Expression Patterns and Prognostic Model Construction of the Major Histocompatibility Complex-Related Gene Granzyme B in Oral Squamous Cell Carcinoma

Expression Patterns and Prognostic Model Construction of the Major Histocompatibility Complex-Related Gene Granzyme B in Oral Squamous Cell Carcinoma | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Expression Patterns and Prognostic Model Construction of the Major Histocompatibility Complex-Related Gene Granzyme B in Oral Squamous Cell Carcinoma Hongrong Zhang, Xue Zhou, Jingyi Li, Wanyue Zhang, Zheyi Sun, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4251746/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 Oral squamous cell carcinoma (OSCC) accounts for 90% of malignant tumors in the oral and maxillofacial region, known for its high metastatic potential and poor prognosis. This study aims to investigate Major Histocompatibility Complex(MHC) related genes' involvement in OSCC, offering new insights for immunotherapy. We identified differentially expressed genes associated with MHC in OSCC, analyzed copy number variations and pathways enrichment, and developed a prognostic model using Cox regression analysis on patient data. Our model proved to be an independent prognostic indicator, effectively predicting patients' prognosis and showcasing its efficacy in estimating 5-year survival rates. Immunoinfiltration analysis highlighted Granzyme B (GZMB) as a key gene significantly correlated with immune cells, showing varied expression across tumor stages. Additionally, we characterized OSCC tissue grading, finding higher GZMB protein expression in well-differentiated compared to moderately differentiated tissues, both exceeding levels in normal tissues. Our findings suggest the developed prognostic model could serve as a reliable biomarker for OSCC prognosis, emphasizing the crucial role of GZMB in tumor progression. Health sciences/Diseases Health sciences/Medical research Oral squamdous cell carcinoma MHC GZMB Risk score Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction OSCC stands as one of the prevalent malignant tumors in the oral and maxillofacial region, renowned for its pronounced invasiveness and a high propensity for cervical lymph node metastasis. The 5-year survival rate among OSCC patients hovers around 50%, indicating a grim prognosis [1-3] . Hence, the identification of regulatory molecules influencing the progression of OSCC holds paramount importance in enhancing patient prognosis. Tumors do not merely consist of cancer cells; rather, they encompass a heterogeneous amalgamation of host tissue cells, immune-infiltrating cells, extracellular matrix(ECM), and secretory mediators. Collectively, these elements give rise to the tumor microenvironment(TME), a complex and dynamic entity [4-6] . These components collectively create an unfavorable environment characterized by immune suppression and nutrient deprivation, fostering tumor cell growth, proliferation and heterogeneity. Within the TME, tumor immune surveillance emerges as an effective strategy to identify and eliminate newly emerging tumor cells. MHC constitutes a cell surface heterotrimer expressed constitutively on nearly all nucleated cells of jawed vertebrates. The fundamental structure and function of MHC-I determine its pivotal role as the initial step in the tumor immune response. This mechanism relies on the presentation of specific antigens within tumor cells by MHC-I molecules [7] . The MHC-I complex is assembled within the endoplasmic reticulum(ER) through the interaction of a polymorphic heavy chain with β2-microglobulin. Following assembly, it loads high-affinity peptides transported from the cytosol by the peptide transporter TAP [8] ，MHC-II forms a heterodimer composed of α and β chains, primarily expressed by professional antigen-presenting cells(PAPCs) like dendritic cells(DCs), B cells, and macrophages. Its primary function involves presenting exogenous peptide antigens to CD4 + T cells [9] . All antigens synthesized endogenously within the cytosol of cells are presented as peptides bound to MHC-I molecules, enabling their presentation to CD8 + T cells. This mechanism permits CD8+ lymphocytes to identify and eliminate virus-infected cells or cancer cells. Within the TME, CD8 + T and natural killer(NK) cells release GZMB and perforin into tumor cells, thereby inducing tumor cell death [10] . Recent studies have revealed that cancer cells can suppress anti-tumor immune responses by diminishing the release of GZMB from CD8 + T cells through methyltransferase, consequently fostering immune evasion [11] . Our research revealed that as the disease progresses in OSCC, the expression of GZMB shows a declining trend. This may be due to the tumor suppressing the expression of GZMB by interfering with the MHC complex function through the interferon pathway. Materials and Methods Data Download and Preprocessing We obtained the OSCC dataset(TCGA-OSCC) from The Cancer Genome Atlas (TCGA,https://portal.gdc.cancer.gov/) for analysis. This dataset encompasses RNA-seq data and clinical information from a total of 361 samples, consisting of 32 adjacent normal samples and 329 OSCC tumor samples. Additionally, we retrieved RNA-seq datasets GSE30784 [12] and GSE74530 [13] from the Gene Expression Omnibus(GEO) database [14] . The GSE30784 dataset comprises 167 OSCC samples and 45 normal oral tissue samples, while the GSE74530 dataset includes 6 OSCC samples and 6 normal oral tissue samples. Subsequently, we conducted batch correction on the GSE30784 and GSE74530 datasets and merged them to form the combined GEO dataset(GEO-Combined). MHCRDEGs selection Firstly, we utilized the limma package(v. 3.44.3) to conduct differential analysis on the OSCC datasets TCGA-OSCC and GEO-Combined individually to identify the differentially expressed genes(DEGs) between different groups(OSCC/Control). DEGs were chosen based on the criteria of |logFC| > 1 and adjusted P-value (P.adj) < 0.05. Subsequently, the resulting DEGs were intersected with the Major Histocompatibility Complex related genes(MHCRGs) using a Venn diagram to derive the MHC-related differentially expressed gene set(MHCRDEGs). Mutation Analysis and Functional Enrichment of MHCRDEGs in OSCC Patients In addition to the previously mentioned analyses, we obtained "Copy Number Segment" data for OSCC patients. GISTIC(v. 2.0) was employed to analyze copy number variation(CNV) segments. We conducted single nucleotide polymorphism(SNP) mutation analysis and visualization for the MHCRDEGs gene set using GISTIC. For functional enrichment analysis, we utilized the “ClusterProfiler” package(v. 4.10.1) to perform Gene Ontology(GO) and Kyoto Encyclopedia of Genes and Genomes(KEGG) pathway enrichment analyses on the MHCRDEGs gene set. Furthermore, we performed Gene Set Enrichment Analysis(GSEA) on all genes associated with logFC values based on the logFC values of TCGA-OSCC-DEGs in descending order. This analysis was conducted using the “ClusterProfiler” package (v. 4.10.1), and the reference gene set "c2.cp.all.v2022.1.Hs.symbols.gmt" was obtained from the Molecular Signatures Database(MSigDB, https://www.gsea-msigdb.org). Building prognostic model to screen key genes and plotting KM curves We integrated the survival information of TCGA-OSCC patients and conducted univariate Cox regression analysis on the MHCRDEGs gene set to preliminarily screen for genes that hold prognostic value. Genes with an adjusted P-value(P.adj) < 0.1 were deemed as key genes and subsequently included in the multivariate Cox regression analysis. We computed the RiskScore based on these key genes and formulated a prognostic model. TCGA-OSCC patients were stratified into groups according to the median value of the RiskScore, and Kaplan-Meier(KM) curves were generated for the TCGA-OSCC cohort. Assessment of the Prognostic Model We incorporated the key genes model as an independent prognostic factor and merged it with clinical information of OSCC patients(including age, gender, histological stage, and clinical grade) for Cox regression analysis. Subsequently, a nomogram plot of the prognostic model was generated. Calibration analysis was conducted to plot calibration curves, and decision curve analysis(DCA) was utilized to evaluate the clinical utility of the prognostic model. Immune Score Analysis between High and Low-Risk Groups in TCGA-OSCC Dataset We employed the ESTIMATE package to conduct quantitative analysis of immune activity in OSCC tumor samples based on the gene expression profiles of TCGA-OSCC. This analysis yielded three scores: Stromal Score, Immune Score, and ESTIMATE Score. Furthermore, within the high and low-risk groups, we performed correlation analysis between key genes and 22 types of immune cells to investigate potential associations and functional interactions between key genes and immune cells.Lastly, we inputted the key genes into the GSCA online platform to determine the correlation between drug sensitivity in the CTRP and GDSC databases and the key genes. The expression of the GZMB gene in OSCC tissues We evaluated the expression levels of the GZMB gene across different clinical stages of OSCC in TCGA-OSCC. Subsequently, we obtained cancer tissues from OSCC patients undergoing inpatient treatment at the Affiliated Stomatology Hospital of Kunming Medical University. Pathological grading of cancer tissues was determined through HE staining. Immunofluorescence was utilized to assess the expression levels of GZMB protein in OSCC tissues at various stages. Lastly, in the TCGA-OSCC expression matrix, we analyzed genes related to GZMB , ranked them by correlation coefficients, and then employed GSEA to explore the perturbation-related pathways of GZMB , thus elucidating its potential biological roles in OSCC.The study was conducted following the Declaration of Helsinki and was approved by the Biomedical Research Ethics Committee of Kunming University[approval number: KYKQ2022MEC0011]. Written informed consent was obtained from all patients prior to sample collection. Results Data Download and Preprocessing We conducted batch correction on GSE30784 and GSE74530 to integrate the GEO-Combined. Following batch removal, the expression matrices of the two datasets overlapped, indicating the successful removal of batch effects(Fig. 1A,B). Through threshold filtering of differentially expressed genes between the two groups in the TCGA-OSCC dataset, we identified a total of 5404 genes. Among these, 2642 genes were significantly upregulated in OSCC, while 2642 genes were significantly downregulated. Similarly, from the GEO-Combined dataset, we obtained 1468 significantly differentially expressed genes, with 714 genes upregulated and 754 genes downregulated in OSCC samples. Visualization of these results was accomplished using volcano plots(Fig. 1C,D). Subsequently, we intersected the differentially expressed gene sets from the TCGA-OSCC dataset and the GEO-Combined dataset with MHCRGs, resulting in 57 MHCRDEGs(Fig. 1E). Furthermore, we depicted the expression patterns of MHCRDEGs in both datasets(Fig. 1F,G)We assessed the gene mutations of the 57 MHCRDEGs in OSCC, predominantly characterized by missense mutations, including nonsense mutations, frameshift deletions, splice site alterations, in-frame deletions, and frameshift insertions. Among these mutation types, SNPs were the most common, with a small proportion of deletions (DEL) and insertions (INS). The most frequent SNP in OSCC patients were C > T, followed by C > G, C > A, and others. Additionally, among the 57 MHCRDEGs in the TCGA-OSCC dataset, FLG had the highest occurrence of single nucleotide polymorphisms involving three main mutation types. Moreover, the nine MHCRDEGs with the highest occurrence of single nucleotide polymorphisms in the TCGA-OSCC dataset were COL11A1 , HLA-B , HLA-A , FN1 , TNC , NLRC5 , TAP1 , STAT1 , and SERPINE1 , primarily characterized by missense mutations(Fig. 1H). GO enrichment analysis revealed that MHCRDEGs mainly participate in biological processes such as antigen processing and presentation, JAK-STAT pathway receptor signaling, and regulation of endogenous antigen presentation(Fig. 1I). Furthermore, KEGG pathway analysis showed that MHCRDEGs are primarily enriched in pathways related to allograft rejection, human papillomavirus infection, Epstein-Barr virus infection, cellular senescence, and malaria(Fig. 1J). Lastly, GSEA enrichment analysis of the entire TCGA-OSCC matrix showed significant activation of pathways such as E2F targets, IFN-α, IFN-γ, epithelial-mesenchymal transition, and angiogenesis in OSCC, while processes such as oxidative phosphorylation, lipid biosynthesis, and fatty acid metabolism were inhibited(Fig. 1K). Constructing Prognostic Cox Model to Screen Key Genes and Plotting KM Curves We employed a forest plot to present the results of single Cox regression analysis for MHCRDEGs(Fig. 2A). Eight key genes have been identified, including PLP1 , IL17D , DDAH2 , AZGP1 , ADA , BLNK , CCL5 , and GZMB . By incorporating these eight key genes into a multifactorial Cox regression, we derived the formula for the RiskScore calculation model as follows: RiskScore = -0.32 DDAH2 - 0.081 BLNK - 0.0683 AZGP1 - 0.0668 CCL5 - 0.0577 GZMB + 0.202 ADA + 0.507 PLP1 + 0.603 IL17D . The prognosis forest plot illustrated IL17D and PLP1 as risk factors, contributing the most to the prognosis Cox model. This plot elucidates the contribution of each gene to the total score of patients and its correlation with survival rates(Fig. 2B). Kaplan-Meier curves demonstrated a significant difference in the survival outcome of OSCC patients between high and low-risk groups based on the prognosis Cox model RiskScore(P<0.0001). Higher RiskScores were associated with poorer prognosis(Fig. 2C). Among the key genes, IL17D and CCL5 also exhibited significant prognostic implications for OSCC patients. Notably, CCL5 served as a favorable prognostic factor (Fig. 2D,E). Prognostic Model Evaluation The multifactorial Cox regression results of the prognosis model incorporating clinical features such as patient age and gender demonstrated that the prognosis model could function as an independent prognostic factor (HR=2.718, P<0.001) for evaluating patient prognosis(Fig. 3A). In comparison to age, the key genes model exhibited a more significant contribution to the model, indicating its superior evaluation capability(Fig. 3B).We assessed the goodness of fit of the model's predicted survival probabilities to actual data through calibration plots. These plots depicted the predictive performance of the model at 1/3/5 years, with different points representing the model's predictions at various time points. Notably, we observed that the predicted points at 3 and 5 years closely approximated the gray ideal line, indicating good predictive performance at these time points (Fig. 3C-E).In the DCA, the stability of the model's line above the lines representing "All positive" and "All negative" served as the basis for judging the results. The range of x-values where the model's line consistently remained above these reference lines indicated the effectiveness of the model. Our findings indicated that the x-value range was the largest at 5 years, suggesting the optimal performance of the model. The predictive model could offer greater "net benefit" and sensitivity at the 5-year mark(Fig. 3F-H). Analysis of Immune and Drug Sensitivity in High and Low-Risk Groups We conducted a comparison of immune scores between high and low-risk groups in the TCGA-OSCC dataset. The results indicated that the ESTIMATE Score(Fig. 4A), Immune Score(Fig. 4B), and Stromal Score(Fig. 4C) were all higher in the low-risk group, with statistical significance. To investigate the reasons for the differences in immune matrix between high and low-risk groups, we conducted correlation analysis between key genes and infiltrating immune cells. The results revealed that key genes were positively correlated with various immune cells. Specifically, in both high and low-risk groups, GZMB exhibited significant positive correlations with all immune cells, with the strongest correlation observed with activated CD8 + T cells. Conversely, DDAH2 showed a significant negative correlation with neutrophils and weak correlations with other immune cells(Fig. 4D,E). Furthermore, we analyzed the relationship between key genes and drug sensitivity. The results unveiled that ADA , CCL5 , and BLNK were significantly negatively correlated with various drugs such as CR-1-31B, alvocidib, dinaciclib, and SNX-2112 in both the GDSC and CTRP databases. Conversely, DDAH2 exhibited a significant positive correlation. This correlation might be associated with the targets of the drugs, providing new insights for potential drug therapies (Fig. 4F,G). The expression status of GZMB in OSCC tissues In the TCGA-OSCC expression matrix, we observed a significant increase in the expression level of the GZMB gene in tumor tissues classified as T 1 /T 2 and T 3 /T 4 stages compared to normal tissues. Additionally, the expression level in T3/T4 stages was lower than in T 1 /T 2 stages(Fig. 5A). HE staining results confirmed the staging of OSCC tissues. Well-differentiated OSCC(WD-OSCC) tissues exhibited prominent intercellular bridges and keratin pearls with fewer mitotic figures, whereas moderately differentiated OSCC(MD-OSCC) showed less distinct keratin pearls, cellular nuclear pleomorphism, and increased mitotic activity(Fig. 5B). Immunofluorescence staining revealed that GZMB protein content was significantly higher in WD-OSCC tissues compared to MD-OSCC tissues, and both were higher than in control tissues(Fig. 5C). GZMB content increased in the early stages of tumor development, but its function and quantity may be suppressed as the tumor progresses. We analyzed the correlation of GZMB with all genes in the TCGA-OSCC expression profile and performed GSEA analysis to identify pathways or biological processes most likely to affect GZMB in OSCC. We found that processes such as interferon and allograft rejection were positively correlated with GZMB , while processes such as epithelial-mesenchymal transition and protein secretion were negatively correlated with GZMB (Fig. 5D). Additionally, we displayed the interferon-alpha pathway, which showed a significant positive correlation with GZMB (p.adj=6.25e-10) (Fig. 5E). Discussion Recent studies have revealed a close association between the TME and immunotherapy, underscoring its significant role in the initiation and progression of tumors [15] . TME consists of stroma, fibroblasts, endothelial cells, immune cells, and various other components, collectively regulating the intricate ecological behavior of cancer cells [16] , Its composition comprises elements that facilitate immune evasion, thus hastening the progression of tumors. This phenomenon can be attributed to factors such as hypoxia, metabolic dysregulation, phenotypic alterations in immune cells, and the release of tumor-derived exosomes [17-20] . In the TME, the pivotal initial stage for CD8 + T cell-mediated anti-tumor immune response involves the presentation and recognition of antigens mediated by MHC-I [21] . The MHC-I complex exhibits a heterodimeric structure, comprising a polymorphic heavy chain and a light chain known as β2-microglobulin [22-24] ，The MHC-I complex undergoes scanning by the T cell receptor (TCR), facilitating the recognition of antigenic peptides by CD8 + T cells [25] . MHC-II molecules are expressed on immune cells such as B cells, monocytes, macrophages, and dendritic cells, as well as on epithelial cells following inflammatory signaling, while MHC-I molecules exhibit broader expression. Dendritic cells present antigens to naïve CD4+ T cells via MHC-II molecules to activate them, subsequently engaging in interactions between B cells and macrophages with these specific CD4 + effector T cells. Together with MHC-I, they stimulate CD8 + T cells to release GZMB and perforin into tumor cells, thereby promoting their apoptosis [26] . This study, anchored on a prognosis model, elucidates a noteworthy correlation between the GZMB gene and CD8 + T cells. To delve into this correlation, the study initially evaluated the expression levels of the GZMB gene across various clinical stages of OSCC using TCGA-OSCC data. The findings revealed a substantial elevation in GZMB content in stages T1-T2 compared to stages T3-T4, with both stages exhibiting higher levels than normal tissues. Subsequently, the study scrutinized the expression levels of GZMB protein in OSCC tissues at different pathological stages, which was corroborated by immunofluorescence results. GZMB, serving as a cytotoxic factor, is a serine protease produced by CD8 + T cells and NK cells. Through this mechanism, the immune system eradicates potentially harmful cells, thereby preserving tissue homeostasis [27] . In the realm of tumor research, it has been unearthed that tumors impede the antigen presentation process of MHC-I molecules, hindering the recognition of tumor cells by CD8 + T cells and thwarting their cytotoxic effects, thus facilitating tumor immune evasion. This study posits that as the tumor advances, there is a decrement in GZMB expression. In cancer, cancer cells undergo a loss of sensitivity to apoptosis, resulting in the uncontrolled proliferation of aberrant cells, thereby fostering the progression of cancer [28] . This study delves into a cluster of synergistic molecules that co-vary with GZMB to probe potential mechanisms through which tumors influence GZMB. Consequently, GSEA was undertaken on a gene set associated with GZMB . The findings reveal that these genes are extensively implicated in biological processes such as interferon response, cell apoptosis, inflammation, and infection. Similarly, GSEA analysis of TCGA-OSCC also indicates the activation of interferon-alpha and interferon-gamma processes within tumors. Interferons (IFNs) are generated by the innate immune system through Toll-like receptor (TLR) stimulation and other signal transduction cascade reactions [29] . Previously identified IFNs are categorized into three types: type I, type II, and type III. Type I interferons encompass IFNα and IFNβ [30, 31] IFNs have the capability to upregulate MHC-II and other components associated with antigen presentation, while also modulating the composition and equilibrium of intricate cytokine responses [32] . Tumor cells have the ability to evade CD8 + T cell-mediated tumor killing by manipulating the inhibitor Serpinb9 through activation of radiation-induced type I IFN signaling [33] . Research suggests that tumor cells evade recognition and attack by immune cells through inhibition of IFN signaling within the tumor, consequently impacting the function of MHC and its associated components [34] . Certainly, our findings corroborate this perspective. However, additional exploration is imperative to elucidate the mechanism by which IFN-I inhibits the antigen presentation of MHC-I molecules in the progression of OSCC, thereby diminishing the release of GZMB Conclusion In summary, the predictive model, built with key genes, could serve as a reliable biomarker for the prognosis of OSCC. In OSCC, tumor cells may suppress MHC-I antigen presentation through IFN-I, reducing GZMB release and facilitating immune escape. This discovery implies that the predictive model could offer vital insights into OSCC progression and prognosis. Abbreviations OSCC Oral squamous cell carcinoma, MHC Major Histocompatibility Complex, GZMB Granzyme B, ECM Extracellular matrix, TME Tumor microenvironment, ER Endoplasmic reticulum, PAPCs Professional antigen-presenting cells, DCs Dendritic cells, NK Natural killer, TCGA The Cancer Genome Atlas, GEO Gene Expression Omnibus, GEO-Combined Combined GEO dataset, MHCRGs Major Histocompatibility Complex related genes, MHCRDEGs MHC-related differentially expressed gene set, CNV Copy number variation, SNP Single nucleotide polymorphism, GO Gene Ontology, KEGG Kyoto Encyclopedia of Genes and Genomes, GSEA Gene Set Enrichment Analysis, KM Kaplan-Meier, DCA Decision curve analysis, WD-OSCC Well-differentiated OSCC, MD-OSCC Moderately differentiated OSCC, TCR T cell receptor, IFNs Interferons, TLR Toll-like receptor. Declarations Ethics approval and consent to participate Written informed consent was obtained from all patients prior to sample collection. This study was approved by the Biomedical Research Ethics Committee of Kunming University [approval number: KYKQ2022MEC0011]. Consent for publication Not applicable Author contribution statement H.R.Z.and X.Z.:Contributed to manuscript writing, conducted literature searches, and participated in data cleaning, processing, data visualization, specimen processing and hematoxylin and eosin(HE) taining.J.Y.L.andW.Y.Z.:Contributed to Specimen collection .Z.Y.S.andW.H.W.: Provided major contributions, including project design, determination of research direction, supervised data analysis, and oversaw the entire research process. Funding statement This work was supported by the Yunnan Provincial Department of Science and Technology[202201AY070001-171]. Data availability statement Data will be made available on request. If you need the data, you can contact the corresponding author, Weihong Wang, via email at [email protected] . 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Proceedings of the National Academy of Sciences of the United States of America, 2015, 112(50): 15408-13. BORST K, FLINDT S, BLANK P, et al. Selective reconstitution of IFN‑γ gene function in Ncr1+ NK cells is sufficient to control systemic vaccinia virus infection [J]. PLoS pathogens, 2020, 16(2): e1008279. BUTTERFIELD L H, VUJANOVIC L, SANTOS P M, et al. Multiple antigen-engineered DC vaccines with or without IFNα to promote antitumor immunity in melanoma [J]. Journal for immunotherapy of cancer, 2019, 7(1): 113. MASSA C, WANG Y, MARR N, et al. Interferons and Resistance Mechanisms in Tumors and Pathogen-Driven Diseases-Focus on the Major Histocompatibility Complex (MHC) Antigen Processing Pathway [J]. International journal of molecular sciences, 2023, 24(7). Additional Declarations No competing interests reported. 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TCGA-OSCC dataset (C), GEO-Combined dataset (D). Red represents upregulated genes, blue represents downregulated genes, and gray represents genes with insignificant changes.(E) Venn diagram depicting the overlap between MHCRGs and differentially expressed genes (DEGs) in the TCGA-OSCC dataset and GEO-Combined dataset, with 57 genes intersecting.(F,G) Heatmaps showing the expression levels of MHCRDEGs in the TCGA-OSCC dataset (F) and GEO-Combined dataset (G). Red indicates high expression in OSCC, while blue indicates low expression.(H) Overview of single nucleotide polymorphisms (SNPs) in MHCRDEGs in the TCGA-OSCC dataset, highlighting the top 10 genes with the highest cumulative mutations.(I) Gene Ontology (GO) pathway enrichment analysis of MHCRDEGs, with the x-axis representing the adjusted P-value and the y-axis representing the enriched results for biological processes, cellular components, and molecular functions.(J) Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of MHCRDEGs, with \"counts\" indicating the number of enriched genes.(K) Gene Set Enrichment Analysis (GSEA) pathway enrichment analysis of MHCRDEGs, with the left side showing activated pathways and the right side showing inhibited pathways.\u003c/p\u003e","description":"","filename":"figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4251746/v1/ae2094c9d289e07df5bff2ac.png"},{"id":55763971,"identity":"7dc9dec4-4009-4fdd-9f82-e537e1705401","added_by":"auto","created_at":"2024-05-02 19:49:59","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":525843,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Forest plot of single-factor Cox regression results for key genes, where HR represents hazard ratio, and the black horizontal line indicates the 95% confidence interval.\u003c/p\u003e\n\u003cp\u003e(B) Column line chart (nomogram) characterizing the situation of each variable in the multi-factor regression model by setting a certain scale to score, ultimately calculating the total score to predict the probability of event occurrence. Points represent the scores of individual genes in the high and low-risk groups, and Total Points represent the total score. IL17D and PLP1 contribute the most to the prognosis Cox model.(C-E) Prognostic KM curves of the RiskScore and key genes combined with the TCGA-OSCC dataset's prognosis information for OSCC patients. The red curve represents the high-risk group, while the blue curve represents the low-risk group. KM curves demonstrate significant differences in survival outcomes between OSCC patients grouped by RiskScore (C), IL17D (D), and CCL5 (E) high and low-risk groups (P \u0026lt; 0.001 for (C) and P \u0026lt; 0.05 for (D) and (E)).\u003c/p\u003e","description":"","filename":"figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4251746/v1/0a837dc4c862f4684fcd5a97.png"},{"id":55763510,"identity":"dad3ffcb-bbd0-42d6-954e-51e07cc57561","added_by":"auto","created_at":"2024-05-02 19:41:59","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1003809,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Single-factor Cox regression forest plot of key genes Model with age, gender, histological grade, and clinical stage indicators. HR represents hazard ratio, and the black horizontal line indicates the 95% confidence interval.(B) Nomogram analysis to assess the prognostic ability of the clinical-related prognosis model, where the key genes Model contributes the most to the model's utility.(C-E) Calibration curve plots of the 1/3/5-year clinical-related Cox prognosis model nomogram analysis. The horizontal axis represents the predicted survival probability of the model, while the vertical axis represents the actual survival probability displayed by the data. Different points represent the model's predictions at different time points. The closer the line is to the gray ideal line, the better the prediction performance at that time point.(F-H) Decision curve analysis (DCA) evaluates the clinical utility of the constructed clinical-related prognosis model at 1/3/5 years. The x-axis represents the probability threshold or threshold probability, and the y-axis represents the net benefit. By observing the stability of the model's line above the All positive and All negative lines within the range of x values, the result can be determined. A larger range of x values indicates better model performance.\u003c/p\u003e","description":"","filename":"figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4251746/v1/2ecbacdcdb1d53864d73007e.png"},{"id":55763513,"identity":"ecc600a5-7aa6-4af1-ae00-d6f128aa5799","added_by":"auto","created_at":"2024-05-02 19:41:59","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":3849803,"visible":true,"origin":"","legend":"\u003cp\u003e(A-C) Comparative plots of ESTIMATE Score (A), Immune Score (B), and Stromal Score (C) between high and low-risk groups in the TCGA-OSCC dataset. Blue represents the low-risk group, while red represents the high-risk group, with the vertical axis indicating the scores.(D-E) Scatter plots showing the correlation between key genes and immune cell infiltration abundance in the high-risk (D) and low-risk (E) datasets of TCGA-OSCC. Red indicates positive correlation, blue indicates negative correlation, and darker colors signify stronger correlations.(F-G) Bubble plots illustrating the correlation between drug sensitivity in CTRP and GDSC databases and key genes. The horizontal axis represents drugs, the vertical axis represents key genes, red indicates positive correlation, blue indicates negative correlation, and darker colors indicate stronger correlations.\u003c/p\u003e","description":"","filename":"figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4251746/v1/0d7df014fabe208ed749448d.png"},{"id":55763514,"identity":"8271da4b-032d-4156-a21e-0c07e66e9418","added_by":"auto","created_at":"2024-05-02 19:42:00","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":13207361,"visible":true,"origin":"","legend":"\u003cp\u003e(\u003cstrong\u003eA\u003c/strong\u003e)Expression levels of the \u003cem\u003eGZMB\u003c/em\u003e gene in different stages of TCGA-OSCC,***\u003cem\u003e P \u003c/em\u003e\u0026lt; 0.001. (\u003cstrong\u003eB\u003c/strong\u003e)Pathological grading of OSCC by HE staining。(\u003cstrong\u003eC\u003c/strong\u003e) Immunofluorescence staining results for GZMB\u003cem\u003e \u003c/em\u003eprotein in OSCC tissue, with a bar chart illustrating the analysis of immunofluorescence staining. ****\u003cem\u003eP \u003c/em\u003e\u0026lt; 0.0001. (\u003cstrong\u003eD\u003c/strong\u003e) In GSEA enrichment of gzmb related gene sets, the activated pathway on the left is positively correlated with \u003cem\u003eGZMB\u003c/em\u003e, while the inhibited pathway on the right is negatively correlated with \u003cem\u003eGZMB\u003c/em\u003e. (\u003cstrong\u003eE\u003c/strong\u003e) The TNF-α pathway is highly positively correlated with the \u003cem\u003eGZMB\u003c/em\u003e gene\u003c/p\u003e","description":"","filename":"figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-4251746/v1/f28e489b2777a1bf21704f9a.png"},{"id":79640598,"identity":"3e062479-0a03-4ad1-9223-b874c0d5bcbe","added_by":"auto","created_at":"2025-04-01 06:03:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":36360656,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4251746/v1/b6e394a6-9772-4c56-8c41-73acff00d69c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Expression Patterns and Prognostic Model Construction of the Major Histocompatibility Complex-Related Gene Granzyme B in Oral Squamous Cell Carcinoma","fulltext":[{"header":"Introduction","content":"\u003cp\u003eOSCC stands as one of the prevalent malignant tumors in the oral and maxillofacial region, renowned for its pronounced invasiveness and a high propensity for cervical lymph node metastasis. The 5-year survival rate among OSCC patients hovers around 50%, indicating a grim prognosis\u003csup\u003e[1-3]\u003c/sup\u003e.\u0026nbsp;Hence, the identification of regulatory molecules influencing the progression of OSCC holds paramount importance in enhancing patient prognosis. Tumors do not merely consist of cancer cells; rather, they encompass a heterogeneous amalgamation of host tissue cells, immune-infiltrating cells, extracellular matrix(ECM), and secretory mediators. Collectively, these elements give rise to the tumor microenvironment(TME), a complex and dynamic entity\u003csup\u003e[4-6]\u003c/sup\u003e.\u0026nbsp;These components collectively create an unfavorable environment characterized by immune suppression and nutrient deprivation, fostering tumor cell growth, proliferation and heterogeneity. Within the TME, tumor immune surveillance emerges as an effective strategy to identify and eliminate newly emerging tumor cells. MHC constitutes a cell surface heterotrimer expressed constitutively on nearly all nucleated cells of jawed vertebrates. The fundamental structure and function of MHC-I determine its pivotal role as the initial step in the tumor immune response. This mechanism relies on the presentation of specific antigens within tumor cells by MHC-I molecules\u003csup\u003e[7]\u003c/sup\u003e.\u0026nbsp;The MHC-I complex is assembled within the endoplasmic reticulum(ER) through the interaction of a polymorphic heavy chain with \u0026beta;2-microglobulin. Following assembly, it loads high-affinity peptides transported from the cytosol by the peptide transporter TAP\u003csup\u003e[8]\u003c/sup\u003e，MHC-II forms a heterodimer composed of \u0026alpha; and \u0026beta; chains, primarily expressed by professional antigen-presenting cells(PAPCs) like dendritic cells(DCs), B cells, and macrophages. Its primary function involves presenting exogenous peptide antigens to CD4\u003csup\u003e+\u003c/sup\u003e T cells\u003csup\u003e[9]\u003c/sup\u003e.\u0026nbsp;All antigens synthesized endogenously within the cytosol of cells are presented as peptides bound to MHC-I molecules, enabling their presentation to CD8\u003csup\u003e+\u003c/sup\u003e T cells. This mechanism permits CD8+ lymphocytes to identify and eliminate virus-infected cells or cancer cells.\u0026nbsp;Within the TME,\u0026nbsp;CD8\u003csup\u003e+\u003c/sup\u003e T\u0026nbsp;and natural killer(NK) cells release GZMB and perforin into tumor cells, thereby inducing tumor cell death\u003csup\u003e[10]\u003c/sup\u003e.\u0026nbsp;Recent studies have revealed that cancer cells can suppress anti-tumor immune responses by diminishing the release of GZMB from CD8\u003csup\u003e+\u003c/sup\u003e T cells through methyltransferase, consequently fostering immune evasion\u003csup\u003e[11]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eOur research revealed that as the disease progresses in OSCC, the expression of GZMB shows a declining trend. This may be due to the tumor suppressing the expression of GZMB by interfering with the MHC complex function through the interferon pathway.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003eData Download and Preprocessing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe obtained the OSCC dataset(TCGA-OSCC) from The Cancer Genome Atlas (TCGA,https://portal.gdc.cancer.gov/) for analysis. This dataset encompasses RNA-seq data and clinical information from a total of 361 samples, consisting of 32 adjacent normal samples and 329 OSCC tumor samples. Additionally, we retrieved RNA-seq datasets GSE30784\u003csup\u003e[12]\u003c/sup\u003e and GSE74530\u003csup\u003e[13]\u003c/sup\u003e from the Gene Expression Omnibus(GEO) database\u003csup\u003e[14]\u003c/sup\u003e. The GSE30784 dataset comprises 167 OSCC samples and 45 normal oral tissue samples, while the GSE74530 dataset includes 6 OSCC samples and 6 normal oral tissue samples. Subsequently, we conducted batch correction on the GSE30784 and GSE74530 datasets and merged them to form the combined GEO dataset(GEO-Combined).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMHCRDEGs selection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFirstly, we utilized the limma package(v. 3.44.3) to conduct differential analysis on the OSCC datasets TCGA-OSCC and GEO-Combined individually to identify the differentially expressed genes(DEGs) between different groups(OSCC/Control). DEGs were chosen based on the criteria of |logFC| \u0026gt; 1 and adjusted P-value (P.adj) \u0026lt; 0.05. Subsequently, the resulting DEGs were intersected with the Major Histocompatibility Complex related genes(MHCRGs) using a Venn diagram to derive the MHC-related differentially expressed gene set(MHCRDEGs).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMutation Analysis and Functional Enrichment of MHCRDEGs in OSCC Patients\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn addition to the previously mentioned analyses, we obtained \u0026quot;Copy Number Segment\u0026quot; data for OSCC patients. GISTIC(v. 2.0) was employed to analyze copy number variation(CNV) segments. We conducted single nucleotide polymorphism(SNP) mutation analysis and visualization for the MHCRDEGs gene set using GISTIC.\u003c/p\u003e\n\u003cp\u003eFor functional enrichment analysis, we utilized the \u0026ldquo;ClusterProfiler\u0026rdquo; package(v. 4.10.1) to perform Gene Ontology(GO) and Kyoto Encyclopedia of Genes and Genomes(KEGG) pathway enrichment analyses on the MHCRDEGs gene set.\u003c/p\u003e\n\u003cp\u003eFurthermore, we performed Gene Set Enrichment Analysis(GSEA) on all genes associated with logFC values based on the logFC values of TCGA-OSCC-DEGs in descending order. This analysis was conducted using the \u0026ldquo;ClusterProfiler\u0026rdquo; package (v. 4.10.1), and the reference gene set \u0026quot;c2.cp.all.v2022.1.Hs.symbols.gmt\u0026quot; was obtained from the Molecular Signatures Database(MSigDB,\u0026nbsp;https://www.gsea-msigdb.org).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBuilding prognostic model to screen key genes and plotting KM curves\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe integrated the survival information of TCGA-OSCC patients and conducted univariate Cox regression analysis on the MHCRDEGs gene set to preliminarily screen for genes that hold prognostic value. Genes with an adjusted P-value(P.adj) \u0026lt; 0.1 were deemed as key genes and subsequently included in the multivariate Cox regression analysis. We computed the RiskScore based on these key genes and formulated a prognostic model. TCGA-OSCC patients were stratified into groups according to the median value of the RiskScore, and Kaplan-Meier(KM) curves were generated for the TCGA-OSCC cohort.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssessment of the Prognostic Model\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe incorporated the key genes model as an independent prognostic factor and merged it with clinical information of OSCC patients(including age, gender, histological stage, and clinical grade) for Cox regression analysis. Subsequently, a nomogram plot of the prognostic model was generated. Calibration analysis was conducted to plot calibration curves, and decision curve analysis(DCA) was utilized to evaluate the clinical utility of the prognostic model.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImmune Score Analysis between High and Low-Risk Groups in TCGA-OSCC Dataset\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe employed the ESTIMATE package to conduct quantitative analysis of immune activity in OSCC tumor samples based on the gene expression profiles of TCGA-OSCC. This analysis yielded three scores: Stromal Score, Immune Score, and ESTIMATE Score. Furthermore, within the high and low-risk groups, we performed correlation analysis between key genes and 22 types of immune cells to investigate potential associations and functional interactions between key genes and immune cells.Lastly, we inputted the key genes into the GSCA online platform to determine the correlation between drug sensitivity in the CTRP and GDSC databases and the key genes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe expression of the \u003cem\u003eGZMB\u003c/em\u003e gene in OSCC tissues\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe evaluated the expression levels of the \u003cem\u003eGZMB\u003c/em\u003e gene across different clinical stages of OSCC in TCGA-OSCC. Subsequently, we obtained cancer tissues from OSCC patients undergoing inpatient treatment at the Affiliated Stomatology Hospital of Kunming Medical University. Pathological grading of cancer tissues was determined through HE staining. Immunofluorescence was utilized to assess the expression levels of GZMB protein in OSCC tissues at various stages. Lastly, in the TCGA-OSCC expression matrix, we analyzed genes related to\u003cem\u003e\u0026nbsp;GZMB\u003c/em\u003e, ranked them by correlation coefficients, and then employed GSEA to explore the perturbation-related pathways of \u003cem\u003eGZMB\u003c/em\u003e, thus elucidating its potential biological roles in OSCC.The study was conducted following the Declaration of Helsinki and was approved by the Biomedical Research Ethics Committee of Kunming University[approval number: KYKQ2022MEC0011]. Written informed consent was obtained from all patients prior to sample collection. \u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eData Download and Preprocessing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe conducted batch correction on GSE30784 and GSE74530 to integrate the GEO-Combined. Following batch removal, the expression matrices of the two datasets overlapped, indicating the successful removal of batch effects(Fig. 1A,B). Through threshold filtering of differentially expressed genes between the two groups in the TCGA-OSCC dataset, we identified a total of 5404 genes. Among these, 2642 genes were significantly upregulated in OSCC, while 2642 genes were significantly downregulated. Similarly, from the GEO-Combined dataset, we obtained 1468 significantly differentially expressed genes, with 714 genes upregulated and 754 genes downregulated in OSCC samples. Visualization of these results was accomplished using volcano plots(Fig. 1C,D). Subsequently, we intersected the differentially expressed gene sets from the TCGA-OSCC dataset and the GEO-Combined dataset with MHCRGs, resulting in 57 MHCRDEGs(Fig. 1E). Furthermore, we depicted the expression patterns of MHCRDEGs in both datasets(Fig. 1F,G)We assessed the gene mutations of the 57 MHCRDEGs in OSCC, predominantly characterized by missense mutations, including nonsense mutations, frameshift deletions, splice site alterations, in-frame deletions, and frameshift insertions. Among these mutation types, SNPs were the most common, with a small proportion of deletions (DEL) and insertions (INS). The most frequent SNP in OSCC patients were C \u0026gt; T, followed by C \u0026gt; G, C \u0026gt; A, and others. Additionally, among the 57 MHCRDEGs in the TCGA-OSCC dataset, FLG had the highest occurrence of single nucleotide polymorphisms involving three main mutation types. Moreover, the nine MHCRDEGs with the highest occurrence of single nucleotide polymorphisms in the TCGA-OSCC dataset were \u003cem\u003eCOL11A1\u003c/em\u003e, \u003cem\u003eHLA-B\u003c/em\u003e, \u003cem\u003eHLA-A\u003c/em\u003e, \u003cem\u003eFN1\u003c/em\u003e, \u003cem\u003eTNC\u003c/em\u003e, \u003cem\u003eNLRC5\u003c/em\u003e, \u003cem\u003eTAP1\u003c/em\u003e, \u003cem\u003eSTAT1\u003c/em\u003e, and \u003cem\u003eSERPINE1\u003c/em\u003e, primarily characterized by missense mutations(Fig. 1H). GO enrichment analysis revealed that MHCRDEGs mainly participate in biological processes such as antigen processing and presentation, JAK-STAT pathway receptor signaling, and regulation of endogenous antigen presentation(Fig. 1I). Furthermore, KEGG pathway analysis showed that MHCRDEGs are primarily enriched in pathways related to allograft rejection, human papillomavirus infection, Epstein-Barr virus infection, cellular senescence, and malaria(Fig. 1J). Lastly, GSEA enrichment analysis of the entire TCGA-OSCC matrix showed significant activation of pathways such as E2F targets, IFN-\u0026alpha;, IFN-\u0026gamma;, epithelial-mesenchymal transition, and angiogenesis in OSCC, while processes such as oxidative phosphorylation, lipid biosynthesis, and fatty acid metabolism were inhibited(Fig. 1K).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConstructing Prognostic Cox Model to Screen Key Genes and Plotting KM Curves\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe employed a forest plot to present the results of single Cox regression analysis for MHCRDEGs(Fig. 2A). Eight key genes have been identified, including \u003cem\u003ePLP1\u003c/em\u003e, \u003cem\u003eIL17D\u003c/em\u003e, \u003cem\u003eDDAH2\u003c/em\u003e, \u003cem\u003eAZGP1\u003c/em\u003e, \u003cem\u003eADA\u003c/em\u003e, \u003cem\u003eBLNK\u003c/em\u003e, \u003cem\u003eCCL5\u003c/em\u003e, and \u003cem\u003eGZMB\u003c/em\u003e. By incorporating these eight key genes into a multifactorial Cox regression, we derived the formula for the RiskScore calculation model as follows: RiskScore = -0.32\u003cem\u003eDDAH2\u003c/em\u003e - 0.081\u003cem\u003eBLNK\u0026nbsp;\u003c/em\u003e- 0.0683\u003cem\u003eAZGP1\u003c/em\u003e - 0.0668\u003cem\u003eCCL5\u003c/em\u003e - 0.0577\u003cem\u003eGZMB\u003c/em\u003e + 0.202\u003cem\u003eADA\u003c/em\u003e + 0.507\u003cem\u003ePLP1\u003c/em\u003e + 0.603\u003cem\u003eIL17D\u003c/em\u003e. The prognosis forest plot illustrated \u003cem\u003eIL17D\u003c/em\u003e and \u003cem\u003ePLP1\u003c/em\u003e as risk factors, contributing the most to the prognosis Cox model. This plot elucidates the contribution of each gene to the total score of patients and its correlation with survival rates(Fig. 2B).\u003c/p\u003e\n\u003cp\u003eKaplan-Meier curves demonstrated a significant difference in the survival outcome of OSCC patients between high and low-risk groups based on the prognosis Cox model RiskScore(P\u0026lt;0.0001). Higher RiskScores were associated with poorer prognosis(Fig. 2C). Among the key genes, \u003cem\u003eIL17D\u003c/em\u003e and \u003cem\u003eCCL5\u003c/em\u003e also exhibited significant prognostic implications for OSCC patients. Notably, \u003cem\u003eCCL5\u003c/em\u003e served as a favorable prognostic factor (Fig. 2D,E).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePrognostic Model Evaluation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe multifactorial Cox regression results of the prognosis model incorporating clinical features such as patient age and gender demonstrated that the prognosis model could function as an independent prognostic factor (HR=2.718, P\u0026lt;0.001) for evaluating patient prognosis(Fig. 3A). In comparison to age, the key genes model exhibited a more significant contribution to the model, indicating its superior evaluation capability(Fig. 3B).We assessed the goodness of fit of the model\u0026apos;s predicted survival probabilities to actual data through calibration plots. These plots depicted the predictive performance of the model at 1/3/5 years, with different points representing the model\u0026apos;s predictions at various time points. Notably, we observed that the predicted points at 3 and 5 years closely approximated the gray ideal line, indicating good predictive performance at these time points (Fig. 3C-E).In the DCA, the stability of the model\u0026apos;s line above the lines representing \u0026quot;All positive\u0026quot; and \u0026quot;All negative\u0026quot; served as the basis for judging the results. The range of x-values where the model\u0026apos;s line consistently remained above these reference lines indicated the effectiveness of the model. Our findings indicated that the x-value range was the largest at 5 years, suggesting the optimal performance of the model. The predictive model could offer greater \u0026quot;net benefit\u0026quot; and sensitivity at the 5-year mark(Fig. 3F-H).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnalysis of Immune and Drug Sensitivity in High and Low-Risk Groups\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe conducted a comparison of immune scores between high and low-risk groups in the TCGA-OSCC dataset. The results indicated that the ESTIMATE Score(Fig. 4A), Immune Score(Fig. 4B), and Stromal Score(Fig. 4C) were all higher in the low-risk group, with statistical significance. To investigate the reasons for the differences in immune matrix between high and low-risk groups, we conducted correlation analysis between key genes and infiltrating immune cells. The results revealed that key genes were positively correlated with various immune cells. Specifically, in both high and low-risk groups, GZMB exhibited significant positive correlations with all immune cells, with the strongest correlation observed with activated CD8\u003csup\u003e+\u003c/sup\u003e T cells. Conversely, DDAH2 showed a significant negative correlation with neutrophils and weak correlations with other immune cells(Fig. 4D,E). Furthermore, we analyzed the relationship between key genes and drug sensitivity. The results unveiled that \u003cem\u003eADA\u003c/em\u003e, \u003cem\u003eCCL5\u003c/em\u003e, and \u003cem\u003eBLNK\u003c/em\u003e were significantly negatively correlated with various drugs such as CR-1-31B, alvocidib, dinaciclib, and SNX-2112 in both the GDSC and CTRP databases. Conversely, \u003cem\u003eDDAH2\u003c/em\u003e exhibited a significant positive correlation. This correlation might be associated with the targets of the drugs, providing new insights for potential drug therapies (Fig. 4F,G).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe expression status of GZMB in OSCC tissues\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the TCGA-OSCC expression matrix, we observed a significant increase in the expression level of the GZMB gene in tumor tissues classified as T\u003csub\u003e1\u003c/sub\u003e/T\u003csub\u003e2\u003c/sub\u003e and T\u003csub\u003e3\u003c/sub\u003e/T\u003csub\u003e4\u003c/sub\u003e stages compared to normal tissues. Additionally, the expression level in T3/T4 stages was lower than in T\u003csub\u003e1\u003c/sub\u003e/T\u003csub\u003e2\u003c/sub\u003e stages(Fig. 5A). HE staining results confirmed the staging of OSCC tissues. Well-differentiated OSCC(WD-OSCC) tissues exhibited prominent intercellular bridges and keratin pearls with fewer mitotic figures, whereas moderately differentiated OSCC(MD-OSCC) showed less distinct keratin pearls, cellular nuclear pleomorphism, and increased mitotic activity(Fig. 5B). Immunofluorescence staining revealed that GZMB protein content was significantly higher in WD-OSCC tissues compared to MD-OSCC tissues, and both were higher than in control tissues(Fig. 5C). GZMB content increased in the early stages of tumor development, but its function and quantity may be suppressed as the tumor progresses.\u003c/p\u003e\n\u003cp\u003eWe analyzed the correlation of \u003cem\u003eGZMB\u003c/em\u003e with all genes in the TCGA-OSCC expression profile and performed GSEA analysis to identify pathways or biological processes most likely to affect \u003cem\u003eGZMB\u003c/em\u003e in OSCC. We found that processes such as interferon and allograft rejection were positively correlated with \u003cem\u003eGZMB\u003c/em\u003e, while processes such as epithelial-mesenchymal transition and protein secretion were negatively correlated with \u003cem\u003eGZMB\u003c/em\u003e(Fig. 5D). Additionally, we displayed the interferon-alpha pathway, which showed a significant positive correlation with \u003cem\u003eGZMB\u003c/em\u003e(p.adj=6.25e-10) (Fig. 5E).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eRecent studies have revealed a close association between the TME and immunotherapy, underscoring its significant role in the initiation and progression of tumors\u0026nbsp;\u003csup\u003e[15]\u003c/sup\u003e.\u0026nbsp;TME consists of stroma, fibroblasts, endothelial cells, immune cells, and various other components, collectively regulating the intricate ecological behavior of cancer cells\u0026nbsp;\u003csup\u003e[16]\u003c/sup\u003e,\u0026nbsp;Its composition comprises elements that facilitate immune evasion, thus hastening the progression of tumors. This phenomenon can be attributed to factors such as hypoxia, metabolic dysregulation, phenotypic alterations in immune cells, and the release of tumor-derived exosomes\u003csup\u003e[17-20]\u003c/sup\u003e.\u0026nbsp;In the TME, the pivotal initial stage for CD8\u003csup\u003e+\u003c/sup\u003e T cell-mediated anti-tumor immune response involves the presentation and recognition of antigens mediated by MHC-I\u0026nbsp;\u003csup\u003e[21]\u003c/sup\u003e. The MHC-I complex exhibits a heterodimeric structure, comprising a polymorphic heavy chain and a light chain known as \u0026beta;2-microglobulin\u0026nbsp;\u003csup\u003e[22-24]\u003c/sup\u003e，The MHC-I complex undergoes scanning by the T cell receptor (TCR), facilitating the recognition of antigenic peptides by CD8\u003csup\u003e+\u003c/sup\u003e T cells\u0026nbsp;\u003csup\u003e[25]\u003c/sup\u003e.\u0026nbsp;MHC-II molecules are expressed on immune cells such as B cells, monocytes, macrophages, and dendritic cells, as well as on epithelial cells following inflammatory signaling, while MHC-I molecules exhibit broader expression. Dendritic cells present antigens to na\u0026iuml;ve CD4+ T cells via MHC-II molecules to activate them, subsequently engaging in interactions between B cells and macrophages with these specific CD4\u003csup\u003e+\u003c/sup\u003e effector T cells. Together with MHC-I, they stimulate CD8\u003csup\u003e+\u003c/sup\u003e T cells to release GZMB and perforin into tumor cells, thereby promoting their apoptosis\u003csup\u003e[26]\u003c/sup\u003e.\u0026nbsp;This study, anchored on a prognosis model, elucidates a noteworthy correlation between the\u003cem\u003e\u0026nbsp;GZMB\u003c/em\u003e gene and CD8\u003csup\u003e+\u003c/sup\u003e T cells. To delve into this correlation, the study initially evaluated the expression levels of the \u003cem\u003eGZMB\u003c/em\u003e gene across various clinical stages of OSCC using TCGA-OSCC data. The findings revealed a substantial elevation in \u003cem\u003eGZMB\u003c/em\u003e content in stages T1-T2 compared to stages T3-T4, with both stages exhibiting higher levels than normal tissues. Subsequently, the study scrutinized the expression levels of GZMB protein in OSCC tissues at different pathological stages, which was corroborated by immunofluorescence results. GZMB, serving as a cytotoxic factor, is a serine protease produced by CD8\u003csup\u003e+\u003c/sup\u003e T cells and NK cells. Through this mechanism, the immune system eradicates potentially harmful cells, thereby preserving tissue homeostasis\u003csup\u003e[27]\u003c/sup\u003e.\u0026nbsp;In the realm of tumor research, it has been unearthed that tumors impede the antigen presentation process of MHC-I molecules, hindering the recognition of tumor cells by CD8\u003csup\u003e+\u003c/sup\u003e T cells and thwarting their cytotoxic effects, thus facilitating tumor immune evasion. This study posits that as the tumor advances, there is a decrement in GZMB expression. In cancer, cancer cells undergo a loss of sensitivity to apoptosis, resulting in the uncontrolled proliferation of aberrant cells, thereby fostering the progression of cancer\u0026nbsp;\u003csup\u003e[28]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThis study delves into a cluster of synergistic molecules that co-vary with GZMB to probe potential mechanisms through which tumors influence GZMB. Consequently, GSEA was undertaken on a gene set associated with \u003cem\u003eGZMB\u003c/em\u003e. The findings reveal that these genes are extensively implicated in biological processes such as interferon response, cell apoptosis, inflammation, and infection. Similarly, GSEA analysis of TCGA-OSCC also indicates the activation of interferon-alpha and interferon-gamma processes within tumors. Interferons (IFNs) are generated by the innate immune system through Toll-like receptor (TLR) stimulation and other signal transduction cascade reactions\u0026nbsp;\u003csup\u003e[29]\u003c/sup\u003e.\u0026nbsp;Previously identified IFNs are categorized into three types: type I, type II, and type III. Type I interferons encompass IFN\u0026alpha; and IFN\u0026beta;\u003csup\u003e[30, 31]\u003c/sup\u003e IFNs have the capability to upregulate MHC-II and other components associated with antigen presentation, while also modulating the composition and equilibrium of intricate cytokine responses\u0026nbsp;\u003csup\u003e[32]\u003c/sup\u003e.\u0026nbsp;Tumor cells have the ability to evade CD8\u003csup\u003e+\u003c/sup\u003e T cell-mediated tumor killing by manipulating the inhibitor Serpinb9 through activation of radiation-induced type I IFN signaling\u0026nbsp;\u003csup\u003e[33]\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eResearch suggests that tumor cells evade recognition and attack by immune cells through inhibition of IFN signaling within the tumor, consequently impacting the function of MHC and its associated components\u003csup\u003e[34]\u003c/sup\u003e. Certainly, our findings corroborate this perspective. However, additional exploration is imperative to elucidate the mechanism by which IFN-I inhibits the antigen presentation of MHC-I molecules in the progression of OSCC, thereby diminishing the release of GZMB\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn summary, the predictive model, built with key genes, could serve as a reliable biomarker for the prognosis of OSCC. In OSCC, tumor cells may suppress MHC-I antigen presentation through IFN-I, reducing \u003cem\u003eGZMB\u003c/em\u003e release and facilitating immune escape. This discovery implies that the predictive model could offer vital insights into OSCC progression and prognosis.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eOSCC Oral squamous cell carcinoma, MHC Major Histocompatibility Complex, GZMB Granzyme B, ECM Extracellular matrix, TME Tumor microenvironment, ER Endoplasmic reticulum, PAPCs Professional antigen-presenting cells, DCs Dendritic cells, NK Natural killer, TCGA The Cancer Genome Atlas, GEO Gene Expression Omnibus, GEO-Combined Combined GEO dataset, MHCRGs Major Histocompatibility Complex related genes, MHCRDEGs MHC-related differentially expressed gene set, CNV Copy number variation, SNP Single nucleotide polymorphism, GO Gene Ontology, KEGG Kyoto Encyclopedia of Genes and Genomes, GSEA Gene Set Enrichment Analysis, KM Kaplan-Meier, DCA Decision curve analysis, WD-OSCC Well-differentiated OSCC, MD-OSCC Moderately differentiated OSCC, TCR \u0026nbsp;T cell receptor, IFNs Interferons, TLR Toll-like receptor.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWritten informed consent was obtained from all patients prior to sample collection.\u0026nbsp;This study was approved by the Biomedical Research Ethics Committee of Kunming University [approval number: KYKQ2022MEC0011].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contribution statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eH.R.Z.and X.Z.:Contributed to manuscript writing, conducted literature searches, and participated in data cleaning, processing, data visualization, specimen processing and hematoxylin and eosin(HE) taining.J.Y.L.andW.Y.Z.:Contributed to Specimen collection .Z.Y.S.andW.H.W.: Provided major contributions, including project design, determination of research direction, supervised data analysis, and oversaw the entire research process.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Yunnan Provincial Department of Science and Technology[202201AY070001-171].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData will be made available on request.\u0026nbsp;If you need the data, you can contact the corresponding author, Weihong Wang, via email at\u0026nbsp;\u003ca href=\"mailto:[email protected]\" target=\"_blank\"\[email protected]\u003c/a\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of Interest\u0026rsquo;s statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSUNG H, FERLAY J, SIEGEL R L, et al. 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Annual review of immunology, 2016, 34: 265-97.\u003c/li\u003e\n\u003cli\u003e CHANG H F, SCHIRRA C, NINOV M, et al. Identification of distinct cytotoxic granules as the origin of supramolecular attack particles in T lymphocytes [J]. Nature communications, 2022, 13(1): 1029.\u003c/li\u003e\n\u003cli\u003e MORANA O, WOOD W, GREGORY C D. The Apoptosis Paradox in Cancer [J]. International journal of molecular sciences, 2022, 23(3).\u003c/li\u003e\n\u003cli\u003e GOENKA A, KHAN F, VERMA B, et al. Tumor microenvironment signaling and therapeutics in cancer progression [J]. Cancer communications (London, England), 2023, 43(5): 525-61.\u003c/li\u003e\n\u003cli\u003e YU R, ZHU B, CHEN D. Type I interferon-mediated tumor immunity and its role in immunotherapy [J]. Cellular and molecular life sciences : CMLS, 2022, 79(3): 191.\u003c/li\u003e\n\u003cli\u003e DEMARIA O, DE GASSART A, COSO S, et al. STING activation of tumor endothelial cells initiates spontaneous and therapeutic antitumor immunity [J]. Proceedings of the National Academy of Sciences of the United States of America, 2015, 112(50): 15408-13.\u003c/li\u003e\n\u003cli\u003e BORST K, FLINDT S, BLANK P, et al. Selective reconstitution of IFN‑\u0026gamma; gene function in Ncr1+\u0026nbsp;NK cells is sufficient to control systemic vaccinia virus infection [J]. PLoS pathogens, 2020, 16(2): e1008279.\u003c/li\u003e\n\u003cli\u003e BUTTERFIELD L H, VUJANOVIC L, SANTOS P M, et al. Multiple antigen-engineered DC vaccines with or without IFN\u0026alpha; to promote antitumor immunity in melanoma [J]. Journal for immunotherapy of cancer, 2019, 7(1): 113.\u003c/li\u003e\n\u003cli\u003e MASSA C, WANG Y, MARR N, et al. Interferons and Resistance Mechanisms in Tumors and Pathogen-Driven Diseases-Focus on the Major Histocompatibility Complex (MHC) Antigen Processing Pathway [J]. International journal of molecular sciences, 2023, 24(7).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Oral squamdous cell carcinoma, MHC, GZMB, Risk score","lastPublishedDoi":"10.21203/rs.3.rs-4251746/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4251746/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Oral squamous cell carcinoma (OSCC) accounts for 90% of malignant tumors in the oral and maxillofacial region, known for its high metastatic potential and poor prognosis. This study aims to investigate Major Histocompatibility Complex(MHC) related genes' involvement in OSCC, offering new insights for immunotherapy. We identified differentially expressed genes associated with MHC in OSCC, analyzed copy number variations and pathways enrichment, and developed a prognostic model using Cox regression analysis on patient data. Our model proved to be an independent prognostic indicator, effectively predicting patients' prognosis and showcasing its efficacy in estimating 5-year survival rates. Immunoinfiltration analysis highlighted Granzyme B (GZMB) as a key gene significantly correlated with immune cells, showing varied expression across tumor stages. Additionally, we characterized OSCC tissue grading, finding higher GZMB protein expression in well-differentiated compared to moderately differentiated tissues, both exceeding levels in normal tissues. Our findings suggest the developed prognostic model could serve as a reliable biomarker for OSCC prognosis, emphasizing the crucial role of GZMB in tumor progression.","manuscriptTitle":"Expression Patterns and Prognostic Model Construction of the Major Histocompatibility Complex-Related Gene Granzyme B in Oral Squamous Cell Carcinoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-02 19:41:54","doi":"10.21203/rs.3.rs-4251746/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"49378260-54c0-45d8-a906-0b1d353f536e","owner":[],"postedDate":"May 2nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":31259563,"name":"Health sciences/Diseases"},{"id":31259564,"name":"Health sciences/Medical research"}],"tags":[],"updatedAt":"2025-04-01T05:38:42+00:00","versionOfRecord":[],"versionCreatedAt":"2024-05-02 19:41:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4251746","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4251746","identity":"rs-4251746","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}