FKBP10 may affect the malignant phenotype of oral squamous cell carcinoma cells through the ECM/WNT signaling pathway

FKBP10 may affect the malignant phenotype of oral squamous cell carcinoma cells through the ECM/WNT signaling pathway | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article FKBP10 may affect the malignant phenotype of oral squamous cell carcinoma cells through the ECM/WNT signaling pathway Chengyi Shen, Rui Hou, Jingzhe Zhang, Qian Zhang, Meina Li, Jia Wang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9094874/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Oral squamous cell carcinoma (OSCC) is a frequent malignant tumor in the oral and maxillofacial region with a poor prognosis, and its pathophysiology has not been fully understood. Although FKBP10 is overexpressed in a number of malignancies, its function and regulation mechanism in OSCC are yet unknown. Method༚Collect OSCC and normal tissue samples adjacent to the cancer, combine with public datasets such as TCGA and GEO, and use bioinformatics methods such as WGCNA, limma differential analysis, and machine learning to screen target genes; through single-cell and spatial transcriptome analysis, clarify the main expression area of FKBP10 and its distribution in macrophages; use CCK-8, cell clone formation, Transwell, cell scratch, and flow cytometry experiments to detect the effects of FKBP10 knockdown on the biological behaviors of SCC-9 cells; use Western blot and GSEA to explore its potential molecular mechanism. Result༚Bioinformatics research identified FKBP10 as a significant target gene for OSCC. It is mostly abundant in fibroblasts and is significantly expressed in OSCC tissues. It has a strong positive correlation with M2-type macrophage infiltration and is linked to a negative outcome for patients. FKBP10 knockdown can cause cell apoptosis, stop the cell cycle at the G0/G1 phase, and prevent SCC-9 cells from proliferating, migrating, and invading. At the same time, it down-regulates the expression of POSTN, ITGB5, and WNT pathway-related proteins. Conclusion༚FKBP10 may influence the recruitment of M2-type macrophages by regulating the ECM/integrin adhesion signal and the WNT pathway, thereby changing the tumor immune milieu and encouraging the occurrence and progression of OSCC. It is projected to become a promising biomarker and target for prognostic evaluation and targeted therapy of OSCC. Health sciences/Biomarkers Biological sciences/Cancer Biological sciences/Cell biology Biological sciences/Computational biology and bioinformatics Health sciences/Oncology OSCC FKBP10 WNT pathway tumor immune microenvironment Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Oral squamous cell carcinoma (OSCC) is a malignant tumor that arises in the oral epithelium and is the predominant type of head and neck squamous cell carcinoma (HNSCC)[ 1 ]. It makes up 1.8% of all newly diagnosed cancer cases globally annually[ 2 ]. The 5-year overall survival rate (OS) for OSCC is still roughly 50%, despite the fact that treatment has changed from surgical resection to multimodal therapy, which includes surgery, radiation, and chemotherapy[ 3 ]Dysplasia of oral leukoplakia (OLK) is regarded as the most prevalent type of precancerous lesion in the oral cavity. A recent cohort analysis demonstrated that the malignant transformation rate of OLK is approximately 11.7%–23.1%[ 4 , 5 ]. Therefore, discovering potential new preventative and therapeutic targets is of tremendous significance for studying the malignant genesis, progression and poor prognosis of OSCC. The FKBP family belongs to the immunomodulatory protein family. It has peptidyl-prolyl isomerase (PPIase) activity and can bind to immunosuppressive medications like rapamycin and FK-506, taking part in a number of biological processes like tumor growth and chemotherapy resistance [ 6 – 8 ]. Protein stability is maintained by the FKBP family of proteins. By directly binding to the target proteins and changing their structure, they stop the target proteins from abnormally aggregating. For instance, FKBP12 interacts to α-synuclein, altering the aggregation of this protein in brain cells[ 9 ]. Transcriptional regulation is a function of the FKBP proteins. They bind to transcription factors and hormone receptors. Studies have indicated that FKBP51 and FKBP52 are related with the glucocorticoid receptor. They aid in the formation of the receptor complex and impact the transcriptional mode of genes [ 10 ]. Under normal and stress settings, FKBP51 works as a co-factor of HSP90 [ 11 , 12 ] and modulates the inflammatory response through the nuclear factor κB (NF-κB) signaling pathway [ 13 ]. FKBPs are adaptor molecules involved in several physiological processes, such as protein folding, protein stability, cell signaling, apoptosis, and transcription, which can contribute to a number of illnesses, including inflammation, fibrosis development, neurological diseases, and cancer [ 14 – 18 ]. FKBP10 is one among the members of the FKBP family. Chromosome 17 contains it (17q21.2). Due to the molecular weight of the endoplasmic reticulum chaperone protein encoded by it being 65 KDa, it is also named FKBP65[ 19 ]. FKBP10 comprises 4 PPIase domains and is the one with the most PPIase domains in the FKBP family[ 20 ]. Yoshihiro Ishikawa et al. showed that FKBP10 can directly interact with type I collagen [ 21 ], and works as a regulatory factor to repair the extracellular matrix (ECM), making it a key prospective target for the therapy of idiopathic pulmonary fibrosis[ 22 , 23 ]. FKBP10 is associated with the occurrence and development of various tumors. FKBP10 inhibits the ubiquitination of β-catenin and promotes the malignant transformation of breast epithelial cells [ 24 ]. It is overexpressed in colorectal cancer [ 25 ], renal cancer [ 26 ], glioma [ 27 ] and gastric cancer [ 28 , 29 ], and promotes the proliferation, invasion and migration of tumor cells. However, the role of FKBP10 in OSCC has not been reported yet. Materials and Methods Tissue specimens and cells Oral squamous cell carcinoma and adjacent normal tissues were acquired from patients who underwent complete resection surgery at the Affiliated Stomatology Hospital of Jiamusi University and did not undergo preoperative radiation or chemotherapy. All specimens were immediately kept in liquid nitrogen after being obtained. The samples were used after being approved by the Medical Ethics Committee of the Affiliated Stomatology Hospital of Jiamusi University and with the informed consent of all patients. Bolson Biotechnology Co., Ltd. supplied the SCC-9 cells. The SCC-9 cells were grown in DMEM media containing 1% penicillin-streptomycin and 10% fetal bovine serum. All the cells were cultivated in a cell culture box at 37°C and 5% CO2. Obtain public datasets of OSCC patients Retrieval of expression data for HNSC samples and OSCC samples from the Cancer Genome Atlas (TCGA) ( https://cancergenome.nih.gov/ ) was accomplished retrospectively. The two suitable HNSC cohorts were annotated and evaluated based on clinical features, containing 567 samples (522 tumor samples and 45 normal samples) from TCGA-HNSC (excluding data from the areas of hypopharynx, larynx, oropharynx, and tonsils). Download the transcriptome datasets (GSE74530, GSE31056, GSE85446) and spatial transcriptome data (GSE181300) from the GEO database ( https://www.ncbi.nlm.nih.gov/ ). Dataset download date, TCGA: August 10, 2024. GSE31056: August 7, 2024. GSE74530: August 7, 2024.GSE181300: September 6, 2024. Weighted Gene Co-Expression Network Analysis (WGCNA) and limma differential analysis The R package "WGCNA" was used to discover biologically meaningful co-expression gene modules, and to study the link between the gene network and OSCC. First, every gene in GSE74530 was chosen for further examination. Second, a scale-free network was built using the "pickSoftThreshold" function to choose the ideal soft threshold power β between 1 and 20; the average connectivity R^2 threshold was set at 0.86. Then, the adjacency relationship was turned into a topological overlap matrix (TOM) to calculate the gene ratio and dissimilarity. Finally, hierarchical clustering and dynamic tree-cut methods were used to cut and identify the co-expression modules, and the co-expression modules were merged and assessed based on the similar expression patterns. MEDissThres was set to 0.4 and minModuleSize was set at 50. Gene significance (GS) and module membership (MM) were used to relate modules to illnesses. The genes with the strongest connection with the disease within the module were used for additional investigation. Finally, common genes were obtained by overlapping the pd-related and pss-related modules using the "venn" R program. Limma is a method for differential expression screening based on generalized linear models. The dataset GSE31056 was subjected to differential analysis using the R software package "limma" (version 3.40.6) in order to determine which genes were differently expressed between tumor and normal tissues. Finally, we picked genes with a 1.5-fold difference for additional study. machine learning We have integrated 10 machine learning algorithms, including Random Survival Forest (RSF), Elastic Net (Enet), Lasso, Ridge Regression, Stepwise Cox Regression (StepCox), Cox Boost, Cox Partial Least Squares Regression (plsRcox), Supervised Principal Component (SuperPC), Generalized Boosted Regression Modeling (GBM), and Survival Support Vector Machine (survival-svm). Based on these procedures, a consensus model was produced. Using the Leave-One-Out Cross-Validation (LOOCV) framework, 58 algorithm combinations were undertaken to match the prediction model. The TCGA-OSCC dataset was utilized as the training dataset, and the GSE85446 dataset was used as the external validation dataset. Furthermore, the consistency index (C-index) of each pattern in all validation datasets was determined. A total of 80 intersecting genes were included in the research. Based on the gene expression levels of distinct patterns, we used the linear combination function of each pattern to generate the risk score. The average C-index value of the training set and the validation set was the highest, and was eventually considered the ideal model. Analysis of the correlation between KM and clinical factors, univariate and multivariate Cox analysis Following the identification of the best model, we separated the patients in the training and validation datasets into high-risk and low-risk groups using the training dataset's median risk score as the cutoff value. Kaplan-Meier (KM) survival analysis was undertaken for the two groups of patients using the "survival" R package. Additionally, we ran univariate and multivariate Cox analyses to show the independence of the model. We employed the Cox proportional hazards model (Cox Proportional Hazards Model) to assess the correlation between the risk score and survival time. By calling the coxph function, we created the Cox model and utilized the regplot tool to draw the column plot. This graph predicts survival and displays the distribution of risk ratings. We created the calibration curve in order to assess the column plot's predictive accuracy. We did survival analysis using the Cox model and calibrated the survival rates at 1 year, 3 years, and 5 years. The calibration curve at each time point was constructed using the calibrate function. Single-cell transcriptome and spatial transcriptome analysis Load the data using the Read10X function, generate Seurat objects (scRNA1 to scRNA8) for each sample using the Seurat (V4.3.0) package, and merge scRNA1 to scRNA8. Apply the Percentage FunctionFor quality control, set a function to determine the ratio of mitochondrial genes to erythrocyte genes. Use the NormalizeData function to standardize the data, locate variable genes and do data scaling. For PCA dimensionality reduction, use the RunPCA function. The FindNeighbors and FindClusters functions are used to locate the nearby points and do the clustering analysis. The UMAP dimensionality reduction is performed and shown using the RunUMAP function. The expression of marker genes for distinct cell types can be seen with DotPlot. Cell names are assigned depending on the clustering results. Specific cell types (such as fibroblasts) are selected for cell communication study. The CellChat software is used to construct cell communication objects, and overexpressed genes and ligand-receptor pairs are identified by extracting a subset supported by the database. Communication probabilities are calculated and filtered. Heatmaps and interaction graphs of cell communication are generated using the ktplots package to show the communication links between different cell types. Use the Load10X_Spatial function to read the spatial transcriptomic data (H5 file), use the GetTissueCoordinates function to obtain the tissue coordinates, mark whether each point in the sample is located on the tissue based on the tissue coordinates, filter out the points that are not on the tissue, perform SCT transformation standardization on the data, and use the NormalizeData function for standardization. Then, perform data scaling and principal component analysis (PCA), and apply UMAP for dimensionality reduction. Perform deconvolution analysis (RCTD) after loading the previously stored spatial transcriptomic and single-cell RNA sequencing data. Build the reference dataset and prepare the spatial data for RCTD analysis. Combine the findings with the spatial transcriptomic data's metadata. To see how particular cell types are distributed spatially, use SpatialFeaturePlot. GSEA enrichment analysis The GSEA software (version 3.0) was obtained from the GSEA website ( http://software.broadinstitute.org/gsea/index.jsp ). The samples were separated into two groups based on cancer tissues and surrounding tissues. To assess the associated pathways and molecular mechanisms, the c2.cp.kegg.v7.4.symbols.gmt subset was downloaded from the Molecular Signatures Database ( http://www.gsea-msigdb.org/gsea/downloads.jsp ). Based on the gene expression profile and phenotypic grouping, a minimum gene set of 5 and a maximum gene set of 5000 were selected. A P value of less than 0.05 was deemed statistically significant after a thousand resamplings. Cell transfection As directed by lipo8000 (Biyuntian), 125 ul of serum-free medium + 4 ul of lipo8000 + 5 ul of interference reagent were combined and allowed to stand for 20 minutes when the cell concentration in the 6-well plate reached 60% to 80%. Then, 2 ml of the total liquid volume in the 6-well plate was added. After 6 hours, the cell status was detected. Sangon Biotech Co., Ltd. supplied the FKBP10 siRNA and the negative control siRNA. After transfection, the transfection efficiency was evaluated by qPCR and Western blot analysis. Primer sequence of FKBP10: FKBP10-229-s cuaccacuacaacggcacuuu, h FKBP10-229-a aaagugccguuguagugguag;h FKBP10-961-s ccacaccuacaauaccuauau, h FKBP10-961-a auauagguauuguaggugugg༛h FKBP10-712-s gaagauuaucaucccuccauu, h FKBP10-712-a aauggagggaugauaaucuuc。 Real-time fluorescence quantitative PCR The RNA extraction kit (Sangon Biotech) was used to extract the total mRNA from SCC-9 cells following transfection. The concentration and purity of the RNA were evaluated by an ultramicro spectrophotometer. The RNA was reverse-transcribed into cDNA. The real-time fluorescence quantitative PCR kit was used for RT-PCR amplification. The results were examined using the 7300 instrument. The 2-ΔΔCt method was used to determine the relative expression levels of FKBP10 gene mRNA in each cell group (with GAPDH as the internal reference). Western blot analysis Gather the transfected SCC-9 cells, use protein lysate to extract total protein, and use the BCA method to calculate the extracted protein's concentration. Take 50 µg of protein for SDS-PAGE (10%) electrophoresis separation. Use the wet transfer technique to transfer the protein onto the PVDF membrane. For one hour, seal the membrane with 5% skim milk at 37°C. Place the membrane in the corresponding FKBP10 rabbit monoclonal antibody (1∶1000, ABclonal), POSTN mouse monoclonal antibody (1∶5000, Proteintech), ITGB5 mouse monoclonal antibody (1∶3000, Proteintech), WNT2 mouse monoclonal antibody (1∶25000, Proteintech), WISP1 mouse monoclonal antibody (1∶2000, Proteintech), and ACTIN mouse monoclonal antibody (1∶10000, Proteintech) for 4 hours at 4 ℃. Use TBS-T to wash out the unbound primary antibodies four times for five minutes each. Then incubate with horseradish peroxidase-labeled goat anti-mouse IgG at 37 ℃ for 1 hour. Wash out the unbound secondary antibody with TBS-T, and wash 4 times, each for 5 minutes. Use an automatic chemiluminescence/fluorescence image analysis device to view the results, then use ImageJ to analyze them. Detection of cell proliferation ability by CCK-8 method Cell proliferation was detected using the Cell Counting Kit-8 (CCK-8 kit, Sangon Biotech). 100 µL of a 2000-cell density solution was applied to each well of a 96-well plate to inoculate SCC-9 cells. After then, si-FKBP10 was co-incubated with the cells for 0, 24, 48, and 72 hours. The control group was Negative Control. Each well received 100 µL of complete media containing 1% CCK-8, and the plates were incubated for two hours at 37°C. The OD values were recorded using an enzyme reader (Λllsheng) at a wavelength of 450 nm. The colony formation assay detects the ability of cells to form colonies. After 24 hours of cell transfection, the cells were centrifuged and digested. 500 cells were put into each 6-well plate. The cells were carefully pipetted to ensure uniform distribution. The cells were grown for 2 weeks. The cell culture was halted when obvious clone clusters could be observed. The culture medium was discarded, and the plates were washed twice with PBS. 1 mL of 4% paraformaldehyde was added for fixation for 30 minutes, then rinsed with PBS, then 1 mL of crystal violet was added for staining for 10 minutes. After a gentle rinse with tap water, the plates were left to air dry. The cells were photographed under a microscope and the number of cell clone forms in each group was tallied for statistical analysis. Transwell invade Dilute the Matrix-GelTM matrix gel with serum-free media to the working concentration (with a dilution ratio of 1:8). Spread out 60 µL of the matrix gel uniformly in the Transwell chamber's upper chamber. Put it in the incubator for three hours. Remove the unbound matrix gel, add 200 µL of serum-free media, and let it hydrate in a 37°C 5% CO2 cell incubator for 30 minutes. Fill the upper chamber with 200 µL of serum-free basic media that contains 50,000 SCC-9 cells. Then, 600 µL of complete medium containing 10% serum was introduced to the lower chamber. After 48 hours of cultivation, the cells were fixed with 4% paraformaldehyde for 30 minutes, stained with crystal violet for 30 minutes, and then photographed under a microscope for recording. The number of cells that had penetrated the membrane was counted, and the average value was determined. cell scratch Saturate SCC-9 cells at an acceptable density in a 6-well plate. When the cell fusion rate reaches around 90% following transfection, replace the culture media with a serum-free baseline medium and use a 200/1000 µL pipette tip to generate consistent gaps. After the scratch, take images right away, and 24 hours later, record the cell placements once more. All photos are collected from 3 independent scratches using an inverted microscope and the average values are determined. Flow cytometry for cell cycle detection After 48 hours of transfection, SCC-9 cells were harvested. After one PBS wash, they were resuspended in a 4% paraformaldehyde solution and kept overnight at -20°C. During the detection process, after centrifugation, 500 µL staining buffer, 25 µL 20 × propidium iodide (PI) staining, and 10 µL 50 × RNase were added to each sample, and incubated at room temperature in the dark for 30 minutes. Finally, the analysis was performed using a flow cytometer. Calcein-AM/PI dyeing Following a 72-hour si-RNA transfection of SCC-9 cells, the cells were twice washed with PBS and the original culture media was discarded. 100 µL of Calcein-AM/PI staining mixture was applied to each well, and the cells were stained in the dark at 37 ℃ in a CO2 incubator for 30 minutes. The cells were then rinsed twice with PBS and photographed using a 20× inverted fluorescence microscope. Five fields of vision were randomly selected from each group for observation to track the changes in cell fluorescence intensity. Result 1 Bioinformatics analysis identifies target genes The GSE74530 was submitted to WGCNA co-analysis, resulting in the identification of 6 modules. Among them, the black module (R = 0.89, P = 1.1e-4) contained 737 genes positively connected with the disease (Fig. 1 A). Using the R software package limma (version 3.40.6), a differential analysis was conducted on GSE31056. A 1.5-fold difference led to a total of 381 up-regulated genes and 723 down-regulated genes as the screening results (Fig. 1 B). A total of 80 important genes were obtained by cross-matching the 737 genes in black with 1104 DEGs. Following machine learning, the LOOCV framework in TCGA-OSCC was used to fit these 80 core genes into 58 prediction models. The C-index of each model was determined in all validation datasets. It was noted that the best model combination was RSF and Enet (alpha = 0.1), with the greatest average C-index of 0.63 (containing SPOCK1, BLNK, BSPRY, PCDH17, C1orf116, FKBP10, RBM38, KRT14) (Fig. 2A). Moreover, it was revealed that the survival duration of the high-expression group was considerably shorter (Fig. 2B). The 1-year, 3-year and 5-year PFS data suggested that this model was a stable and effective prognostic tool with good specificity and sensitivity (Fig. 2C). Univariate and multivariate Cox regression studies demonstrated that this model, age, and stage could be independent predictive factors for OSCC patients (P < 0.05) (Fig. 2D and 2E). This model outperformed traditional clinical factors, according to the time-dependent C-index (Fig. 2F). Based on the current literature research, FKBP10 was finally picked as the final target gene for additional research examination. Figure 2. Establishment and validation of the integration process based on machine learning. A: 58 prediction models were validated using the LOOCV framework, and the C-index of each model in all validation datasets was further calculated. B: KM curve. C: ROC curves showing 1-year, 3-year, and 5-year PFS. D: Univariate Cox analysis. E: Multivariate Cox analysis. F: Time-dependent C-index curve. 2 Single-cell transcriptome and spatial transcriptome analysis Principal component analysis (PCA) was used for dimension reduction, clustering, and UMAP visualization in the quality control and integration of single-cell transcriptome study. GSE181300 was categorized into 14 subcellular cell type clusters and annotated as 6 types of cells. FKBP10 was primarily expressed in fibroblasts (Figs. 3A and 3B). Significant variations in FKBP10 expression and macrophage communication were found by the cell communication study (Fig. 3C). Moreover, it can be noted that FKBP10 might interact with pathways such as POSTN-ITGAV-ITGB5 (Fig. 3D). Furthermore, the GSEA enrichment analysis also indicated that increased expression of FKBP10 would also activate ECM RECEPTOR INTERACTION (Fig. 3E). There was some overlap between FKBP10 and macrophages in the expression areas when they were deconvoluted onto the sections (Fig. 3F). When FKBP10 was deconvoluted onto the other four portions, it could be seen that there were considerable variances among the four sections as well as within each segment (Fig. 3G). These findings imply that FKBP10 is crucial for both patient prognosis and cell invasion. This stimulates further research of the carcinogenic effect of FKBP10. Figure 3. Analysis of single-cell transcriptome and spatial transcriptome. A. Cell clusters after single-cell transcriptome annotation B. Main expression region of FKBP10 C. Cell communication between FKBP10 positive and negative cells D. Heatmap and interaction diagram of cell communication drawn by cellchat E. GSEA results of FKBP10 F. RCTD identifies the main distribution of FKBP10 and macrophages in the spatial map G. RCTD identifies the main distribution of FKBP10 in other spatial maps. 3 Immune cell infiltration analysis The CIBERSORT algorithm was used to determine the percentage of immune cells infiltrated with FKBP10 (Fig. 4A). The distribution of immune cells varied between the two risk categories. The percentages of each type of immune cell in the two risk groups were then compared (Fig. 4B). The results showed that in the high-risk group, T cells CD4 memory resting, Macrophages M0, and Macrophages M2 were significantly enriched. Additionally, the correlation coefficients between the risk score and tumor-infiltrating immune cells assessed using seven different methodologies also demonstrated a strong positive link with Macrophages M2 (Fig. 4D). Additionally, the tumor immune microenvironment's ESTIMATEScore was statistically significant (Fig. 4C). Figure 4 Immune cell infiltration analysis A Bar graph showing the proportions of tumor-infiltrating immune cells in TCGA by CIBERSORT. B Differential graph of immune cells between high and low expression groups C Tumor immune microenvironment analysis D Correlation between different immune cells and risk scores calculated by seven different algorithms *p < 0.05; **p < 0.01; ***p < 0.001. 4 Expression of FKBP10 in OSCC and Identification of Cell Transfection Three pairs of paired oral squamous cell carcinoma (T) and normal tissues surrounding the cancer (N) were randomly selected from the sample collection. The three pairs of paired tissues were examined for FKBP10 expression using immunofluorescence. The results showed that the relative expression level of FKBP10 in all OSCC tissues was higher than that in the neighboring normal tissues (Fig. 5A), suggesting that FKBP10 plays a role as a cancer gene in OSCC. According to the RT-qPCR data, the transfected group's FKBP10 expression level was significantly lower than that of the control group, and the si-FKBP10-1 group had the highest transfection efficiency (Fig. 5B). The Western blot results were comparable with the RT-PCR results, and the protein expression level of FKBP10 in the si-FKBP10-1 group was the lowest (Fig. 5C). Therefore, the si-FKBP10-1 was employed for transfection in the future investigations. Figure 5. Organizational expression and transfection. A: Expression of FKBP10 in OSCC. Blue represents DAPI, red represents FKBP10. B: After transfection with interfering RNA, the mRNA expression in the si-FKBP10-1 group was the lowest, ****p < 0.0001. C: The protein expression in the si-FKBP10-1 transfection group was the lowest, ****p < 0.0001. 5 Effects of FKBP10 knockdown on proliferation, migration, invasion, cycle and apoptosis of SCC-9 cells The CCK-8 results showed that the cell proliferation ability of the si-FKBP10-1 transfection group was significantly lower than that of the control group (Fig. 6 A); the cell clone formation experiment results indicated that after knockdown of FKBP10 expression, the clone formation ability of SCC-9 cells significantly decreased (Fig. 6 B); thus, it can be seen that the cell proliferation was inhibited after the expression of FKBP10 was knocked down. The Transwell invasion and scratch assays indicated that the cell invasion and migration capacities of the si-FKBP10-1 transfection group were greatly suppressed (Figs. 6 C and 6 D), and the results were statistically significant. During this process, one or more stages of the cell cycle may be disturbed and halted. The results of flow cytometry analysis suggested that the cells in the si-FKBP10 group were in a trend of G0/G1 phase arrest and a drop in S phase cells (Fig. 6 E), confirming that FKBP10 can boost cell proliferation by regulating the cell cycle. Calcein-AM may penetrate the cell membrane and stain live cells, showing green fluorescence. PI can stain dead cells and show red fluorescence. Therefore, when Calcein-AM is coupled with PI, it may conduct dual fluorescence labeling on both living and dead cells. After the expression level of FKBP10 declines, the number of cells emitting red fluorescence increases, demonstrating that knocking down FKBP10 can induce death in SCC-9 cells (Fig. 6 F). 6 Effects of FKBP10 knockdown on related pathway proteins First, we investigated the associated mechanisms of apoptosis and cell cycle arrest in SCC-9 cells brought on by FKBP10 knockdown. The main proteins of the WNT pathway were found by WB. The results showed that the expression levels of periostin (POSTN), integrin ITGB5 and WNT pathway-related proteins were decreased following FKBP10 knockdown (Fig. 7A). Thus, FKBP10 may influence apoptosis and G0/G1 phase arrest via the WNT pathway. Figure 7. The protein expression levels of POSTN, ITGB5, WNT2, and WISP1 in the FKBP10 knockdown group were significantly lower than those in the control group (***P < 0.001). Discussion Early research on FKBP 10 mostly focused on pulmonary fibrosis and osteochondrodysplasia. Bruck syndrome is an extremely uncommon hereditary condition marked by osteoporosis, gradual joint contractures, small stature, and increased fracture risk. The mutation mostly affects the FKBP10 gene on chromosome 17p12, and it is inherited in an autosomal recessive fashion[ 30 ]. A number of uncommon autosomal recessive phenotypes, such as osteogenesis imperfecta type XI (OI XI), Brook's syndrome type I (BS I), and a congenital arthrogryposis-like phenotype (AG), can result from pathogenic variations of the FKBP10 gene[ 31 ]. What is essential is that the absence of FKBP 10 expression dramatically limits the secretion of collagen by primary human lung fibroblasts[ 32 ]. Research on the connection between FKBP 10 and cancer has been done recently. FKBP 10 regulates the folding, transport and secretion of proteins during the synthesis of extracellular matrix proteins[ 33 ]. FKBP10 is considerably enhanced in colorectal cancer tissues and exhibits three unique subcellular expression patterns, designated as "FKBP10-C" (concentrated), "FKBP10-T" (transitional), and "FKBP10-D" (dispersed). Among these, the FKBP10-D expression pattern is only observed in tumor tissues and is associated with poor prognosis in CRC patients[ 34 ]. FKBP10 plays a vital part in the malignant transition process from normal tissues to ductal carcinoma in situ (DCIS) and invasive ductal carcinoma (IDC) by preventing the ubiquitination of β-catenin [ 24 ]. FKBP10 interacts with pre-lamin A and prevents pre-lamin A from entering the nucleus, thereby reducing nuclear lamin A. This enhances the nuclear atypia of bladder cancer cells. Studies have shown that the FKBP10/prelamin A/lamin A axis leads to MIBC (muscle-invasive bladder cancer)[ 35 ]. Using WGCNA in GSE74530, the black module in this study was found to be substantially linked with the tumor phenotype (with a R value of 0.89), and by intersecting with the differentially expressed genes in GSE31056, 80 potential core genes were found. Further, utilizing the LOOCV framework in the TCGA-OSCC cohort, 58 types of prediction models were trained and compared. Finally, a reliable prognostic model combination (RSF and Enet) including FKBP10 was produced (with an average C-index of 0.63), and the survival outcome of the high-risk group was worse. This implies that FKBP10 may be associated to the poor prognosis of OSCC. Single-cell transcriptome analysis then showed that FKBP10 was mostly enriched in the fibroblast population, which is extremely compatible with earlier research demonstrating that FKBP10 is an endoplasmic reticulum chaperone protein and takes role in ECM remodeling and collagen maturation. More importantly, cell communication research suggested that the FKBP10-related signals had significant disparities in communication with macrophages and may include the POSTN–ITGAV–ITGB5 axis. Spatial transcriptomic deconvolution further revealed that the expression regions of FKBP10 and macrophage expression regions had a substantial degree of overlap. Combined with the results of immune cell infiltration analysis: high expression of FKBP10 was significantly positively correlated with Macrophages M2, and was statistically significant in the ESTIMATE score and GSEA results: high expression of FKBP10 activated ECM RECEPTOR INTERACTION, WNT SIGNALING PATHWAY, and FOCAL ADHESION. This suggests that FKBP10 may be implicated in ECM and WNT SIGNALING PATHWAY, consequently impacting the occurrence and progression of OSCC. As essential receptors that facilitate cell adhesion and signal transmission, integrins have been thoroughly investigated and shown to have multifaceted regulatory roles in the formation and occurrence of tumors. In order to regulate complex cellular biological behaviors like cell survival, proliferation, migration, and different cell fate decisions, this receptor can mediate the rearrangement of the cytoskeleton and activate downstream intracellular signaling pathways when it binds to the extracellular matrix (ECM)[ 36 ]. Collagen's extracellular matrix cross-linking can be controlled by FKBP10. Silencing FKBP10 in GC cells and altering the expression of the integrin family members αV and α6 can prevent the adherence of GC cells[ 37 ]. The extracellular matrix (ECM) components such as fibronectin (FN) and integrins interact with each other, controlling carcinogenic signal transduction and encouraging the formation of OC (ovarian cancer) spheroids through the Wnt/β-catenin pathway[ 38 ]. According to the aforementioned hypothesis, FKBP10 may control the adhesion signals between integrins and extracellular matrix, which could impact the downstream WNT signaling pathway, mediate M2-type macrophage recruitment, homing, and polarization, and ultimately modify the tumor immune microenvironment. According to this study, OSCC tissues had substantially greater levels of FKBP10 expression than nearby tissues. After suppression of FKBP10, the proliferation ability of SCC-9 cells was decreased. Through flow cytometry, it was determined that the cell cycle was aberrant. After suppression of FKBP10, the cell cycle was halted at the G1 phase, and the live/dead cell labeling suggested that the number of live cells was reduced while the number of dead cells rose. An extracellular matrix-related secreted protein called POSTN is crucial to the extracellular matrix's development, remodeling, and operation[ 39 ], It interacts with receptors such as integrins on the cell surface, thereby affecting cell behavior[ 40 ]. WISP1 (WNT1-inducible signaling protein 1), also known as CCN4, is a protein belonging to the CCN family and is a secreted protein that can interact with the extracellular matrix[ 41 ]. WISP1 is a key downstream effector molecule of the WNT signaling pathway. Its expression is regulated by this route and can stimulate the expression of Cyclin D1 [ 42 ], while Cyclin D1 is a crucial factor for transitioning from the G1 phase to the S phase [ 43 ]. Therefore, it can be extrapolated that WISP1 is directly associated to the G1 phase arrest and growth of malignancies. After knocking down FKBP10 in SCC-9 cells, the expressions of POSTN, ITGB5, WNT2, and WISP1 dramatically dropped, and the cell migration and invasion abilities were hindered. In conclusion, FKBP10 may affect cell proliferation and a series of malignant biological behaviors through the ECM/WNT signaling pathway. However, further in vivo studies are needed to confirm whether FKBP10 is involved in the cancer development process of oral squamous cell carcinoma. Abbreviations OSCC Oral squamous cell carcinoma HNSCC Head and neck squamous cell carcinoma OS Overall survival rate OLK Oral leukoplakia PPIase Peptidyl-prolyl isomerase NF-κB Nuclear factor κB ECM Extracellular matrix TCGA The Cancer Genome Atlas NCBI National Center for Biotechnology Information GEO Gene Expression Omnibus WGCNA Weighted Gene Co-Expression Network Analysis GS Gene significance MM Module membership DEGs Differentially Expressed Genes RSF Random Survival Forest Enet Elastic Net LOOCV Leave-One-Out Cross-Validation C-index Consistency index KM Kaplan-Meier PCA Principal Component Analysis UMAP Uniform Manifold Approximation and Projection RCTD Robust Cell Type Decomposition GSEA Gene Set Enrichment Analysis siRNA Small interfering RNA qPCR Quantitative Polymerase Chain Reaction RT-PCR Reverse Transcription-Polymerase Chain Reaction SDS-PAGE Sodium Dodecyl Sulfate-Polyacrylamide Gel Electrophoresis PVDF Polyvinylidene Fluoride CCK-8 Cell Counting Kit-8 PI Propidium iodide OD Optical Density TBS-T Tris-Buffered Saline with Tween-20 Calcein-AM Calcein Acetoxymethyl Ester ESTIMATE Estimation of Stromal and Immune cells in Malignant Tumor tissues using Expression data DCIS Ductal carcinoma in situ IDC Invasive ductal carcinoma MIBC Muscle-invasive bladder cancer GC Gastric Cancer OC Ovarian Cancer FN Fibronectin POSTN Periostin ITGB5 Integrin Subunit Beta 5 WNT2 Wnt Family Member 2 WISP1 WNT1-inducible signaling protein 1 Declarations Funding: This research was funded by the Basic Research Project of the Education Department of Heilongjiang Province, with the project number: 2024-KYYWF-0585. Institutional Review Board Statement: This research was approved by the Ethics Committee of the Affiliated Stomatological Hospital of Jiamusi University. Ethical Approval Number: KQYXY-2025-XS-M025. All the methods in this article were carried out in accordance with the relevant guidelines and regulations. Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. Data Availability Statement: All data included in the article are available from the corresponding author upon reasonable request. Conflicts of Interest: The authors declare no conflicts of interest. References Wang, P., Tian, J., Zheng, G., Chen, Y. & Ren, X. Application of artificial intelligence in head and neck squamous cell carcinoma. Ann. Med. 58 10.1080/07853890.2026.2620191 (2026). Sung, H. et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J. Clin. 71 , 209–249. 10.3322/caac.21660 (2021). 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Gastroenterol. 36 , 247–254. 10.5152/tjg.2024.23524 (2024). Liu, Y. et al. C1222C Deletion in Exon 8 of ABL1 Is Involved in Carcinogenesis and Cell Cycle Control of Colorectal Cancer Through IRS1/PI3K/Akt Pathway. Front. Oncol. 10 10.3389/fonc.2020.01385 (2020). Additional Declarations No competing interests reported. Supplementary Files originaldata.zip TheuncutPVDFmembrane.zip Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 03 May, 2026 Reviews received at journal 01 May, 2026 Reviews received at journal 30 Apr, 2026 Reviewers agreed at journal 16 Apr, 2026 Reviewers agreed at journal 14 Apr, 2026 Reviewers invited by journal 14 Apr, 2026 Editor assigned by journal 14 Apr, 2026 Editor invited by journal 23 Mar, 2026 Submission checks completed at journal 21 Mar, 2026 First submitted to journal 21 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9094874","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":625324723,"identity":"3ca1c875-e82b-4874-868e-e676b07cb747","order_by":0,"name":"Chengyi Shen","email":"","orcid":"","institution":"Jiamusi University","correspondingAuthor":false,"prefix":"","firstName":"Chengyi","middleName":"","lastName":"Shen","suffix":""},{"id":625324724,"identity":"81424c95-acdb-4797-be8d-3187c077809f","order_by":1,"name":"Rui Hou","email":"","orcid":"","institution":"Jiamusi University","correspondingAuthor":false,"prefix":"","firstName":"Rui","middleName":"","lastName":"Hou","suffix":""},{"id":625324725,"identity":"265a3601-1e45-4ec6-9b90-40e84b04cd24","order_by":2,"name":"Jingzhe Zhang","email":"","orcid":"","institution":"Affiliated Stomatological Hospital of Jiamusi University","correspondingAuthor":false,"prefix":"","firstName":"Jingzhe","middleName":"","lastName":"Zhang","suffix":""},{"id":625324726,"identity":"a55fa77a-82dc-4772-9455-fc995aad4d6b","order_by":3,"name":"Qian Zhang","email":"","orcid":"","institution":"Jiamusi University","correspondingAuthor":false,"prefix":"","firstName":"Qian","middleName":"","lastName":"Zhang","suffix":""},{"id":625324730,"identity":"3cc7caa6-6922-4c9c-8e89-37ac1dffedb9","order_by":4,"name":"Meina Li","email":"","orcid":"","institution":"Affiliated Stomatological Hospital of Jiamusi University","correspondingAuthor":false,"prefix":"","firstName":"Meina","middleName":"","lastName":"Li","suffix":""},{"id":625324733,"identity":"1054a9a0-3282-4fb7-88a2-21f950b9c625","order_by":5,"name":"Jia Wang","email":"","orcid":"","institution":"Jiamusi University","correspondingAuthor":false,"prefix":"","firstName":"Jia","middleName":"","lastName":"Wang","suffix":""},{"id":625324734,"identity":"ed17a940-8013-4f4a-ad4d-054f3d65c873","order_by":6,"name":"Jian Guan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA70lEQVRIiWNgGAWjYLACCQMGBjb2xvYfHwxs7IjXwsdz+IDkjIK0ZOJtkpNIS5Dm+XCIsYGQSvmI5GcPLArs8tgYcgyMbQwOMDOwHz66AZ8Wwxtp5gYSBsnFbAxnDJJzDO7wMfCkpd3Aq2VGgpmEhAFzYhtjj8HhHINnzAwSPGYEtKR/A2qpT2xj5jFstjA4zNhASIu8RA7IlsOJbWxsycwMxGgx4HlTBtRyPLGNh/kY0G1pyWyE/CLfnr5NWuJPdeL8+Q/bGH78sbHjZz98DL8tBxgYmCWQRdjwKQfb0sDAwPiBkKpRMApGwSgY2QAAdgVE+SKyBBUAAAAASUVORK5CYII=","orcid":"","institution":"Affiliated Stomatological Hospital of Jiamusi University","correspondingAuthor":true,"prefix":"","firstName":"Jian","middleName":"","lastName":"Guan","suffix":""}],"badges":[],"createdAt":"2026-03-11 13:16:48","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9094874/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9094874/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107708290,"identity":"e3a27065-75d6-4112-8e1e-0edf5b89c7b6","added_by":"auto","created_at":"2026-04-24 09:25:58","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":388611,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification of key genes. A: WGCNA analysis was performed on GSE74530. 2: limma analysis was performed on GSE31056.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-9094874/v1/c2bb6d6db255dd5a0e71f690.png"},{"id":107708281,"identity":"d9fcdc57-3a51-4b54-ad83-dd8fa9713188","added_by":"auto","created_at":"2026-04-24 09:25:47","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":439682,"visible":true,"origin":"","legend":"\u003cp\u003eEstablishment and validation of the integration process based on machine learning. A: 58 prediction models were validated using the LOOCV framework, and the C-index of each model in all validation datasets was further calculated. B: KM curve. C: ROC curves showing 1-year, 3-year, and 5-year PFS. D: Univariate Cox analysis. E: Multivariate Cox analysis. F: Time-dependent C-index curve.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-9094874/v1/58d1dd8d9e8e60acbbafae99.png"},{"id":107712577,"identity":"e6418051-8516-4051-8f1a-b8da1c163126","added_by":"auto","created_at":"2026-04-24 09:49:29","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":729085,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of single-cell transcriptome and spatial transcriptome. A. Cell clusters after single-cell transcriptome annotation B. Main expression region of FKBP10 C. Cell communication between FKBP10 positive and negative cells D. Heatmap and interaction diagram of cell communication drawn by cellchat E. GSEA results of FKBP10 F. RCTD identifies the main distribution of FKBP10 and macrophages in the spatial map G. RCTD identifies the main distribution of FKBP10 in other spatial maps.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-9094874/v1/2e16e33d7875ebd91957e8dd.png"},{"id":107708295,"identity":"4616388f-4316-4acf-bc17-0c2189e9d458","added_by":"auto","created_at":"2026-04-24 09:26:06","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":539677,"visible":true,"origin":"","legend":"\u003cp\u003eImmune cell infiltration analysis A Bar graph showing the proportions of tumor-infiltrating immune cells in TCGA by CIBERSORT. B Differential graph of immune cells between high and low expression groups C Tumor immune microenvironment analysis D Correlation between different immune cells and risk scores calculated by seven different algorithms *p \u0026lt; 0.05; **p \u0026lt; 0.01; ***p \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-9094874/v1/3ca23a9ea8dd3a25241f351a.png"},{"id":107708282,"identity":"d8363969-ed5a-42b9-afc8-4ec190da3e45","added_by":"auto","created_at":"2026-04-24 09:25:47","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":335127,"visible":true,"origin":"","legend":"\u003cp\u003eOrganizational expression and transfection. A: Expression of FKBP10 in OSCC. Blue represents DAPI, red represents FKBP10. B: After transfection with interfering RNA, the mRNA expression in the si-FKBP10-1 group was the lowest, ****p \u0026lt; 0.0001. C: The protein expression in the si-FKBP10-1 transfection group was the lowest, ****p \u0026lt; 0.0001.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-9094874/v1/1713d729600477d7fa8df3e5.png"},{"id":107711289,"identity":"8709442f-c58b-4bac-be58-202e8584c312","added_by":"auto","created_at":"2026-04-24 09:45:02","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":554564,"visible":true,"origin":"","legend":"\u003cp\u003eEffects of si-FKBP10-1 transfection on SCC-9 cells. A Proliferation of SCC-9 cells after FKBP10 knockdown B Colony formation of SCC-9 cells after FKBP10 knockdown C Cell invasion after FKBP10 knockdown D Cell migration after FKBP10 knockdown E Cell cycle distribution of FKBP10 before and after knockdown F Calcein-AM/PI staining.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-9094874/v1/78703046ce718fb6ff744770.png"},{"id":107710136,"identity":"35b7af02-908e-4302-a46a-fabe0222577d","added_by":"auto","created_at":"2026-04-24 09:39:32","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":125196,"visible":true,"origin":"","legend":"\u003cp\u003eThe protein expression levels of POSTN, ITGB5, WNT2, and WISP1 in the FKBP10 knockdown group were significantly lower than those in the control group (***P\u0026lt;0.001).\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-9094874/v1/ab8ee81ec5ecbccbdaece511.png"},{"id":107869016,"identity":"e6dd0290-6a21-4761-9360-8ebff994ab1d","added_by":"auto","created_at":"2026-04-27 07:35:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3445360,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9094874/v1/3d1fba87-1b14-4c5d-9b99-01342a30bdb9.pdf"},{"id":107708230,"identity":"f057800f-aa10-4f3a-a457-b236aae27526","added_by":"auto","created_at":"2026-04-24 09:25:40","extension":"zip","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":83631112,"visible":true,"origin":"","legend":"","description":"","filename":"originaldata.zip","url":"https://assets-eu.researchsquare.com/files/rs-9094874/v1/cad5496b24fe989a99982e6c.zip"},{"id":107708898,"identity":"8275dad1-0612-4de9-b614-b42eb4b0c3a1","added_by":"auto","created_at":"2026-04-24 09:33:17","extension":"zip","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":10771927,"visible":true,"origin":"","legend":"","description":"","filename":"TheuncutPVDFmembrane.zip","url":"https://assets-eu.researchsquare.com/files/rs-9094874/v1/9e4357866dcbbe03e1035299.zip"}],"financialInterests":"No competing interests reported.","formattedTitle":"FKBP10 may affect the malignant phenotype of oral squamous cell carcinoma cells through the ECM/WNT signaling pathway","fulltext":[{"header":"Introduction","content":"\u003cp\u003eOral squamous cell carcinoma (OSCC) is a malignant tumor that arises in the oral epithelium and is the predominant type of head and neck squamous cell carcinoma (HNSCC)[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. It makes up 1.8% of all newly diagnosed cancer cases globally annually[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The 5-year overall survival rate (OS) for OSCC is still roughly 50%, despite the fact that treatment has changed from surgical resection to multimodal therapy, which includes surgery, radiation, and chemotherapy[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]Dysplasia of oral leukoplakia (OLK) is regarded as the most prevalent type of precancerous lesion in the oral cavity. A recent cohort analysis demonstrated that the malignant transformation rate of OLK is approximately 11.7%\u0026ndash;23.1%[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Therefore, discovering potential new preventative and therapeutic targets is of tremendous significance for studying the malignant genesis, progression and poor prognosis of OSCC.\u003c/p\u003e \u003cp\u003eThe FKBP family belongs to the immunomodulatory protein family. It has peptidyl-prolyl isomerase (PPIase) activity and can bind to immunosuppressive medications like rapamycin and FK-506, taking part in a number of biological processes like tumor growth and chemotherapy resistance [\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Protein stability is maintained by the FKBP family of proteins. By directly binding to the target proteins and changing their structure, they stop the target proteins from abnormally aggregating. For instance, FKBP12 interacts to α-synuclein, altering the aggregation of this protein in brain cells[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Transcriptional regulation is a function of the FKBP proteins. They bind to transcription factors and hormone receptors. Studies have indicated that FKBP51 and FKBP52 are related with the glucocorticoid receptor. They aid in the formation of the receptor complex and impact the transcriptional mode of genes [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Under normal and stress settings, FKBP51 works as a co-factor of HSP90 [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] and modulates the inflammatory response through the nuclear factor κB (NF-κB) signaling pathway [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. FKBPs are adaptor molecules involved in several physiological processes, such as protein folding, protein stability, cell signaling, apoptosis, and transcription, which can contribute to a number of illnesses, including inflammation, fibrosis development, neurological diseases, and cancer [\u003cspan additionalcitationids=\"CR15 CR16 CR17\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFKBP10 is one among the members of the FKBP family. Chromosome 17 contains it (17q21.2). Due to the molecular weight of the endoplasmic reticulum chaperone protein encoded by it being 65 KDa, it is also named FKBP65[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. FKBP10 comprises 4 PPIase domains and is the one with the most PPIase domains in the FKBP family[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Yoshihiro Ishikawa et al. showed that FKBP10 can directly interact with type I collagen [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], and works as a regulatory factor to repair the extracellular matrix (ECM), making it a key prospective target for the therapy of idiopathic pulmonary fibrosis[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. FKBP10 is associated with the occurrence and development of various tumors. FKBP10 inhibits the ubiquitination of β-catenin and promotes the malignant transformation of breast epithelial cells [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. It is overexpressed in colorectal cancer [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], renal cancer [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], glioma [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] and gastric cancer [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], and promotes the proliferation, invasion and migration of tumor cells. However, the role of FKBP10 in OSCC has not been reported yet.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eTissue specimens and cells\u003c/h2\u003e \u003cp\u003eOral squamous cell carcinoma and adjacent normal tissues were acquired from patients who underwent complete resection surgery at the Affiliated Stomatology Hospital of Jiamusi University and did not undergo preoperative radiation or chemotherapy. All specimens were immediately kept in liquid nitrogen after being obtained. The samples were used after being approved by the Medical Ethics Committee of the Affiliated Stomatology Hospital of Jiamusi University and with the informed consent of all patients.\u003c/p\u003e \u003cp\u003eBolson Biotechnology Co., Ltd. supplied the SCC-9 cells. The SCC-9 cells were grown in DMEM media containing 1% penicillin-streptomycin and 10% fetal bovine serum. All the cells were cultivated in a cell culture box at 37°C and 5% CO2.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eObtain public datasets of OSCC patients\u003c/h3\u003e\n\u003cp\u003eRetrieval of expression data for HNSC samples and OSCC samples from the Cancer Genome Atlas (TCGA) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cancergenome.nih.gov/\u003c/span\u003e\u003cspan class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was accomplished retrospectively. The two suitable HNSC cohorts were annotated and evaluated based on clinical features, containing 567 samples (522 tumor samples and 45 normal samples) from TCGA-HNSC (excluding data from the areas of hypopharynx, larynx, oropharynx, and tonsils). Download the transcriptome datasets (GSE74530, GSE31056, GSE85446) and spatial transcriptome data (GSE181300) from the GEO database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/\u003c/span\u003e\u003cspan class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Dataset download date, TCGA: August 10, 2024. GSE31056: August 7, 2024. GSE74530: August 7, 2024.GSE181300: September 6, 2024.\u003c/p\u003e\n\u003ch3\u003eWeighted Gene Co-Expression Network Analysis (WGCNA) and limma differential analysis\u003c/h3\u003e\n\u003cp\u003eThe R package \"WGCNA\" was used to discover biologically meaningful co-expression gene modules, and to study the link between the gene network and OSCC. First, every gene in GSE74530 was chosen for further examination. Second, a scale-free network was built using the \"pickSoftThreshold\" function to choose the ideal soft threshold power β between 1 and 20; the average connectivity R^2 threshold was set at 0.86. Then, the adjacency relationship was turned into a topological overlap matrix (TOM) to calculate the gene ratio and dissimilarity. Finally, hierarchical clustering and dynamic tree-cut methods were used to cut and identify the co-expression modules, and the co-expression modules were merged and assessed based on the similar expression patterns. MEDissThres was set to 0.4 and minModuleSize was set at 50. Gene significance (GS) and module membership (MM) were used to relate modules to illnesses. The genes with the strongest connection with the disease within the module were used for additional investigation. Finally, common genes were obtained by overlapping the pd-related and pss-related modules using the \"venn\" R program.\u003c/p\u003e \u003cp\u003eLimma is a method for differential expression screening based on generalized linear models. The dataset GSE31056 was subjected to differential analysis using the R software package \"limma\" (version 3.40.6) in order to determine which genes were differently expressed between tumor and normal tissues. Finally, we picked genes with a 1.5-fold difference for additional study.\u003c/p\u003e\n\u003ch3\u003emachine learning\u003c/h3\u003e\n\u003cp\u003eWe have integrated 10 machine learning algorithms, including Random Survival Forest (RSF), Elastic Net (Enet), Lasso, Ridge Regression, Stepwise Cox Regression (StepCox), Cox Boost, Cox Partial Least Squares Regression (plsRcox), Supervised Principal Component (SuperPC), Generalized Boosted Regression Modeling (GBM), and Survival Support Vector Machine (survival-svm). Based on these procedures, a consensus model was produced. Using the Leave-One-Out Cross-Validation (LOOCV) framework, 58 algorithm combinations were undertaken to match the prediction model. The TCGA-OSCC dataset was utilized as the training dataset, and the GSE85446 dataset was used as the external validation dataset. Furthermore, the consistency index (C-index) of each pattern in all validation datasets was determined. A total of 80 intersecting genes were included in the research. Based on the gene expression levels of distinct patterns, we used the linear combination function of each pattern to generate the risk score. The average C-index value of the training set and the validation set was the highest, and was eventually considered the ideal model.\u003c/p\u003e\n\u003ch3\u003eAnalysis of the correlation between KM and clinical factors, univariate and multivariate Cox analysis\u003c/h3\u003e\n\u003cp\u003eFollowing the identification of the best model, we separated the patients in the training and validation datasets into high-risk and low-risk groups using the training dataset's median risk score as the cutoff value. Kaplan-Meier (KM) survival analysis was undertaken for the two groups of patients using the \"survival\" R package. Additionally, we ran univariate and multivariate Cox analyses to show the independence of the model. We employed the Cox proportional hazards model (Cox Proportional Hazards Model) to assess the correlation between the risk score and survival time. By calling the coxph function, we created the Cox model and utilized the regplot tool to draw the column plot. This graph predicts survival and displays the distribution of risk ratings. We created the calibration curve in order to assess the column plot's predictive accuracy. We did survival analysis using the Cox model and calibrated the survival rates at 1 year, 3 years, and 5 years. The calibration curve at each time point was constructed using the calibrate function.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eSingle-cell transcriptome and spatial transcriptome analysis\u003c/h2\u003e \u003cp\u003eLoad the data using the Read10X function, generate Seurat objects (scRNA1 to scRNA8) for each sample using the Seurat (V4.3.0) package, and merge scRNA1 to scRNA8. Apply the Percentage FunctionFor quality control, set a function to determine the ratio of mitochondrial genes to erythrocyte genes. Use the NormalizeData function to standardize the data, locate variable genes and do data scaling. For PCA dimensionality reduction, use the RunPCA function. The FindNeighbors and FindClusters functions are used to locate the nearby points and do the clustering analysis. The UMAP dimensionality reduction is performed and shown using the RunUMAP function. The expression of marker genes for distinct cell types can be seen with DotPlot. Cell names are assigned depending on the clustering results. Specific cell types (such as fibroblasts) are selected for cell communication study. The CellChat software is used to construct cell communication objects, and overexpressed genes and ligand-receptor pairs are identified by extracting a subset supported by the database. Communication probabilities are calculated and filtered. Heatmaps and interaction graphs of cell communication are generated using the ktplots package to show the communication links between different cell types. Use the Load10X_Spatial function to read the spatial transcriptomic data (H5 file), use the GetTissueCoordinates function to obtain the tissue coordinates, mark whether each point in the sample is located on the tissue based on the tissue coordinates, filter out the points that are not on the tissue, perform SCT transformation standardization on the data, and use the NormalizeData function for standardization. Then, perform data scaling and principal component analysis (PCA), and apply UMAP for dimensionality reduction. Perform deconvolution analysis (RCTD) after loading the previously stored spatial transcriptomic and single-cell RNA sequencing data. Build the reference dataset and prepare the spatial data for RCTD analysis. Combine the findings with the spatial transcriptomic data's metadata. To see how particular cell types are distributed spatially, use SpatialFeaturePlot.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eGSEA enrichment analysis\u003c/h3\u003e\n\u003cp\u003eThe GSEA software (version 3.0) was obtained from the GSEA website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://software.broadinstitute.org/gsea/index.jsp\u003c/span\u003e\u003cspan class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The samples were separated into two groups based on cancer tissues and surrounding tissues. To assess the associated pathways and molecular mechanisms, the c2.cp.kegg.v7.4.symbols.gmt subset was downloaded from the Molecular Signatures Database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.gsea-msigdb.org/gsea/downloads.jsp\u003c/span\u003e\u003cspan class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Based on the gene expression profile and phenotypic grouping, a minimum gene set of 5 and a maximum gene set of 5000 were selected. A P value of less than 0.05 was deemed statistically significant after a thousand resamplings.\u003c/p\u003e\n\u003ch3\u003eCell transfection\u003c/h3\u003e\n\u003cp\u003eAs directed by lipo8000 (Biyuntian), 125 ul of serum-free medium + 4 ul of lipo8000 + 5 ul of interference reagent were combined and allowed to stand for 20 minutes when the cell concentration in the 6-well plate reached 60% to 80%. Then, 2 ml of the total liquid volume in the 6-well plate was added. After 6 hours, the cell status was detected. Sangon Biotech Co., Ltd. supplied the FKBP10 siRNA and the negative control siRNA. After transfection, the transfection efficiency was evaluated by qPCR and Western blot analysis. Primer sequence of FKBP10: FKBP10-229-s cuaccacuacaacggcacuuu, h FKBP10-229-a aaagugccguuguagugguag;h FKBP10-961-s ccacaccuacaauaccuauau, h FKBP10-961-a auauagguauuguaggugugg༛h FKBP10-712-s gaagauuaucaucccuccauu, h FKBP10-712-a aauggagggaugauaaucuuc。\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eReal-time fluorescence quantitative PCR\u003c/h2\u003e \u003cp\u003eThe RNA extraction kit (Sangon Biotech) was used to extract the total mRNA from SCC-9 cells following transfection. The concentration and purity of the RNA were evaluated by an ultramicro spectrophotometer. The RNA was reverse-transcribed into cDNA. The real-time fluorescence quantitative PCR kit was used for RT-PCR amplification. The results were examined using the 7300 instrument. The 2-ΔΔCt method was used to determine the relative expression levels of FKBP10 gene mRNA in each cell group (with GAPDH as the internal reference).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eWestern blot analysis\u003c/h2\u003e \u003cp\u003eGather the transfected SCC-9 cells, use protein lysate to extract total protein, and use the BCA method to calculate the extracted protein's concentration. Take 50 µg of protein for SDS-PAGE (10%) electrophoresis separation. Use the wet transfer technique to transfer the protein onto the PVDF membrane. For one hour, seal the membrane with 5% skim milk at 37°C. Place the membrane in the corresponding FKBP10 rabbit monoclonal antibody (1∶1000, ABclonal), POSTN mouse monoclonal antibody (1∶5000, Proteintech), ITGB5 mouse monoclonal antibody (1∶3000, Proteintech), WNT2 mouse monoclonal antibody (1∶25000, Proteintech), WISP1 mouse monoclonal antibody (1∶2000, Proteintech), and ACTIN mouse monoclonal antibody (1∶10000, Proteintech) for 4 hours at 4 ℃. Use TBS-T to wash out the unbound primary antibodies four times for five minutes each. Then incubate with horseradish peroxidase-labeled goat anti-mouse IgG at 37 ℃ for 1 hour. Wash out the unbound secondary antibody with TBS-T, and wash 4 times, each for 5 minutes. Use an automatic chemiluminescence/fluorescence image analysis device to view the results, then use ImageJ to analyze them.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eDetection of cell proliferation ability by CCK-8 method\u003c/h2\u003e \u003cp\u003eCell proliferation was detected using the Cell Counting Kit-8 (CCK-8 kit, Sangon Biotech). 100 µL of a 2000-cell density solution was applied to each well of a 96-well plate to inoculate SCC-9 cells. After then, si-FKBP10 was co-incubated with the cells for 0, 24, 48, and 72 hours. The control group was Negative Control. Each well received 100 µL of complete media containing 1% CCK-8, and the plates were incubated for two hours at 37°C. The OD values were recorded using an enzyme reader (Λllsheng) at a wavelength of 450 nm.\u003c/p\u003e \u003cp\u003e \u003cb\u003eThe colony formation assay detects the ability of cells to form colonies.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAfter 24 hours of cell transfection, the cells were centrifuged and digested. 500 cells were put into each 6-well plate. The cells were carefully pipetted to ensure uniform distribution. The cells were grown for 2 weeks. The cell culture was halted when obvious clone clusters could be observed. The culture medium was discarded, and the plates were washed twice with PBS. 1 mL of 4% paraformaldehyde was added for fixation for 30 minutes, then rinsed with PBS, then 1 mL of crystal violet was added for staining for 10 minutes. After a gentle rinse with tap water, the plates were left to air dry. The cells were photographed under a microscope and the number of cell clone forms in each group was tallied for statistical analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eTranswell invade\u003c/h2\u003e \u003cp\u003eDilute the Matrix-GelTM matrix gel with serum-free media to the working concentration (with a dilution ratio of 1:8). Spread out 60 µL of the matrix gel uniformly in the Transwell chamber's upper chamber. Put it in the incubator for three hours. Remove the unbound matrix gel, add 200 µL of serum-free media, and let it hydrate in a 37°C 5% CO2 cell incubator for 30 minutes. Fill the upper chamber with 200 µL of serum-free basic media that contains 50,000 SCC-9 cells. Then, 600 µL of complete medium containing 10% serum was introduced to the lower chamber. After 48 hours of cultivation, the cells were fixed with 4% paraformaldehyde for 30 minutes, stained with crystal violet for 30 minutes, and then photographed under a microscope for recording. The number of cells that had penetrated the membrane was counted, and the average value was determined.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003ecell scratch\u003c/h2\u003e \u003cp\u003eSaturate SCC-9 cells at an acceptable density in a 6-well plate. When the cell fusion rate reaches around 90% following transfection, replace the culture media with a serum-free baseline medium and use a 200/1000 µL pipette tip to generate consistent gaps. After the scratch, take images right away, and 24 hours later, record the cell placements once more. All photos are collected from 3 independent scratches using an inverted microscope and the average values are determined.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eFlow cytometry for cell cycle detection\u003c/h2\u003e \u003cp\u003eAfter 48 hours of transfection, SCC-9 cells were harvested. After one PBS wash, they were resuspended in a 4% paraformaldehyde solution and kept overnight at -20°C. During the detection process, after centrifugation, 500 µL staining buffer, 25 µL 20 × propidium iodide (PI) staining, and 10 µL 50 × RNase were added to each sample, and incubated at room temperature in the dark for 30 minutes. Finally, the analysis was performed using a flow cytometer.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eCalcein-AM/PI dyeing\u003c/h2\u003e \u003cp\u003eFollowing a 72-hour si-RNA transfection of SCC-9 cells, the cells were twice washed with PBS and the original culture media was discarded. 100 µL of Calcein-AM/PI staining mixture was applied to each well, and the cells were stained in the dark at 37 ℃ in a CO2 incubator for 30 minutes. The cells were then rinsed twice with PBS and photographed using a 20× inverted fluorescence microscope. Five fields of vision were randomly selected from each group for observation to track the changes in cell fluorescence intensity.\u003c/p\u003e \u003c/div\u003e "},{"header":"Result","content":"\u003cp\u003e \u003cb\u003e1 Bioinformatics analysis identifies target genes\u003c/b\u003e \u003c/p\u003e\u003cp\u003eThe GSE74530 was submitted to WGCNA co-analysis, resulting in the identification of 6 modules. Among them, the black module (R = 0.89, P = 1.1e-4) contained 737 genes positively connected with the disease (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eA). Using the R software package limma (version 3.40.6), a differential analysis was conducted on GSE31056. A 1.5-fold difference led to a total of 381 up-regulated genes and 723 down-regulated genes as the screening results (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eB). A total of 80 important genes were obtained by cross-matching the 737 genes in black with 1104 DEGs. Following machine learning, the LOOCV framework in TCGA-OSCC was used to fit these 80 core genes into 58 prediction models. The C-index of each model was determined in all validation datasets. It was noted that the best model combination was RSF and Enet (alpha = 0.1), with the greatest average C-index of 0.63 (containing SPOCK1, BLNK, BSPRY, PCDH17, C1orf116, FKBP10, RBM38, KRT14) (Fig.\u0026nbsp;2A). Moreover, it was revealed that the survival duration of the high-expression group was considerably shorter (Fig.\u0026nbsp;2B). The 1-year, 3-year and 5-year PFS data suggested that this model was a stable and effective prognostic tool with good specificity and sensitivity (Fig.\u0026nbsp;2C). Univariate and multivariate Cox regression studies demonstrated that this model, age, and stage could be independent predictive factors for OSCC patients (P \u0026lt; 0.05) (Fig.\u0026nbsp;2D and 2E). This model outperformed traditional clinical factors, according to the time-dependent C-index (Fig.\u0026nbsp;2F). Based on the current literature research, FKBP10 was finally picked as the final target gene for additional research examination.\u003c/p\u003e\u003cp\u003eFigure 2. Establishment and validation of the integration process based on machine learning. A: 58 prediction models were validated using the LOOCV framework, and the C-index of each model in all validation datasets was further calculated. B: KM curve. C: ROC curves showing 1-year, 3-year, and 5-year PFS. D: Univariate Cox analysis. E: Multivariate Cox analysis. F: Time-dependent C-index curve.\u003c/p\u003e\u003cp\u003e \u003cb\u003e2 Single-cell transcriptome and spatial transcriptome analysis\u003c/b\u003e \u003c/p\u003e\u003cp\u003ePrincipal component analysis (PCA) was used for dimension reduction, clustering, and UMAP visualization in the quality control and integration of single-cell transcriptome study. GSE181300 was categorized into 14 subcellular cell type clusters and annotated as 6 types of cells. FKBP10 was primarily expressed in fibroblasts (Figs.\u0026nbsp;3A and 3B). Significant variations in FKBP10 expression and macrophage communication were found by the cell communication study (Fig.\u0026nbsp;3C). Moreover, it can be noted that FKBP10 might interact with pathways such as POSTN-ITGAV-ITGB5 (Fig.\u0026nbsp;3D). Furthermore, the GSEA enrichment analysis also indicated that increased expression of FKBP10 would also activate ECM RECEPTOR INTERACTION (Fig.\u0026nbsp;3E). There was some overlap between FKBP10 and macrophages in the expression areas when they were deconvoluted onto the sections (Fig.\u0026nbsp;3F). When FKBP10 was deconvoluted onto the other four portions, it could be seen that there were considerable variances among the four sections as well as within each segment (Fig.\u0026nbsp;3G). These findings imply that FKBP10 is crucial for both patient prognosis and cell invasion. This stimulates further research of the carcinogenic effect of FKBP10.\u003c/p\u003e\u003cp\u003eFigure 3. Analysis of single-cell transcriptome and spatial transcriptome. A. Cell clusters after single-cell transcriptome annotation B. Main expression region of FKBP10 C. Cell communication between FKBP10 positive and negative cells D. Heatmap and interaction diagram of cell communication drawn by cellchat E. GSEA results of FKBP10 F. RCTD identifies the main distribution of FKBP10 and macrophages in the spatial map G. RCTD identifies the main distribution of FKBP10 in other spatial maps.\u003c/p\u003e\u003cp\u003e \u003cb\u003e3 Immune cell infiltration analysis\u003c/b\u003e \u003c/p\u003e\u003cp\u003eThe CIBERSORT algorithm was used to determine the percentage of immune cells infiltrated with FKBP10 (Fig.\u0026nbsp;4A). The distribution of immune cells varied between the two risk categories. The percentages of each type of immune cell in the two risk groups were then compared (Fig.\u0026nbsp;4B). The results showed that in the high-risk group, T cells CD4 memory resting, Macrophages M0, and Macrophages M2 were significantly enriched. Additionally, the correlation coefficients between the risk score and tumor-infiltrating immune cells assessed using seven different methodologies also demonstrated a strong positive link with Macrophages M2 (Fig.\u0026nbsp;4D). Additionally, the tumor immune microenvironment's ESTIMATEScore was statistically significant (Fig.\u0026nbsp;4C).\u003c/p\u003e\u003cp\u003eFigure 4 Immune cell infiltration analysis A Bar graph showing the proportions of tumor-infiltrating immune cells in TCGA by CIBERSORT. B Differential graph of immune cells between high and low expression groups C Tumor immune microenvironment analysis D Correlation between different immune cells and risk scores calculated by seven different algorithms *p \u0026lt; 0.05; **p \u0026lt; 0.01; ***p \u0026lt; 0.001.\u003c/p\u003e\u003cp\u003e \u003cb\u003e4 Expression of FKBP10 in OSCC and Identification of Cell Transfection\u003c/b\u003e \u003c/p\u003e\u003cp\u003eThree pairs of paired oral squamous cell carcinoma (T) and normal tissues surrounding the cancer (N) were randomly selected from the sample collection. The three pairs of paired tissues were examined for FKBP10 expression using immunofluorescence. The results showed that the relative expression level of FKBP10 in all OSCC tissues was higher than that in the neighboring normal tissues (Fig.\u0026nbsp;5A), suggesting that FKBP10 plays a role as a cancer gene in OSCC. According to the RT-qPCR data, the transfected group's FKBP10 expression level was significantly lower than that of the control group, and the si-FKBP10-1 group had the highest transfection efficiency (Fig.\u0026nbsp;5B). The Western blot results were comparable with the RT-PCR results, and the protein expression level of FKBP10 in the si-FKBP10-1 group was the lowest (Fig.\u0026nbsp;5C). Therefore, the si-FKBP10-1 was employed for transfection in the future investigations.\u003c/p\u003e\u003cp\u003eFigure 5. Organizational expression and transfection. A: Expression of FKBP10 in OSCC. Blue represents DAPI, red represents FKBP10. B: After transfection with interfering RNA, the mRNA expression in the si-FKBP10-1 group was the lowest, ****p \u0026lt; 0.0001. C: The protein expression in the si-FKBP10-1 transfection group was the lowest, ****p \u0026lt; 0.0001.\u003c/p\u003e\u003cp\u003e \u003cb\u003e5 Effects of FKBP10 knockdown on proliferation, migration, invasion, cycle and apoptosis of SCC-9 cells\u003c/b\u003e \u003c/p\u003e\u003cp\u003eThe CCK-8 results showed that the cell proliferation ability of the si-FKBP10-1 transfection group was significantly lower than that of the control group (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eA); the cell clone formation experiment results indicated that after knockdown of FKBP10 expression, the clone formation ability of SCC-9 cells significantly decreased (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eB); thus, it can be seen that the cell proliferation was inhibited after the expression of FKBP10 was knocked down. The Transwell invasion and scratch assays indicated that the cell invasion and migration capacities of the si-FKBP10-1 transfection group were greatly suppressed (Figs.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eC and \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eD), and the results were statistically significant. During this process, one or more stages of the cell cycle may be disturbed and halted. The results of flow cytometry analysis suggested that the cells in the si-FKBP10 group were in a trend of G0/G1 phase arrest and a drop in S phase cells (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eE), confirming that FKBP10 can boost cell proliferation by regulating the cell cycle. Calcein-AM may penetrate the cell membrane and stain live cells, showing green fluorescence. PI can stain dead cells and show red fluorescence. Therefore, when Calcein-AM is coupled with PI, it may conduct dual fluorescence labeling on both living and dead cells. After the expression level of FKBP10 declines, the number of cells emitting red fluorescence increases, demonstrating that knocking down FKBP10 can induce death in SCC-9 cells (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eF).\u003c/p\u003e\u003cp\u003e \u003cb\u003e6 Effects of FKBP10 knockdown on related pathway proteins\u003c/b\u003e \u003c/p\u003e\u003cp\u003eFirst, we investigated the associated mechanisms of apoptosis and cell cycle arrest in SCC-9 cells brought on by FKBP10 knockdown. The main proteins of the WNT pathway were found by WB. The results showed that the expression levels of periostin (POSTN), integrin ITGB5 and WNT pathway-related proteins were decreased following FKBP10 knockdown (Fig.\u0026nbsp;7A). Thus, FKBP10 may influence apoptosis and G0/G1 phase arrest via the WNT pathway. \u003c/p\u003e\u003cp\u003eFigure 7. The protein expression levels of POSTN, ITGB5, WNT2, and WISP1 in the FKBP10 knockdown group were significantly lower than those in the control group (***P \u0026lt; 0.001).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eEarly research on FKBP 10 mostly focused on pulmonary fibrosis and osteochondrodysplasia. Bruck syndrome is an extremely uncommon hereditary condition marked by osteoporosis, gradual joint contractures, small stature, and increased fracture risk. The mutation mostly affects the FKBP10 gene on chromosome 17p12, and it is inherited in an autosomal recessive fashion[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. A number of uncommon autosomal recessive phenotypes, such as osteogenesis imperfecta type XI (OI XI), Brook's syndrome type I (BS I), and a congenital arthrogryposis-like phenotype (AG), can result from pathogenic variations of the FKBP10 gene[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. What is essential is that the absence of FKBP 10 expression dramatically limits the secretion of collagen by primary human lung fibroblasts[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eResearch on the connection between FKBP 10 and cancer has been done recently. FKBP 10 regulates the folding, transport and secretion of proteins during the synthesis of extracellular matrix proteins[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. FKBP10 is considerably enhanced in colorectal cancer tissues and exhibits three unique subcellular expression patterns, designated as \"FKBP10-C\" (concentrated), \"FKBP10-T\" (transitional), and \"FKBP10-D\" (dispersed). Among these, the FKBP10-D expression pattern is only observed in tumor tissues and is associated with poor prognosis in CRC patients[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. FKBP10 plays a vital part in the malignant transition process from normal tissues to ductal carcinoma in situ (DCIS) and invasive ductal carcinoma (IDC) by preventing the ubiquitination of β-catenin [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. FKBP10 interacts with pre-lamin A and prevents pre-lamin A from entering the nucleus, thereby reducing nuclear lamin A. This enhances the nuclear atypia of bladder cancer cells. Studies have shown that the FKBP10/prelamin A/lamin A axis leads to MIBC (muscle-invasive bladder cancer)[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eUsing WGCNA in GSE74530, the black module in this study was found to be substantially linked with the tumor phenotype (with a R value of 0.89), and by intersecting with the differentially expressed genes in GSE31056, 80 potential core genes were found. Further, utilizing the LOOCV framework in the TCGA-OSCC cohort, 58 types of prediction models were trained and compared. Finally, a reliable prognostic model combination (RSF and Enet) including FKBP10 was produced (with an average C-index of 0.63), and the survival outcome of the high-risk group was worse. This implies that FKBP10 may be associated to the poor prognosis of OSCC. Single-cell transcriptome analysis then showed that FKBP10 was mostly enriched in the fibroblast population, which is extremely compatible with earlier research demonstrating that FKBP10 is an endoplasmic reticulum chaperone protein and takes role in ECM remodeling and collagen maturation. More importantly, cell communication research suggested that the FKBP10-related signals had significant disparities in communication with macrophages and may include the POSTN\u0026ndash;ITGAV\u0026ndash;ITGB5 axis. Spatial transcriptomic deconvolution further revealed that the expression regions of FKBP10 and macrophage expression regions had a substantial degree of overlap. Combined with the results of immune cell infiltration analysis: high expression of FKBP10 was significantly positively correlated with Macrophages M2, and was statistically significant in the ESTIMATE score and GSEA results: high expression of FKBP10 activated ECM RECEPTOR INTERACTION, WNT SIGNALING PATHWAY, and FOCAL ADHESION. This suggests that FKBP10 may be implicated in ECM and WNT SIGNALING PATHWAY, consequently impacting the occurrence and progression of OSCC.\u003c/p\u003e \u003cp\u003eAs essential receptors that facilitate cell adhesion and signal transmission, integrins have been thoroughly investigated and shown to have multifaceted regulatory roles in the formation and occurrence of tumors. In order to regulate complex cellular biological behaviors like cell survival, proliferation, migration, and different cell fate decisions, this receptor can mediate the rearrangement of the cytoskeleton and activate downstream intracellular signaling pathways when it binds to the extracellular matrix (ECM)[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Collagen's extracellular matrix cross-linking can be controlled by FKBP10. Silencing FKBP10 in GC cells and altering the expression of the integrin family members αV and α6 can prevent the adherence of GC cells[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. The extracellular matrix (ECM) components such as fibronectin (FN) and integrins interact with each other, controlling carcinogenic signal transduction and encouraging the formation of OC (ovarian cancer) spheroids through the Wnt/β-catenin pathway[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. According to the aforementioned hypothesis, FKBP10 may control the adhesion signals between integrins and extracellular matrix, which could impact the downstream WNT signaling pathway, mediate M2-type macrophage recruitment, homing, and polarization, and ultimately modify the tumor immune microenvironment.\u003c/p\u003e \u003cp\u003eAccording to this study, OSCC tissues had substantially greater levels of FKBP10 expression than nearby tissues. After suppression of FKBP10, the proliferation ability of SCC-9 cells was decreased. Through flow cytometry, it was determined that the cell cycle was aberrant. After suppression of FKBP10, the cell cycle was halted at the G1 phase, and the live/dead cell labeling suggested that the number of live cells was reduced while the number of dead cells rose. An extracellular matrix-related secreted protein called POSTN is crucial to the extracellular matrix's development, remodeling, and operation[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], It interacts with receptors such as integrins on the cell surface, thereby affecting cell behavior[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. WISP1 (WNT1-inducible signaling protein 1), also known as CCN4, is a protein belonging to the CCN family and is a secreted protein that can interact with the extracellular matrix[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. WISP1 is a key downstream effector molecule of the WNT signaling pathway. Its expression is regulated by this route and can stimulate the expression of Cyclin D1 [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e], while Cyclin D1 is a crucial factor for transitioning from the G1 phase to the S phase [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Therefore, it can be extrapolated that WISP1 is directly associated to the G1 phase arrest and growth of malignancies. After knocking down FKBP10 in SCC-9 cells, the expressions of POSTN, ITGB5, WNT2, and WISP1 dramatically dropped, and the cell migration and invasion abilities were hindered. In conclusion, FKBP10 may affect cell proliferation and a series of malignant biological behaviors through the ECM/WNT signaling pathway. However, further in vivo studies are needed to confirm whether FKBP10 is involved in the cancer development process of oral squamous cell carcinoma.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOSCC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOral squamous cell carcinoma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHNSCC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHead and neck squamous cell carcinoma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOverall survival rate\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOLK\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOral leukoplakia\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePPIase\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePeptidyl-prolyl isomerase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNF-κB\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNuclear factor κB\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eECM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eExtracellular matrix\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTCGA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eThe Cancer Genome Atlas\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNCBI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNational Center for Biotechnology Information\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGEO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGene Expression Omnibus\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eWGCNA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWeighted Gene Co-Expression Network Analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGene significance\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eModule membership\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDEGs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDifferentially Expressed Genes\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRSF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRandom Survival Forest\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eEnet\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eElastic Net\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLOOCV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLeave-One-Out Cross-Validation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eC-index\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eConsistency index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eKM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eKaplan-Meier\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePCA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePrincipal Component Analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eUMAP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eUniform Manifold Approximation and Projection\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRCTD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRobust Cell Type Decomposition\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGSEA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGene Set Enrichment Analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003esiRNA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSmall interfering RNA\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eqPCR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eQuantitative Polymerase Chain Reaction\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRT-PCR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eReverse Transcription-Polymerase Chain Reaction\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSDS-PAGE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSodium Dodecyl Sulfate-Polyacrylamide Gel Electrophoresis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePVDF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePolyvinylidene Fluoride\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCCK-8\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCell Counting Kit-8\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePropidium iodide\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOptical Density\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTBS-T\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTris-Buffered Saline with Tween-20\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCalcein-AM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCalcein Acetoxymethyl Ester\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eESTIMATE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEstimation of Stromal and Immune cells in Malignant Tumor tissues using Expression data\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDCIS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDuctal carcinoma in situ\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIDC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInvasive ductal carcinoma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMIBC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMuscle-invasive bladder cancer\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGastric Cancer\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOvarian Cancer\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFN\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFibronectin\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePOSTN\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePeriostin\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eITGB5\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eIntegrin Subunit Beta 5\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eWNT2\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWnt Family Member 2\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eWISP1\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWNT1-inducible signaling protein 1\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThis research was funded by the Basic Research Project of the Education Department of Heilongjiang Province, with the project number: 2024-KYYWF-0585.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInstitutional Review Board Statement:\u003c/strong\u003e This research was approved by the Ethics Committee of the Affiliated Stomatological Hospital of Jiamusi University. Ethical Approval Number: KQYXY-2025-XS-M025. All the methods in this article were carried out in accordance with the relevant guidelines and regulations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed Consent Statement:\u003c/strong\u003e Informed consent was obtained from all subjects involved in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement:\u003c/strong\u003e All data included in the article are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest:\u003c/strong\u003e The authors declare no conflicts of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWang, P., Tian, J., Zheng, G., Chen, Y. \u0026amp; Ren, X. Application of artificial intelligence in head and neck squamous cell carcinoma. \u003cem\u003eAnn. 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Oncol.\u003c/em\u003e \u003cb\u003e10\u003c/b\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fonc.2020.01385\u003c/span\u003e\u003cspan address=\"10.3389/fonc.2020.01385\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"OSCC, FKBP10, WNT pathway, tumor immune microenvironment","lastPublishedDoi":"10.21203/rs.3.rs-9094874/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9094874/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eOral squamous cell carcinoma (OSCC) is a frequent malignant tumor in the oral and maxillofacial region with a poor prognosis, and its pathophysiology has not been fully understood. Although FKBP10 is overexpressed in a number of malignancies, its function and regulation mechanism in OSCC are yet unknown. Method༚Collect OSCC and normal tissue samples adjacent to the cancer, combine with public datasets such as TCGA and GEO, and use bioinformatics methods such as WGCNA, limma differential analysis, and machine learning to screen target genes; through single-cell and spatial transcriptome analysis, clarify the main expression area of FKBP10 and its distribution in macrophages; use CCK-8, cell clone formation, Transwell, cell scratch, and flow cytometry experiments to detect the effects of FKBP10 knockdown on the biological behaviors of SCC-9 cells; use Western blot and GSEA to explore its potential molecular mechanism. Result༚Bioinformatics research identified FKBP10 as a significant target gene for OSCC. It is mostly abundant in fibroblasts and is significantly expressed in OSCC tissues. It has a strong positive correlation with M2-type macrophage infiltration and is linked to a negative outcome for patients. FKBP10 knockdown can cause cell apoptosis, stop the cell cycle at the G0/G1 phase, and prevent SCC-9 cells from proliferating, migrating, and invading. At the same time, it down-regulates the expression of POSTN, ITGB5, and WNT pathway-related proteins. Conclusion༚FKBP10 may influence the recruitment of M2-type macrophages by regulating the ECM/integrin adhesion signal and the WNT pathway, thereby changing the tumor immune milieu and encouraging the occurrence and progression of OSCC. It is projected to become a promising biomarker and target for prognostic evaluation and targeted therapy of OSCC.\u003c/p\u003e","manuscriptTitle":"FKBP10 may affect the malignant phenotype of oral squamous cell carcinoma cells through the ECM/WNT signaling pathway","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-22 05:49:03","doi":"10.21203/rs.3.rs-9094874/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-05-04T03:44:20+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-01T08:16:46+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-30T23:50:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"38809688829682142534936188957918393668","date":"2026-04-16T13:24:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"299615605040649383956442023098372870925","date":"2026-04-14T15:00:01+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-14T13:13:46+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-14T13:07:41+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-23T16:59:23+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-21T09:11:25+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-03-21T09:06:41+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6974c55f-1b73-4cb2-abdb-4602f2c0523b","owner":[],"postedDate":"April 22nd, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-04T03:44:20+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-01T08:16:46+00:00","index":95,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-30T23:50:12+00:00","index":94,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":66562870,"name":"Health sciences/Biomarkers"},{"id":66562871,"name":"Biological sciences/Cancer"},{"id":66562872,"name":"Biological sciences/Cell biology"},{"id":66562873,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":66562874,"name":"Health sciences/Oncology"}],"tags":[],"updatedAt":"2026-05-17T14:53:03+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-22 05:49:03","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9094874","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9094874","identity":"rs-9094874","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}