ed3","added_by":"auto","created_at":"2024-09-11 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01:33:19","extension":"pdf","order_by":16,"title":"","display":"","copyAsset":false,"role":"supplement","size":6306803,"visible":true,"origin":"","legend":"","description":"","filename":"sFig7.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4589423/v1/2fa7508ebafe622ae8809297.pdf"},{"id":62655644,"identity":"d940468f-9d11-4f95-802f-ca4d3a34e72a","added_by":"auto","created_at":"2024-08-17 01:41:19","extension":"pdf","order_by":17,"title":"","display":"","copyAsset":false,"role":"supplement","size":377015,"visible":true,"origin":"","legend":"","description":"","filename":"sFig8.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4589423/v1/67f4ae2ba1c1f0886d58dbfd.pdf"},{"id":62655249,"identity":"08bb172b-66e2-474d-b59b-be576323ac74","added_by":"auto","created_at":"2024-08-17 01:33:19","extension":"docx","order_by":18,"title":"","display":"","copyAsset":false,"role":"supplement","size":14799,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigurelegend.docx","url":"https://assets-eu.researchsquare.com/files/rs-4589423/v1/e065266c1523ad5061a64d02.docx"}],"financialInterests":"","formattedTitle":"Single-cell transcriptomic analyses reveal heterogeneity and key subsets associated with survival and response to PD-1 blockade in cervical squamous cell carcinoma","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCervical cancer (CC) is one of the most prevalent female malignancies globally in low- and middle-income regions, accounting for 7.5% of all female cancer deaths(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). The most prevalent type of CC is cervical squamous cell carcinoma (CSCC), especially in patients with human papillomavirus (HPV) infection, which was considered as the leading risk factor for CC(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Currently, the efficacy of surgery, chemotherapy, and radiotherapy for early-stage and low-risk CC is satisfactory(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). However, survival for metastatic CC is still poor(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Recent immunotherapies that block immune checkpoints, such as programmed cell death-1 (PD-1) and programmed cell death ligand-1 (PD-L1), have exhibited substantial anti-tumor activity and a good biosafety profile in clinical trials for the treatment of recurrent CC or metastatic CC(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). However, these treatments have not achieved better results in most patients because of the complexity and heterogeneity of the tumor microenvironment (TME).\u003c/p\u003e \u003cp\u003eTME is a dynamic system sculpted by tumor cells, surrounding cells, and molecules, including stromal cells, immune cells, extracellular matrix, chemokines, and cytokines(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Accumulating evidence suggests that TME components are linked to the progression of patients with CC(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Therefore, understanding the complexity of TME is essential for tumor treatment. In our previous work, we performed a comprehensive characterization of immune infiltration through bulk sequencing data, we found that tumor cells highly expressed Keratin, type I cytoskeletal 23 (KRT23) to inhibit the accumulation of CD8\u003csup\u003e+\u003c/sup\u003eT cells(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Recent studies have utilized single-cell RNA sequencing (scRNA-seq) to investigate the heterogeneity of TME in various tumor types, such as hepatocellular carcinoma, colon cancer, lung cancer, and ovary cancer(\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e)at single-cell resolution. Some studies have performed scRNA-seq to uncover the TME of CC. Keqin Hua et al. found intra-tumoral heterogeneity and transcriptional activities of endothelial cells and constructed a cell landscape during CC pregression(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). However, the precise composition and function of the CSCC cell landscape are still unclear.\u003c/p\u003e \u003cp\u003eHerein, we profile the transcriptome of 50649 cells derived from six tumor tissues and two adjacent normal tissues of CSCC through scRNA-seq.\u0026nbsp;We displayed a comprehensive cell landscape of the TME and investigated its function and lineage tracking. Of note, we found that PCLAF-TAEpis were negatively correlated with tumor-specific CXCL13\u003csup\u003e+\u003c/sup\u003eCD8\u003csup\u003e+\u003c/sup\u003eT cells and infiltration of these two subsets of cells was correlated with tumor progression. Furthermore, we used in vivo experiments to explore the role of PCLAF-TAEpis in limiting the efficiency of anti-PD-1 treatment.Our analysis sheds light on the heterogeneity of CSCC and demonstrates that PCLAF- TAEpis are a viable therapeutic target.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eHuman specimens\u003c/h2\u003e \u003cp\u003eSix tumor tissues and two adjacent normal tissues were promptly obtained post-surgical resection from treatment-naive CSCC patients for scRNA-seq.\u0026nbsp;Tissues for immunohistochemistry (IHC) and multi-color immunofluorescence (IF) were procured during surgery and fixed in formalin for 48 hours. This study included fifty patients pathologically diagnosed with CSCC to assess the correlation between CD24 and PD-1 expression and survival. Furthermore, thirty-seven patients who underwent radiotherapy and anti-PD-1 therapy were enrolled to investigate the link between CD24 expression and clinical response. The clinical response for each target lesion was evaluated based on RECIST v.1.1 criteria.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eCell lines and regents\u003c/h2\u003e \u003cp\u003eU14 cell cervical cancer cell line were purchased from Hefei Wanwu Biotechnology Co., LTD. U14 cells were cultured in Dulbecco\u0026rsquo;s Modified Eagle\u0026rsquo;s Medium (DMEM) (Gibco) with 10% fetal bovine serum (Gibco) and 1\u0026times;Penicillin-Streptomycin Solution (Gibco) at 37℃ and 5% CO2. Murine PD-1(clone2.43) antibody were purchased from BioXCell.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eAnimal experiments\u003c/h2\u003e \u003cp\u003eAll animal protocols were conducted in accordance with the National Institutes of Health (NIH) Guidelines for the Care and Use of Laboratory Animals and were approved by the institutional animal care committee. Female C57BL/6J mice aged 6\u0026ndash;8 weeks were procured from Charles River. For the animal experiments, 5 x 10^6 U14 cells were initially implanted subcutaneously into the right flank of the mice in 100 \u0026micro;L of PBS. Upon reaching a tumor volume of 1000 mm^3, the tumor, adjacent skin tissue, and cervical tissue of the mice were surgically excised. The tissues were then sectioned into small pieces and enzymatic digestion was carried out using the tumor dissociation kit (Miltenyi Biotec) for one hour at 37\u0026deg;C. Afterward, single cells were isolated using flow cytometry (BD FACSaria III). The cells were tagged with APC anti-mouse EpCAM (#118213, BioLegend) and FITC anti-mouse CD24 (#101805, BioLegend) for the isolation of PCLAF-tumor-associated epithelial (TAEpis) and normal epithelial cells. The epithelial cells were then cultured and expanded using a specific culture medium for epithelial cells obtained from MINGZHOUBIO. Subsequently, 5 x 10^6 U14 cells and either TAEpis or normal epithelial cells were subcutaneously implanted into the right flank of mice in 100 \u0026micro;L of PBS. Anti-PD-1 antibody (200 \u0026micro;g per mouse) was administered intraperitoneally every two days for a total of three doses.Tumor volume was calculated every two days using the formula: (length x width^2)/2. The survival of tumor-bearing mice was assessed daily. If the experimental endpoints were reached or the tumor volume reached 2000 mm^3, all mice were humanely euthanized following the NIH guidelines.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eIsolation of single cell\u003c/h2\u003e \u003cp\u003eTumor and normal tissues were washed thrice with PBS and subsequently sliced into 1\u0026ndash;3 mm^3 pieces. These tissue sections were then placed in a 10-mL digestion medium comprising 0.2% collagenase I/II, DNAse I (Sigma), and 25 units dispase in DMEM. The samples were incubated on an orbital shaker at 37\u0026deg;C and 250 RPM for 15 minutes. Following this, approximately 20 mL of ice-cold PBS containing 5% fetal bovine serum was introduced, and the mixture was filtered through a 40-\u0026micro;m cell strainer. After centrifugation at 1500 RPM for 5 minutes at 4\u0026deg;C, 5 mL of red blood cell lysis buffer was added and mixed for 15 minutes. The samples were subsequently centrifuged; the resulting single cells were suspended in a sorting buffer and quantified using an automatic cell counter.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eSingle-cell sequencing, filtering, and normalization\u003c/h2\u003e \u003cp\u003eSingle-cell data library preparation was conducted using the Chromium Single Cell 3\u0026rsquo; Library, Gel Bead \u0026amp; Multiplex Kit, and Chip Kit (10x Genomics) following the manufacturer\u0026rsquo;s guidelines. The raw data was processed into unique molecular identifier (UMI) counts using Cellranger 3.0.2 (10x Genomics) with the GRCh38 reference genome. Further analyses were carried out utilizing Seruat (version 4.2.1) in R (version 4.0.1). Each sample underwent filtering based on gene numbers, gene counts, mitochondrial gene fraction, and hemoglobin genes as detailed in Additional file1: Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. The filtered UMI counts were log-transformed for normalization, and 2000 variable genes were identified using the variance stabilizing transformation method for subsequent cell analysis. The data were then scaled using the ScaleData function, and a principal component analysis (PCA) was conducted with default parameters.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eUnsupervised cell clustering and cell type annotation\u003c/h2\u003e \u003cp\u003eCanonical correlation analysis (CCA) was utilized to mitigate batch effects among the samples, integrating all samples into a comprehensive data matrix. Following CCA, clusters were discerned at a resolution of 0.3, revealing ten primary cell types characterized by canonical marker gene expression: epithelial cells, fibroblasts, endothelial cells, myeloid cells, B cells, mast cells, CD4\u003csup\u003e+\u003c/sup\u003e and CD8\u003csup\u003e+\u003c/sup\u003e T cells, NK cells, and plasma cells. Subsequently, epithelial cells, fibroblasts, myeloid cells, and T cells were selected using the subset function for a repeated PCA analysis, excluding B cells and endothelial cells due to their limited numbers. The identified major cell types underwent a re-clustering process, with marker genes pinpointed using the findAllMarkers function based on log2 fold change\u0026thinsp;\u0026gt;\u0026thinsp;0.5 and P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05, and visualization was achieved with the DotPlot function.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eSCENIC analysis\u003c/h2\u003e \u003cp\u003eSCENIC (version 1.0.0.3) was used to analyze TF activity across a subsets of each major cell type. Row count matrix was used as input; the regulons and TF activity for each subsets of major cell types were calculated using the pySCENIC (version 0.8.9) pipeline with motif collection version mc9nr. The differentially activated TFs of each subcluster were identified using the Wilcoxon rank sum test against TFs with log-fold-change\u0026thinsp;\u0026gt;\u0026thinsp;0.5, and \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered as significantly upregulated.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eTrajectory analysis\u003c/h2\u003e \u003cp\u003eR package \u0026ldquo;Slingshot\u0026rdquo; was used to infer lineage tracking of epithelial cells, ECs, fibroblasts, neutrophils, CD4\u003csup\u003e+\u003c/sup\u003e and CD8\u003csup\u003e+\u003c/sup\u003e T cells, respectively.Integrated data were\u003c/p\u003e \u003cp\u003etransformed into the format of SingleCellExperiment. The analysis were performed using default parameters.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eEstimate the activity of the metabolic and signaling pathways\u003c/h2\u003e \u003cp\u003eThe metabolic and signaling pathways were selected from the KEGG database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.kegg.jp/\u003c/span\u003e\u003cspan address=\"https://www.kegg.jp/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) using the R package \u0026ldquo;KEGGREST\u0026rdquo;; seven metabolic pathways were used as previously described dataset(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). The GSVA package was used to calculate the pathway score of these terms. A \u003cem\u003et\u003c/em\u003e-value was used to compare the difference in score between the tumor and normal tissue. Heatmap was used to visualize the average expression of score across each subcluster.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eCell communication analysis\u003c/h2\u003e \u003cp\u003eThe Cellchat package was used to investigate communication across cells by integrating gene expression with prior knowledge of the interactions between signaling ligands, receptors, and their cofactors(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Normalized matrix and Metadata were used as input. The CellchatDB database, which contains 2021 validated molecular interactions, was used to analyze the receptor-ligand interaction. Significant interactions were calculated using default parameters.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eIdentification and enrichment analysis of DEGs\u003c/h2\u003e \u003cp\u003eTo identify the DEGs between the two subsets of cells, we first transformed UMI counts to transcripts per million (TPM) using hg19 genome references. The \u0026ldquo;Limma\u0026rdquo; package was used to identify DEGs with logFC\u0026thinsp;\u0026gt;\u0026thinsp;1, p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05, and then DEGs were utilized to perform Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analysis through \u0026ldquo;clusterProfiler\u0026rdquo; and \u0026ldquo;org.Hs.eg.db\u0026rdquo; packages.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eTCGA analysis\u003c/h2\u003e \u003cp\u003eTo explore the effect of cell types on the survival of CSCC, maker genes of each subsets were selected (log2 fold change\u0026thinsp;\u0026gt;\u0026thinsp;0.5, \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and housekeeping genes were used as previously identified(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). TCGA data of CSCC were downloaded from the UCSC Xena database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://xena.ucsc.edu/\u003c/span\u003e\u003cspan address=\"http://xena.ucsc.edu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) with the formation of log2(x\u0026thinsp;+\u0026thinsp;1) transformed RSEM normalized count. Subsequently, GSVA was used to calculate the cell score and housekeeping gene score, and the cell score was normalized with the housekeeping gene score. The survival cut-off point value of the risk score was calculated by maximally selected rank statistics using \u0026ldquo;survminer\u0026rdquo; and visualized using the \u0026ldquo;survival\u0026rdquo; package.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eImmunohistochemistry and multi-color immunofluorescence\u003c/h2\u003e \u003cp\u003eThe solid tumor specimens were cut into 5-mm sections and affixed to glass slides. Following this, the slides underwent deparaffinization and antigen retrieval using standard procedures. Subsequently, H2O2 was applied to block the slides for 20 minutes in the dark, followed by a 15-minute PBS (pH 7.4) wash. Blocking of nonspecific antigens was carried out using 10% goat serum for 30 minutes. The slides were then rinsed with PBS and exposed to primary antibodies CD24 (#ab290730, Abcam, Cambridge, MA, USA), PD-1 (#ab52857, Abcam), and CD8 (#ab237709, Abcam) overnight at 4\u0026deg;C. After another PBS wash, the slides were incubated with an HRP-labeled secondary antibody for 50 minutes at 25\u0026deg;C. Subsequently, CY3 and CY5-TSA were applied, incubated for 15 minutes at 25\u0026deg;C, and followed by antigen retrieval. A mixture of PD-1 and CD8 was added to the slides and left overnight at 4\u0026deg;C. Mixed secondary antibodies were applied and incubated for 50 minutes. Finally, the images were examined using confocal microscopy (IX71, Olympus).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eSurvival analysis of PD-1 and CD24\u003c/h2\u003e \u003cp\u003eThe immunohistochemistry (IHC) score for PD-1 and CD24 was determined as previously outlined(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Each sample underwent assessment for both nucleic and membrane staining intensity (blank\u0026thinsp;=\u0026thinsp;0, light yellow\u0026thinsp;=\u0026thinsp;1, yellow\u0026thinsp;=\u0026thinsp;2, brown\u0026thinsp;=\u0026thinsp;3) and the percentage of stained cells (0% = 0, 1\u0026ndash;24% = 1, 25\u0026ndash;49% = 2, \u0026gt;\u0026thinsp;50% = 3). An IHC score of \u0026ge;\u0026thinsp;4 indicated high expression, while an IHC score of \u0026lt;\u0026thinsp;4 indicated low expression. The relationship between PD-1 and CD24 expression and the overall survival of CSCC was analyzed using the log-rank test.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eFlow cytometry analysis\u003c/h2\u003e \u003cp\u003eSingle cells were obtained from tumor tissues as described above. Cells were labeled with Zombie dye, Percp/Cyanine 5.5 anti-mouse CD45(#157208, BioLegend), APC anti-mouse CD3 (#100236,BioLegend), FITC anti-mouse CD4(#100406, BioLegend), PE/Cyanine7 anti-mouse CD8 (#100722, BioLegend), PE anti-mouse CD11b (#101208, BioLegend), Brilliant Violet 421 anti-mouse B220(#103251, BioLegend), APC/Cyanine7 anti-mouse NK1.1(#156510, BioLegend), PE anti-mouse IFN-γ(#505808, BioLegend), APC/Cyanine7 anti-mouse(#506344,BioLegend) for 15 minute at 4 ℃. For intracellular cytokine staining, cells were activated with Cell Activation Cocktail (#423303, Biolegend) for 6h and then fixed and permeabilized. Flow cytometry were performed by CytoFLEX(/Beckman Coulter).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analyses\u003c/h2\u003e \u003cp\u003eStatistical analyses were conducted using GraphPad Prism (version 8.0) and R (version 4.0.1). The Wilcoxon test was utilized to compare group differences, ANOVA was employed for comparisons involving more than two groups, and the log-rank test was applied to examine survival disparities between the two groups. A significance level of p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was deemed statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eA single-cell transcriptomic atlas of CSCC\u003c/h2\u003e \u003cp\u003eTo investigate the cellular composition of TME in CSCC, we collected six tumor tissues and two adjacent normal tissues from six treatment-na\u0026iuml;ve CSCC patients for scRNA-seq (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). The clinical details of the patients are provided in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Following quality filtering, a total of 50,649 cells, with an average of 2,282 genes per cell, were analyzed. This dataset comprised 31,232 cells from tumor tissues and 19,437 cells from normal tissues (Additional file1: Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eA and 1B, Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). Unsupervised clustering using uniform manifold approximation and projection (UMAP) demonstrated distinct clustering based on tissue origin, mitigating batch effects from the samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB, Additional file1:Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eA). Ten major cell types were identified based on canonical markers, comprising three stromal cell types (epithelial cells, fibroblasts, and endothelial cells (ECs)), and seven immune cell types (myeloid cells, mast cells, B cells, plasma cells, NK cells, CD4\u003csup\u003e+\u003c/sup\u003e T cells, and CD8\u003csup\u003e+\u003c/sup\u003e T cells) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC, Additional file1:Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e). Cell type ratios exhibited variations between tumor and normal tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). Normal tissues showed abundance in ECs and fibroblasts, while B cells and mast cells were prevalent in tumor tissues, indicating an immune activation state within TME (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE). Additionally, the cellular compositions differed significantly between tumor and normal tissues. Tumor tissues displayed a higher proportion of epithelial cells followed by immune cells, whereas normal tissues had higher proportions of fibroblasts and endothelial cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF). Survival analysis indicated that high infiltration of CD4\u003csup\u003e+\u003c/sup\u003e T cells, CD8\u003csup\u003e+\u003c/sup\u003e T cells, NK cells, plasma cells, and B cells correlated with favorable overall survival, suggesting a positive association between an activated immune response and tumor control; however, no correlation was observed between stromal cells and survival (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eG, Additional file1:Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eB). Analysis of cell communication and correlations revealed complex interactions within the TME, with most cells showing positive correlations indicative of cooperation in the TME (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eH, Additional file1:Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eC and 2D). In conclusion, this study provides a comprehensive overview of the major cell types in CSCC.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClinical information of included patients\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=\"char\" char=\".\" 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 \u003cp\u003ePatients\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSamples\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStage\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDifferentiation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePathological type\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePatient1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIIA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCSCC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePatient1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePN1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIIA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCSCC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePatient2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIIIA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCSCC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePatient3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIIB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCSCC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePatient4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePT4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIIB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCSCC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePatient5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePT5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCSCC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePatient5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePN5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCSCC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePatient6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePT6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCSCC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eIntratumoral heterogeneity of epithelial cells in CSCC\u003c/h2\u003e \u003cp\u003eTo delve deeper into understanding the phenotype and function of epithelial cells within TME dominated by CSCC, we performed subsetting and re-clustering of these cells. Through unsupervised clustering using UMAP, we delineated eight distinct clusters of epithelial cells. The distribution of these clusters varied depending on the sample source and tissue origin (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB; Additional file1:Fig. \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003eA).Cluster 3 (C3), defined by its high expression of carcinoembryonic antigen (CEACAM5, CEACAM6) and mucin (MUC20) genes, exhibited characteristics typically enriched in tumor cells, specifically excluding those found in normal tissues. Clusters 4 (C4) and 6 (C6), designated as C4-KRTDAP and C6-TFF3, respectively, showed some shared marker genes with C3 but displayed a lower level of carcinoembryonic antigen expression. Notably, these two clusters demonstrated activation of immune-related pathways such as antigen processing and presentation, leukocyte chemotaxis, and antimicrobial function, categorizing them as immune-associated epithelial cells (IAEpis) (Additional file1:Fig. \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003eB).Cluster 5 (C5), denoted as C5-PCLAF, was predominantly present in tumor tissues and exhibited high expression of cell cycle-related genes (PCNA, CLSPN), signifying tumor-associated epithelial cells (TAEpis). Conversely, clusters 7 (C7) and 8 (C8) identified as C7-CENPF and C8-NEURL1B - tended to aggregate in tumor tissues, displaying similar patterns of metabolic pathways and signal transduction as C5. This suggested a transitional state of TAEpis within these clusters. Cluster 2 (C2), labeled as C2-DST, showcased a high expression of stromal genes (CCN1, COL17A1) associated with wound healing and matrix remodeling, predominantly found in normal tissues, representing normal epithelial cells.Cluster 1 (C1) did not exhibit distinct markers, indicating a transitional state that was less well-defined (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD, Additional file1:Table \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn examining the functions of epithelial cells, gene set enrichment analysis (GSVA) was employed to quantify the activity levels of metabolic and signaling pathways. The findings revealed heightened metabolic activity in epithelial cells within tumor tissues, particularly evident in the increased activity of sphingolipid metabolism, the pentose phosphate pathway, and glycerophospholipid metabolism. Conversely, tryptophan metabolism, taurine and hypotaurine metabolism, and fatty acid biosynthesis exhibited higher scores in normal epithelial cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE; Additional file1:Fig. \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003eC).Further analysis showed that intratumoral epithelial cells were characterized by the activation of signaling pathways such as HIF-1, mTOR, notch, and VEGF, while displaying a reduction in immune-related signaling (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF). Examination of specific clusters highlighted that C5-PCLAF and C8-NEURL1B demonstrated similar metabolic profiles, with heightened activation in TME. In contrast, C2-DST appeared to be in a state of metabolic equilibrium (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eG; Additional file1:Fig. \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003eD).Interestingly, distinct signaling patterns were observed between C2 and C8, with C2 predominately activating calcium and cytokine-cytokine receptor signaling pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eH). Transcription factor (TF) analysis using SCENIC identified specific TFs associated with each cluster, although C1 did not exhibit any unique TFs (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eI).Survival analysis indicated that a greater abundance of C8 correlated with a more favorable overall prognosis, suggestive of an \u0026ldquo;immune-hot\u0026rdquo; TME in this subtype of CSCC due to the activation of the NF-κB and TNF signaling pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eJ; Additional file1:Fig. \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003eE). In summary, these results elucidate the intratumoral diversity and plasticity of epithelial cells in CSCC.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eThe interconversion of inflammatory and cancer-associated fibroblasts correlates with the survival outcome of CSCC patients\u003c/h2\u003e \u003cp\u003eReclustering of fibroblasts identified five distinct clusters, with these subsets displaying universal distribution across samples but showing disparities between tumor and adjacent normal tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB, Additional file1:Fig. \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003eA). Cluster 1 (C1), labeled as C1-SPER4, exhibited elevated expression of SPER4 along with several chemokines such as CXCL1, CXCL14, and PDGFRA. This fibroblast cluster, prevalent in normal tissues, was classified as inflammatory-associated fibroblasts (IAFs). Cluster 2 (C2), known as C2-MMP11, showed high expression of matrix metallopeptidase genes like MMP11, MMP2, and MMP14, a range of collagens, and other extracellular matrix components including COL1A1, COL3A1, COL1A2, COL5A2, and COL12A1. Moreover, the presence of FAP, a characteristic gene of cancer-associated fibroblasts (CAFs), designated this cluster as CAFs. Functional analysis indicated that CAFs were associated with the activation of extracellular matrix organization and collagen fibril organization (Additional file1:Fig. \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003eB).Clusters 3 (C3) and 5 (C5) were identified as subsets of myofibroblasts, both expressing typical myofibroblast marker genes such as ACTA2 and genes involved in myogenesis like MYH11, MUSTN1, and DES. However, while C3 exhibited moderate expression of these genes, suggesting an immature state, C5-MYH11 was predominant in normal tissues, whereas C3-MUSTN1 showed higher levels in tumor tissues. Cluster 4 (C4), identified as C4-RGS5, comprised pericytes characterized by the expression of the pericyte marker RGS5 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD, Additional file1:Table \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eMetabolic analysis unveiled heightened activity in intratumoral fibroblasts, showcasing increased metabolic pathways like glycolysis, gluconeogenesis, and pyrimidine metabolism, alongside reduced cholesterol metabolism and steroid biosynthesis levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE, Additional file1:Fig. \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003eC). Observation of the fibroblasts within TME demonstrated concurrent activation of oncogenic and immune-related signaling, emphasizing the diverse nature of fibroblasts within the TME (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF).Further exploration of each cluster revealed that C2-MMP11-CAFs exhibited the most vigorous metabolic processes, in contrast to a shared metabolic pattern between C3-MUST1 and C5-DES, while C1-SPER4 displayed a more quiescent metabolic profile (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eG, Additional file1:Fig. \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003eD). Consistent with these findings, C2-MMP11 showed activation across various oncogenic signaling pathways, C3 activated the calcium signaling pathway, and C5 activated the phosphatidylinositol signaling system (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eH).TF analysis identified distinct TFs specific to each cluster. The similar TF expression between the two subsets of cancer-associated fibroblasts (CAFs) suggested the potential for mutual transformation (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eI). Trajectory analysis revealed a differentiation pathway from C1-SPER4-IAFs to C2-MMP11-CAFs (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eJ). Moreover, survival analysis indicated that a high infiltration of IAFs correlated with favorable survival outcomes in CSCC patients, while the presence of CAFs displayed an opposing trend. Conversely, the other subsets showed no significant correlation with patient survival (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eK, Additional file1:Fig. \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003eE).In summary, these findings illuminate the intricate landscape of fibroblasts in CSCC.\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eMyeloid cell heterogeneity in CSCC correlates with tumor progression\u003c/h2\u003e \u003cp\u003eMyeloid cells encompass significant immune cell populations within TME that exert anti-tumor effects. Sub-clustering of myeloid cells post-batch correction revealed eight distinctive subsets (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). While non-unique subsets were evident across samples, notable differences were observed between tumor and adjacent normal tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB, Additional file1:Fig. \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003eA). Specifically, C1-C1QA was characterized as macrophages (C1QA, C1QB), C4-CD163 identified as tumor-associated tumor-associated macrophages (TAMs) expressing key metabolic genes like SLC40A1 and FOLR2, linked to TAMs proliferation and polarization. Dendritic cell subsets were also identified, with C5-CD1C representing conventional type 2 dendritic cells (cDC2) expressing CD1C, FCER1A, and HLA-DQB1, and C8-LAMP3 denoted as mature dendritic cells, or cDC3, with high expression of LAMP3 and FCN1 enabling migration to tumors via elevated CCR7 expression.Four subsets of neutrophils were clustered in CSCC, with C2-S100A8 exhibiting high expression of pro-inflammatory genes like IL1B, indicative of conventional neutrophils. On the other hand, C3-CXCR4 and C6-CXCL8 displayed high expression of CXCL8 and CSF3R predominantly in tumor tissues, representing a subset of tumor-associated neutrophils (TANs). C7-ISG15 exhibited high expression of interferon-induced and stimulated genes (IFIT2, IFIT3, ISG15, and ISG20), characterizing a type I interferon-producing neutrophil phenotype, both displaying high expression of the neutrophil-specific antigen CD16B (encoded by FCGR3B) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD, Additional file1:Fig. \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003eB, Table \u003cspan refid=\"MOESM6\" class=\"InternalRef\"\u003eS6\u003c/span\u003e).Further characterization of the C6 and C7 neutrophil subsets involved differential gene expression and pathway enrichment analysis. Results showed distinct transcription profiles between the two clusters, with C6-CXCL8 activating oncogenic signaling pathways like MAPK and NF-κB, while C7-ISG15 enhancing immune-related pathways such as chemokine signaling, Toll-like receptor signaling, and antigen processing and presentation (Additional file1:Fig. \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003eC and 5D).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eUnlike stromal cells, the metabolism and signal transduction of myeloid cells in tumor tissues are significantly suppressed (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF, Additional file1:Fig. \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003eE). Upon detailed analysis of each cluster, it was evident that macrophages and dendritic cells displayed enhanced metabolic activity and signaling capabilities compared to neutrophils, underscoring their crucial role in TME (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eG and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eH, Additional file1:Fig.\u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003eF). While unique TFs were identified in each cluster, C6-CXCL8-TANs and C7-ISG15-Neutrophils exhibited similar TF expression levels, suggesting a potential interconversion between these two neutrophil subsets (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eI). Trajectory analysis of the four neutrophil subsets revealed three distinct differentiation paths originating from C2-S100A8 conventional neutrophils (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eJ). Consistent with the aforementioned findings, survival analysis of these clusters indicated that increased infiltration of C6-CXCL8-TANs was associated with unfavorable overall survival, while the trend was reversed for C7. Additionally, high infiltration of the two dendritic cell subsets predicted a more favorable overall survival outcome (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eK, Additional file1:Fig.\u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003eG).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eCharacterization of T cells in CSCC\u003c/h2\u003e \u003cp\u003eT cells play a pivotal role in immune responses. Re-clustering of T and NK cells unveiled four clusters of CD8\u003csup\u003e+\u003c/sup\u003e T cells, five clusters of CD4\u003csup\u003e+\u003c/sup\u003e T cells, one cluster of double-positive cells, and one cluster of NK cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). While no specific clusters were consistent across patients, distinct tissue origins were evident in some clusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB, Additional file1:Fig.\u003cspan refid=\"MOESM6\" class=\"InternalRef\"\u003eS6\u003c/span\u003eA). Noteworthy CD8 clusters included CD8-C1-ZNF683, characterized by high expression of ZNF683 and T cell-related chemokines and cytokines like CCL4, CCL5, GZMB, and IFNG, labeled as tissue-resident memory T cells (Trm). CD8-C3-GZMK displayed heightened GZMK expression and TNFSF9 co-stimulatory molecules, being newly recognized as a transitional state denoted as effector memory T cells (Tem). CD8-C6-CXCL13, with a pronounced expression of inhibitory genes like HAVCR2, LAG3, and PDCD1, denoted a state of exhaustion (Tex). Notably, this cluster showed high levels of cytokines GZMB, PRF1, and IFNG, underscoring its anti-tumor function, identified as tumor-specific T cells expressing CD39 (ENTPD1-encoded) and CD103 (ITGAE-encoded). Consistently, CD8-C6-CXCL13 T cells were exclusively observed in tumor tissues (Additional file1:Fig. \u003cspan refid=\"MOESM6\" class=\"InternalRef\"\u003eS6\u003c/span\u003eB). CD8-C8-GPR183 were classified as central memory T cells (Tcm). Regulatory T cell (Treg) subsets, CD4-C4 and CD4-C9, expressed Treg markers FOXP3 and IL2RA (CD25), with CD4-C4 exhibiting higher gene expression and a tumor-exclusive presence, indicating an enhanced suppressive nature. CD4-C2-IL7R, notably expressing homing receptor CCR7, was categorized as central memory T cells (Tcm). CD4-C7-CD40LG notably expressed TNF, defining it as effector T cells (Teff). CD4-C10-CXCL13 exhibited elevated CXCL13 and BHLHE40 expression, recently classified as Th1-like cells (Th1). NK cells were distinguished by the expression of NKG7. The CD4 and CD8 double-positive T cell subset, DP-C11-HIST1H1B, displayed high expression of cell-cycle-related genes like MKI67, STMN1, and TOP2A, indicating proliferative potential (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD, Additional file1:Table \u003cspan refid=\"MOESM7\" class=\"InternalRef\"\u003eS7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMetabolic analysis unveiled a metabolic response of T cells to hypoxia in TME. Both CD4\u003csup\u003e+\u003c/sup\u003e and CD8\u003csup\u003e+\u003c/sup\u003e T cells upregulated oxidative phosphorylation, glycolysis, gluconeogenesis, and fatty acid metabolism (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE). Furthermore, T cells within the tumor tissue exhibited heightened activity in seven metabolic processes (Additional file1:Fig. \u003cspan refid=\"MOESM6\" class=\"InternalRef\"\u003eS6\u003c/span\u003eC). Consistent with these findings, activation of HIF-1 signaling in T cells was observed (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eF). Subsequent analysis of each cluster revealed that CD4-C9-FOXP3low, CD4-C10-CXCL13, and CD8-C6-CXCL13 displayed robust metabolic activity (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eG, Additional file1:Fig. \u003cspan refid=\"MOESM6\" class=\"InternalRef\"\u003eS6\u003c/span\u003eD). Interestingly, both CXCL13\u003csup\u003e+\u003c/sup\u003e CD8\u003csup\u003e+\u003c/sup\u003e and CD4\u003csup\u003e+\u003c/sup\u003e T cells displayed activation of apoptosis signaling pathways, including ferroptosis, necroptosis, and general apoptosis signaling pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eH).SCENIC analysis revealed a novel TF, nuclear factor interleukin-3-regulated (NFIL3), was specific to CD8-C6-CXCL13 and is recognized as a pivotal immune regulator. NFIL3 overexpression inhibits Tregs function and regulates cytokine expression in Th2 cells (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Conversely, the role of NFIL3 in the formation or function of the CD8-C6-CXCL13 cluster remains largely unexplored (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eI). Trajectory analysis indicated that CD8-C1-ZNF683 and CD4-C2-IL7R represent the initial states of CD8\u003csup\u003e+\u003c/sup\u003e and CD4\u003csup\u003e+\u003c/sup\u003e T cells, respectively. Furthermore, CD8-C3-GZMK, CD8-C6-CXCL13, and CD8-C8-GPR183 delineate distinct differentiation pathways from CD8-C1-ZNF683. Additionally, a potential association between CD4-C10-CXCL13 and Tregs was observed (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eJ). Survival analysis revealed a positive correlation between high T cell infiltration levels and improved survival outcomes (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eK, Additional file1:Fig. \u003cspan refid=\"MOESM6\" class=\"InternalRef\"\u003eS6\u003c/span\u003eE). In conclusion, our findings suggest an enhanced activity of T cells within the TME.\u003c/p\u003e \u003cp\u003e \u003cb\u003eThe presence of PCLAF\u003c/b\u003e \u003csup\u003e \u003cb\u003e+\u003c/b\u003e \u003c/sup\u003e \u003cb\u003eTAEpis showed a negative correlation with the abundance of CXCL13\u003c/b\u003e\u003csup\u003e\u003cb\u003e+\u003c/b\u003e\u003c/sup\u003e \u003cb\u003eCD8\u003c/b\u003e\u003csup\u003e\u003cb\u003e+\u003c/b\u003e\u003c/sup\u003e \u003cb\u003eT cells.\u003c/b\u003e\u003c/p\u003e \u003cp\u003eTo investigate the cellular interactions within TME of CSCC, we conducted Cellchat analysis of the identified clusters. Our findings demonstrated extensive interactions among most clusters, particularly between epithelial cells and fibroblasts (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA, Additional file1:Table \u003cspan refid=\"MOESM8\" class=\"InternalRef\"\u003eS8\u003c/span\u003e). Specifically, we observed that the collagen signaling pathway played a significant role in mediating these interactions (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). Further scrutiny revealed fibroblasts and epithelial cells as the primary cells engaged in interactions (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). Subsequent receptor-ligand analysis unveiled interactions between collagen-related genes like COL1A1 with ITGA1 or ITGB1, predominantly occurring in T cells and stromal cells (Additional file1:Fig \u003cspan refid=\"MOESM7\" class=\"InternalRef\"\u003eS7\u003c/span\u003eA and 7B). Correlation analysis further supported the interrelationships among these cell types. Moreover, a negative correlation was noted between C5-PCLAF TAEpis and C6-CD8 CXCL13 T cells, identified as novel tumor-reactive T cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD) (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e).To characterize C5-PCLAF TAEpis, we initially conducted an intersection analysis of the marker genes of C5-PCLAF TAEpis with epithelial cells, identifying CD24 as a specific membrane marker for C5-PCLAF TAEpis suitable for immunofluorescence labeling (Additional file1: Figure \u003cspan refid=\"MOESM7\" class=\"InternalRef\"\u003eS7\u003c/span\u003eC, Table \u003cspan refid=\"MOESM9\" class=\"InternalRef\"\u003eS9\u003c/span\u003e). Subsequently, utilizing multi-immunofluorescence, we labeled CD24, CD8A, and PD-1 for two distinct cell types. The findings revealed a negative correlation between CD24 expression and CD8\u003csup\u003e+\u003c/sup\u003e PDCD1\u003csup\u003e+\u003c/sup\u003e T cells, implying the formation of a physical barrier (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE). Consistent with these observations, Immunohistochemistry (IHC) analysis indicated a negative correlation between CD24 and PD-1 expression (Additional file1:Fig. \u003cspan refid=\"MOESM7\" class=\"InternalRef\"\u003eS7\u003c/span\u003eD and 7E). Survival analysis elucidated that increased CD24 expression correlated with poorer survival outcomes, while elevated PD-1 expression was associated with a more favorable prognosis (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eF). Furthermore, high PD-1 expression was linked to greater tumor cell differentiation, while CD24 showed no significant correlation (Tables\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Subsequently, we assessed the expression of CD24 in response to anti-PD-1 therapy among CSCC patients who underwent radiotherapy combined with anti-PD-1 blockade, a detailed listing of patient characteristics in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The analysis revealed higher CD24 expression levels in tumor tissues of non-responsive patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eG and \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eH). Additionally, CD24 expression was identified as a significant predictor of the response to anti-PD-1 therapy with enhanced specificity and sensitivity (AUC:0.768). These collective findings suggest a potential pro-tumor role of PCLAF\u003csup\u003e+\u003c/sup\u003e TAEpis in suppressing tumor-specific CD8\u003csup\u003e+\u003c/sup\u003e T cells.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCorrelation of PCNA expression and clinical parameters\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"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=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal (n\u0026thinsp;=\u0026thinsp;50)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh (n\u0026thinsp;=\u0026thinsp;25)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLow (n\u0026thinsp;=\u0026thinsp;25)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50.08\u0026thinsp;\u0026plusmn;\u0026thinsp;11.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52.16\u0026thinsp;\u0026plusmn;\u0026thinsp;9.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46.68\u0026thinsp;\u0026plusmn;\u0026thinsp;12.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.124\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.919\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24(\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10 (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20(\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7 (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage2,n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.825\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24(\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10 (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eII\u0026thinsp;+\u0026thinsp;III\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26(\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDifferentiation,n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.273\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17 (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18 (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15 (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDifferentiation2,n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.445\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL\u0026thinsp;+\u0026thinsp;M\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35 (70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20 (65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15 (79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15 (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCorrelation of PD-1 expression and clinical parameters\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"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=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal (n\u0026thinsp;=\u0026thinsp;50)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh (n\u0026thinsp;=\u0026thinsp;25)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLow (n\u0026thinsp;=\u0026thinsp;25)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50.08\u0026thinsp;\u0026plusmn;\u0026thinsp;11.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49.4\u0026thinsp;\u0026plusmn;\u0026thinsp;10.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50.76\u0026thinsp;\u0026plusmn;\u0026thinsp;12.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.676\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.571\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24(\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14(56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10(\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20(\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9(\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11(\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage2,n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.396\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24(\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14(56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10(\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eII\u0026thinsp;+\u0026thinsp;III\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26(\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11(\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15(60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDifferentiation,n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17 (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7 (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18 (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18 (72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15 (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDifferentiation2,n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL\u0026thinsp;+\u0026thinsp;M\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35 (70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15 (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003echaracteristics of CESC patients treated with radiotherapy and anti-PD-1 blockade\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eResponse(CR\u0026thinsp;+\u0026thinsp;PR)\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;25)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-response(PD\u0026thinsp;+\u0026thinsp;SD)\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;12)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50.04\u0026thinsp;\u0026plusmn;\u0026thinsp;14.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48.08\u0026thinsp;\u0026plusmn;\u0026thinsp;9.811\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHistological grade\u003c/b\u003e,\u003c/p\u003e \u003cp\u003e\u003cb\u003en (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWell or moderately\u003c/p\u003e \u003cp\u003edifferentiated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18(72%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2(17%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoorly differentiated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4(16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8(66%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3(12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2(17%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eExpression of PD-L1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7(28%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5(42%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;1\u0026ndash;49%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8(32%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3(25%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;50%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8(32%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3(25%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2(8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1(8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSurgery\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9(36%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2(17%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16(64%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10(83%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTumor(T)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2(8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9(36%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1(8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11(44%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7(58%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2(8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4(34%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNode(N)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7(28%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2(16%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12(48%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5(42%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6(24%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5(42%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMetastasis(M)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20(80%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8(66%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5(20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4(34%)\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 \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003ePCLAF\u003csup\u003e+\u003c/sup\u003eTAEpis inhibit infiltration and function of T cells\u003c/h2\u003e \u003cp\u003eTo elucidate the role of PCLAF\u003csup\u003e+\u003c/sup\u003e TAEpis, we initially isolated these cell types from tumor and adjacent tissues in tumor-bearing mice based on the membrane markers EpCAM and CD24 using flow cytometry, defining them as PCLAF\u003csup\u003e+\u003c/sup\u003e TAEpis (TAEpis). Concurrently, we isolated EpCAM\u003csup\u003e+\u003c/sup\u003e epithelial cells from cervical tissue of tumor-free mice, referred to as normal epithelial cells (NAEpis). Subsequently, U14 mouse cervical tumor cells were co-implanted with TAEpis or NAEpis into mice (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). The baseline tumor volumes were similar among the groups before anti-PD-1 treatment (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB). Notably, TAEpis promoted tumor growth and attenuated the efficacy of anti-PD-1 treatment, while NAEpis had no impact on tumor progression (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC and \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eD). Consistent with these findings, TAEpis reduced the survival of tumor-bearing mice following anti-PD-1 treatment compared to NAEpis (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eE). Furthermore, we examined the tumor-infiltrating immune subsets within five groups (Additional file1:Fig. \u003cspan refid=\"MOESM8\" class=\"InternalRef\"\u003eS8\u003c/span\u003e), revealing a significant decline in T cells, including CD3, CD4, and CD8\u003csup\u003e+\u003c/sup\u003e T cells, in the TAEpis plus U14 group. Although PD-1 treatment partially restored the T cell ratios, a sustained decrease was observed in the TAEpis plus U14 group compared to the NAEpis plus U14 group under PD-1 treatment (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eF-K). Notably, the ratios of CD4\u003csup\u003e+\u003c/sup\u003e and CD8\u003csup\u003e+\u003c/sup\u003e T cells remained consistent across these groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eL-M). Additionally, functional analysis indicated that co-implantation with TAEpis resulted in reduced secretion of IFN-γ in both CD4 and CD8\u003csup\u003e+\u003c/sup\u003e T cells, while TNF-α levels exhibited a slight decrease in the TAEpis group (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eN-Q).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we performed scRNA-seq to profile 55469 cells from six tumor samples and two adjacent normal samples. Some major cell types were identified: three stromal cells (epithelial cells, endothelial cells, and fibroblasts) and five immune cells (myeloid cells, T cells, NK cells, B cells, and mast cells). No specific cell types were found in CSCC, and this is consistent with recent studies described in esophageal squamous cell carcinoma and lung cancer(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). However, fractions of these cells varied between tumor and normal tissues, and the same subsets of these cells were correlated with the progression of CSCC. Our study provided a deep characterization of single cells in the TME of CSCC.\u003c/p\u003e \u003cp\u003eBy comparing the fractions of major cell types between tumor and normal tissues, we observed that B cells tend to accumulate in tumor tissues. B cells are a major component of adaptive immune cells(\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Recent studies have revealed that B cells are involved in the construction of tertiary lymphoid structures (TLS), which were correlated with the response to immunotherapy(\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). The interaction of antigen-specific T cells and B cells in TLS is crucial for T cell-based tumor control and the presence of TLS was correlated with the survival of patients with ovarian cancer, endometrial cancer, and head and neck squamous cell carcinoma(\u003cspan additionalcitationids=\"CR30\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). Furthermore, long-lived B cells also expressed genes (MHC class II, CD80, and CD86) associated with antigen presentation(\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Notably, the role of B cells in TME is controversial. For several cancer types, increased infiltration of B cells was linked to increased invasiveness in bladder cancer and reduced survival of patients with renal cell carcinoma(\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). In mouse models, depletion of B cells increased the responsiveness to oxaliplatin treatment, whereas adoptive transfer of B cells promoted tumor growth in prostate cancer(\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). These B cells are also identified as \u0026ldquo;regulatory B cells\u0026rdquo; with immunosuppressive function. Recent studies have identified subsets of B cells through scRNA-seq.\u0026nbsp;In breast cancer, B cells can be subdivided into naive B cells, memory B cells, plasma cells, and germinal cells(\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). In human papillomavirus (HPV)-positive head and neck cancer, three subsets of B cells were observed, including activated B cells, germinal center B cells, and HPV-specific antibody-secreting cells(\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). We observed a small fraction of B cells in the TME of CSCC, and this is in line with the findings of a previous study(\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). Therefore, no subsets of B cells were further explored.\u003c/p\u003e \u003cp\u003eEpithelial cells were most abundant in tumor tissues, and re-clustering of epithelial cells identified eight subsets. Although previous studies have performed scRNA-seq in CC, the characterization of epithelial cells has not been explored(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). Most human cancers are derived from epithelial cells, including the cervix(\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). We found that epithelial cells in tumor tissue exhibited more activated metabolic pathways and signal transduction. Furthermore, we observed three subsets of tumor-associated epithelial cells. These subsets of epithelial cells exhibited higher metabolic activity and signal transduction than tumor cells, indicating the important role of these subsets. Previous studies have focused on the plasticity of epithelial cells, specifically in epithelial-mesenchymal transition, in promoting metastasis of tumor cells(\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). However, little is known about tumor-associated epithelial cells. In TME, the cytokines and other molecules secreted by tumor cells interact with normal epithelial cells, leading to the change of this group of cells that has the function of promoting tumor progression. We observed that C5-PCLAF TAEpis were negatively correlated with CXCL13\u003csup\u003e+\u003c/sup\u003eCD8\u003csup\u003e+\u003c/sup\u003eT cells. CXCL13\u003csup\u003e+\u003c/sup\u003eCD8\u003csup\u003e+\u003c/sup\u003eT cells highly expressed some inhibitory molecules, such as PDCD1, HAVCR2 (TIM-3), CTLA4, and TIGIT, demonstrated a phenotype of \u0026ldquo;exhausted-like\u0026rdquo; and are consistent with the findings of previous studies(\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). Recent studies have demonstrated that this subtype of CD8\u003csup\u003e+\u003c/sup\u003eT cells were tumor-specific T cells and respond to anti-PD-1 and PD-L1 immunotherapy in lung cancer and breast cancer(\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e). Multi-color immunofluorescence revealed that C5-PCLAF TAEpis were negatively correlated with CXCL13\u003csup\u003e+\u003c/sup\u003eCD8\u003csup\u003e+\u003c/sup\u003eT cells, and high infiltration of C5-PCLAF TAEpis were associated with poor survival. Moreover, our in vivo experiments demonstrated that PCLAF TAEpis limited the anti-PD-1 treatment by inhibiting the infiltration and function of T cells.\u003c/p\u003e \u003cp\u003eMyeloid cells and T cells are the major components of immune cells. In the present study, we identified eight myeloid clusters: four CD8\u003csup\u003e+\u003c/sup\u003eT clusters and five CD4\u003csup\u003e+\u003c/sup\u003eT clusters. Most of these clusters were well defined by previous studies(\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e). Notably, four clusters of neutrophils were found. Neutrophils are short-lived and predominantly compromise approximately 50\u0026ndash;70% of total white blood cells(\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e). Neutrophils have long been known to have an essential role both in acute and chronic inflammation(\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e). We observed two subsets of neutrophils linked to inflammation, C2-S100A8 highly expressed IL-1β; C7-ISG15 highly expressed genes involved in type I interferon. Recent studies have proved that increased infiltration of neutrophils was found in various tumors, and these neutrophils were defined as TANs(\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e). In TME, secretion of growth factors (GM-CSF and G-CSF) and inflammatory cytokines (IL-6) by tumor cells and other stromal cells altered the maturation stage of neutrophils and polarized it to TANs(\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e). Neutrophils secreted neutrophil elastase, reactive oxygen species (ROS), and reactive nitrogen species (RNS) to promote tumor initiation and metastasis(\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e). Currently, few studies have explored the phenotype and function of neutrophils using scRNA-seq.\u0026nbsp;In liver cancer, a total of 34,307 neutrophils were divided into 11 subsets, of which six subsets were TANs. Of these TANs, CCL4\u003csup\u003e+\u003c/sup\u003eTANs expressed high levels of CCL4 and CCL3 to recruit macrophages, and PD-L1\u003csup\u003e+\u003c/sup\u003eTANs can suppress T cell function(\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e). In another study, four subsets of TANs were identified in non-small cell lung cancer, and tissue-resistant neutrophil signatures can predict the response to immunotherapy(\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e). We observed two subsets of TANs in this study: C3-CXCR4 and C6-CXCL8. These two subsets of TANs demonstrated a higher alanine aspartate and glutamate metabolism as well as taurine and hypotaurine metabolism compared with other myeloid cells. The role of glutamate and taurine metabolism in tumor progression has been reported; however, its effect on the formation and function of TANs remains unclear(\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn summary, our scRNA-seq analysis of single cells in tumor tissue and corresponding normal tissues revealed heterogeneity of the composition in the TME of CSCC. Meanwhile, a comprehensive characterization of epithelial cells and neutrophils perfects the cell atlas of CSCC. The interactions of these cells and their function provide novel potential therapeutic targets. There are some limitations in this study. First, many adjacent normal tissues were small because of the challenge of obtaining samples. Second, some findings require \u003cem\u003ein vitro\u003c/em\u003e and \u003cem\u003ein vivo\u003c/em\u003e experiment validation. Overall, our study performed a comprehensive characterization of the cell atlas in CSCC and provided evidence that PCLAF TAEpis is a potential therapeutic target.\u003c/p\u003e \u003cp\u003eIn conclusion,this study performed a single cell transcriptomic analysis of TME in CSCC, and identified total of 31 subsets of immune and stromal cells.We observed that PCLAF-TAEpis were correlated with resistance to anti-PD-1treatment through inhibiting the infiltration and function of T cells.This work provides a deep understanding of the complexity of TME in CSCC and evidence of PCLAF-TAEpis as a therapeutic target.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank team of TCGA for providing available RNA-sequencing data and clinical information of patients with CSCC.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXL\u0026nbsp;and ZZ\u0026nbsp;conceived the study, analyzed data, write paper and palatially support the study;\u0026nbsp;YMC and JQY\u0026nbsp;collected tumor sample and performed IHC,\u0026nbsp;multi-color\u0026nbsp;IF\u0026nbsp;, analyzed the data\u0026nbsp;and\u0026nbsp;performed in vivo experiments;\u0026nbsp;FR,YYJ, JHL, BHW and JQL\u0026nbsp;performed parts of experiments\u0026nbsp;and constructed parts of figures;CYW, MMG, HG and MLF\u0026nbsp;checked the sample pathology and performed parts of IHC and IF imaging;\u0026nbsp;MJ and HRS\u0026nbsp;provided tumor samples for scRNA-seq,designed study and revised the paper;\u0026nbsp;QTZ\u0026nbsp;analyzed scRNA-data, write paper and support study.All authors reviewed and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by National funded postdoctoral researcher program(GZC20232435),the Henan Medical Science and Technology Project (LHGJ2090116) .\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRaw single cell RNA sequencing data are available in GEO database with accession number GSE224327. Bulk RNA-seq data from online website UCSCxena (http://xena.ucsc.edu/). Other data used in the study are available from the\u003c/p\u003e\n\u003cp\u003ecorresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe present study was approved by the Ethics Committee of the First Affiliated Hospital of Zhengzhou University(Ethics number:2022-KY-0093-002), and all patients provided written informed consent in accordance with the tenets of the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eArbyn M, Weiderpass E, Bruni L, de Sanjos\u0026eacute; S, Saraiya M, Ferlay J, et al. 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J Neural Transm. 2014;121(8):933-44.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Cervical squamous cell carcinoma, Single-cell RNA sequencing, Heterogeneity, Tumor-associated epithelial cells, Immunotherapy response","lastPublishedDoi":"10.21203/rs.3.rs-4589423/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4589423/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eUnderstanding the intricate tumor microenvironment (TME) is crucial for elucidating the mechanisms underlying the progression of cervical squamous cell carcinoma (CSCC) and its response to anti-PD-1 therapy. In this study, we characterized 50,649 cells obtained from CSCC for single-cell RNA sequencing and integrated bulk sequencing data from The Cancer Genome Atlas (TCGA) and clinical specimens to explore cell composition, metabolic processes, signaling pathways, specific transcription factors, lineage tracking and response to immunotherapy. We identified 31 subsets of stromal and immune cells in the tumor microenvironment (TME) and observed distinct patterns in the metabolic processes and signaling pathways of these cells between tumor and normal tissues. Collagen signaling was found to be crucial for the interaction between stromal and immune cells. Furthermore, PCLAF-TAEpis were negatively correlated with CXCL13\u003csup\u003e+\u003c/sup\u003eCD8\u003csup\u003e+\u003c/sup\u003e tumor-reactive T cells, overall survival, and the response to anti-PD-1therapy in patients with CSCC. In vivo experiments demonstrated that PCLAF-TAEpis promoted tumor growth and hindered the therapeutic efficacy of anti-PD-1 treatment by inhibiting the infiltration and function of T cells. Collectively, our findings illuminate the heterogeneity of the complex TME in CSCC and offer evidence supporting PCLAF-TAEpis as a promising therapeutic target.\u003c/p\u003e","manuscriptTitle":"Single-cell transcriptomic analyses reveal heterogeneity and key subsets associated with survival and response to PD-1 blockade in cervical squamous cell carcinoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-17 01:33:13","doi":"10.21203/rs.3.rs-4589423/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"710ef90d-2723-4ef5-b183-d4d151a3986b","owner":[],"postedDate":"August 17th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-09-11T15:20:26+00:00","versionOfRecord":[],"versionCreatedAt":"2024-08-17 01:33:13","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4589423","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4589423","identity":"rs-4589423","version":["v1"]},"buildId":"veTbxFhMMB0_faC6-Wkog","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}