{"paper_id":"28e2cc05-a3ac-4e0b-80d2-f94b515b08ed","body_text":"Expression and Clinical Significance of IBSP in Head and Neck Squamous Cell Carcinoma: A Bioinformatics Analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Expression and Clinical Significance of IBSP in Head and Neck Squamous Cell Carcinoma: A Bioinformatics Analysis Jun Li, Fang Wang, Jianyang Sun, Yongqiang Shi, Geng Deng, Hongxing Min This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6490272/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Head and neck squamous cell carcinoma (HNSC) poses considerable clinical challenges, characterized by high incidence and mortality rates, which underscores the urgent need for novel biomarkers to enhance diagnosis and prognosis. Methods This study explores the expression of integrin-binding sialoprotein (IBSP) in HNSC, employing RNA sequencing data sourced from The Cancer Genome Atlas (TCGA) databases to investigate its potential role. Results We conducted an analysis of IBSP expression in a cohort of 502 patients with head and neck squamous cell carcinoma (HNSC) and 44 normal controls, which revealed that IBSP levels were significantly higher in HNSC tissues (P < 0.001). This finding was further validated through paired comparison analysis. Additionally, receiver operating characteristic (ROC) curve analysis indicated that IBSP has strong diagnostic potential, achieving an area under the curve (AUC) of 0.929. Kaplan-Meier survival analysis demonstrated that elevated IBSP expression is associated with poorer overall survival (OS), progression-free interval (PFI), and disease-specific survival (DSS) among HNSC patients. Subgroup analyses revealed significant links between high IBSP expression and negative outcomes across various patient demographics and clinical stages. Both univariate and multivariate Cox regression analyses identified IBSP as an independent prognostic factor. Furthermore, single-sample gene set enrichment analysis (ssGSEA) showed a positive correlation between IBSP expression and immune cell infiltration, specifically by natural killer (NK) cells and macrophages. Conclusion The findings indicate that IBSP may serve as a valuable biomarker for diagnosing head and neck squamous cell carcinoma (HNSC), while also acting as a prognostic indicator and influencing the tumor immune microenvironment. Future studies should aim to clarify the mechanistic pathways through which IBSP operates in HNSC and investigate its potential as a therapeutic target to improve patient outcomes. IBSP head and neck squamous cell carcinoma immune infiltration clinical significance bioinformatics Figures Figure 1 Figure 2 Figure 3 Figure 4 INTRODUCTION Head and neck squamous cell carcinoma (HNSC) poses a significant public health challenge due to its high rates of incidence and mortality, leading to considerable economic burdens on healthcare systems and a reduced quality of life for those affected [ 1 – 3 ]. The current treatment options for HNSC, which include surgery, radiotherapy, and chemotherapy, often show limited effectiveness and come with substantial side effects. This situation highlights the urgent need for new biomarkers and therapeutic targets to improve patient outcomes. Previous research has suggested that integrin-binding sialoprotein (IBSP) may play a crucial role in tumor progression and metastasis in various cancers, indicating a possible connection between IBSP expression and the underlying mechanisms of HNSC. However, the specific relationship between IBSP levels and clinical characteristics in HNSC has not been thoroughly investigated, revealing a significant gap in research that this study intends to fill. By examining the expression of IBSP in HNSC and its correlation with clinical features and prognosis, this research aims to clarify the potential of IBSP as a valuable biomarker that could enhance diagnostic and therapeutic approaches in the management of HNSC. The present study explores the expression levels of Integrin Binding Sialoprotein (IBSP) in Head and Neck Squamous Cell Carcinoma (HNSC), emphasizing its potential as a new biomarker for this type of cancer. Previous studies have shown that IBSP is significantly involved in tumor progression and metastasis in various cancers [ 4 , 5 ], indicating its important role in the underlying mechanisms of HNSC. Our results demonstrate that IBSP expression is significantly higher in HNSC tissues compared to normal tissues, suggesting a strong link to the disease. Additionally, the relationship between IBSP expression and various clinicopathological features, as well as patient prognosis, highlights its potential use in clinical practice. The thorough statistical analyses conducted in this study, which include survival analysis and assessments of immune cell infiltration, provide strong evidence for the significance of IBSP in HNSC. This research not only enhances our understanding of the biology of HNSC but also paves the way for future studies focused on the mechanisms of IBSP and its potential therapeutic applications, ultimately aiming to improve patient outcomes. This study utilizes RNA sequencing data analysis alongside various bioinformatics techniques, such as survival analysis and immune infiltration assessment, to thoroughly investigate the expression of Integrin Binding Sialoprotein (IBSP) in Head and Neck Squamous Cell Carcinoma (HNSC) and its clinical implications. A key strength of this approach is the use of extensive data from The Cancer Genome Atlas (TCGA), which bolsters the reliability and applicability of the results. The main goal of this research is to explore the relationship between IBSP expression levels and clinical features, as well as patient prognosis in those with HNSC, highlighting its potential as a biomarker. By merging detailed data analysis with clinical perspectives, this study seeks to enhance the understanding of IBSP's function in HNSC, ultimately aiding in the discovery of new therapeutic targets and improving patient outcomes. MATERIALS AND METHODS RNA-seq data and bioinformatics analysis This investigation utilized data obtained from the TCGA database ( https://portal.gdc.cancer.gov ). RNA-sequencing datasets from the TCGA Head and Neck Squamous Cell Carcinoma project were processed through the STAR workflow. Subsequently, data were extracted in the Transcripts Per Million (TPM) format to facilitate the acquisition of gene expression and clinical data. Data processing and visualization were conducted utilizing R software (version 4.2.1), during which entries that were clinically irrelevant or duplicates were eliminated. Statistically appropriate methods were employed, utilizing the R packages 'stats' and 'car', in accordance with the characteristics of the data. For data visualization, the R package 'ggplot2' was employed. This study adhered to the principles outlined in the Declaration of Helsinki (revised in 2013) and complied with the publication guidelines established by TCGA. Notably, the authors did not conduct any research involving human or animal subjects. Receiver operating characteristic curve analysis The Receiver Operating Characteristic (ROC) curve analysis represents a statistical technique employed to assess the efficacy of binary classification systems. This method is particularly advantageous for evaluating the sensitivity and specificity of diagnostic tests. The ROC curve graphically represents the true positive rate (sensitivity) in relation to the false positive rate (1 - specificity) across a spectrum of threshold values. Data analysis and visualization were executed utilizing R software (version 4.2.1). We applied the R package ‘pROC [1.18.0]’ to conduct the ROC analysis on the processed dataset. The results were depicted using the R package ‘ggplot2’. Kaplan–Meier curve analysis Data analysis and visualization were conducted utilizing R software (version 4.2.1). The 'survival' package facilitated the Kaplan-Meier (KM) analysis of the processed dataset, while the visualization of the results was achieved through the 'survminer' package. Immune infiltration analysis The immune infiltration matrix data were sourced from the online repository of Xiantao Academic ( https://www.xiantao.love ). We performed an analysis to investigate the correlations between the main variables and the matrix data within the preprocessed dataset, utilizing R software (version 4.2.1). The results were then illustrated through visualizations created with the R package 'ggplot2'. Statistical analysis All statistical analyses were performed utilizing R software (version 4.2.1), which incorporated the Kruskal–Wallis test to assess the expression levels of IBSP in both head and neck squamous cell carcinoma tissues and normal tissues. A p-value of less than 0.05 was deemed to be statistically significant. The thresholds for p-values were established as follows: *P < 0.05, **P < 0.01, and ***P < 0.001. RESULTS Differences in IBSP expression between Head and neck squamous cell carcinoma and normal tissues The Cancer Genome Atlas (TCGA) provided data that facilitated an examination of IBSP expression levels across various cancer types. The analysis revealed significant variations in IBSP expression among distinct tumor categories (P < 0.001, Fig. 1 A). We gathered gene expression profiles from 502 patients diagnosed with Head and Neck Squamous Cell Carcinoma (HNSC) alongside 44 normal control subjects, all of which were obtained from TCGA databases. The findings demonstrated that IBSP expression was markedly elevated in HNSC patients compared to normal controls (P < 0.001, Fig. 1 B). Subsequently, a paired comparison analysis involving 43 HNSC patients and 43 normal controls was conducted. This analysis further confirmed that IBSP expression was significantly increased within the HNSC cohort (P < 0.001, Fig. 1 C). The relationship between IBSP expression and HNSC was thoroughly validated employing two comparative approaches. Finally, we executed a receiver operating characteristic (ROC) curve analysis to evaluate IBSP's effectiveness in distinguishing tumor tissues from non-tumor tissues. The calculated area under the curve (AUC) for IBSP was 0.929 (CI = 0.900–0.958), indicating that IBSP is highly proficient in differentiating tumor tissues from non-tumor tissues (Fig. 1 D). The Association Between IBSP Expression and Clinicopathological Features in Head and neck squamous cell carcinoma We conducted a statistical analysis of the baseline characteristics of 504 individuals diagnosed with head and neck squamous cell carcinoma, utilizing data sourced from The Cancer Genome Atlas (TCGA) database (refer to Table 1). RNA sequencing data pertinent to the TCGA-HNSC project were procured and systematically organized through the STAR pipeline, accessible via the TCGA database ( https://portal.gdc.cancer.gov ). The retrieved data were formatted in Transcripts Per Million (TPM) and incorporated essential clinical details. During the data preprocessing phase, normal samples and those devoid of clinical information were excluded. The final dataset was categorized into two distinct groups: one comprising 252 patients with low expression levels of IBSP and the other containing 252 patients with high IBSP expression. Statistical analyses indicated no significant differences between the two cohorts regarding gender (P = 0.840), age (P = 0.141), Pathologic T stage (P = 0.345), or Pathologic M stage (P = 1.000). However, noteworthy differences were identified in patient distributions relative to N stages (P = 0.010) and Pathologic stage (P = 0.009). Furthermore, significant discrepancies were noted between the two groups concerning Histologic grade (P = 0.046). Tabel 1 Correlation between IBSP expression and clinicopathologic characteristics of HNSC. Characteristics Low expression of IBSP High expression of IBSP P value n 252 252 Gender, n (%) 0.840 Female 68 (13.5%) 66 (13.1%) Male 184 (36.5%) 186 (36.9%) Age, n (%) 0.141 <= 60 132 (26.2%) 115 (22.9%) > 60 120 (23.9%) 136 (27%) Pathologic T stage, n (%) 0.345 T1 26 (5.8%) 19 (4.2%) T2 68 (15.2%) 67 (15%) T3 51 (11.4%) 45 (10%) T4 77 (17.2%) 95 (21.2%) Pathologic N stage, n (%) 0.010 N0 93 (22.6%) 78 (19%) N1 42 (10.2%) 24 (5.8%) N2 69 (16.8%) 98 (23.8%) N3 3 (0.7%) 4 (1%) Pathologic M stage, n (%) 1.000 M0 102 (54%) 86 (45.5%) M1 1 (0.5%) 0 (0%) Pathologic stage, n (%) 0.009 Stage I 15 (3.4%) 10 (2.3%) Stage II 34 (7.8%) 36 (8.3%) Stage III 51 (11.7%) 28 (6.4%) Stage IV 115 (26.4%) 147 (33.7%) Histologic grade, n (%) 0.046 G1 39 (8.1%) 23 (4.8%) G2 151 (31.2%) 150 (31%) G3 52 (10.7%) 67 (13.8%) G4 0 (0%) 2 (0.4%) The TCGA database was utilized to investigate the association between clinical features and IBSP expression levels in patients diagnosed with Head and Neck Squamous Cell Carcinoma (HNSC). This examination concentrated on the correlation between IBSP expression and four distinct clinicopathological parameters: T-stage, N-stage, M-stage, and pathological stage (Fig. 2 ). Initially, we assessed the relationship between IBSP and the different pathological stages of HNSC by analyzing its expression levels in samples classified by these stages. The results indicated a notable association between increased IBSP expression and the T stage, revealing that levels were significantly higher in HNSC samples across the four T stages when compared to normal head and neck tissues; however, no substantial differences were observed among the samples within the four T stages (Fig. 2 A). Furthermore, IBSP expression was significantly elevated in HNSC samples across all four N stages relative to normal head and neck tissues, although no significant differences were detected among the samples of the four N stages (Fig. 2 B). In terms of M0 stages, IBSP expression was markedly greater in HNSC samples compared to normal head and neck tissues (Fig. 2 C). Additionally, IBSP expression was significantly heightened in HNSC samples across the four pathological stages when compared to normal head and neck tissues, with particularly noteworthy differences observed between stage III and stage IV samples (Fig. 2 D). Overall, these results indicate that IBSP is significantly upregulated in HNSC and correlates with various clinical characteristics. High IBSP expression affects the prognosis of Head and neck squamous cell carcinoma patients in various clinical and pathological stages The Kaplan-Meier survival analysis revealed that elevated levels of IBSP expression were linked to unfavorable prognostic outcomes in patients diagnosed with head and neck squamous cell carcinoma, particularly regarding overall survival (OS) (HR = 1.45, P = 0.027), progression-free interval (PFI) (HR = 1.44, P = 0.020), and disease-specific survival (DSS) (HR = 1.85, P = 0.002) as illustrated in Figs. 3 A-C. Further subgroup analyses categorized patients according to various clinicopathological features. These investigations indicated that high IBSP expression was notably correlated with adverse prognostic results in head and neck squamous cell carcinoma, especially in: individuals aged over 60 years (HR = 1.93, P = 0.001), female patients (HR = 3.03, P = 0.004), those classified with T3 status (HR = 1.92, P = 0.049), patients exhibiting M0 status (HR = 1.81, P = 0.024), individuals in stage III (HR = 2.61, P = 0.028), and smokers (HR = 1.41, P = 0.033). The pertinent data is presented in Figs. 3 D-I. Tabel 2 Univariate and multivariate Cox regression analyses of prognostic factors for OS in HNSC. Characteristics Total(N) Univariate analysis Multivariate analysis Hazard ratio (95% CI) P value Hazard ratio (95% CI) P value Gender 503 Female 134 Reference Reference Male 369 0.760 (0.571–1.012) 0.061 0.670 (0.371–1.209) 0.183 Age 503 <= 60 247 Reference Reference > 60 256 1.262 (0.964–1.653) 0.090 0.964 (0.553–1.683) 0.898 Pathologic T stage 447 T1&T2 179 Reference Reference T3&T4 268 1.934 (1.413–2.649) < 0.001 2.432 (1.092–5.417) 0.030 Pathologic N stage 410 N0&N1 236 Reference Reference N2&N3 174 2.296 (1.686–3.127) < 0.001 2.662 (1.529–4.635) < 0.001 Pathologic M stage 188 M0 187 Reference Reference M1 1 22.631 (2.830–180.948) 0.003 19.894 (2.224–177.930) 0.007 Pathologic stage 435 Stage I&Stage II 94 Reference Reference Stage III&Stage IV 341 1.839 (1.236–2.737) 0.003 1.409 (0.346–5.743) 0.632 Histologic grade 483 G1&G2 362 Reference G3&G4 121 0.942 (0.690–1.286) 0.706 We employed both univariate and multivariate Cox proportional hazards regression models to ascertain independent prognostic determinants influencing head and neck squamous cell carcinoma, with a particular focus on T stage, N stage, M stage, and pathologic stage. The results from the univariate analysis revealed a noteworthy correlation between overall survival (OS) and various factors: T1 and T2 stages compared to T3 and T4 stages (HR = 1.934, 95% CI [1.413–2.649], P < 0.001), N0 and N1 stages as opposed to N2 and N3 stages (HR = 2.296, 95% CI [1.686–3.127], P < 0.001), M0 stage versus M1 stage (HR = 22.631, 95% CI [2.830–180.948], P = 0.003), and stage I and stage II compared to stage III and stage IV (HR = 1.839, 95% CI [1.236–2.737], P = 0.003). Following this, we executed a multivariate analysis, which indicated that T1 and T2 stages relative to T3 and T4 stages (HR = 2.432, 95% CI [1.092–5.417], P = 0.030), N0 and N1 stages against N2 and N3 stages (HR = 2.662, 95% CI [1.529–4.635], P < 0.001), and M0 stage in comparison with M1 stage (HR = 19.894, 95% CI [2.224–177.930], P = 0.007) were all significantly associated with overall survival (Table 2). Correlation between IBSP expression and immune infiltration We employed the single-sample gene set enrichment analysis (ssGSEA) technique to explore the association between the expression levels of IBSP and 24 distinct types of immune cells involved in immune infiltration. Our results were then illustrated visually. In Fig. 4 A, we present a graphical depiction where the diameter of each circle reflects the intensity of the correlation between immune cell enrichment and IBSP expression levels. The color coding of the circles corresponds to the respective P-values. Additionally, the proximity of each circle to the baseline signifies the strength of the correlation between immune cell enrichment and IBSP expression. Subsequently, we pinpointed the two immune cell types that demonstrated the strongest correlations along with statistically significant differences in their P-values for further analysis. Notably, we identified a positive correlation between IBSP expression and levels of immune infiltration across several cell types, including NK cells (R = 0.347, P < 0.001) and macrophages (R = 0.337, P < 0.001) as illustrated in Figs. 4 B-C. Our statistical evaluations indicated that NK cells and macrophages exhibited significantly elevated infiltration levels in samples characterized by high IBSP expression when compared to those with lower expression levels (P < 0.001) as shown in Figs. 4 D-E. DISCUSSION Head and neck squamous cell carcinoma poses a significant global health challenge, marked by high rates of incidence and mortality [ 6 ]. This type of cancer not only places a heavy economic strain on healthcare systems but also greatly diminishes the quality of life for those affected [ 7 , 8 ]. Current treatment options, such as surgery, radiation therapy, and chemotherapy, often show limited effectiveness and come with considerable side effects [ 9 , 10 ]. Therefore, there is an urgent need to identify new biomarkers and therapeutic targets that can improve patient outcomes and enhance overall quality of life. Gaining a deeper understanding of the molecular mechanisms behind HNSC is essential for developing more effective diagnostic and treatment strategies [ 11 , 12 ]. In this study, we focused on the role of Integrin Binding Sialoprotein in HNSC by utilizing extensive RNA sequencing data from The Cancer Genome Atlas (TCGA) and applying advanced bioinformatics techniques. Our results indicate a significant increase in IBSP expression in HNSC tissues compared to normal controls, and we found a strong link between high levels of IBSP and poor clinical outcomes. Additionally, we investigated the relationship between IBSP expression and immune cell infiltration, which may have important implications for the tumor microenvironment. These findings highlight the potential of IBSP as a valuable biomarker for HNSC and open avenues for further research into its mechanistic role and possible therapeutic targeting in this cancer type. The variance analysis results from our study indicate significant differences in IBSP expression levels between patients with head and neck squamous cell carcinoma (HNSC) and normal controls, highlighting the potential of IBSP as a biomarker in the development of HNSC. Specifically, we observed a substantial increase in IBSP expression in HNSC patients (P < 0.001), suggesting that this protein may play a crucial role in tumor progression. This observation is consistent with previous research that has reported similar biomarker expression patterns across various cancers [ 13 , 14 ], indicating that IBSP may have broader implications in oncology. Our inter-group analysis further clarifies the clinical significance of IBSP expression, particularly concerning the N stage and pathological stage of HNSC. The significant differences noted (P = 0.010 for N stage and P = 0.009 for pathological stage) imply that IBSP could be an important factor in tumor staging and progression, aligning with existing literature that suggests biomarkers can offer insights into tumor behavior and patient prognosis [ 15 – 17 ]. Additionally, our conditional effect analysis, especially regarding immune response, reveals a significant correlation between high IBSP expression and the presence of natural killer (NK) cells and macrophages (R = 0.347, P < 0.001; R = 0.337, P < 0.001, respectively). This finding indicates that IBSP may not only serve as a marker for tumor presence but also influence the immune microenvironment, potentially affecting therapeutic responses. These results are crucial as they pave the way for further exploration of IBSP's role in immunotherapy, an area of cancer treatment that is rapidly advancing. In conclusion, our variance analysis, inter-group comparisons, and conditional effect assessments collectively underscore the complex role of IBSP in HNSC, suggesting its potential as both a diagnostic and prognostic biomarker, as well as a target for therapeutic strategies. Further research is needed to confirm these findings and investigate the underlying mechanisms involved. In conclusion, our study emphasizes the important role of IBSP in head and neck squamous cell carcinoma, indicating its potential as both a biomarker and a prognostic factor. We found that higher levels of IBSP expression in HNSC patients are linked to poorer clinical outcomes, which suggests that it could be useful for early diagnosis and guiding treatment decisions. Additionally, the relationship between IBSP expression and the infiltration of immune cells highlights its significance in influencing the tumor microenvironment, potentially playing a role in mechanisms that allow tumors to evade the immune system. While our study has limitations, such as the absence of validation through laboratory experiments and possible biases in sample representation, the results provide a strong foundation for further research into the pathways involving IBSP and its potential clinical applications. Future studies should aim to clarify the mechanisms by which IBSP operates in HNSC and investigate how it can be integrated into treatment strategies to improve patient outcomes. CONCLUSION The findings indicate that IBSP may serve as a valuable biomarker for diagnosing head and neck squamous cell carcinoma (HNSC), while also acting as a prognostic indicator and influencing the tumor immune microenvironment. Future studies should aim to clarify the mechanistic pathways through which IBSP operates in HNSC and investigate its potential as a therapeutic target to improve patient outcomes. Declarations 1.The funds for this project come from the self-provided fund of the department and no other specific project funds have been used. 2. Consent to Publish declaration: not applicable. 3.Ethics and Consent to Participate declarations: not applicable. 4. The data in this article is sourced from the TCGA database and is genuine and reliable. 5. There is no competitive interest relationship among all the authors of this article. 6.Jun Li and Fang Wang wrote the main manuscript text and Jianyang Sun prepared figures 1-2, and Yongqiang Shi prepared figures 3-4，and Geng Deng prepared tables 1-2, and Hongxing Min experimental design and guidance . References Järbrink K, Ni G, Sönnergren H, et al. The humanistic and economic burden of chronic wounds: a protocol for a systematic review. 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PLoS One. 2022;17(9):e0274140. https://doi:10.1371/journal.pone.0274140 Shah S, Nag A, Sachithanandam SV, Lucke-Wold B. Predictive and Prognostic Significance of Molecular Biomarkers in Glioblastoma. Biomedicines. 2024;12(12)2664. https://doi:10.3390/biomedicines12122664 Miao Y, Dong M, Zhou Q, et al. Single-cell RNA-seq reveals FGF12 as a prognostic biomarker in low-grade endometrial stromal sarcoma. Front Immunol. 2024;15:1513076. https://doi:10.3389/fimmu.2024.1513076 Ye L, Tong S, Wang Y, Wang Y, Ma W. Grade scoring system reveals distinct molecular subtypes and identifies KIF20A as a novel biomarker for predicting temozolomide treatment efficiency in gliomas. J Cancer Res Clin Oncol. 2023;149(12):9857–9876. https://doi:10.1007/s00432-023-04898-6 Additional Declarations No competing interests reported. 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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-6490272\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":449817675,\"identity\":\"b5c95edd-6641-4421-9d42-a2868573e49a\",\"order_by\":0,\"name\":\"Jun Li\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"General Hospital of Ningxia Medical University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Jun\",\"middleName\":\"\",\"lastName\":\"Li\",\"suffix\":\"\"},{\"id\":449817677,\"identity\":\"d5199709-2d9c-4085-86cc-81ccdeed9431\",\"order_by\":1,\"name\":\"Fang Wang\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Ningxia Hui Autonomous Region People's Hospital\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Fang\",\"middleName\":\"\",\"lastName\":\"Wang\",\"suffix\":\"\"},{\"id\":449817678,\"identity\":\"04b08b02-88de-4b98-86e3-43877c84fa8a\",\"order_by\":2,\"name\":\"Jianyang Sun\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"General Hospital of Ningxia Medical University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Jianyang\",\"middleName\":\"\",\"lastName\":\"Sun\",\"suffix\":\"\"},{\"id\":449817679,\"identity\":\"b4d71164-6b7a-43ae-82eb-34a68f7d45b8\",\"order_by\":3,\"name\":\"Yongqiang Shi\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"General Hospital of Ningxia Medical University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Yongqiang\",\"middleName\":\"\",\"lastName\":\"Shi\",\"suffix\":\"\"},{\"id\":449817680,\"identity\":\"0094f3b5-8208-4f7c-b4c4-1adf56419681\",\"order_by\":4,\"name\":\"Geng Deng\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"General Hospital of Ningxia Medical University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Geng\",\"middleName\":\"\",\"lastName\":\"Deng\",\"suffix\":\"\"},{\"id\":449817681,\"identity\":\"9c3c805f-af85-4650-a6eb-cfa6b44793cb\",\"order_by\":5,\"name\":\"Hongxing Min\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAx0lEQVRIiWNgGAWjYBACNvkDiY//VNQw87M3EKmFT4LhsQHPmWPskj0HiNQiJ8H4TIC3jZnf4EYCsQ6Tbk5jkDjDJi058/HGGww1NtGEtcgcS3tgUCFjzC+dVmzBcCwtt4GgFoacdIOEM2zJkrNzzCQYGw4ToyX/m8TBNub6DTfPEKtFIiFNsrGNmdngBg+xWngOJBsznDnGLNkD9EsCMX6Rb29IfMwAjsrDG298qLEhrAUZGEgkkKIcooVUHaNgFIyCUTAyAAB3gz1XtUJSxgAAAABJRU5ErkJggg==\",\"orcid\":\"\",\"institution\":\"General Hospital of Ningxia Medical University\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Hongxing\",\"middleName\":\"\",\"lastName\":\"Min\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2025-04-20 16:08:13\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-6490272/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-6490272/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":82159868,\"identity\":\"96c5996e-68bf-44a4-93de-81e0b61445c7\",\"added_by\":\"auto\",\"created_at\":\"2025-05-07 08:27:40\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":238189,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eDifferential expression of IBSP in HNSC and various other cancers. (A) Differential expression of IBSP in various cancers. (B)Difference in IBSP\\u003cem\\u003e \\u003c/em\\u003eexpression between patients with HNSC and normal individuals. (C) Difference in IBSP\\u003cem\\u003e \\u003c/em\\u003eexpression between patients with HNSC and paired adjacent normal samples. (D) ROC curve showing the efficiency of\\u003cem\\u003e IBSP \\u003c/em\\u003ein distinguishing HNSC tissues from normal tissues. *\\u003cem\\u003eP \\u003c/em\\u003e\\u0026lt; 0.05, **\\u003cem\\u003eP \\u003c/em\\u003e\\u0026lt; 0.01, and ***\\u003cem\\u003eP \\u003c/em\\u003e\\u0026lt; 0.001.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6490272/v1/41078023dc3244d203e65802.png\"},{\"id\":82159866,\"identity\":\"73a24cec-5d94-4083-ab50-f6071d7db672\",\"added_by\":\"auto\",\"created_at\":\"2025-05-07 08:27:40\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":221321,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eExpression levels in tumor tissues of patients with different clinical features of IBSP in TCGA database. (A) T-stage, (B) N-stage, (C) M-stage, (D) Pathological stage. *\\u003cem\\u003eP \\u003c/em\\u003e\\u0026lt; 0.05, **\\u003cem\\u003eP \\u003c/em\\u003e\\u0026lt; 0.01, and ***\\u003cem\\u003eP \\u003c/em\\u003e\\u0026lt; 0.001.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6490272/v1/12776aaf8f4ec20d579154b9.png\"},{\"id\":82160490,\"identity\":\"72715bd2-7e9f-4577-85fc-8d74fb108182\",\"added_by\":\"auto\",\"created_at\":\"2025-05-07 08:35:40\",\"extension\":\"png\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":242709,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003ePrognostic relationship between IBSP and HNSC. (A–C) Analysis of the relationship between IBSP expression and OS and PFI and DSS of HNSC patients based on the TCGA database. (D–I) Prognostic subgroup analyses of HNSC patients with different clinicopathological status.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Onlinefloatimage3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6490272/v1/3e0f00e0d8daffb151c7294b.png\"},{\"id\":82160491,\"identity\":\"214c536a-8347-426d-adf9-74ec12ccf74d\",\"added_by\":\"auto\",\"created_at\":\"2025-05-07 08:35:40\",\"extension\":\"png\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":633599,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eRelationship between IBSP expression and immune infiltration in the HNSC microenvironment. (A) Correlation between the relative abundance of 24 immune cells and IBSP\\u003cem\\u003e \\u003c/em\\u003eexpression. The size of the dots indicates the absolute value of Spearman’s correlation coefficient R. (B,C) Correlation between IBSP\\u003cem\\u003e \\u003c/em\\u003eexpression and infiltration levels of \\u0026nbsp;NK cells and Macrophages. (D,E) Correlation between high and low IBSP expression and the infiltration levels of \\u0026nbsp;NK cells and Macrophages. *\\u003cem\\u003eP \\u003c/em\\u003e\\u0026lt; 0.05, **\\u003cem\\u003eP \\u003c/em\\u003e\\u0026lt; 0.01, and ***\\u003cem\\u003eP \\u003c/em\\u003e\\u0026lt; 0.001.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage4.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6490272/v1/7033f424bcd2486f69f23498.png\"},{\"id\":100373352,\"identity\":\"720b6978-35d3-4944-8f6c-a84d0a3d8f12\",\"added_by\":\"auto\",\"created_at\":\"2026-01-16 08:14:08\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":2176848,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6490272/v1/3c2e2b18-ee18-421e-bdf5-a3b16cba5c6f.pdf\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Expression and Clinical Significance of IBSP in Head and Neck Squamous Cell Carcinoma: A Bioinformatics Analysis\",\"fulltext\":[{\"header\":\"INTRODUCTION\",\"content\":\"\\u003cp\\u003eHead and neck squamous cell carcinoma (HNSC) poses a significant public health challenge due to its high rates of incidence and mortality, leading to considerable economic burdens on healthcare systems and a reduced quality of life for those affected [\\u003cspan additionalcitationids=\\\"CR2\\\" citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e]. The current treatment options for HNSC, which include surgery, radiotherapy, and chemotherapy, often show limited effectiveness and come with substantial side effects. This situation highlights the urgent need for new biomarkers and therapeutic targets to improve patient outcomes. Previous research has suggested that integrin-binding sialoprotein (IBSP) may play a crucial role in tumor progression and metastasis in various cancers, indicating a possible connection between IBSP expression and the underlying mechanisms of HNSC. However, the specific relationship between IBSP levels and clinical characteristics in HNSC has not been thoroughly investigated, revealing a significant gap in research that this study intends to fill. By examining the expression of IBSP in HNSC and its correlation with clinical features and prognosis, this research aims to clarify the potential of IBSP as a valuable biomarker that could enhance diagnostic and therapeutic approaches in the management of HNSC.\\u003c/p\\u003e \\u003cp\\u003eThe present study explores the expression levels of Integrin Binding Sialoprotein (IBSP) in Head and Neck Squamous Cell Carcinoma (HNSC), emphasizing its potential as a new biomarker for this type of cancer. Previous studies have shown that IBSP is significantly involved in tumor progression and metastasis in various cancers [\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e], indicating its important role in the underlying mechanisms of HNSC. Our results demonstrate that IBSP expression is significantly higher in HNSC tissues compared to normal tissues, suggesting a strong link to the disease. Additionally, the relationship between IBSP expression and various clinicopathological features, as well as patient prognosis, highlights its potential use in clinical practice. The thorough statistical analyses conducted in this study, which include survival analysis and assessments of immune cell infiltration, provide strong evidence for the significance of IBSP in HNSC. This research not only enhances our understanding of the biology of HNSC but also paves the way for future studies focused on the mechanisms of IBSP and its potential therapeutic applications, ultimately aiming to improve patient outcomes.\\u003c/p\\u003e \\u003cp\\u003eThis study utilizes RNA sequencing data analysis alongside various bioinformatics techniques, such as survival analysis and immune infiltration assessment, to thoroughly investigate the expression of Integrin Binding Sialoprotein (IBSP) in Head and Neck Squamous Cell Carcinoma (HNSC) and its clinical implications. A key strength of this approach is the use of extensive data from The Cancer Genome Atlas (TCGA), which bolsters the reliability and applicability of the results. The main goal of this research is to explore the relationship between IBSP expression levels and clinical features, as well as patient prognosis in those with HNSC, highlighting its potential as a biomarker. By merging detailed data analysis with clinical perspectives, this study seeks to enhance the understanding of IBSP's function in HNSC, ultimately aiding in the discovery of new therapeutic targets and improving patient outcomes.\\u003c/p\\u003e\"},{\"header\":\"MATERIALS AND METHODS\",\"content\":\"\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eRNA-seq data and bioinformatics analysis\\u003c/h2\\u003e \\u003cp\\u003eThis investigation utilized data obtained from the TCGA database (\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://portal.gdc.cancer.gov\\u003c/span\\u003e\\u003cspan address=\\\"https://portal.gdc.cancer.gov\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e). RNA-sequencing datasets from the TCGA Head and Neck Squamous Cell Carcinoma project were processed through the STAR workflow. Subsequently, data were extracted in the Transcripts Per Million (TPM) format to facilitate the acquisition of gene expression and clinical data. Data processing and visualization were conducted utilizing R software (version 4.2.1), during which entries that were clinically irrelevant or duplicates were eliminated. Statistically appropriate methods were employed, utilizing the R packages 'stats' and 'car', in accordance with the characteristics of the data. For data visualization, the R package 'ggplot2' was employed. This study adhered to the principles outlined in the Declaration of Helsinki (revised in 2013) and complied with the publication guidelines established by TCGA. Notably, the authors did not conduct any research involving human or animal subjects.\\u003c/p\\u003e \\u003c/div\\u003e\\n\\u003ch3\\u003eReceiver operating characteristic curve analysis\\u003c/h3\\u003e\\n\\u003cp\\u003eThe Receiver Operating Characteristic (ROC) curve analysis represents a statistical technique employed to assess the efficacy of binary classification systems. This method is particularly advantageous for evaluating the sensitivity and specificity of diagnostic tests. The ROC curve graphically represents the true positive rate (sensitivity) in relation to the false positive rate (1 - specificity) across a spectrum of threshold values. Data analysis and visualization were executed utilizing R software (version 4.2.1). We applied the R package \\u0026lsquo;pROC [1.18.0]\\u0026rsquo; to conduct the ROC analysis on the processed dataset. The results were depicted using the R package \\u0026lsquo;ggplot2\\u0026rsquo;.\\u003c/p\\u003e\\n\\u003ch3\\u003eKaplan–Meier curve analysis\\u003c/h3\\u003e\\n\\u003cp\\u003eData analysis and visualization were conducted utilizing R software (version 4.2.1). The 'survival' package facilitated the Kaplan-Meier (KM) analysis of the processed dataset, while the visualization of the results was achieved through the 'survminer' package.\\u003c/p\\u003e\\n\\u003ch3\\u003eImmune infiltration analysis\\u003c/h3\\u003e\\n\\u003cp\\u003eThe immune infiltration matrix data were sourced from the online repository of Xiantao Academic (\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://www.xiantao.love\\u003c/span\\u003e\\u003cspan address=\\\"https://www.xiantao.love\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e). We performed an analysis to investigate the correlations between the main variables and the matrix data within the preprocessed dataset, utilizing R software (version 4.2.1). The results were then illustrated through visualizations created with the R package 'ggplot2'.\\u003c/p\\u003e \\u003cdiv id=\\\"Sec7\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eStatistical analysis\\u003c/h2\\u003e \\u003cp\\u003eAll statistical analyses were performed utilizing R software (version 4.2.1), which incorporated the Kruskal\\u0026ndash;Wallis test to assess the expression levels of IBSP in both head and neck squamous cell carcinoma tissues and normal tissues. A p-value of less than 0.05 was deemed to be statistically significant. The thresholds for p-values were established as follows: *P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05, **P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.01, and ***P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"RESULTS\",\"content\":\"\\u003cdiv id=\\\"Sec9\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eDifferences in IBSP expression between Head and neck squamous cell carcinoma and normal tissues\\u003c/h2\\u003e \\u003cp\\u003eThe Cancer Genome Atlas (TCGA) provided data that facilitated an examination of IBSP expression levels across various cancer types. The analysis revealed significant variations in IBSP expression among distinct tumor categories (P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001, Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eA). We gathered gene expression profiles from 502 patients diagnosed with Head and Neck Squamous Cell Carcinoma (HNSC) alongside 44 normal control subjects, all of which were obtained from TCGA databases. The findings demonstrated that IBSP expression was markedly elevated in HNSC patients compared to normal controls (P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001, Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eB). Subsequently, a paired comparison analysis involving 43 HNSC patients and 43 normal controls was conducted. This analysis further confirmed that IBSP expression was significantly increased within the HNSC cohort (P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001, Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eC). The relationship between IBSP expression and HNSC was thoroughly validated employing two comparative approaches. Finally, we executed a receiver operating characteristic (ROC) curve analysis to evaluate IBSP's effectiveness in distinguishing tumor tissues from non-tumor tissues. The calculated area under the curve (AUC) for IBSP was 0.929 (CI\\u0026thinsp;=\\u0026thinsp;0.900\\u0026ndash;0.958), indicating that IBSP is highly proficient in differentiating tumor tissues from non-tumor tissues (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eD).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e\\n\\u003ch3\\u003eThe Association Between IBSP Expression and Clinicopathological Features in Head and neck squamous cell carcinoma\\u003c/h3\\u003e\\n\\u003cp\\u003eWe conducted a statistical analysis of the baseline characteristics of 504 individuals diagnosed with head and neck squamous cell carcinoma, utilizing data sourced from The Cancer Genome Atlas (TCGA) database (refer to Table\\u0026nbsp;1). RNA sequencing data pertinent to the TCGA-HNSC project were procured and systematically organized through the STAR pipeline, accessible via the TCGA database (\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://portal.gdc.cancer.gov\\u003c/span\\u003e\\u003cspan address=\\\"https://portal.gdc.cancer.gov\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e). The retrieved data were formatted in Transcripts Per Million (TPM) and incorporated essential clinical details. During the data preprocessing phase, normal samples and those devoid of clinical information were excluded. The final dataset was categorized into two distinct groups: one comprising 252 patients with low expression levels of IBSP and the other containing 252 patients with high IBSP expression. Statistical analyses indicated no significant differences between the two cohorts regarding gender (P\\u0026thinsp;=\\u0026thinsp;0.840), age (P\\u0026thinsp;=\\u0026thinsp;0.141), Pathologic T stage (P\\u0026thinsp;=\\u0026thinsp;0.345), or Pathologic M stage (P\\u0026thinsp;=\\u0026thinsp;1.000). However, noteworthy differences were identified in patient distributions relative to N stages (P\\u0026thinsp;=\\u0026thinsp;0.010) and Pathologic stage (P\\u0026thinsp;=\\u0026thinsp;0.009). Furthermore, significant discrepancies were noted between the two groups concerning Histologic grade (P\\u0026thinsp;=\\u0026thinsp;0.046).\\u003c/p\\u003e \\u003cp\\u003eTabel 1 \\u0026emsp;Correlation between IBSP expression and clinicopathologic characteristics of HNSC.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"No\\\" id=\\\"Taba\\\" border=\\\"1\\\"\\u003e \\u003ccolgroup cols=\\\"4\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCharacteristics\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eLow expression of IBSP\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eHigh expression of IBSP\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eP value\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003en\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e252\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e252\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eGender, n (%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.840\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eFemale\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e68 (13.5%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e66 (13.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMale\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e184 (36.5%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e186 (36.9%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAge, n (%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.141\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u0026lt;= 60\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e132 (26.2%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e115 (22.9%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u0026gt;\\u0026thinsp;60\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e120 (23.9%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e136 (27%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePathologic T stage, n (%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.345\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eT1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e26 (5.8%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e19 (4.2%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eT2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e68 (15.2%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e67 (15%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eT3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e51 (11.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e45 (10%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eT4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e77 (17.2%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e95 (21.2%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePathologic N stage, n (%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.010\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eN0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e93 (22.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e78 (19%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eN1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e42 (10.2%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e24 (5.8%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eN2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e69 (16.8%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e98 (23.8%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eN3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e3 (0.7%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e4 (1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePathologic M stage, n (%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.000\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eM0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e102 (54%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e86 (45.5%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eM1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1 (0.5%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0 (0%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePathologic stage, n (%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.009\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eStage I\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e15 (3.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e10 (2.3%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eStage II\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e34 (7.8%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e36 (8.3%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eStage III\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e51 (11.7%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e28 (6.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eStage IV\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e115 (26.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e147 (33.7%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHistologic grade, n (%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.046\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eG1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e39 (8.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e23 (4.8%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eG2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e151 (31.2%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e150 (31%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eG3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e52 (10.7%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e67 (13.8%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eG4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0 (0%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2 (0.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003eThe TCGA database was utilized to investigate the association between clinical features and IBSP expression levels in patients diagnosed with Head and Neck Squamous Cell Carcinoma (HNSC). This examination concentrated on the correlation between IBSP expression and four distinct clinicopathological parameters: T-stage, N-stage, M-stage, and pathological stage (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e). Initially, we assessed the relationship between IBSP and the different pathological stages of HNSC by analyzing its expression levels in samples classified by these stages. The results indicated a notable association between increased IBSP expression and the T stage, revealing that levels were significantly higher in HNSC samples across the four T stages when compared to normal head and neck tissues; however, no substantial differences were observed among the samples within the four T stages (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eA). Furthermore, IBSP expression was significantly elevated in HNSC samples across all four N stages relative to normal head and neck tissues, although no significant differences were detected among the samples of the four N stages (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eB). In terms of M0 stages, IBSP expression was markedly greater in HNSC samples compared to normal head and neck tissues (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eC). Additionally, IBSP expression was significantly heightened in HNSC samples across the four pathological stages when compared to normal head and neck tissues, with particularly noteworthy differences observed between stage III and stage IV samples (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eD). Overall, these results indicate that IBSP is significantly upregulated in HNSC and correlates with various clinical characteristics.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003eHigh IBSP expression affects the prognosis of Head and neck squamous cell carcinoma patients in various clinical and pathological stages\\u003c/b\\u003e \\u003c/p\\u003e \\u003cp\\u003eThe Kaplan-Meier survival analysis revealed that elevated levels of IBSP expression were linked to unfavorable prognostic outcomes in patients diagnosed with head and neck squamous cell carcinoma, particularly regarding overall survival (OS) (HR\\u0026thinsp;=\\u0026thinsp;1.45, P\\u0026thinsp;=\\u0026thinsp;0.027), progression-free interval (PFI) (HR\\u0026thinsp;=\\u0026thinsp;1.44, P\\u0026thinsp;=\\u0026thinsp;0.020), and disease-specific survival (DSS) (HR\\u0026thinsp;=\\u0026thinsp;1.85, P\\u0026thinsp;=\\u0026thinsp;0.002) as illustrated in Figs.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eA-C. Further subgroup analyses categorized patients according to various clinicopathological features. These investigations indicated that high IBSP expression was notably correlated with adverse prognostic results in head and neck squamous cell carcinoma, especially in: individuals aged over 60 years (HR\\u0026thinsp;=\\u0026thinsp;1.93, P\\u0026thinsp;=\\u0026thinsp;0.001), female patients (HR\\u0026thinsp;=\\u0026thinsp;3.03, P\\u0026thinsp;=\\u0026thinsp;0.004), those classified with T3 status (HR\\u0026thinsp;=\\u0026thinsp;1.92, P\\u0026thinsp;=\\u0026thinsp;0.049), patients exhibiting M0 status (HR\\u0026thinsp;=\\u0026thinsp;1.81, P\\u0026thinsp;=\\u0026thinsp;0.024), individuals in stage III (HR\\u0026thinsp;=\\u0026thinsp;2.61, P\\u0026thinsp;=\\u0026thinsp;0.028), and smokers (HR\\u0026thinsp;=\\u0026thinsp;1.41, P\\u0026thinsp;=\\u0026thinsp;0.033). The pertinent data is presented in Figs.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eD-I.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eTabel 2 \\u0026emsp;Univariate and multivariate Cox regression analyses of prognostic factors for OS in HNSC.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"No\\\" id=\\\"Tabb\\\" border=\\\"1\\\"\\u003e \\u003ccolgroup cols=\\\"7\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eCharacteristics\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eTotal(N)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c4\\\" namest=\\\"c3\\\"\\u003e \\u003cp\\u003eUnivariate analysis\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c7\\\" namest=\\\"c6\\\"\\u003e \\u003cp\\u003eMultivariate analysis\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eHazard ratio (95% CI)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eP value\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eHazard ratio (95% CI)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eP value\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eGender\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e503\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eFemale\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e134\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eReference\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eReference\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMale\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e369\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.760 (0.571\\u0026ndash;1.012)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.061\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.670 (0.371\\u0026ndash;1.209)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.183\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAge\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e503\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u0026lt;= 60\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e247\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eReference\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eReference\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u0026gt;\\u0026thinsp;60\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e256\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.262 (0.964\\u0026ndash;1.653)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.090\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.964 (0.553\\u0026ndash;1.683)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.898\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePathologic T stage\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e447\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eT1\\u0026amp;T2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e179\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eReference\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eReference\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eT3\\u0026amp;T4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e268\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.934 (1.413\\u0026ndash;2.649)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e2.432 (1.092\\u0026ndash;5.417)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.030\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePathologic N stage\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e410\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eN0\\u0026amp;N1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e236\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eReference\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eReference\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eN2\\u0026amp;N3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e174\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2.296 (1.686\\u0026ndash;3.127)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e2.662 (1.529\\u0026ndash;4.635)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePathologic M stage\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e188\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eM0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e187\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eReference\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eReference\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eM1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e22.631 (2.830\\u0026ndash;180.948)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.003\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e19.894 (2.224\\u0026ndash;177.930)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.007\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePathologic stage\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e435\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eStage I\\u0026amp;Stage II\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e94\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eReference\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eReference\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eStage III\\u0026amp;Stage IV\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e341\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.839 (1.236\\u0026ndash;2.737)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.003\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e1.409 (0.346\\u0026ndash;5.743)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.632\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHistologic grade\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e483\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eG1\\u0026amp;G2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e362\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eReference\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eG3\\u0026amp;G4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e121\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.942 (0.690\\u0026ndash;1.286)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.706\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003eWe employed both univariate and multivariate Cox proportional hazards regression models to ascertain independent prognostic determinants influencing head and neck squamous cell carcinoma, with a particular focus on T stage, N stage, M stage, and pathologic stage. The results from the univariate analysis revealed a noteworthy correlation between overall survival (OS) and various factors: T1 and T2 stages compared to T3 and T4 stages (HR\\u0026thinsp;=\\u0026thinsp;1.934, 95% CI [1.413\\u0026ndash;2.649], P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), N0 and N1 stages as opposed to N2 and N3 stages (HR\\u0026thinsp;=\\u0026thinsp;2.296, 95% CI [1.686\\u0026ndash;3.127], P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), M0 stage versus M1 stage (HR\\u0026thinsp;=\\u0026thinsp;22.631, 95% CI [2.830\\u0026ndash;180.948], P\\u0026thinsp;=\\u0026thinsp;0.003), and stage I and stage II compared to stage III and stage IV (HR\\u0026thinsp;=\\u0026thinsp;1.839, 95% CI [1.236\\u0026ndash;2.737], P\\u0026thinsp;=\\u0026thinsp;0.003). Following this, we executed a multivariate analysis, which indicated that T1 and T2 stages relative to T3 and T4 stages (HR\\u0026thinsp;=\\u0026thinsp;2.432, 95% CI [1.092\\u0026ndash;5.417], P\\u0026thinsp;=\\u0026thinsp;0.030), N0 and N1 stages against N2 and N3 stages (HR\\u0026thinsp;=\\u0026thinsp;2.662, 95% CI [1.529\\u0026ndash;4.635], P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), and M0 stage in comparison with M1 stage (HR\\u0026thinsp;=\\u0026thinsp;19.894, 95% CI [2.224\\u0026ndash;177.930], P\\u0026thinsp;=\\u0026thinsp;0.007) were all significantly associated with overall survival (Table\\u0026nbsp;2).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cdiv id=\\\"Sec11\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eCorrelation between IBSP expression and immune infiltration\\u003c/h2\\u003e \\u003cp\\u003eWe employed the single-sample gene set enrichment analysis (ssGSEA) technique to explore the association between the expression levels of IBSP and 24 distinct types of immune cells involved in immune infiltration. Our results were then illustrated visually. In Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eA, we present a graphical depiction where the diameter of each circle reflects the intensity of the correlation between immune cell enrichment and IBSP expression levels. The color coding of the circles corresponds to the respective P-values. Additionally, the proximity of each circle to the baseline signifies the strength of the correlation between immune cell enrichment and IBSP expression. Subsequently, we pinpointed the two immune cell types that demonstrated the strongest correlations along with statistically significant differences in their P-values for further analysis. Notably, we identified a positive correlation between IBSP expression and levels of immune infiltration across several cell types, including NK cells (R\\u0026thinsp;=\\u0026thinsp;0.347, P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001) and macrophages (R\\u0026thinsp;=\\u0026thinsp;0.337, P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001) as illustrated in Figs.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eB-C. Our statistical evaluations indicated that NK cells and macrophages exhibited significantly elevated infiltration levels in samples characterized by high IBSP expression when compared to those with lower expression levels (P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001) as shown in Figs.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eD-E.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"DISCUSSION\",\"content\":\"\\u003cp\\u003eHead and neck squamous cell carcinoma poses a significant global health challenge, marked by high rates of incidence and mortality [\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e]. This type of cancer not only places a heavy economic strain on healthcare systems but also greatly diminishes the quality of life for those affected [\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e]. Current treatment options, such as surgery, radiation therapy, and chemotherapy, often show limited effectiveness and come with considerable side effects [\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e]. Therefore, there is an urgent need to identify new biomarkers and therapeutic targets that can improve patient outcomes and enhance overall quality of life. Gaining a deeper understanding of the molecular mechanisms behind HNSC is essential for developing more effective diagnostic and treatment strategies [\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e]. In this study, we focused on the role of Integrin Binding Sialoprotein in HNSC by utilizing extensive RNA sequencing data from The Cancer Genome Atlas (TCGA) and applying advanced bioinformatics techniques. Our results indicate a significant increase in IBSP expression in HNSC tissues compared to normal controls, and we found a strong link between high levels of IBSP and poor clinical outcomes. Additionally, we investigated the relationship between IBSP expression and immune cell infiltration, which may have important implications for the tumor microenvironment. These findings highlight the potential of IBSP as a valuable biomarker for HNSC and open avenues for further research into its mechanistic role and possible therapeutic targeting in this cancer type.\\u003c/p\\u003e \\u003cp\\u003eThe variance analysis results from our study indicate significant differences in IBSP expression levels between patients with head and neck squamous cell carcinoma (HNSC) and normal controls, highlighting the potential of IBSP as a biomarker in the development of HNSC. Specifically, we observed a substantial increase in IBSP expression in HNSC patients (P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), suggesting that this protein may play a crucial role in tumor progression. This observation is consistent with previous research that has reported similar biomarker expression patterns across various cancers [\\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e], indicating that IBSP may have broader implications in oncology. Our inter-group analysis further clarifies the clinical significance of IBSP expression, particularly concerning the N stage and pathological stage of HNSC. The significant differences noted (P\\u0026thinsp;=\\u0026thinsp;0.010 for N stage and P\\u0026thinsp;=\\u0026thinsp;0.009 for pathological stage) imply that IBSP could be an important factor in tumor staging and progression, aligning with existing literature that suggests biomarkers can offer insights into tumor behavior and patient prognosis [\\u003cspan additionalcitationids=\\\"CR16\\\" citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e]. Additionally, our conditional effect analysis, especially regarding immune response, reveals a significant correlation between high IBSP expression and the presence of natural killer (NK) cells and macrophages (R\\u0026thinsp;=\\u0026thinsp;0.347, P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001; R\\u0026thinsp;=\\u0026thinsp;0.337, P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001, respectively). This finding indicates that IBSP may not only serve as a marker for tumor presence but also influence the immune microenvironment, potentially affecting therapeutic responses. These results are crucial as they pave the way for further exploration of IBSP's role in immunotherapy, an area of cancer treatment that is rapidly advancing. In conclusion, our variance analysis, inter-group comparisons, and conditional effect assessments collectively underscore the complex role of IBSP in HNSC, suggesting its potential as both a diagnostic and prognostic biomarker, as well as a target for therapeutic strategies. Further research is needed to confirm these findings and investigate the underlying mechanisms involved.\\u003c/p\\u003e \\u003cp\\u003eIn conclusion, our study emphasizes the important role of IBSP in head and neck squamous cell carcinoma, indicating its potential as both a biomarker and a prognostic factor. We found that higher levels of IBSP expression in HNSC patients are linked to poorer clinical outcomes, which suggests that it could be useful for early diagnosis and guiding treatment decisions. Additionally, the relationship between IBSP expression and the infiltration of immune cells highlights its significance in influencing the tumor microenvironment, potentially playing a role in mechanisms that allow tumors to evade the immune system. While our study has limitations, such as the absence of validation through laboratory experiments and possible biases in sample representation, the results provide a strong foundation for further research into the pathways involving IBSP and its potential clinical applications. Future studies should aim to clarify the mechanisms by which IBSP operates in HNSC and investigate how it can be integrated into treatment strategies to improve patient outcomes.\\u003c/p\\u003e\"},{\"header\":\"CONCLUSION\",\"content\":\"\\u003cp\\u003eThe findings indicate that IBSP may serve as a valuable biomarker for diagnosing head and neck squamous cell carcinoma (HNSC), while also acting as a prognostic indicator and influencing the tumor immune microenvironment. Future studies should aim to clarify the mechanistic pathways through which IBSP operates in HNSC and investigate its potential as a therapeutic target to improve patient outcomes.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e1.The funds for this project come from the self-provided fund of the department and no other specific project funds have been used.\\u003c/p\\u003e\\n\\u003cp\\u003e2. Consent to Publish declaration: not applicable.\\u003c/p\\u003e\\n\\u003cp\\u003e3.Ethics and Consent to Participate declarations: not applicable.\\u003c/p\\u003e\\n\\u003cp\\u003e4. The data in this article is sourced from the TCGA database and is genuine and reliable.\\u003c/p\\u003e\\n\\u003cp\\u003e5. There is no competitive interest relationship among all the authors of this article.\\u003c/p\\u003e\\n\\u003cp\\u003e6.Jun Li and Fang Wang wrote the main manuscript text and Jianyang Sun prepared figures 1-2, and Yongqiang Shi prepared figures 3-4，and Geng Deng prepared tables 1-2, and Hongxing Min experimental design and guidance . \\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\u003cli\\u003e\\u003cspan\\u003eJ\\u0026auml;rbrink K, Ni G, S\\u0026ouml;nnergren H, et al. The humanistic and economic burden of chronic wounds: a protocol for a systematic review. 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J Cancer Res Clin Oncol. 2023;149(12):9857\\u0026ndash;9876. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi:10.1007/s00432-023-04898-6\\u003c/span\\u003e\\u003cspan address=\\\"https://doi:10.1007/s00432-023-04898-6\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":true,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true},\"keywords\":\"IBSP, head and neck squamous cell carcinoma, immune infiltration, clinical significance, bioinformatics\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-6490272/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-6490272/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003ch2\\u003eBackground\\u003c/h2\\u003e \\u003cp\\u003eHead and neck squamous cell carcinoma (HNSC) poses considerable clinical challenges, characterized by high incidence and mortality rates, which underscores the urgent need for novel biomarkers to enhance diagnosis and prognosis.\\u003c/p\\u003e\\u003ch2\\u003eMethods\\u003c/h2\\u003e \\u003cp\\u003eThis study explores the expression of integrin-binding sialoprotein (IBSP) in HNSC, employing RNA sequencing data sourced from The Cancer Genome Atlas (TCGA) databases to investigate its potential role.\\u003c/p\\u003e\\u003ch2\\u003eResults\\u003c/h2\\u003e \\u003cp\\u003eWe conducted an analysis of IBSP expression in a cohort of 502 patients with head and neck squamous cell carcinoma (HNSC) and 44 normal controls, which revealed that IBSP levels were significantly higher in HNSC tissues (P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001). This finding was further validated through paired comparison analysis. Additionally, receiver operating characteristic (ROC) curve analysis indicated that IBSP has strong diagnostic potential, achieving an area under the curve (AUC) of 0.929. Kaplan-Meier survival analysis demonstrated that elevated IBSP expression is associated with poorer overall survival (OS), progression-free interval (PFI), and disease-specific survival (DSS) among HNSC patients. Subgroup analyses revealed significant links between high IBSP expression and negative outcomes across various patient demographics and clinical stages. Both univariate and multivariate Cox regression analyses identified IBSP as an independent prognostic factor. Furthermore, single-sample gene set enrichment analysis (ssGSEA) showed a positive correlation between IBSP expression and immune cell infiltration, specifically by natural killer (NK) cells and macrophages.\\u003c/p\\u003e\\u003ch2\\u003eConclusion\\u003c/h2\\u003e \\u003cp\\u003eThe findings indicate that IBSP may serve as a valuable biomarker for diagnosing head and neck squamous cell carcinoma (HNSC), while also acting as a prognostic indicator and influencing the tumor immune microenvironment. Future studies should aim to clarify the mechanistic pathways through which IBSP operates in HNSC and investigate its potential as a therapeutic target to improve patient outcomes.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Expression and Clinical Significance of IBSP in Head and Neck Squamous Cell Carcinoma: A Bioinformatics Analysis\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2025-05-07 08:27:35\",\"doi\":\"10.21203/rs.3.rs-6490272/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"b53ab423-487c-4eb1-a980-32dc821efe2a\",\"owner\":[],\"postedDate\":\"May 7th, 2025\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2026-01-15T02:24:32+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2025-05-07 08:27:35\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-6490272\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-6490272\",\"identity\":\"rs-6490272\",\"version\":[\"v1\"]},\"buildId\":\"8U1c8b4HqxoKbykW_rLl7\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}