Granzyme B activity and apoptotic signatures in head and neck cancers: a multi-parameter immunological study

Granzyme B activity and apoptotic signatures in head and neck cancers: a multi-parameter immunological study | 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 Granzyme B activity and apoptotic signatures in head and neck cancers: a multi-parameter immunological study Filip Lewandowski, Karolina Wiśniewska, Karolina Pierzynowska, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9413065/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Head and neck cancers (HNC) are associated with profound immune dysregulation and high resistance to immunotherapy. Granzyme B (GZMB) plays a pivotal role in cytotoxic lymphocyte function, while apoptotic gene signatures may determine the fate and efficacy of immune responses. This study analyzed peripheral blood and tumor samples from HNC patients and healthy donors using qPCR, flow cytometry, and capillary Western blotting. We quantified GZMB, BAX, BCL-2 , and CASPASE-3 expression in PBMCs and assessed immune cell composition and circulating GZMB levels. Data were integrated through principal component analysis (PCA). PBMCs from HNC patients showed significant upregulation of GZMB and increased frequencies of CD3⁺CD8⁺ cytotoxic T cells and CD3⁻CD56⁺CD16⁺ NK cells. In contrast, pro-apoptotic genes BAX and CASPASE-3 were downregulated, while anti-apoptotic BCL-2 was elevated, leading to a markedly reduced BAX/BCL-2 ratio. Western blot confirmed higher GZMB protein levels in tumor lysates. PCA revealed clear segregation of HNC patients from controls based on cytotoxic and apoptotic profiles. Our findings indicate that although immune effector cells in HNC patients exhibit elevated cytotoxic markers, their apoptotic resistance may reflect systemic immune dysregulation in HNC patients and may reflect systemic immune dysregulation without direct evidence of altered tumor killing capacity, although this cannot be directly extrapolated to tumor infiltrating immune cell. This altered immune signature supports further investigation into combined immuno-stimulatory and pro-apoptotic therapeutic strategies in HNC. Granzyme B Head and Neck Cancer Immunotherapy Cytotoxic Lymphocytes NK cells Tumor Immunology Oral Cancer Immune Response Figures Figure 1 Figure 2 Figure 3 Figure 4 1 Introduction Head and neck cancers (HNC) represent a diverse group of malignancies arising in the oral cavity [ 1 ], pharynx [ 2 ], and larynx [ 3 ], with head and neck squamous cell carcinoma (HNSCC) [ 4 ] accounting for over 90% of cases. HNC is among the most common cancers worldwide, with over 830,000 new cases and 430,000 deaths annually [ 5 , 6 ]. Despite advances in surgery, radiotherapy, and chemotherapy, the overall 5-year survival rate remains stagnant at approximately 50%, particularly due to late diagnosis and high recurrence rates [ 7 ]. The tumor microenvironment (TME) of HNC is often profoundly immunosuppressive, presenting a major barrier to effective immune-mediated tumor clearance [ 8 ]. The immunological landscape of HNC is characterized by extensive immune evasion mechanisms, including downregulation of MHC class I, recruitment of regulatory T cells (Tregs), myeloid-derived suppressor cells (MDSCs), and secretion of immunosuppressive cytokines such as IL-10 and TGF-β [ 9 ]. These features inhibit the activity of cytotoxic T lymphocytes (CTLs) and natural killer (NK) cells, impairing effective antitumor immunity. High infiltration by Tregs and immunosuppressive macrophages correlates with poor prognosis, while a strong CD8⁺ T cell presence is generally associated with better outcomes [ 10 ]. The success of immune checkpoint inhibitors (ICIs), particularly anti-PD-1 agents, in recurrent or metastatic HNC has highlighted the role of the immune system in tumor control [ 11 , 12 ]. However, only a subset of patients respond to such therapies, underlining the need to identify immune biomarkers and functional indicators that predict therapeutic outcomes and guide patient selection [ 13 – 15 ]. Among these, the cytolytic enzyme granzyme B (GZMB) has gained attention as a key effector molecule and a potential marker of immune activity [ 16 – 20 ]. Granzyme B is a serine protease stored in the cytotoxic granules of CD8⁺ T cells and NK cells [ 21 ]. Upon immune synapse formation, it is released alongside perforin to induce apoptosis in target cells via caspase activation and DNA fragmentation [ 22 ]. This mechanism is central to immune surveillance against virally infected and transformed cells [ 23 – 25 ]. Elevated levels of GZMB in tumors or peripheral blood have been linked to improved clinical outcomes and responses to immunotherapy in several malignancies [ 26 ]. Conversely, low GZMB expression may reflect T cell exhaustion or impaired cytotoxicity, which are frequently observed in advanced cancers, including HNC [ 27 – 28 ]. Recent studies have also explored the therapeutic and diagnostic potential of GZMB. Granzyme B-targeted imaging agents have been developed for non-invasive monitoring of immune responses during immunotherapy [ 29 – 33 ]. Additionally, engineered fusion proteins combining GZMB with tumor-targeting domains are being investigated as novel cancer treatments [ 34 ]. These strategies underscore the growing recognition of GZMB not only as an immune effector but also as a clinically relevant biomarker [ 35 ]. In parallel, resistance to apoptosis has been recognized as a hallmark of immune dysfunction in cancer [ 36 ]. Tumors can induce immune cell apoptosis through FasL expression or by altering intracellular apoptotic signaling pathways [ 37 ]. Therefore, analyzing the expression of apoptosis-related genes such as BAX [ 38 ], BCL-2 [ 39 ], and CASPASE-3 [ 40 ] in immune cells may provide insight into their functional state and survival potential. Given the immunosuppressive nature of HNC and the importance of cytotoxic immune responses, we hypothesized that the expression of GZMB and apoptosis-related genes in peripheral blood mononuclear cells (PBMCs) may reflect systemic immune alterations in HNC patients. PBMCs, which include T cells and NK cells, serve as a valuable model for monitoring host immune status [ 41 , 42 ]. In this study, we conducted a multi-parameter immunological analysis to compare the transcriptional profile of GZMB, BAX, BCL-2 , and CASPASE-3 in PBMCs from HNC patients and healthy donors. Our aim was to identify immunological signatures associated with cancer presence, providing a rationale for further research on immune-targeted therapies and blood-based biomarkers in HNC. 2 Materials and Methods 2.1 Biological material collection and ethical approval Peripheral venous blood (3 mL) was collected into EDTA-coated tubes from patients diagnosed with head and neck squamous cell carcinoma (ICD-10 codes C32.0 and C32.8) at the Department of Adult and Pediatric Otolaryngology and Laryngological Oncology, University Clinical Hospital No. 1, Pomeranian Medical University in Szczecin, Poland. In parallel, biopsy specimens were also collected for molecular analyses. The biological material was transported under Good Clinical Practice (GCP) conditions to the Center for Experimental Immunology and Immunobiology of Infectious and Cancer Diseases, University of Szczecin, where all cytometric, gene and protein expression analyses were performed. The study group consisted of 36 patients (26 males and 10 females), aged 38–85 years, all diagnosed with laryngeal cancer. The majority of patients underwent total laryngectomy with adjuvant radiotherapy or chemoradiotherapy, while others received laser cordectomy or palliative radiotherapy, depending on tumor location, stage, and clinical condition. The control group consisted of 20 healthy volunteers (12 females and 8 males), aged 20–57 years, with no history of cancer or autoimmune disease. Peripheral blood samples (3 mL) were collected from these individuals under identical conditions and processed using the same procedures as for the study group. An important limitation of this study is the demographic difference between groups, as the HNC cohort was older and predominantly male, while the control group was younger and included more females. Age and sex are known to influence NK cell frequency, CD8⁺ T cell differentiation, and apoptosis-related gene expression. Therefore, some of the observed differences may be partially influenced by demographic factors rather than cancer alone. All participants provided written informed consent prior to inclusion in the study. The study protocol was approved by the Bioethics Committee of the University of Szczecin (Resolution no. 14/2024 of Bioethical Comission of the University of Szczecin, Poland) and all procedures were conducted in accordance with the Declaration of Helsinki and applicable institutional guidelines. 2.2 Immunophenotyping Immunophenotyping was performed using a BD FACS Canto II flow cytometer (BD Biosciences, USA) and BD FACSDiva software version 9.1. Whole blood samples (100 µL) were incubated with monoclonal antibodies (all from BD Biosciences) according to the manufacturer’s instructions. The initial staining panel included anti-CD5 and anti-CD19 to distinguish T and B lymphocyte populations. Within the T-cell population, further staining was performed using anti-CD3, anti-CD4, and anti-CD8 to identify CD3⁺, CD4⁺, CD8⁺, and CD3⁺CD8⁺ subsets. Percentages of CD3⁺CD8⁺ T cells were calculated within the CD3⁺ T cell gate. Subsequently, intracellular expression of Granzyme B was assessed in CD3⁺CD8⁺ cytotoxic T lymphocytes using an anti-Granzyme B antibody (BD Biosciences). Additionally, NK cells were identified based on the expression of CD3⁻CD56⁺CD16⁺ markers, and Granzyme B levels were also evaluated within this population. Following antibody incubation, samples were incubated in the dark at room temperature. Red blood cell lysis was performed using BD Lysing Solution, followed by another incubation step under the same conditions. Cells were then washed twice with 1,000 µL of BD FACS Flow Solution and centrifuged at 250 RCF for 3 minutes. The final cell pellet was resuspended in 500 µL of BD FACS Flow Solution and immediately analyzed. A minimum of 10,000 events per sample were acquired for analysis. Fluorescence compensation was performed using single-stained compensation controls for each fluorochrome. All gating strategies were applied consistently across all samples and verified using appropriate fluorescence controls. 2.3 Gene expression analysis Peripheral blood mononuclear cells (PBMCs) were isolated from whole blood using density gradient centrifugation with Ficoll (Sigma-Aldrich). Total RNA was subsequently extracted from the obtained PBMCs using the Total RNA Mini kit (A&A Biotechnology, Gdańsk, Poland), following the manufacturer’s protocol. RNA purity and concentration were assessed spectrophotometrically by measuring the absorbance ratios at 260/280 nm and 260/230 nm using a NanoDrop spectrophotometer (ThermoFisher Scientific, USA). Reverse transcription was performed using the RevertAid First Strand cDNA Synthesis Kit (ThermoFisher Scientific), according to the manufacturer's instructions, with Random Hexamer Primers. The reaction was carried out on a Mastercycler nexus thermal cycler (Eppendorf, Germany). Gene Sequence GAPDH- F 5' TGAACGGGAAGCTCACTGG 3' GAPDH- R 5' TCCACCACCCTGTTGCTGTA 3' CASPASE3- F 5' ATGGAAGCGAATCAATGGA 3' CASPASE3- R 5' TGTACCAGACCGAGATGTC 3' BAX- F 5' GCCCTTTTCTACTTTGCCAGC 3' BAX- R 5' TCAGCCCATCTTCTTCCAGAT 3' BCL2- F 5' GGCCTTCTTTGAGTTCGGTGG 3' BCL2- R 5' GATAGGCACCCAGGGTGATGC 3' Quantitative real-time PCR (qPCR) was conducted using the PowerUp™ SYBR™ Green Master Mix for qPCR (ThermoFisher Scientific) and the LightCycler 480 System (Roche, Switzerland). Each reaction was carried out in a total volume of 20 µL, comprising 5 µL of cDNA and 15 µL of master mix with primers. The expression of GZMB, BAX, BCL-2 , and CASPASE-3 genes was analyzed, using GAPDH as the reference gene (primer sequences listed in Table 1). Each reaction for each gene was performed in three independent technical replicates. Table 1. Primer sequences used 2.4 Western blot exploratory analysis Tissue samples were incubated with T-PER™ Tissue Protein Extraction Reagent (cat.no. 78510, Thermo Scientific™) and a mixture of protease and phosphatase inhibitors (Roche Applied Science, Penzberg, Germany; #05892791001 and #11873580001), and homogenized for 30 seconds with maximum speed using Bead Ruptor Elite (cat. no. SKU 19-040E, Omni International) to obtain a homogenous mixture. The samples were then centrifuged for 10 min (12,000 rpm, 4 o C). Protein lysates were transferred to new Eppendorf tubes. Analysis of protein levels in lysates was conducted using Bradford reagent. Proteins were separated using the WES system (WES - Automated Western Blots with Simple Western; ProteinSimple, San Jose, California, USA), with a 12-230 kDa separation module (#SM-W003), and detected using an Anti-Mouse (#DM-002) detection module, according to the manufacturer's instructions. The Total protein module (#DM-TP01, ProteinSimple, San Jose, CA, USA) was used as a loading control. Granzyme B Monoclonal Antibody (GZB01) (Invitrogen, MA5-11587) were used in the study 1:50. Each sample was analyzed in three independent replicates. To determine the quantitative detection range of Granzyme B, a standard curve was prepared using Human Granzyme B Recombinant Protein (PeproTech®, Gibco, #140-18-10UG). The protein stock solution was serially diluted in MiliQ Ultrapure water (Merck, New Jersey, USA) to obtain the following final concentrations: 500, 250, 125, and 62.5 ng/mL. A negative control (C–) was prepared by replacing the protein with distilled water. Each standard and the negative control were processed and separated analogously to the experimental samples. The resulting signal intensities were used to generate a standard curve for subsequent quantification of Granzyme B levels in tested samples. Analyses were conducted in three independent repetitions for every sample. In addition, total protein levels were determined using the Total Protein Detection Module for Chemiluminescence, which served as the loading control. The band intensity was analyzed with an instrument’s Simple Western™ provided software, Compass for Simple Western (Bio-Techne, Minneapolis, MN, USA). Due to the limited sample size, no statistical comparisons were performed for tumor protein data. 2.5 Statistical analysis All statistical analyses were performed using Tibco Statistica 13.3 (StatSoft, Palo Alto, CA, USA) and GraphPad Prism 10.0.3 (GraphPad Software, San Diego, CA, USA). Data normality was assessed using the Shapiro–Wilk test. For normally distributed datasets, comparisons between two groups (HNC vs. control) were performed using unpaired two-tailed Student’s t-tests. In the case of non-normally distributed variables, the Mann–Whitney U test was applied. Significance thresholds were defined as follows: p < 0.05 ( ), p < 0.01 (), p < 0.001 ( ), and p < 0.0001 (****). Quantitative real-time PCR results were expressed as relative gene expression levels calculated using the 2^−ΔΔCt method, normalized to the reference gene GAPDH. Western blot densitometric values were normalized to GAPDH band intensity and reported as relative units. Flow cytometry results were expressed as percentages of gated parent populations (e.g., CD3⁺CD8⁺ T cells, CD3⁻CD56⁺CD16⁺ NK cells) or total event counts (e.g., extracellular granzyme B-positive events), and statistical comparisons were performed using appropriate parametric or non-parametric tests based on distribution. For multivariate pattern recognition, a Principal Component Analysis (PCA) was performed using GraphPad Prism. Prior to PCA, all variables (qPCR expression levels and cytometric data) were standardized (z-scores; mean = 0, SD = 1). Dimensionality reduction was based on parallel analysis, retaining principal components with eigenvalues greater than those obtained from 1000 Monte Carlo simulations at the 95th percentile confidence level. PCA results were visualized using score plots and biplots, displaying sample distribution and variable loadings. All numerical data are presented as mean ± standard deviation (SD) unless otherwise indicated. Graphical representations include bar plots with SD error bars and annotated p -values. Effect sizes were calculated using Cohen’s d to assess the magnitude of differences between groups. To control for multiple comparisons, p-values were adjusted using the Benjamini–Hochberg false discovery rate (FDR) correction. 3 Results The transcriptomic profiling revealed distinct differences between the groups, indicating potential dysregulation of both cytolytic and apoptotic pathways in the context of malignancy. A significant upregulation of GZMB (granzyme B) was observed (Fig. 1) in the cancer group (mean relative expression: 0.27) compared to the control group (0.112), indicating increased transcription of this cytotoxic effector molecule ( p < 0.0001 ). This elevation may reflect compensatory immune activation or heightened cytotoxic potential in response to tumor presence. Conversely, the expression of the proapoptotic gene BAX was markedly reduced (Fig. 1) in the HNC group (0.053) compared to controls (1.247), while the antiapoptotic gene BCL-2 (Fig. 1) was elevated (0.50 in HNC vs. 0.278 in controls). These alterations resulted in a profound decrease in the BAX/BCL-2 expression ratio (Fig. 1) from 4.66 in the control group to 0.110 in the cancer group (**** p < 0.0001). This shift indicates a strong antiapoptotic profile in the immune cells of HNC patients, consistent with an immunosuppressive or survival-oriented phenotype. Supporting this observation, CASPASE-3 , a key effector caspase involved in apoptosis execution, also showed (Fig. 1) significantly lower expression in the cancer group (0.219) versus controls (0.867; **** p < 0.0001). These findings suggest that immune cells from HNC patients may exhibit reduced susceptibility to apoptotic signaling, which likely reflects altered immune cell survival and differentiation status rather than directly indicating impaired cytotoxic function against tumor cells. Collectively, the gene expression data reveal an altered apoptotic and cytolytic gene signature in PBMCs of HNC patients. Despite elevated GZMB, the anti-apoptotic balance (↓BAX, ↑BCL-2, ↓CASPASE-3) suggests an anti-apoptotic survival phenotype in peripheral immune cells, which may reflect systemic immune dysregulation rather than impaired cytotoxic effectiveness per se. These patterns support the rationale for modulating apoptotic and effector pathways such as through melittin stimulation in order to restore functional immune responses against HNC. To quantitatively assess granzyme B protein abundance and complement the gene expression analysis, we performed an automated capillary Western blot (WES) using recombinant human granzyme B to generate a standard curve (62.5–500 ng/mL) and tissue lysates from three HNC patients. The standard curve produced a clear, concentration-dependent increase in signal intensity at ~ 66 kDa, confirming the specificity and quantitative range of the detection system (Fig. 2). No signal was observed in the negative control (distilled water), validating the absence of nonspecific background. In the experimental samples, a distinct band corresponding to granzyme B was detected in all analyzed HNC tissue lysates (Fig. 2). Quantitative analysis revealed substantial inter-individual variability, with Patient 2 showing the highest granzyme B concentration (434.4 ng/mL), consistent with the strongest signal intensity. Patient 3 exhibited an intermediate level (214.6 ng/mL), whereas Patient 1 demonstrated the lowest but clearly detectable protein abundance (80.4 ng/mL). These quantitative differences aligned with the visual variation in band intensities. No additional nonspecific bands were observed, confirming the assay’s analytical specificity. These exploratory results indicate that granzyme B protein is detectable in tumor tissues of HNC patients, although the small sample size and lack of non-tumor controls limit definitive conclusions. When considered alongside transcriptomic data showing elevated GZMB mRNA expression in PBMCs of HNC patients, the Western blot analysis supports the presence of an activated cytotoxic immune signature within the tumor environment. However, the variability in granzyme B abundance may reflect functional heterogeneity of infiltrating immune cells or differential tumor-driven modulation of cytotoxic lymphocyte activity in individual patients. To complement transcriptomic and proteomic data, we performed multiparametric flow cytometry to analyze immune effector populations and secreted granzyme B levels in peripheral blood samples from patients with head and neck cancer (HNC) and healthy donors. The preliminary cytometric profiling included quantification of cytotoxic CD3⁺CD8⁺ T cells, CD3⁻CD56⁺CD16⁺ natural killer (NK) cells, and extracellular granzyme B (GZMB), measured as the number of positive events per sample. In HNC patients, the proportion of CD3⁺CD8⁺ cytotoxic T cells (Fig. 3 ) was markedly elevated (30.84) compared to healthy controls (0.545), with a highly significant difference (p < 0.0001). This expansion may reflect ongoing immune activation or tumor-induced remodeling of the T cell compartment. The percentage of CD3⁻CD56⁺CD16⁺ NK cells was also significantly increased (Fig. 3 ) in the cancer group (5.585) compared to controls (1.240; p < 0.0001), indicating broader engagement of innate cytotoxic responses. Importantly, granzyme B was quantified as free circulating events in whole blood using flow cytometry. A significant increase in extracellular GZMB-positive events was observed (Fig. 3 ) in the HNC group (131.1) compared to controls (38.17; p < 0.0001). To reduce dimensionality and explore relationships among measured variables across all samples (n = 56), we performed a principal component analysis (PCA) (Fig. 4) integrating gene expression ( GZMB , BAX , BCL-2 , CASPASE-3 ) and cytometric data (CD8⁺ T cells %, NK cells %, extracellular GZMB-positive events). All variables were standardized (mean = 0, SD = 1) prior to analysis. The first principal component (PC1) captured the majority of the variance (67.05%), followed by PC2, which explained an additional 13.54%, bringing the cumulative variance explained by the first two components to 80.58%. In the resulting biplot, PC1 appeared to segregate samples based on cytotoxic activity and apoptotic resistance. Loadings revealed that CD8⁺ T cells (%), NK cells (%), GZMB expression, BCL-2 expression, and free granzyme B events were positively associated with PC1, suggesting a cytotoxic effector signature. In contrast, BAX and CASPASE-3 loaded negatively on PC1, consistent with a pro-apoptotic profile reduced in the experimental group. The distribution of sample scores along PC1 demonstrated separation between samples with elevated effector activity and those characterized by reduced pro-apoptotic signaling. PC2 contributed to minor variance (13.5%) and may reflect subtle differences in regulatory or compensatory pathways not captured by PC1. These results confirm that the integrated gene expression and cytometric profile of HNC patients is distinct from controls and is dominated by a shift toward enhanced cytotoxic marker expression ( GZMB , CD8⁺, NK cells) and suppression of apoptosis-related genes ( BAX , CASPASE-3 ). This multivariate pattern demonstrates coordinated changes in cytotoxic and apoptosis-related markers in HNC patients and supports the presence of a distinct systemic immune signature rather than providing direct evidence of functional impairment. PCA was used solely as an exploratory tool to visualize relationships among variables and does not imply predictive or diagnostic modeling. All analyzed parameters remained significant after Benjamini-Hochberg FDR correction (q < 0.05). Effect size analysis demonstrated very large differences between HNC patients and controls across most tested parameters (Cohen’s d range: 1.74–6.32). These findings indicate that the observed differences are not only statistically significant but also quantitatively substantial and consistent across multiple independent immune parameters. 4 Discussion The preliminary data presented in this study indicate a distinctive immunogenomic signature in patients with head and neck cancer (HNC), marked by enhanced cytotoxic marker expression and a concurrent suppression of pro-apoptotic signaling in peripheral immune cells. Our integrated analysis combining gene expression, flow cytometry, and protein-level validation provides new insights into immune dysregulation in HNC. A key finding was the significant upregulation of GZMB (granzyme B) at both the transcript (qPCR) and protein (Western blot) levels in PBMCs and tumor tissues from HNC patients. These results support previous studies demonstrating the involvement of GZMB in tumor immune responses. For example, Bose et al. [ 43 ] showed that despite the presence of cytotoxic cells in HNSCC tumors, key cytolytic genes, including GZMB , were often transcriptionally suppressed in tumor-infiltrating lymphocytes, possibly due to local immunosuppression mechanisms. In contrast, our data show elevated GZMB in peripheral immune cells and blood, suggesting a systemic cytotoxic activation that may not fully translate into effective anti-tumor responses at the tumor site. The overexpression of granzyme B, alongside a significant expansion of CD3⁺CD8⁺ T cells and NK cells, observed via flow cytometry, further supports the hypothesis of heightened immune surveillance. Similar findings were reported by Charap et al. [ 44 ], who observed preserved NK cytolytic potential in HPV⁺ HNSCC patients, although cytotoxic functionality varied depending on immune cell subset and tumor microenvironment. Our PCA analysis reinforces this perspective, showing that cytotoxic markers (CD8⁺ T cells, NK cells, GZMB) load heavily on the first principal component, distinguishing HNC patients from healthy controls. Despite elevated cytotoxic markers, the anti-apoptotic gene expression profile observed in PBMCs suggests an altered survival phenotype of peripheral immune cells, which may represent systemic immune dysregulation rather than a direct measure of cytotoxic effectiveness at the tumor site. Importantly, functional cytotoxic activity was not assessed in this study and therefore no conclusions regarding tumor cell killing can be drawn. This is consistent with findings from Wang et al. [ 45 ], who identified BAX downregulation as a pan-cancer hallmark associated with poor prognosis and reduced response to immunotherapy. Moreover, Tano et al. [ 46 ] previously reported that low BAX and high BCL-2 expression in HNC tissues correlated with worse patient outcomes, highlighting the relevance of apoptosis resistance as a prognostic factor. These transcriptional changes suggest that while cytotoxic cells may be numerically and transcriptionally activated in peripheral blood, their apoptotic machinery is impaired possibly due to tumor-derived signals inducing survival pathways. Importantly, apoptosis-related gene expression in cytotoxic lymphocytes reflects regulation of immune cell survival and activation-induced cell death rather than their ability to induce apoptosis in target tumor cells, which is mediated by granzyme B delivered into target cells following perforin-dependent entry. This hypothesis is supported by Ow et al. [ 47 ], who demonstrated that targeting apoptosis-related molecules in HNC cells can restore immune susceptibility and improve therapeutic responses. Interestingly, the elevation of circulating, extracellular granzyme B in HNC patients, as measured in our study, could represent either active secretion from functional cytotoxic cells or dysregulated degranulation. While such markers have not been extensively studied in peripheral blood, Lechner et al. [ 48 ] highlighted that humoral and innate immune components are increasingly active in certain HNC subtypes, suggesting that immune effector responses may be initiated but rendered ineffective by tumor evasion mechanisms. From a clinical perspective, peripheral blood immune signatures such as the GZMB/BAX/BCL-2 profile may serve as systemic biomarkers of immune status in HNC patients and could potentially be used to monitor immune response dynamics during immunotherapy Overall, our results converge with current literature to support a model of systemic immune dysregulation in HNC: immune effector cells are activated and express cytolytic molecules like GZMB but are simultaneously skewed toward an anti-apoptotic, survival phenotype. Whether this phenotype enhances or limits tumor eradication cannot be determined from the present data and requires functional cytotoxicity assays in future studies. These patterns underline the need for therapeutic strategies that both activate cytotoxic responses and overcome intrinsic apoptosis resistance. These findings should be interpreted as a description of systemic immune status rather than direct evidence of anti-tumor immune function. Given the preliminary nature of this study and the limited sample size, these findings should be interpreted with caution. However, the consistency between mRNA, protein, and functional data and alignment with multiple published reports, strengthens the biological plausibility of our results. Taken together, our findings support a model in which head and neck cancer is associated not with a lack of cytotoxic immune cells, but rather with a state of systemic immune dysregulation characterized by cytotoxic activation accompanied by altered apoptotic signaling and prolonged immune cell survival. 5 Limitations Several limitations of this study should be acknowledged. First, the HNC group was older and predominantly male, whereas the control group was younger and included more females. Age and sex are known to influence NK cell frequency, CD8⁺ T cell differentiation, apoptosis-related gene expression, and circulating granzyme B levels. Therefore, part of the observed differences may be influenced by demographic factors rather than cancer alone. Second, the Western blot analysis was performed in a limited number of tumor samples and should be considered exploratory. Third, functional cytotoxicity assays were not performed, which limits direct conclusions regarding tumor killing capacity. 6 Conclusion Our preliminary results reveal a distinct immunological profile in HNC patients, characterized by increased granzyme B activity and cytotoxic cell expansion, but accompanied by reduced expression of pro-apoptotic genes. Importantly, the observed differences were not only statistically significant but also demonstrated very large effect sizes, indicating that these differences represent biologically meaningful systemic immune alterations rather than minor statistical variations.This imbalance suggests that despite heightened immune activation, effector cells may exhibit an altered survival and activation phenotype characteristic of systemic immune dysregulation. However, functional cytotoxic capacity was not directly measured in this study and requires further investigation. Peripheral immune cells from HNC patients may exhibit reduced susceptibility to apoptotic signaling, indicating systemic immune alterations associated with cancer presence. However, whether this phenotype reflects the behavior of tumor-infiltrating lymphocytes requires further investigation. Declarations Conflict of Interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Funding This research was funded by the Polish Ministry of Science and Higher Education under the program "Studenckie koła naukowe tworzą innowacje" (eng. Student Scientific Clubs create innovation ), Agreement No. KN/SP/603343/2024, and was also financed by the Polish Minister of Science under the “Regional Excellence Initiative” Program, Agreement No. RID/SP/0045/2024/01. Author Contribution FL: Writing – original draft, Writing – review & editing, Conceptualization, Methodology, Formal analysis, Investigation, Visualization, Project administration, Funding acquisition, Validation, Response to reviewers; RH: Writing – review & editing, Investigation, Methodology; KW: Investigation, Resources, Validation; KP: Investigation, Resources; KR: Investigation, Resources; DB: Writing – review & editing, Methodology; ŁG: Writing – review & editing, Methodology; MZ: Writing – review & editing, Methodology;PNR: Supervision, Conceptualization, Methodology, Writing – review & editing, Funding acquisition, Project administration. Acknowledgement The authors would like to thank Dr. Rafał Becht for his invaluable assistance in collecting the biological material used in this study. Data Availability Data are available from the corresponding author upon reasonable request due to ethical restrictions related to patient data. 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Nivolumab in patients with recurrent or metastatic squamous cell carcinoma of the head and neck: Results of Polish multicenter observational study. International Journal of Cancer. https://doi.org/10.1002/ijc.35248 Saito Y, Kondo H, Hojo Y (2011) Granzyme B as a novel factor involved in cardiovascular diseases. J Cardiol (Vol 57:141–147. https://doi.org/10.1016/j.jjcc.2010.10.001 Seiwert TY, Burtness B, Mehra R, Weiss J, Berger R, Eder JP, Heath K, McClanahan T, Lunceford J, Gause C, Cheng JD, Chow LQ (2016) Safety and clinical activity of pembrolizumab for treatment of recurrent or metastatic squamous cell carcinoma of the head and neck (KEYNOTE-012): an open-label, multicentre, phase 1b trial. Lancet Oncol 17:956–965. https://doi.org/10.1016/S1470-2045(16)30066-3 Sindhu SK, Bauman JE (2019) Current Concepts in Chemotherapy for Head and Neck Cancer. Oral and Maxillofacial Surgery Clinics of North America, vol 31. W.B. 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Nature, 520, 373–377. https://doi.org/10.1038/nature14292 Wang H, Huang Y, He J, Zhong L, Zhao Y (2021) Dual roles of granzyme B. In Scandinavian Journal of Immunology (Vol. 94). John Wiley and Sons Inc. https://doi.org/10.1111/sji.13086 Wołaçewicz M, Becht R, Grywalska E, Niedźwiedzka-Rystwej P (2020) Herpesviruses in head and neck cancers. In Viruses (Vol. 12). MDPI AG. https://doi.org/10.3390/v12020172 Xie J, El Rami F, Zhou K, Simonetta F, Chen Z, Zheng X, Chen M, Balakrishnan PB, Dai S-Y, Murty S, Alam IS, Baker J, Negrin RS, Gambhir SS, Rao J (2022) Multiparameter Longitudinal Imaging of Immune Cell Activity in Chimeric Antigen Receptor T Cell and Checkpoint Blockade Therapies. ACS Cent Sci 8:590–602. https://doi.org/10.1021/acscentsci.2c00142 Xie L, Fang J, Yu J, Zhang W, He Z, Ye L, Wang H (2023) The role of CD4 + T cells in tumor and chronic viral immune responses. MedComm 4:e390. https://doi.org/10.1002/mco2.390 Zhang Y, Zhou X, Zhong Y, Chen X, Li Z, Li R, Qin P, Wang S, Yin J, Liu S, Jiang M, Yu Q, Hou Y, Liu S, Wu L (2024) Pan-cancer scRNA-seq analysis reveals immunological and diagnostic significance of the peripheral blood mononuclear cells. Hum Mol Genet 33:342–354. https://doi.org/10.1093/hmg/ddad187 Zhu J, Petit PF, Van den Eynde BJ (2019) Apoptosis of tumor-infiltrating T lymphocytes: a new immune checkpoint mechanism. In Cancer Immunology, Immunotherapy (Vol. 68, pp. 835–847). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/s00262-018-2269-y Anamika Bose T, Chakraborty K, Chakraborty S, Pal (2008) Rathindranath Baral; Dysregulation in immune functions is reflected in tumor cell cytotoxicity by peripheral blood mononuclear cells from head and neck squamous cell carcinoma patients. Cancer Immun 1 January 8(1):10. https://doi.org/10.1158/1424-9634.DCL-10.8.1 Wang S, Chen X, Zhang X, Wen K, Chen X, Gu J, Li J, Wang Z (2024) Pro-apoptotic gene BAX is a pan-cancer predictive biomarker for prognosis and immunotherapy efficacy. Aging 16:11289–11317. https://doi.org/10.18632/aging.206003 Ow TJ, Thomas C, Fulcher CD, Chen J, López A, Reyna DE, Prystowsky MB, Smith RV, Schiff BA, Rosenblatt G, Belbin TJ, Harris TM, Childs GC, Kawachi N, Schlecht NF, Gavathiotis E (2020) Apoptosis signaling molecules as treatment targets in head and neck squamous cell carcinoma. Laryngoscope 130(11):2643–2649 Epub 2020 Jan 2. PMID: 31894587; PMCID: PMC8142150 Charap AJ, Enokida T, Brody R, Sfakianos J, Miles B, Bhardwaj N, Horowitz A (2020) Landscape of natural killer cell activity in head and neck squamous cell carcinoma. J Immunother Cancer 8(2):e001523. 10.1136/jitc-2020-001523 PMID: 33428584; PMCID: PMC7754625 Lechner, A., Schlößer, H. A., Thelen, M., Wennhold, K., Rothschild, S. I., Gilles,R., … von Bergwelt-Baildon, M. (2019). Tumor-associated B cells and humoral immune response in head and neck squamous cell carcinoma. OncoImmunology, 8(3). https://doi.org/10.1080/2162402X.2018.1535293 Tano T, Okamoto M, Kan S, Nakashiro K, Shimodaira S, Koido S, Homma S, Sato M, Fujita T, Kawakami Y, Hamakawa H (2013) Prognostic impact of expression of Bcl-2 and Bax genes in circulating immune cells derived from patients with head and neck carcinoma. Neoplasia 15(3):305–314. 10.1593/neo.121528 PMID: 23479508; PMCID: PMC3593153 Additional Declarations No competing interests reported. Supplementary Files SuplementaryMaterial.pdf Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 19 May, 2026 Reviewers agreed at journal 11 May, 2026 Reviewers agreed at journal 21 Apr, 2026 Reviewers invited by journal 21 Apr, 2026 Editor assigned by journal 16 Apr, 2026 Submission checks completed at journal 16 Apr, 2026 First submitted to journal 14 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-9413065","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":631255503,"identity":"5fe98f58-c868-4d8d-850b-d82a62deeb9c","order_by":0,"name":"Filip 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University","correspondingAuthor":false,"prefix":"","firstName":"Katarzyna","middleName":"","lastName":"Radomska","suffix":""},{"id":631255511,"identity":"c13ed4ec-e78b-4426-a8ec-feb69d8f8805","order_by":8,"name":"Paulina Niedźwiedzka-Rystwej","email":"","orcid":"","institution":"University of Szczecin","correspondingAuthor":false,"prefix":"","firstName":"Paulina","middleName":"","lastName":"Niedźwiedzka-Rystwej","suffix":""}],"badges":[],"createdAt":"2026-04-14 09:08:46","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9413065/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9413065/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108397350,"identity":"4cd708bd-e4aa-4983-960b-d26b3cd2800a","added_by":"auto","created_at":"2026-05-04 08:21:09","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":219437,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifferential expression of cytotoxicity- and apoptosis-related genes in PBMCs from HNC patients and healthy donors.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-9413065/v1/5eb34e7efca74f1deebfbcd9.png"},{"id":108397346,"identity":"a309a5aa-54a9-4cc0-9eec-07c34149b6ae","added_by":"auto","created_at":"2026-05-04 08:21:09","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":24793,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eQuantitative WES Western blot analysis of granzyme B protein in tumor lysates from HNC patients (P1–P3) alongside recombinant standards (62.5–500 ng/mL).\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage28.png","url":"https://assets-eu.researchsquare.com/files/rs-9413065/v1/6034594af1e8c1eb10d5f535.png"},{"id":108397347,"identity":"c8d11e99-0381-4d7b-bd50-794f0dcc2bfe","added_by":"auto","created_at":"2026-05-04 08:21:09","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":184553,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlow cytometry analysis of peripheral CD3⁺CD8⁺ T cells, CD3⁻CD56⁺CD16⁺ NK cells, and free granzyme B (GZMB) events in whole blood of head and neck cancer (HNC) patients and healthy controls. All parameters were significantly elevated in the HNC group. Data are shown as mean ± SD. ****p \u0026lt; 0.0001 (unpaired two-tailed t-test).\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-9413065/v1/d8acb637d0bb04805941f3a7.png"},{"id":108492806,"identity":"85d2c8fb-38d9-4260-8752-a1062ef55a71","added_by":"auto","created_at":"2026-05-05 09:58:40","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":87530,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePrincipal Component Analysis (PCA) integrating gene expression (\u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eBAX, BCL-2, CASPASE-3, GZMB\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e), flow cytometry (% of CD3⁺CD8⁺ T cells and CD3⁻CD56⁺CD16⁺ NK cells), and free granzyme B levels in peripheral blood. The biplot displays PC1 and PC2, which together explain 80.58% of the total variance (PC1 = 67.05%, PC2 = 13.54%). Arrows indicate variable loadings, with strong contributions from \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eGZMB\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003eexpression, CD8⁺ T cells, NK cells, and circulating granzyme B. Patients with HNC cluster separately from controls along PC1, suggesting distinct immunogenomic profiles between groups.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-9413065/v1/a33a198a0929b391abc007ea.png"},{"id":108495227,"identity":"5c7c29ae-80fe-4174-9b58-3f0027ddf375","added_by":"auto","created_at":"2026-05-05 10:09:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":775035,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9413065/v1/7d935c46-db27-450c-8fc7-3b5e4fc3812d.pdf"},{"id":108493833,"identity":"f97ef6bd-8e65-476c-8583-2a2e059e1577","added_by":"auto","created_at":"2026-05-05 10:01:56","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":207762,"visible":true,"origin":"","legend":"","description":"","filename":"SuplementaryMaterial.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9413065/v1/2a7b6b5f3966724e642b9da5.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Granzyme B activity and apoptotic signatures in head and neck cancers: a multi-parameter immunological study","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eHead and neck cancers (HNC) represent a diverse group of malignancies arising in the oral cavity [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], pharynx [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], and larynx [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], with head and neck squamous cell carcinoma (HNSCC) [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] accounting for over 90% of cases. HNC is among the most common cancers worldwide, with over 830,000 new cases and 430,000 deaths annually [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Despite advances in surgery, radiotherapy, and chemotherapy, the overall 5-year survival rate remains stagnant at approximately 50%, particularly due to late diagnosis and high recurrence rates [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. The tumor microenvironment (TME) of HNC is often profoundly immunosuppressive, presenting a major barrier to effective immune-mediated tumor clearance [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The immunological landscape of HNC is characterized by extensive immune evasion mechanisms, including downregulation of MHC class I, recruitment of regulatory T cells (Tregs), myeloid-derived suppressor cells (MDSCs), and secretion of immunosuppressive cytokines such as IL-10 and TGF-β [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. These features inhibit the activity of cytotoxic T lymphocytes (CTLs) and natural killer (NK) cells, impairing effective antitumor immunity. High infiltration by Tregs and immunosuppressive macrophages correlates with poor prognosis, while a strong CD8⁺ T cell presence is generally associated with better outcomes [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe success of immune checkpoint inhibitors (ICIs), particularly anti-PD-1 agents, in recurrent or metastatic HNC has highlighted the role of the immune system in tumor control [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. However, only a subset of patients respond to such therapies, underlining the need to identify immune biomarkers and functional indicators that predict therapeutic outcomes and guide patient selection [\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Among these, the cytolytic enzyme granzyme B (GZMB) has gained attention as a key effector molecule and a potential marker of immune activity [\u003cspan additionalcitationids=\"CR17 CR18 CR19\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eGranzyme B is a serine protease stored in the cytotoxic granules of CD8⁺ T cells and NK cells [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Upon immune synapse formation, it is released alongside perforin to induce apoptosis in target cells via caspase activation and DNA fragmentation [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. This mechanism is central to immune surveillance against virally infected and transformed cells [\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Elevated levels of GZMB in tumors or peripheral blood have been linked to improved clinical outcomes and responses to immunotherapy in several malignancies [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Conversely, low GZMB expression may reflect T cell exhaustion or impaired cytotoxicity, which are frequently observed in advanced cancers, including HNC [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Recent studies have also explored the therapeutic and diagnostic potential of GZMB. Granzyme B-targeted imaging agents have been developed for non-invasive monitoring of immune responses during immunotherapy [\u003cspan additionalcitationids=\"CR30 CR31 CR32\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Additionally, engineered fusion proteins combining GZMB with tumor-targeting domains are being investigated as novel cancer treatments [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. These strategies underscore the growing recognition of GZMB not only as an immune effector but also as a clinically relevant biomarker [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. In parallel, resistance to apoptosis has been recognized as a hallmark of immune dysfunction in cancer [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Tumors can induce immune cell apoptosis through FasL expression or by altering intracellular apoptotic signaling pathways [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Therefore, analyzing the expression of apoptosis-related genes such as \u003cem\u003eBAX\u003c/em\u003e [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], \u003cem\u003eBCL-2\u003c/em\u003e [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], and \u003cem\u003eCASPASE-3\u003c/em\u003e [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] in immune cells may provide insight into their functional state and survival potential.\u003c/p\u003e \u003cp\u003eGiven the immunosuppressive nature of HNC and the importance of cytotoxic immune responses, we hypothesized that the expression of \u003cem\u003eGZMB\u003c/em\u003e and apoptosis-related genes in peripheral blood mononuclear cells (PBMCs) may reflect systemic immune alterations in HNC patients. PBMCs, which include T cells and NK cells, serve as a valuable model for monitoring host immune status [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. In this study, we conducted a multi-parameter immunological analysis to compare the transcriptional profile of \u003cem\u003eGZMB, BAX, BCL-2\u003c/em\u003e, and \u003cem\u003eCASPASE-3\u003c/em\u003e in PBMCs from HNC patients and healthy donors. Our aim was to identify immunological signatures associated with cancer presence, providing a rationale for further research on immune-targeted therapies and blood-based biomarkers in HNC.\u003c/p\u003e"},{"header":"2 Materials and Methods","content":"\u003ch2\u003e2.1\u0026nbsp; \u0026nbsp; \u0026nbsp;Biological material collection and ethical approval\u003c/h2\u003e\n\u003cp\u003ePeripheral venous blood (3 mL) was collected into EDTA-coated tubes from patients diagnosed with head and neck squamous cell carcinoma (ICD-10 codes C32.0 and C32.8) at the Department of Adult and Pediatric Otolaryngology and Laryngological Oncology, University Clinical Hospital No. 1, Pomeranian Medical University in Szczecin, Poland. In parallel, biopsy specimens were also collected for molecular analyses. The biological material was transported under Good Clinical Practice (GCP) conditions to the Center for Experimental Immunology and Immunobiology of Infectious and Cancer Diseases, University of Szczecin, where all cytometric, gene and protein expression analyses were performed.\u003c/p\u003e\n\u003cp\u003eThe study group consisted of 36 patients (26 males and 10 females), aged 38\u0026ndash;85 years, all diagnosed with laryngeal cancer. The majority of patients underwent total laryngectomy with adjuvant radiotherapy or chemoradiotherapy, while others received laser cordectomy or palliative radiotherapy, depending on tumor location, stage, and clinical condition.\u003c/p\u003e\n\u003cp\u003eThe control group consisted of 20 healthy volunteers (12 females and 8 males), aged 20\u0026ndash;57 years, with no history of cancer or autoimmune disease. Peripheral blood samples (3 mL) were collected from these individuals under identical conditions and processed using the same procedures as for the study group.\u003c/p\u003e\n\u003cp\u003eAn important limitation of this study is the demographic difference between groups, as the HNC cohort was older and predominantly male, while the control group was younger and included more females. Age and sex are known to influence NK cell frequency, CD8⁺ T cell differentiation, and apoptosis-related gene expression. Therefore, some of the observed differences may be partially influenced by demographic factors rather than cancer alone.\u003c/p\u003e\n\u003cp\u003eAll participants provided written informed consent prior to inclusion in the study. The study protocol was approved by the Bioethics Committee of the University of Szczecin (Resolution no. 14/2024 of Bioethical Comission of the University of Szczecin, Poland) and all procedures were conducted in accordance with the Declaration of Helsinki and applicable institutional guidelines.\u003c/p\u003e\n\u003ch2\u003e2.2\u0026nbsp; \u0026nbsp; \u0026nbsp;Immunophenotyping\u003c/h2\u003e\n\u003cp\u003eImmunophenotyping was performed using a BD FACS Canto II flow cytometer (BD Biosciences, USA) and BD FACSDiva software version 9.1. Whole blood samples (100 \u0026micro;L) were incubated with monoclonal antibodies (all from BD Biosciences) according to the manufacturer\u0026rsquo;s instructions. The initial staining panel included anti-CD5 and anti-CD19 to distinguish T and B lymphocyte populations. Within the T-cell population, further staining was performed using anti-CD3, anti-CD4, and anti-CD8 to identify CD3⁺, CD4⁺, CD8⁺, and CD3⁺CD8⁺ subsets. Percentages of CD3⁺CD8⁺ T cells were calculated within the CD3⁺ T cell gate. Subsequently, intracellular expression of Granzyme B was assessed in CD3⁺CD8⁺ cytotoxic T lymphocytes using an anti-Granzyme B antibody (BD Biosciences). Additionally, NK cells were identified based on the expression of CD3⁻CD56⁺CD16⁺ markers, and Granzyme B levels were also evaluated within this population. Following antibody incubation, samples were incubated in the dark at room temperature. Red blood cell lysis was performed using BD Lysing Solution, followed by another incubation step under the same conditions. Cells were then washed twice with 1,000 \u0026micro;L of BD FACS Flow Solution and centrifuged at 250 RCF for 3 minutes. The final cell pellet was resuspended in 500 \u0026micro;L of BD FACS Flow Solution and immediately analyzed. A minimum of 10,000 events per sample were acquired for analysis. Fluorescence compensation was performed using single-stained compensation controls for each fluorochrome. All gating strategies were applied consistently across all samples and verified using appropriate fluorescence controls.\u003c/p\u003e\n\u003ch2\u003e2.3\u0026nbsp; \u0026nbsp; \u0026nbsp;Gene expression analysis\u003c/h2\u003e\n\u003cp\u003ePeripheral blood mononuclear cells (PBMCs) were isolated from whole blood using density gradient centrifugation with Ficoll (Sigma-Aldrich). Total RNA was subsequently extracted from the obtained PBMCs using the Total RNA Mini kit (A\u0026amp;A Biotechnology, Gdańsk, Poland), following the manufacturer\u0026rsquo;s protocol. RNA purity and concentration were assessed spectrophotometrically by measuring the absorbance ratios at 260/280 nm and 260/230 nm using a NanoDrop spectrophotometer (ThermoFisher Scientific, USA).\u003c/p\u003e\n\u003cp\u003eReverse transcription was performed using the RevertAid First Strand cDNA Synthesis Kit (ThermoFisher Scientific), according to the manufacturer\u0026apos;s instructions, with Random Hexamer Primers. The reaction was carried out on a Mastercycler nexus thermal cycler (Eppendorf, Germany).\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" align=\"left\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 27.2727%;\"\u003e\n \u003cp\u003eGene\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72.7273%;\"\u003e\n \u003cp\u003eSequence\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 27.2727%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eGAPDH-\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003eF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72.7273%;\"\u003e\n \u003cp\u003e5\u0026apos; TGAACGGGAAGCTCACTGG 3\u0026apos;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 27.2727%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eGAPDH-\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003eR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72.7273%;\"\u003e\n \u003cp\u003e5\u0026apos; TCCACCACCCTGTTGCTGTA 3\u0026apos;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 27.2727%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eCASPASE3-\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003eF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72.7273%;\"\u003e\n \u003cp\u003e5\u0026apos; ATGGAAGCGAATCAATGGA 3\u0026apos;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 27.2727%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eCASPASE3-\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003eR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72.7273%;\"\u003e\n \u003cp\u003e5\u0026apos; TGTACCAGACCGAGATGTC 3\u0026apos;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 27.2727%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eBAX-\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003eF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72.7273%;\"\u003e\n \u003cp\u003e5\u0026apos; GCCCTTTTCTACTTTGCCAGC 3\u0026apos;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 27.2727%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eBAX-\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003eR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72.7273%;\"\u003e\n \u003cp\u003e5\u0026apos; TCAGCCCATCTTCTTCCAGAT 3\u0026apos;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 27.2727%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eBCL2-\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003eF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72.7273%;\"\u003e\n \u003cp\u003e5\u0026apos; GGCCTTCTTTGAGTTCGGTGG 3\u0026apos;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 27.2727%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eBCL2-\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003eR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72.7273%;\"\u003e\n \u003cp\u003e5\u0026apos; GATAGGCACCCAGGGTGATGC 3\u0026apos;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eQuantitative real-time PCR (qPCR) was conducted using the PowerUp\u0026trade; SYBR\u0026trade; Green Master Mix for qPCR (ThermoFisher Scientific) and the LightCycler 480 System (Roche, Switzerland). Each reaction was carried out in a total volume of 20 \u0026micro;L, comprising 5 \u0026micro;L of cDNA and 15 \u0026micro;L of master mix with primers. The expression of \u003cem\u003eGZMB, BAX, BCL-2\u003c/em\u003e, and \u003cem\u003eCASPASE-3\u003c/em\u003e genes was analyzed, using \u003cem\u003eGAPDH\u003c/em\u003e as the reference gene (primer sequences listed in Table 1). Each reaction for each gene was performed in three independent technical replicates.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1. Primer sequences used\u003c/strong\u003e\u003c/p\u003e\n\u003ch2\u003e2.4\u0026nbsp; \u0026nbsp; \u0026nbsp;Western blot exploratory analysis\u003c/h2\u003e\n\u003cp\u003eTissue samples were incubated with T-PER\u0026trade; Tissue Protein Extraction Reagent (cat.no. 78510, Thermo Scientific\u0026trade;) and a mixture of protease and phosphatase inhibitors (Roche Applied Science, Penzberg, Germany; #05892791001 and #11873580001), and homogenized for 30 seconds with maximum speed using Bead Ruptor Elite (cat. no. SKU 19-040E, Omni International) to obtain a homogenous mixture. The samples were then centrifuged for 10 min (12,000 rpm, 4\u003csup\u003eo\u003c/sup\u003eC). Protein lysates were transferred to new Eppendorf tubes. Analysis of protein levels in lysates was conducted using Bradford reagent. Proteins were separated using the WES system (WES - Automated Western Blots with Simple Western; ProteinSimple, San Jose, California, USA), with a 12-230 kDa separation module (#SM-W003), and detected using an Anti-Mouse (#DM-002) detection module, according to the manufacturer\u0026apos;s instructions. The Total protein module (#DM-TP01, ProteinSimple, San Jose, CA, USA) was used as a loading control. Granzyme B Monoclonal Antibody (GZB01) (Invitrogen, MA5-11587) were used in the study 1:50. Each sample was analyzed in three independent replicates.\u003c/p\u003e\n\u003cp\u003eTo determine the quantitative detection range of Granzyme B, a standard curve was prepared using Human Granzyme B Recombinant Protein (PeproTech\u0026reg;, Gibco, #140-18-10UG). The protein stock solution was serially diluted in MiliQ Ultrapure water (Merck, New Jersey, USA) to obtain the following final concentrations: 500, 250, 125, and 62.5 ng/mL. A negative control (C\u0026ndash;) was prepared by replacing the protein with distilled water. Each standard and the negative control were processed and separated analogously to the experimental samples. The resulting signal intensities were used to generate a standard curve for subsequent quantification of Granzyme B levels in tested samples. Analyses were conducted in three independent repetitions for every sample.\u0026nbsp;In addition, total protein levels were determined using the Total Protein Detection Module for Chemiluminescence, which served as the loading control. The band intensity was analyzed with an instrument\u0026rsquo;s Simple Western\u0026trade; provided software, Compass for Simple Western (Bio-Techne, Minneapolis, MN, USA). Due to the limited sample size, no statistical comparisons were performed for tumor protein data.\u003c/p\u003e\n\u003ch2\u003e2.5\u0026nbsp; \u0026nbsp; \u0026nbsp;Statistical analysis\u003c/h2\u003e\n\u003cp\u003eAll statistical analyses were performed using Tibco Statistica 13.3 (StatSoft, Palo Alto, CA, USA) and GraphPad Prism 10.0.3 (GraphPad Software, San Diego, CA, USA). Data normality was assessed using the Shapiro\u0026ndash;Wilk test. For normally distributed datasets, comparisons between two groups (HNC vs. control) were performed using unpaired two-tailed Student\u0026rsquo;s t-tests. In the case of non-normally distributed variables, the Mann\u0026ndash;Whitney U test was applied. Significance thresholds were defined as follows: \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05 (\u003cem\u003e),\u0026nbsp;p\u0026nbsp;\u0026lt; 0.01 (),\u0026nbsp;p\u0026nbsp;\u0026lt; 0.001 (\u003c/em\u003e), and \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.0001 (****). Quantitative real-time PCR results were expressed as relative gene expression levels calculated using the 2^\u0026minus;\u0026Delta;\u0026Delta;Ct method, normalized to the reference gene GAPDH. Western blot densitometric values were normalized to GAPDH band intensity and reported as relative units. Flow cytometry results were expressed as percentages of gated parent populations (e.g., CD3⁺CD8⁺ T cells, CD3⁻CD56⁺CD16⁺ NK cells) or total event counts (e.g., extracellular granzyme B-positive events), and statistical comparisons were performed using appropriate parametric or non-parametric tests based on distribution. For multivariate pattern recognition, a Principal Component Analysis (PCA) was performed using GraphPad Prism. Prior to PCA, all variables (qPCR expression levels and cytometric data) were standardized (z-scores; mean = 0, SD = 1). Dimensionality reduction was based on parallel analysis, retaining principal components with eigenvalues greater than those obtained from 1000 Monte Carlo simulations at the 95th percentile confidence level. PCA results were visualized using score plots and biplots, displaying sample distribution and variable loadings. All numerical data are presented as mean \u0026plusmn; standard deviation (SD) unless otherwise indicated. Graphical representations include bar plots with SD error bars and annotated \u003cem\u003ep\u003c/em\u003e-values. Effect sizes were calculated using Cohen\u0026rsquo;s d to assess the magnitude of differences between groups. To control for multiple comparisons, p-values were adjusted using the Benjamini\u0026ndash;Hochberg false discovery rate (FDR) correction.\u003c/p\u003e"},{"header":"3 Results","content":"\u003cp\u003eThe transcriptomic profiling revealed distinct differences between the groups, indicating potential dysregulation of both cytolytic and apoptotic pathways in the context of malignancy.\u003c/p\u003e\n\u003cp\u003eA significant upregulation of \u003cem\u003eGZMB\u003c/em\u003e (granzyme B) was observed (Fig. 1) in the cancer group (mean relative expression: 0.27) compared to the control group (0.112), indicating increased transcription\u003c/p\u003e\n\u003cp\u003eof this cytotoxic effector molecule (\u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/em\u003e). This elevation may reflect compensatory immune activation or heightened cytotoxic potential in response to tumor presence.\u003c/p\u003e\n\u003cp\u003eConversely, the expression of the proapoptotic gene \u003cem\u003eBAX\u003c/em\u003e was markedly reduced (Fig. 1) in the HNC group (0.053) compared to controls (1.247), while the antiapoptotic gene \u003cem\u003eBCL-2\u003c/em\u003e (Fig. 1) was elevated (0.50 in HNC vs. 0.278 in controls). These alterations resulted in a profound decrease in the \u003cem\u003eBAX/BCL-2\u003c/em\u003e expression ratio (Fig. 1) from 4.66 in the control group to 0.110 in the cancer group (**** \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). This shift indicates a strong antiapoptotic profile in the immune cells of HNC patients, consistent with an immunosuppressive or survival-oriented phenotype.\u003c/p\u003e\n\u003cp\u003eSupporting this observation, \u003cem\u003eCASPASE-3\u003c/em\u003e, a key effector caspase involved in apoptosis execution, also showed (Fig. 1) significantly lower expression in the cancer group (0.219) versus controls (0.867; **** \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). These findings suggest that immune cells from HNC patients may exhibit reduced susceptibility to apoptotic signaling, which likely reflects altered immune cell survival and differentiation status rather than directly indicating impaired cytotoxic function against tumor cells. Collectively, the gene expression data reveal an altered apoptotic and cytolytic gene signature in PBMCs of HNC patients. Despite elevated GZMB, the anti-apoptotic balance (\u0026darr;BAX, \u0026uarr;BCL-2, \u0026darr;CASPASE-3) suggests an anti-apoptotic survival phenotype in peripheral immune cells, which may reflect systemic immune dysregulation rather than impaired cytotoxic effectiveness per se. These patterns support the rationale for modulating apoptotic and effector pathways such as through melittin stimulation in order to restore functional immune responses against HNC.\u003c/p\u003e\n\u003cp\u003eTo quantitatively assess granzyme B protein abundance and complement the gene expression analysis, we performed an automated capillary Western blot (WES) using recombinant human granzyme B to generate a standard curve (62.5\u0026ndash;500 ng/mL) and tissue lysates from three HNC patients. The standard curve produced a clear, concentration-dependent increase in signal intensity at ~\u0026thinsp;66 kDa, confirming the specificity and quantitative range of the detection system (Fig. 2). No signal was observed in the negative control (distilled water), validating the absence of nonspecific background.\u003c/p\u003e\n\u003cp\u003eIn the experimental samples, a distinct band corresponding to granzyme B was detected in all analyzed HNC tissue lysates (Fig.\u0026nbsp;2). Quantitative analysis revealed substantial inter-individual variability, with Patient 2 showing the highest granzyme B concentration (434.4 ng/mL), consistent with the strongest signal intensity. Patient 3 exhibited an intermediate level (214.6 ng/mL), whereas Patient 1 demonstrated the lowest but clearly detectable protein abundance (80.4 ng/mL). These quantitative differences aligned with the visual variation in band intensities. No additional nonspecific bands were observed, confirming the assay\u0026rsquo;s analytical specificity. These exploratory results indicate that granzyme B protein is detectable in tumor tissues of HNC patients, although the small sample size and lack of non-tumor controls limit definitive conclusions. When considered alongside transcriptomic data showing elevated GZMB mRNA expression in PBMCs of HNC patients, the Western blot analysis supports the presence of an activated cytotoxic immune signature within the tumor environment. However, the variability in granzyme B abundance may reflect functional heterogeneity of infiltrating immune cells or differential tumor-driven modulation of cytotoxic lymphocyte activity in individual patients.\u003c/p\u003e\n\u003cp\u003eTo complement transcriptomic and proteomic data, we performed multiparametric flow cytometry to analyze immune effector populations and secreted granzyme B levels in peripheral blood samples from patients with head and neck cancer (HNC) and healthy donors. The preliminary cytometric profiling included quantification of cytotoxic CD3⁺CD8⁺ T cells, CD3⁻CD56⁺CD16⁺ natural killer (NK) cells, and extracellular granzyme B (GZMB), measured as the number of positive events per sample. In HNC patients, the proportion of CD3⁺CD8⁺ cytotoxic T cells (Fig. \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e3\u003c/span\u003e) was markedly elevated (30.84) compared to healthy controls (0.545), with a highly significant difference (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). This expansion may reflect ongoing immune activation or tumor-induced remodeling of the T cell compartment. The percentage of CD3⁻CD56⁺CD16⁺ NK cells was also significantly increased (Fig. \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e3\u003c/span\u003e) in the cancer group (5.585) compared to controls (1.240; p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), indicating broader engagement of innate cytotoxic responses. Importantly, granzyme B was quantified as free circulating events in whole blood using flow cytometry. A significant increase in extracellular GZMB-positive events was observed (Fig. \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e3\u003c/span\u003e) in the HNC group (131.1) compared to controls (38.17; p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001).\u003c/p\u003e\n\u003cp\u003eTo reduce dimensionality and explore relationships among measured variables across all samples (n\u0026thinsp;=\u0026thinsp;56), we performed a principal component analysis (PCA) (Fig.\u0026nbsp;4) integrating gene expression (\u003cem\u003eGZMB\u003c/em\u003e, \u003cem\u003eBAX\u003c/em\u003e, \u003cem\u003eBCL-2\u003c/em\u003e, \u003cem\u003eCASPASE-3\u003c/em\u003e) and cytometric data (CD8⁺ T cells %, NK cells %, extracellular GZMB-positive events). All variables were standardized (mean\u0026thinsp;=\u0026thinsp;0, SD\u0026thinsp;=\u0026thinsp;1) prior to analysis. The first principal component (PC1) captured the majority of the variance (67.05%), followed by PC2, which explained an additional 13.54%, bringing the cumulative variance explained by the first two components to 80.58%. In the resulting biplot, PC1 appeared to segregate samples based on cytotoxic activity and apoptotic resistance. Loadings revealed that CD8⁺ T cells (%), NK cells (%), \u003cem\u003eGZMB\u003c/em\u003e expression, \u003cem\u003eBCL-2\u003c/em\u003e expression, and free granzyme B events were positively associated with PC1, suggesting a cytotoxic effector signature. In contrast, \u003cem\u003eBAX\u003c/em\u003e and \u003cem\u003eCASPASE-3\u003c/em\u003e loaded negatively on PC1, consistent with a pro-apoptotic profile reduced in the experimental group. The distribution of sample scores along PC1 demonstrated separation between samples with elevated effector activity and those characterized by reduced pro-apoptotic signaling. PC2 contributed to minor variance (13.5%) and may reflect subtle differences in regulatory or compensatory pathways not captured by PC1. These results confirm that the integrated gene expression and cytometric profile of HNC patients is distinct from controls and is dominated by a shift toward enhanced cytotoxic marker expression (\u003cem\u003eGZMB\u003c/em\u003e, CD8⁺, NK cells) and suppression of apoptosis-related genes (\u003cem\u003eBAX\u003c/em\u003e, \u003cem\u003eCASPASE-3\u003c/em\u003e). This multivariate pattern demonstrates coordinated changes in cytotoxic and apoptosis-related markers in HNC patients and supports the presence of a distinct systemic immune signature rather than providing direct evidence of functional impairment. PCA was used solely as an exploratory tool to visualize relationships among variables and does not imply predictive or diagnostic modeling.\u003c/p\u003e\n\u003cp\u003eAll analyzed parameters remained significant after Benjamini-Hochberg FDR correction (q\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Effect size analysis demonstrated very large differences between HNC patients and controls across most tested parameters (Cohen\u0026rsquo;s d range: 1.74\u0026ndash;6.32). These findings indicate that the observed differences are not only statistically significant but also quantitatively substantial and consistent across multiple independent immune parameters.\u003c/p\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eThe preliminary data presented in this study indicate a distinctive immunogenomic signature in patients with head and neck cancer (HNC), marked by enhanced cytotoxic marker expression and a concurrent suppression of pro-apoptotic signaling in peripheral immune cells. Our integrated analysis combining gene expression, flow cytometry, and protein-level validation provides new insights into immune dysregulation in HNC. A key finding was the significant upregulation of GZMB (granzyme B) at both the transcript (qPCR) and protein (Western blot) levels in PBMCs and tumor tissues from HNC patients. These results support previous studies demonstrating the involvement of GZMB in tumor immune responses. For example, Bose et al. [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e] showed that despite the presence of cytotoxic cells in HNSCC tumors, key cytolytic genes, including \u003cem\u003eGZMB\u003c/em\u003e, were often transcriptionally suppressed in tumor-infiltrating lymphocytes, possibly due to local immunosuppression mechanisms. In contrast, our data show elevated GZMB in peripheral immune cells and blood, suggesting a systemic cytotoxic activation that may not fully translate into effective anti-tumor responses at the tumor site. The overexpression of granzyme B, alongside a significant expansion of CD3⁺CD8⁺ T cells and NK cells, observed via flow cytometry, further supports the hypothesis of heightened immune surveillance. Similar findings were reported by Charap et al. [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e], who observed preserved NK cytolytic potential in HPV⁺ HNSCC patients, although cytotoxic functionality varied depending on immune cell subset and tumor microenvironment. Our PCA analysis reinforces this perspective, showing that cytotoxic markers (CD8⁺ T cells, NK cells, GZMB) load heavily on the first principal component, distinguishing HNC patients from healthy controls. Despite elevated cytotoxic markers, the anti-apoptotic gene expression profile observed in PBMCs suggests an altered survival phenotype of peripheral immune cells, which may represent systemic immune dysregulation rather than a direct measure of cytotoxic effectiveness at the tumor site. Importantly, functional cytotoxic activity was not assessed in this study and therefore no conclusions regarding tumor cell killing can be drawn. This is consistent with findings from Wang et al. [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e], who identified \u003cem\u003eBAX\u003c/em\u003e downregulation as a pan-cancer hallmark associated with poor prognosis and reduced response to immunotherapy. Moreover, Tano et al. [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e] previously reported that low \u003cem\u003eBAX\u003c/em\u003e and high \u003cem\u003eBCL-2\u003c/em\u003e expression in HNC tissues correlated with worse patient outcomes, highlighting the relevance of apoptosis resistance as a prognostic factor. These transcriptional changes suggest that while cytotoxic cells may be numerically and transcriptionally activated in peripheral blood, their apoptotic machinery is impaired possibly due to tumor-derived signals inducing survival pathways. Importantly, apoptosis-related gene expression in cytotoxic lymphocytes reflects regulation of immune cell survival and activation-induced cell death rather than their ability to induce apoptosis in target tumor cells, which is mediated by granzyme B delivered into target cells following perforin-dependent entry. This hypothesis is supported by Ow et al. [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e], who demonstrated that targeting apoptosis-related molecules in HNC cells can restore immune susceptibility and improve therapeutic responses. Interestingly, the elevation of circulating, extracellular granzyme B in HNC patients, as measured in our study, could represent either active secretion from functional cytotoxic cells or dysregulated degranulation. While such markers have not been extensively studied in peripheral blood, Lechner et al. [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e] highlighted that humoral and innate immune components are increasingly active in certain HNC subtypes, suggesting that immune effector responses may be initiated but rendered ineffective by tumor evasion mechanisms. From a clinical perspective, peripheral blood immune signatures such as the GZMB/BAX/BCL-2 profile may serve as systemic biomarkers of immune status in HNC patients and could potentially be used to monitor immune response dynamics during immunotherapy\u003c/p\u003e \u003cp\u003eOverall, our results converge with current literature to support a model of systemic immune dysregulation in HNC: immune effector cells are activated and express cytolytic molecules like GZMB but are simultaneously skewed toward an anti-apoptotic, survival phenotype. Whether this phenotype enhances or limits tumor eradication cannot be determined from the present data and requires functional cytotoxicity assays in future studies. These patterns underline the need for therapeutic strategies that both activate cytotoxic responses and overcome intrinsic apoptosis resistance. These findings should be interpreted as a description of systemic immune status rather than direct evidence of anti-tumor immune function. Given the preliminary nature of this study and the limited sample size, these findings should be interpreted with caution. However, the consistency between mRNA, protein, and functional data and alignment with multiple published reports, strengthens the biological plausibility of our results. Taken together, our findings support a model in which head and neck cancer is associated not with a lack of cytotoxic immune cells, but rather with a state of systemic immune dysregulation characterized by cytotoxic activation accompanied by altered apoptotic signaling and prolonged immune cell survival.\u003c/p\u003e"},{"header":"5 Limitations","content":"\u003cp\u003eSeveral limitations of this study should be acknowledged. First, the HNC group was older and predominantly male, whereas the control group was younger and included more females. Age and sex are known to influence NK cell frequency, CD8⁺ T cell differentiation, apoptosis-related gene expression, and circulating granzyme B levels. Therefore, part of the observed differences may be influenced by demographic factors rather than cancer alone. Second, the Western blot analysis was performed in a limited number of tumor samples and should be considered exploratory. Third, functional cytotoxicity assays were not performed, which limits direct conclusions regarding tumor killing capacity.\u003c/p\u003e"},{"header":"6 Conclusion","content":"\u003cp\u003eOur preliminary results reveal a distinct immunological profile in HNC patients, characterized by increased granzyme B activity and cytotoxic cell expansion, but accompanied by reduced expression of pro-apoptotic genes. Importantly, the observed differences were not only statistically significant but also demonstrated very large effect sizes, indicating that these differences represent biologically meaningful systemic immune alterations rather than minor statistical variations.This imbalance suggests that despite heightened immune activation, effector cells may exhibit an altered survival and activation phenotype characteristic of systemic immune dysregulation. However, functional cytotoxic capacity was not directly measured in this study and requires further investigation. Peripheral immune cells from HNC patients may exhibit reduced susceptibility to apoptotic signaling, indicating systemic immune alterations associated with cancer presence. However, whether this phenotype reflects the behavior of tumor-infiltrating lymphocytes requires further investigation.\u003c/p\u003e"},{"header":"Declarations","content":" \u003ch2\u003eConflict of Interest\u003c/h2\u003e \u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e \u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis research was funded by the Polish Ministry of Science and Higher Education under the program \"Studenckie koła naukowe tworzą innowacje\" (eng. \u003cem\u003eStudent Scientific Clubs create innovation\u003c/em\u003e), Agreement No. KN/SP/603343/2024, and was also financed by the Polish Minister of Science under the \u0026ldquo;Regional Excellence Initiative\u0026rdquo; Program, Agreement No. RID/SP/0045/2024/01.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eFL: Writing \u0026ndash; original draft, Writing \u0026ndash; review \u0026amp; editing, Conceptualization, Methodology, Formal analysis, Investigation, Visualization, Project administration, Funding acquisition, Validation, Response to reviewers; RH: Writing \u0026ndash; review \u0026amp; editing, Investigation, Methodology; KW: Investigation, Resources, Validation; KP: Investigation, Resources; KR: Investigation, Resources; DB: Writing \u0026ndash; review \u0026amp; editing, Methodology; ŁG: Writing \u0026ndash; review \u0026amp; editing, Methodology; MZ: Writing \u0026ndash; review \u0026amp; editing, Methodology;PNR: Supervision, Conceptualization, Methodology, Writing \u0026ndash; review \u0026amp; editing, Funding acquisition, Project administration.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors would like to thank Dr. Rafał Becht for his invaluable assistance in collecting the biological material used in this study.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData are available from the corresponding author upon reasonable request due to ethical restrictions related to patient data.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBabakanrad E, Mohammadian T, Esmaeili D, Behzadi P (2023) Studying the effect of gene fusion of A and C types capsular synthesizing enzymes and anticancer sequence on inducing the expression of apoptotic BCL-2, BAX, and Caspase-3 genes by Real-time RT-PCR method. \u003cem\u003eHeliyon\u003c/em\u003e, \u003cem\u003e9\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.heliyon.2023.e16326\u003c/span\u003e\u003cspan address=\"10.1016/j.heliyon.2023.e16326\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBasheeth N, Patil N (2019) Biomarkers in Head and Neck Cancer an Update. 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Neoplasia 15(3):305\u0026ndash;314. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1593/neo.121528\u003c/span\u003e\u003cspan address=\"10.1593/neo.121528\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003ePMID: 23479508; PMCID: PMC3593153\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"cancer-immunology-immunotherapy","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ciim","sideBox":"Learn more about [Cancer Immunology, Immunotherapy](http://link.springer.com/journal/262)","snPcode":"262","submissionUrl":"https://submission.nature.com/new-submission/262/3","title":"Cancer Immunology, Immunotherapy","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Granzyme B, Head and Neck Cancer, Immunotherapy, Cytotoxic Lymphocytes, NK cells, Tumor Immunology, Oral Cancer, Immune Response","lastPublishedDoi":"10.21203/rs.3.rs-9413065/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9413065/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eHead and neck cancers (HNC) are associated with profound immune dysregulation and high resistance to immunotherapy. Granzyme B (GZMB) plays a pivotal role in cytotoxic lymphocyte function, while apoptotic gene signatures may determine the fate and efficacy of immune responses. This study analyzed peripheral blood and tumor samples from HNC patients and healthy donors using qPCR, flow cytometry, and capillary Western blotting. We quantified \u003cem\u003eGZMB, BAX, BCL-2\u003c/em\u003e, and \u003cem\u003eCASPASE-3\u003c/em\u003e expression in PBMCs and assessed immune cell composition and circulating GZMB levels. Data were integrated through principal component analysis (PCA). PBMCs from HNC patients showed significant upregulation of \u003cem\u003eGZMB\u003c/em\u003e and increased frequencies of CD3⁺CD8⁺ cytotoxic T cells and CD3⁻CD56⁺CD16⁺ NK cells. In contrast, pro-apoptotic genes \u003cem\u003eBAX\u003c/em\u003e and CASPASE-3 were downregulated, while anti-apoptotic \u003cem\u003eBCL-2\u003c/em\u003e was elevated, leading to a markedly reduced \u003cem\u003eBAX/BCL-2\u003c/em\u003e ratio. Western blot confirmed higher GZMB protein levels in tumor lysates. PCA revealed clear segregation of HNC patients from controls based on cytotoxic and apoptotic profiles. Our findings indicate that although immune effector cells in HNC patients exhibit elevated cytotoxic markers, their apoptotic resistance may reflect systemic immune dysregulation in HNC patients and may reflect systemic immune dysregulation without direct evidence of altered tumor killing capacity, although this cannot be directly extrapolated to tumor infiltrating immune cell. This altered immune signature supports further investigation into combined immuno-stimulatory and pro-apoptotic therapeutic strategies in HNC.\u003c/p\u003e","manuscriptTitle":"Granzyme B activity and apoptotic signatures in head and neck cancers: a multi-parameter immunological study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-04 08:21:05","doi":"10.21203/rs.3.rs-9413065/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-19T13:15:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"331327746308956664587338686940764834745","date":"2026-05-11T10:20:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"141830809108986752091470580890796350579","date":"2026-04-21T17:44:50+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-21T17:09:50+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-16T08:50:58+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-16T08:50:00+00:00","index":"","fulltext":""},{"type":"submitted","content":"Cancer Immunology, Immunotherapy","date":"2026-04-14T08:54:28+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"cancer-immunology-immunotherapy","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ciim","sideBox":"Learn more about [Cancer Immunology, Immunotherapy](http://link.springer.com/journal/262)","snPcode":"262","submissionUrl":"https://submission.nature.com/new-submission/262/3","title":"Cancer Immunology, Immunotherapy","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"e1ffbfad-254d-4e08-9046-758539bf8c60","owner":[],"postedDate":"May 4th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-19T13:15:48+00:00","index":84,"fulltext":""},{"type":"reviewerAgreed","content":"331327746308956664587338686940764834745","date":"2026-05-11T10:20:59+00:00","index":76,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-04T08:21:05+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-04 08:21:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9413065","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9413065","identity":"rs-9413065","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}