Identification and validation of PANoptosis-based HNSCPAN-index as a prognostic model for head and neck squamous cell carcinoma

Identification and validation of PANoptosis-based HNSCPAN-index as a prognostic model for head and neck squamous cell carcinoma | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Identification and validation of PANoptosis-based HNSCPAN-index as a prognostic model for head and neck squamous cell carcinoma Yekai Feng, Qinglai Tang, Xiaojun Tang, Miao Zeng, Yuming Zang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5379601/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract PANoptosis, a recently characterized form of programmed cell death, remains incompletely understood in the context of Head and Neck Squamous Cell Carcinoma (HNSCC). In this study, we identified a prognostically relevant set of PANoptosis genes within The Cancer Genome Atlas (TCGA) database for HNSCC and uncovered three molecular subtypes based on their expression profiles. Each subtype exhibited distinct prognostic outcomes and immune cell infiltration patterns. To further elucidate the clinical relevance, we constructed a PANoptosis risk score model, termed the HNSCPAN-index, using least absolute shrinkage and selection operator (LASSO) Cox regression based on differentially expressed genes across the subtypes. Patients were stratified into high-risk and low-risk groups according to the HNSCPAN-index. The predictive power of the model was evaluated using Kaplan-Meier analysis, ROC, nomogram and validated using an external dataset. A lower HNSCPAN-index correlated with longer overall survival and enhanced immunotherapy responses, whereas a higher HNSCPAN-index indicated increased sensitivity to small-molecule targeted therapies. Moreover, the HNSCPAN-index demonstrated a strong correlation with chemotherapeutic drug sensitivity. Finally, DSCAM was identified as a key regulator in HNSCC, where silencing DSCAM expression enhanced cell death mediated by pyroptosis inducers. In conclusion, we constructed a risk model of PANoptosis in HNSCC and revealed its potential role in prognosis, TME, chemotherapy. These findings may provide a deeper understanding of PANoptosis in HNSCC and pave the way for the development of more personalized therapeutic strategies. Biological sciences/Cancer Biological sciences/Genetics Biological sciences/Immunology Biological sciences/Molecular biology PANoptosis Head and neck squamous cell carcinoma prognosis Cancer immunotherapy Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 INTRODUCTION Head and Neck Squamous Cell Carcinoma (HNSCC) represent a heterogeneous group of malignancies, which are typically categorized according to their site of origin. These include oral cavity carcinoma, nasopharyngeal carcinoma, mucosal melanoma, hypopharyngeal carcinoma, sinonasal carcinoma, oropharyngeal carcinoma laryngeal carcinoma, salivary gland neoplasms, and other related subtypes [ 1 ]. In 2019, HNSCC accounted for > 870 000 new cases and 440 000 deaths worldwide [ 2 ]. By 2022, China alone is projected to report 150 000 new cases and 80 000 deaths, with approximately 60% of patients presenting with locally or regionally advanced disease [ 3 ]. HNSCC is often accompanied by severe pain, disfigurement, functional impairments, and profound psychosocial distress, all of which significantly diminish quality of life of the patient [ 4 , 5 ]. Cell death can be orchestrated through various subprograms, among which PANoptosis emerges as a distinctive mode of inflammatory cell death characterized by the interplay among pyroptosis, apoptosis, and necroptotic pathways [ 6 ]. This intricate process is orchestrated by the PANoptosome complex, a sophisticated assembly that integrates components from diverse cell death modalities[ 7 ]. Emerging evidence suggests that PANoptosis is triggered by various stimuli, including viral and bacterial infections, cytokine storms, and cancer. Several biomarkers implicated in PANoptosis, such as NLRP3, caspase-1 (CASP1), GSDMD (pyroptosis), ZBP1, CASP-3, CASP-8, (apoptosis), and RIPK3/RIPK1 (necroptosis), have demonstrated potential for cancer suppression [ 8 – 10 ]. HNSCC, a cancer associated with chronic inflammation, has been linked to upregulation of the NLRP3 inflammasome that is thought to drive tumor development and enhance the self-renewal capacity of cancer stem cells [ 11 ]. Recent studies on the regulation of cell death have provided new insights into the biological behaviour and precision treatment of HNSCC [ 12 ]. Moreover, interleukin-1β has been demonstrated to reduce cisplatin-induced apoptosis in caspase-3-mediated squamous carcinoma cells by inhibiting caspase-3 activity, thereby contributing to cisplatin resistance [ 13 ]. Treatment with the natural compound triptolide induces the Bad/Bax-caspase-3 cascade by suppressing mitochondrial HK-II expression and triggering GSDME-mediated pyroptosis in head and neck tumor cells [ 14 ]. Additionally, afatinib exerts antitumor effects on HNSCC by inducing apoptosis via mTORC1 inhibition [ 15 ]. In summary, dysregulation of these cell death pathways in HNSCC not only contributes to tumor resistance but also highlights promising avenues for the development of targeted therapies. However, studies investigating the role of PANoptosis in HNSCC are limited. We conducted co-clustering analyses to delineate three subgroups based on prognostically relevant PANoptosis genes. Subsequently, we examined the prognostic implications and immune cell infiltration within the tumor microenvironment (TME) of these subgroups and developed a PANoptosis risk score model for HNSCC (HNSCPAN-index). This index serves to stratify prognostic risk and predict responses to immunotherapy. Additionally, we validated drug responses across various HNSCPAN-index groups using public databases and explored the role and mechanism of the DSCAM gene in HNSCC through in vitro experiments. The workflow of this study is illustrated in Fig. 1 . In summary, our findings offer significant clinical promise for advancing personalized decision-making in HNSCC immunotherapy. MATERIALS AND METHODS Data collection and processing RNA-Seq data for 44 non-cancerous samples, 522 HNSCC samples, and 528 HNSCC clinical data were downloaded from TCGA database ( https://www.cancer.gov/tcga ) [ 16 ]. The clinical data included variables such as age, sex, TNM stage, and survival outcome. Somatic mutation data were retrieved separately from TCGA. Ensembl IDs were translated into an expression matrix profile using Perl, enabling the conversion of these identifiers into gene symbols. The validation dataset (GSE65858, n = 270) was acquired from the GEO database ( https://www.ncbi.nlm.nih.gov/geo/ ) [ 17 ]. The PANoptosis gene list was compiled from the literature (Supplementary Table S1 ) [ 18 , 19 ]. Differential expression and survival analysis of PANoptosis genes in HNSCC To evaluate the differentially expressed of PANoptosis genes between normal and HNSCC tissues, we conducted two sets of t-tests on the log2-transformed expression data. Box plots were generated using the "ggpubr" package. Overall survival (OS) was selected as the endpoint to elucidate the correlation between PANoptosis genes and prognostic outcomes. Consensus clustering of PANoptosis in molecular subtypes of HNSCC To elucidate the characteristics of PANoptosis genes, we employed the “ConsensusClusterPlus” R package to stratify patients with HNSCC into three distinct clusters. Kaplan-Meier method was used to assess OS across three distinct clusters. Additionally, single-sample Gene Set Enrichment Analysis (ssGSEA) method was performed to compare immune cell infiltration in different clusters to understand differences in the immune microenvironment of different molecular subtypes. [ 20 ]. Identification of HNSCC differential genes associated with PANoptosis Gene Set Variation Analysis (GSVA) was performed using gene expression profiles and phenotype classifications to assess the pertinent pathways and underlying molecular mechanisms [ 21 ]. Criteria for identifying significantly enriched pathways between the three distinct PANoptosis clusters included | logFC| > 0.1 and adj. p 0.585 and adj. p < 0.05 to identify significantly changed genes. The “clusterProfiler” package is employed to conduct the KEGG and GO enrichment analyses of DEGs [ 22 ]. Survival analysis and machine learning To identify the prognostically relevant DEGs in the TCGA-HNSCC dataset, univariate Cox regression analyses were conducted, with DEGs serving as the independent variables. The entire dataset was randomly split into training and validation cohorts in an approximate 1:1 ratio. To refine the number of prognostically relevant DEGs, LASSO Cox regression analyses were conducted using the R "glmnet" package. The final prognostic risk score was subsequently constructed through multivariate Cox regression. The formula for the prognostic risk score is presented below: $$\:Risk\:score\:=\:{\sum\:}_{i=1}^{n}bi*\:Ei$$ , where n denotes the number of DEGs associated with prognosis within the risk model, b represents the regression coefficients for multivariate Cox regression analysis, and E indicates the expression levels of the risk-related DEGs. Risk difference analysis was performed to evaluate variations in risk scores across subgroups. Utilizing data from the TCGA and GEO databases, we classified patients into high-risk and low-risk groups based on the 50th percentile of their risk coefficients. Prognostic differences were analyzed using the R "survival" package, with long-rank tests determining statistical significance. Additionally, we developed a nomogram to predict OS for patients with HNSCC by integrating risk scores and clinical data, utilizing the R packages "survival" and "rms". Predicting the immune landscape, immunotherapy response, and chemotherapy sensitivity The CIBERSORT algorithm was applied to estimate the relative proportions of immune cell subsets in the TCGA-HNSCC dataset by quantifying the relative abundance of RNA transcripts specific to various immune cell types [ 23 ]. Immuno-score, Stromal-score, and ESTIMATE score were computed using the ESTIMATE algorithm. TIDE score was used to predict tumor response to immune checkpoint inhibitors. Somatic mutations were evaluated utilizing the "maftools" R package. The TMB grade was estimated by dividing the number of non-synonymous mutations through the total genome size in megabases. Additionally, the chemotherapeutic drugs' IC50 was predicted using the "prophytic" R package. Data on immunotherapy cohorts are available from the public database IMvigor210 [ 24 ]. Validation and functional characterization of the HNSCPAN-index Cell culture and transfection HNSCC cell lines CAL-27 and HN30 were cultured in a complete DMEM medium supplemented with 10% fetal bovine serum (Gibco), while Human bronchial epithelial cells (16HBE) were cultured in complete RPMI-1640 medium. All cell lines were purchased from the Cell Bank of the Type Culture Collection of Chinese Academy of Sciences (Shanghai, China). Transfection for CAL-27 and HN30 cells was performed using Lipofectamine® 3000 (Invitrogen). Small interfering RNA (siRNA) was utilized to knock down the expression of the target gene. The siRNA sequence of DSCAM were as follows: si-1 5’-CGUGUUGGUUCAACCACAATT-3’, si-2 5’-GGACAAGCUGUCCGCUAATT-3’, and si-NC 5’-UUCUCCGAACGUGUCACGUTT-3’. HNSCC cell lines were treated with LPS and ATP to induce pyroptosis, following the protocol outlined in Ref [ 25 ]. qRT-PCR Using a 6-well plate, cells are washed twice with PBS and treated with 1 mL Trizol reagent (Invitrogen) per well. After pipetting and incubation, chloroform is added, shaken, and centrifuged to separate phases. The upper phase is collected, mixed with isopropanol to precipitate RNA, which is then washed with 75% ethanol, air-dried, and dissolved in nuclease-free water for quality assessment. Reverse transcription kit (Yeasen, Shanghai, China) is performed to generate cDNA, with GAPDH as the internal control for Real-time PCR, run in triplicate. Gene expression is analyzed via the 2 −△△Ct method. Primer sequences are listed in Table S2 . Western blotting Western blotting was performed as previously standard techniques [ 26 ]. Cells were washed with phosphate-buffered saline (PBS) and lysed on ice for 30 minutes in RIPA buffer supplemented with protease and phosphatase inhibitors. Following sonication and centrifugation, protein concentration was quantified using the BCA assay (Elabscience, Wuhan, China) at 595 nm. Proteins were denatured in 5× loading buffer at 95°C for 10 minutes and then separated by SDS-PAGE. After transferring the proteins to a PVDF membrane at 300 mA for 90 minutes, the membranes were blocked with 5% skim milk and incubated overnight at 4°C with the primary antibody. The membranes were then incubated with a secondary antibody for one hour at room temperature. Detection was performed using enhanced chemiluminescence (ECL), and images were captured for further analysis. Antibody information is listed in Table S3. In vitro experiments After treating cells in a 96-well plate, a working solution was prepared by mixing CCK8 reagent (Elabscience, Wuhan, China) with complete medium at a 9:1 ratio, using 10 µL of CCK8 per well. Following the removal of the old medium, 100 µL of the CCK8 solution was added to each well, including control wells without cells, and incubated at 37°C in the dark for 2 hours. Absorbance at 450 nm was measured using a microplate reader, and cell viability was calculated according to the appropriate formula, with at least three replicates per group. Cell migration was evaluated using a wound-healing assay. HNSCC cells were seeded in 6-well plates. After the transfection procedure was accomplished, 200 µL pipette tip was employed to create a scratch in the center of each well. PBS was utilized for washing to remove the detached cells, and the remaining cells were cultivated under starvation conditions. Gap distances at 0, 24 and 36 hours were recorded using an inverted microscope. Cell invasion was further investigated using a transwell assay. First, thaw the ECM gel (Invitrogen) and dilute it with serum-free medium at a 1:8 ratio. Add the diluted solution to the upper chamber of the Transwell and incubate overnight at 37°C for gelation. Next, prepare a serum-free cell suspension, introduce approximately 2000 cells into the upper chamber, and add 500 µL of 10% FBS medium to the lower chamber. Finally, after 36 hours, wash the upper chamber, fix and stain the cells, and assess invasion capacity by counting them under a microscope. Statistical analysis Data analysis in this study utilized R software (version 4.2.1) and GraphPad Prism software (version 9.4.1). The Wilcoxon rank-sum test assessed mean differences between two groups, while the Kruskal-Wallis test evaluated differences among three or more groups. For survival analysis, the Kaplan-Meier method generated survival curves, with the log-rank test used to assess the significance of differences between these curves. RESULTS Differential expression and survival analysis of PANoptosis genes in HNSCC The PANoptosis gene set comprised 28 pyroptosis genes, 32 apoptosis genes, and 8 necrosis genes. We explored the differential expression of these 68 genes in samples from 528 patients with HNSCC. Among them, 59 genes (86.7%) exhibited differential expression between normal and tumor tissues (Fig. 2 A). Furthermore, univariate Cox regression analysis identified 14 genes (Fig. 2 B), accounting for 20.5% of the total, that are significantly related with patient prognosis. Elevated expression levels of these genes appear to be correlated with poor clinical outcomes. These results provide the foundation for the further exploration of the role of PANoptosis genes in HNSCC. Consensus clustering of PANoptosis genes in molecular subtypes of HNSCC Based on the expression profiles of PANoptosis genes, the cohort of 520 HNSCC samples was stratified into three distinct molecular clusters: A, B, and C, consisting of 147, 195, and 178 patients, respectively. The optimum number of clusters was determined to be K = 3 (Fig. 3 A-C). To assess the reliability of the molecular subtypes, a KM survival analysis was performed, which showed significant differences in OS across subtypes. Notably, Cluster B exhibited markedly poorer OS outcomes than those of clusters A and C. ( p < 0.05; Fig. 3 D). PCA analyses of the HNSCC population confirmed that patients with the three subtypes were unevenly distributed in the TCGA dataset. Heatmaps, generated using the ssGSEA algorithm, demonstrated the distribution of immune cell infiltration across the three clusters (Fig. 3 E). Cluster C exhibited the highest degree of immune cell infiltration, correlating with a more favorable prognosis. Conversely, Clusters A and B that demonstrated poorer OS were characterized by lower levels of immune infiltration. In addition to antitumor immune cells, immunosuppression-associated myeloid-derived suppressor cells and CD8 + T cells significantly infiltrated Cluster C (Fig. 3 F). The enhanced prognosis observed in HNSCC tumors with high CD8 + T cell infiltration may be attributed to the augmented effector function of these cells and their increased capacity for tumor cell eradication [ 27 ]. Differentially expressed genes identified by single-cell sequencing of HNSCC samples have been linked to distinct tumor subtypes [ 28 ]. Notably, certain genes such as the PANoptosis-related gene TNF were associated with immune activation and may exhibit context-specific responses within various TMEs. This observation implies that the degree of immune cell infiltration may reflect the expression levels of PANoptosis genes. Functional analysis of differentially expressed genes associated with PANoptosis in HNSCC GSVA pathway analysis revealed that clusters A, B, and C were all enriched in immune response and apoptosis pathways. For example, PANoptosis Cluster C demonstrated significantly higher enrichment in pathways related to immune function compared to Clusters A and B. These pathways included the T cell receptor signaling pathway, primary immunodeficiency, antigen processing and presentation, the intestinal immune network for IgA production, natural killer cell-mediated cytotoxicity, and the NOD-like receptor signaling pathway. In contrast, PANoptosis Cluster B exhibited greater enrichment in RIG-I-like receptor signaling and apoptosis pathways than Cluster A (Fig. 4 A–C). Mapping the gene sets across the three clusters identified 179 DEGs associated with PANoptosis in HNSCC (HNSCPAN_DEGs) (Fig. 4 D). Enrichment analysis of these DEGs revealed their involvement in immune system processes, inflammatory responses, and cell signaling. The principal biological process enriched among these DEGs was the cytokine-mediated signaling pathway, with chemokine-mediated signaling also playing a prominent role. In terms of cellular composition, DEGs were primarily enriched on the external side of the plasma membrane. Molecular functions analysis showed substantial enrichment in chemokine activity and receptor-ligand interactions. KEGG pathway enrichment analysis further demonstrated that HNSCPAN_DEGs were predominantly enriched in pathways such as NOD-like receptor, cytokine–cytokine receptor interactions, JAK-STAT, TNF, and the AGE-RAGE signaling pathway in diabetic complications (Fig. 4 E, F). Construction of the HNSCPAN-index prognostic model To assess the effect of HNSCPAN_DEGs on survival outcomes, six gene features that exhibited a robust correlation with prognosis were identified by LASSO and univariate Cox regression analyses. These findings culminated in the construction of a PANoptosis risk score model based on HNSCPAN_DEGs, designated as the HNSCPAN-index (Fig. 5 A, B). The HNSCPAN-index was calculated as follows: HNSCPAN-index = 0.4198 * DSCAM + 0.1734 * NT5E + 0.1147 * CXCL1–0.3343 * MIAT − 0.2405 * IL12RB2–0.1253 * AKR1C3. Using the median risk score, patients with HNSCC were stratified into low-risk and high-risk cohorts. To validate the predictive accuracy of these prognostic characteristics, we randomly assigned 519 patients with complete survival data into training and test groups. Within the training set (n = 260), patients were categorized into a high-risk group (n = 130) and a low-risk group (n = 130) based on the HNSCPAN-index. OS was significantly better in the low-risk group than in the high-risk group (P < 0.001, Fig. 5 E); Similar outcomes were observed in the TCGA test (n = 259) and GEO validation sets (GSE65858, n = 270) (Fig. 5 D–F). To elucidate the relationships among the HNSCPAN_DEGs subtypes, risk groups, and individual clinical characteristics, we employed Sankey plots that indicated that Cluster B was predominantly represented in the high-risk group, whereas Clusters A and C were primarily associated with the low-risk group (Fig. 5 H). The independent prognostic value of the HNSCPAN-index was further confirmed via both multivariate and univariate Cox regression analyses (Fig. 6 A, B). Analysis of TNM staging revealed that patients classified as stage I and II were predominantly found in the low-risk group, while stages III and IV were primarily enriched in the high-risk group (Fig. 6 C, D). ROC curve analysis was employed to validate the model's specificity and sensitivity, yielding area under the curve (AUC) values for the HNSCPAN-index of 0.666, 0.667, and 0.642 for 1-, 3-, and 5-year overall survival, respectively, surpassing those of other factors (Fig. 6 E, F). Moreover, the concordance index (C-index) indicated that the HNSCPAN-index exhibited superior predictive power compared to other clinical characteristics (Fig. 6 G). We also constructed a nomogram integrating the risk scores with clinical factors, enhancing its applicability for predicting patient survival in clinical settings (Fig. 6 H). In conclusion, the HNSCPAN-index we established demonstrates robust predictive accuracy for survival in HNSCC patients and serves as a valuable independent prognostic marker, supporting its potential role in guiding clinical decision-making. Analysis of the immune infiltration and mutational landscapes Previous studies have indicated that PANoptosis modulates tumor mutation rates and immune infiltration[ 29 , 30 ]. To elucidate the immunological characteristics of the two risk groups defined by the HNSCPAN-index, we conducted immunological landscape analysis using the CIBERSORT and ESTIMATE algorithms. As illustrated in Fig. 7 A, the waterfall plot delineates the distribution of 22 immune cell types. Notably, we identified an upregulation of M0 and M2 macrophages, accompanied by a downregulation of M1 macrophages, CD8 + T cells, follicular helper T cells, and memory-activated CD4 + T cells in patients classified as high-risk (Fig. 7 B). Subsequently, we evaluated Immune-Score, Stromal-Score, and Microenvironment-Score between the two groups. Our findings demonstrated substantial differences in both immune and Microenvironmental scores, with the low-risk group exhibiting higher scores than the high-risk group, while stromal scores showed no significant changes (Fig. 7 E). These observations suggest that the high-risk group exhibits more susceptibility to immunosuppressive microenvironments. Immune checkpoint inhibitors (ICIs) exert their anti-tumour effects by leveraging the patient’s immune system to suppress tumour growth[ 31 ]. In our analysis of immune checkpoint gene expression profiles between the two risk groups, we identified 16 genes exhibiting significant differential expression. Among these, BTNL2, TNFSF14, TNFSF18, CD276, CD200, VTCN1, NRP1, CD200R1, CD160, and ADORA2A were upregulated in the high-risk cohort. Conversely, LGALS9, PD-1 (PDCD1), LAG3, PD-L1 (CD274), TNFRSF14, and IDO1 levels were elevated in the low-risk group (Fig. 7 F). Patients in the high-risk group exhibited elevated TIDE scores, suggesting an increased propensity for immune evasion and a diminished response to immune checkpoint therapies compared to those in the low-risk group (Fig. 7 G). Further analysis of the IMvigor210 cohort corroborated these findings, revealing that the risk scores were significantly lower in the immunotherapy responder group (CR/PR) than non-responder group (SD/PD) (Fig. 7 H). Following immunotherapy, OS was markedly improved in the low-risk group relative to the high-risk group ( p = 0.002, Fig. 7 I), suggesting that patients with lower risk scores demonstrated a favorable response to immunotherapy. We subsequently elucidated the somatic mutation landscape across high-risk and low-risk groups. The 15 most frequently mutated genes included TP53, MUC16, TTN, FAT1, SYNE1, KMT2D, CDKN2A, CSMD3, NOTCH1, PIK3CA, USH2A, KMT2D, PCLO, DNAH5, FLG, and LRP1B. These mutations play a crucial role in cancer development and progression, particularly mutations in TP53, which exhibited a mutation rate of 75% in the high-risk group compared to 57% in the low-risk group (Fig. 7 C, D). This significant disparity underscores the differences in mutation frequency between the two groups and offers vital insights into the biological characteristics of the tumors. Specifically, TP53 mutations, a key tumor suppressor gene, are closely linked to the malignancy of various cancers and patient prognosis, suggesting that individuals in the high-risk group may experience more severe disease progression [ 32 ]. In summary, the HNSCPAN-index may emerge as a valuable biomarker for predicting responsiveness to immunotherapy. The HNSCPAN-index serves as a reliable predictor of the immune landscape, mutational landscape, and immunotherapy efficacy. ( A ) The CIBERSORT algorithm quantified 22 types of tumor-infiltrating immune cells in two risk groups. ( B ) The proportion of different immune cell infiltrations. ( C-D ) Waterfall plots illustrated the somatic mutation profiles of both risk groups. ( E ) Stromal-score, Immuno-score and Microenvironment-score in two groups. ( F ) Differences in immune checkpoint gene expression across different groups. ( G ) Comparison of TIDE scores between the two groups. ( H ) Correlation of risk scores with CR/PR and SD/PD was explored in the IMvigor210 cohort. ( I ) Comparison of OS between two risk groups in the IMvigor210 cohort. HNSCPAN-index predicts HNSCC chemotherapy drug sensitivity ICIs have demonstrated promising clinical outcomes in patients with HNSCC and elevated survival rates among treatment-responsive individuals[ 33 ]. However, the persistence of multidrug resistance is a primary impediment to ICI efficacy. Nonetheless, combination therapies, including chemotherapy, continue to serve as the cornerstones of treatment. This study aimed to assess the IC50 values for predicting the sensitivity of distinct HNSCC populations to various chemotherapeutic agents. Our findings revealed that the high-risk cohort displayed heightened sensitivity to numerous agents such as Dasatinib, Trametinib, Staurosporine, ERK_6604, SCH772984, PD0325901, VX-11e, and BI-2536 (p < 0.001, Fig. 8 A–H). Conversely, the low-risk group exhibited sensitivity to Navitoclax, Daporinad, AMG-319, Vorinostat, Olaparib, Venetoclax, and Sorafenib (p < 0.001, Fig. 8 I–O). Identification of key molecule DSCAM on HNSCC Using qRT-PCR, we observed that DSCAM mRNA was significantly overexpressed in HNSCC cell lines (CAL-27 and HN30) compared to 16HBE cells (p < 0.05, Fig. 9 A). Immunohistochemical results from the Human Protein Atlas database revealed that DSCAM protein expression was higher in tumors than normal tissues (Fig. 9 B). Knockdown of DSCAM in HNSCC cells was successfully achieved (Fig. 9 C, D). CCK-8 assay results revealed that the DSCAM inhibition reduced the proliferative capacity of HNSCC cells (p < 0.05). Furthermore, transwell assays and wound healing indicated that DSCAM knockdown significantly suppressed HNSCC cell and invasion and migration (p < 0.05, Fig. 9 E-G), indicating that DSCAM promotes HNSCC progression. To elucidate the impact of DSCAM interference on PANoptosis in HNSCC, we induced pyroptosis in CAL-27 cells using LPS and ATP and subsequently assessed the expression levels of cleaved CASP1 (p20). Our findings revealed that DSCAM downregulation facilitated CASP1 cleavage, leading to the activation of GSDMD and the generation of biologically active GSDMD-N fragments. These fragments induce pyroptosis through the formation of pores in the cell membrane. Furthermore, GSDME, another member of the Gasdermin family, is also recognized for its role in inducing pyroptosis under specific conditions. In DSCAM-knockdown CAL-27 cells, the cleavage of GSDME was significantly enhanced, indicating its potential involvement in pyroptosis. In addition to pyroptosis, DSCAM knockdown resulted in the upregulation of apoptosis, as evidenced by the cleavage of CASP8 (p18) and CASP3 (p17). DSCAM knockdown also increased MLKL phosphorylation, indicating the induction of necroptosis (original blots are presented in Supplementary Fig. 1). Collectively, our findings indicate that DSCAM knockdown not only promotes pyroptosis in CAL-27 cells but also underscores its pivotal role in the regulation of PANoptosis. These insights offer novel avenues for targeting DSCAM for the treatment of HNSCC (Fig. 9 H). DISCUSSION Although substantial progress has been made in the field of immunotherapy for advanced HNSCC, the overall response rate remains below 20% [ 34 – 36 ]. Combining immunotherapy with chemotherapy has demonstrated improved efficacy, yet its outcomes remain comparable to those of chemotherapy alone, with only marginal gains in survival rates over immunotherapy monotherapy [ 35 , 37 ] Patients with HNSCC continue to encounter formidable challenges in achieving optimal treatment outcomes. The marked heterogeneity and immunosuppressive nature of the TME, scarcity of reliable biomarkers, and resistance of tumor cells to radiotherapy and chemotherapy present major obstacles for effective treatment [ 4 , 37 ]. Personalized treatment strategies targeting the underlying mechanisms of cell death are urgently needed to improve therapeutic success. Recently, PANoptosis, an emerging modality of cell death, has garnered increasing attention. This phenomenon integrates the intricate interplay between the pyroptosis, apoptosis, and necroptosis pathways, indicating remarkable potential for tumor suppression [ 6 ]. PANoptosis not only regulates tumor growth but also enhances the immune response by influencing the TME [ 38 ]. Although certain molecular mechanisms related to PANoptosis in other types of cancers have been studied, research on its role in HNSCC remains relatively limited. This study constructed an HNSCPAN-index to reveal the potential role of PANoptosis in HNSCC and demonstrated its application in prognostic assessment, drug response prediction, and personalized therapy, offering new insights for future therapeutic strategies. Three distinct PANoptosis clusters were identified in patients with HNSCC. PANoptosis Cluster C exhibited a markedly higher level of CD8 + T cell infiltration in the TME compared to that of Clusters A and B. Furthermore, Cluster C exhibited greater enrichment in antigen processing and presentation pathways, consistent with previous findings that HNSCC tumors with robust CD8 + T cell infiltration are associated with improved prognoses, likely due to enhanced effector cell function and increased tumor cell killing [ 4 ]. The expression profiles of these T cells may also predict the responses to checkpoint immunotherapy [ 39 ]. Single-cell sequencing of HNSCC further revealed distinct populations of cytotoxic CD8 + T cells, including CD8 + T and CD8 + T exhausted cells, with differential expression of co-inhibitory receptors such as PD1 and CTLA4, along with other genes linked to T cell dysfunction. This suggests that immune checkpoint inhibitors (e.g., PD-1/PD-L1 or CTLA-4 inhibitors) may relieve T cell inhibition and enhance tumor cell killing in this subset of patients [ 28 ]. This implies that modulation of these pathways in populations with high HNSCPAN-index scores may produce significant enhancement of HNSCC immunotherapy. To enhance prognostic prediction for patients with HNSCC, we identified DEGs across the three PANoptosis clusters that were significantly associated with prognosis. Based on these DEGs, we constructed an HNSCPAN-index that was validated for predictive accuracy, independence, and clinical applicability. Additionally, we developed a prognostic nomogram by integrating factors such as sex, tumor stage and age, thereby enabling individualized survival probability prediction. Consequently, the HNSCPAN-index emerged as a reliable and effective tool for predicting the HNSCC prognosis. We further evaluated its ability to predict the immune landscape and therapeutic response in patients with HNSCC. Patients classified as low-risk exhibited higher levels of M1 macrophages and CD8 + T cells, correlating with better prognoses than that of those in the high-risk group, who possessed elevated levels of M0 macrophages and resting CD4 + memory T cells, both of which contributed to immune suppression and tumor progression. These findings highlight the critical role of macrophages in HNSCC prognosis and suggest their potential as macrophage-targeted therapies. Notably, CD8 + T cell and macrophage interactions in the HNSCC TME were characterized by predicted HAVCR2 (TIM-3) -LGALS9, CD274 (PD-L1)-PDCD1(PD-1) and TIGIT-NECTIN2 interactions that are likely key to tumor rejection [ 27 ]. This implies that modulation of these pathways in populations with high HNSCPAN-index scores may produce significant enhancement of HNSCC immunotherapy. Previous studies have demonstrated co-localization of T cells with PD-L1 + macrophages in inflammatory HNSCC lesions [ 40 ]. This co-localization suggests that these cells may be jointly involved in the immune escape mechanism of tumor suppression through the immune checkpoint pathway. Therefore, as new ICR-targeting therapies are approved for the treatment of various malignancies, a better understanding of the primary cellular sources and expression patterns of ICR ligands in the HNSCC TME is of paramount importance [ 41 ]. Our findings suggest that low-risk patients, characterized by a high expression of immune checkpoint genes, may respond favorably to ICI therapy, as corroborated by the TIDE scores, IMvigor210 cohort data, and TMB analysis. Notably, patients with higher TMB, which is often associated with improved outcomes, were enriched in the low-risk group that also exhibited lower TIDE scores [ 42 ]. Furthermore, in patients with HNSCC receiving immunotherapy, the incidence of CR/PR was higher in the low-risk group, demonstrating that a lower risk score was closely associated with better clinical outcomes and greater responsiveness to immunotherapy. Given the potential impact of PANoptosis on HNSCC heterogeneity and associated clinical outcomes, we developed a prognostic model based on six PANoptosis-related DEGs to quantify the HNSCPAN-index. To validate our findings, we explored the role of DSCAM, a previously unstudied gene in cancer. DSCAM expression was significantly upregulated in HNSCC cell lines compared to that in normal endothelial cells, and knockdown of DSCAM markedly inhibited the proliferation and migration of HNSCC cells, suggesting that DSCAM acts as an oncogene in HNSCC and negatively affects patient prognosis. The precise relationship between DSCAM and PANoptosis remains unclear, thus necessitating further in vitro and in vivo studies to elucidate its underlying mechanisms. This study possesses certain limitations. Although we demonstrated the ability of the HNSCPAN-index to predict HNSCC prognosis and ICI therapy response, the study relied exclusively on data from the TCGA and GEO databases, potentially introducing biases in case selection and affecting the generalizability of the results. Therefore, large-scale clinical studies and prospective sample collection are required to confirm the robustness and clinical utility of the HNSCPAN-index. Furthermore, the risk score was based solely on gene expression levels without accounting for other factors, such as genetic mutations, that may influence patient outcomes. Conclusion In conclusion, this study identified three distinct molecular subtypes of PANoptosis in patients with HNSCC, each associated with varying prognostic outcomes. The HNSCPAN-index, derived from these subtypes, emerged as a robust and independent predictor of prognosis, chemotherapeutic drug sensitivity, and immunotherapeutic responsiveness in HNSCC. Thus, the HNSCPAN-index possesses significant potential for refining risk stratification and advancing personalized immunotherapy for HNSCC, offering a strong theoretical framework for future research and clinical applications. Declarations Data Availability Data of HNSC cohort were downloaded from TCGA database. Validation dataset of GSE65858 was acquired from the GEO database. Data of IMvigor210 cohort: http://research-pub.gene.com/IMvigor210CoreBiologies/. Acknowledgements Not applicable. Author contributions FY conceived and designed the study, and FY, TQ, TX, ZM, and ZY participated in data collection, analysis, interpretation, cell experiments, and manuscript writing, review, and revision. LS supervised and monitored the data, participated in fund acquisition, reviewed and revised the manuscript. All authors reviewed and approved the final manuscript. Funding This study was supported by the Clinical Medical Research Center for Otology in Hunan Province (2023SK4030). Ethics approval and consent to participate No administrative permissions or licenses were obtained to access the original data used in this study. Consent for publication Not applicable. Competing interests The authors declare no competing interests. References Chow, L. Q. M. Head and Neck Cancer. N Engl. J. Med. 382 , 60–72 (2020). Sung, H. et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J. Clin. 71 , 209–249 (2021). Han, B. et al. Cancer incidence and mortality in China, 2022 (Journal of the National Cancer Center, 2024). Johnson, D. E. et al. Head and neck squamous cell carcinoma. Nat. Rev. Dis. 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PD-1 Expression in Head and Neck Squamous Cell Carcinomas Derives Primarily from Functionally Anergic CD4(+) TILs in the Presence of PD-L1(+) TAMs. Cancer Res. 77 , 6365–6374 (2017). Ferris, R. L. et al. Nivolumab for Recurrent Squamous-Cell Carcinoma of the Head and Neck. N Engl. J. Med. 375 , 1856–1867 (2016). Marabelle, A. et al. Association of tumour mutational burden with outcomes in patients with advanced solid tumours treated with pembrolizumab: prospective biomarker analysis of the multicohort, open-label, phase 2 KEYNOTE-158 study. Lancet Oncol. 21 , 1353–1365 (2020). Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterial.docx Supplementaryfigure1.tif Cite Share Download PDF Status: Posted Version 1 posted 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5379601","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":382525927,"identity":"86938cc4-e8ab-4bb9-b63c-56488bb69914","order_by":0,"name":"Yekai Feng","email":"","orcid":"","institution":"Second Xiangya Hospital of Central South University","correspondingAuthor":false,"prefix":"","firstName":"Yekai","middleName":"","lastName":"Feng","suffix":""},{"id":382525928,"identity":"44f9c606-c9d8-4a9a-aaa9-6db472ef0906","order_by":1,"name":"Qinglai Tang","email":"","orcid":"","institution":"Second Xiangya Hospital of Central South University","correspondingAuthor":false,"prefix":"","firstName":"Qinglai","middleName":"","lastName":"Tang","suffix":""},{"id":382525929,"identity":"59835b9f-ba7b-493d-8b1a-4492eabc6691","order_by":2,"name":"Xiaojun Tang","email":"","orcid":"","institution":"Second Xiangya Hospital of Central South University","correspondingAuthor":false,"prefix":"","firstName":"Xiaojun","middleName":"","lastName":"Tang","suffix":""},{"id":382525930,"identity":"9e75f3e6-c042-472b-8c52-287368309d58","order_by":3,"name":"Miao Zeng","email":"","orcid":"","institution":"Second Xiangya Hospital of Central South University","correspondingAuthor":false,"prefix":"","firstName":"Miao","middleName":"","lastName":"Zeng","suffix":""},{"id":382525931,"identity":"fe9034a2-4e11-44ad-b6bb-0b00ccc97524","order_by":4,"name":"Yuming Zang","email":"","orcid":"","institution":"Second Xiangya Hospital of Central South University","correspondingAuthor":false,"prefix":"","firstName":"Yuming","middleName":"","lastName":"Zang","suffix":""},{"id":382525932,"identity":"b27f028a-de61-42cd-b979-f2589737a578","order_by":5,"name":"Shisheng Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0klEQVRIiWNgGAWjYBACPmYgwQNmMh+ACB0goIUNqkUCyEwgUgsDXAuPAZFa2Nkff3hTYVfHL33m48efbQxyfDcSGD8X4HUYj5nknDPJEpJ9uZuledsYjCVvJDBLz8CvhY2Zt+2AhMEZ3m3MjG0MiRtuJAAF8Wphf/wZooXnGSPQYfVEaGEwkIZqYWMAOizBgLAWiF8kZ/awGUvznJMwnHnmYbM0Pi38/MfBIcbPz8P88OOPMht5vuPJBz/j04IOgPHDwNhAgoZRMApGwSgYBdgAAH+sPmWVIkf4AAAAAElFTkSuQmCC","orcid":"","institution":"Second Xiangya Hospital of Central South University","correspondingAuthor":true,"prefix":"","firstName":"Shisheng","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2024-11-02 18:53:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5379601/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5379601/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":71548432,"identity":"96b00a63-eef1-4755-98cc-b204e97360d5","added_by":"auto","created_at":"2024-12-16 15:25:00","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":210035,"visible":true,"origin":"","legend":"\u003cp\u003eGraphical abstract of the study. Created with BioRender.com.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5379601/v1/77098772f48cda2aee9c6b5c.png"},{"id":71548431,"identity":"e7d6e039-40c4-4b3a-8d79-dd08dcfbb468","added_by":"auto","created_at":"2024-12-16 15:25:00","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":134792,"visible":true,"origin":"","legend":"\u003cp\u003eDifferential expression and survival analysis of PANoptosis genes in HNSCC. (\u003cstrong\u003eA\u003c/strong\u003e) Differential mRNA expression of PANoptosis genes in tumors and adjacent non-cancerous tissues; (\u003cstrong\u003eB\u003c/strong\u003e) Forest plot lists the PANoptosis genes associated with the prognosis of HNSCC. * \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, ** \u003cem\u003ep \u003c/em\u003e\u0026lt; 0.01, and *** \u003cem\u003ep\u003c/em\u003e\u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5379601/v1/fc4fdd83618d447c2f6c41d9.png"},{"id":71550607,"identity":"2dc129df-773e-4463-97f9-ce27eda430dc","added_by":"auto","created_at":"2024-12-16 15:41:00","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":217794,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification of potential PANoptosis-related subtypes in TCGA-HNSCC cohort. (\u003cstrong\u003eA\u003c/strong\u003e) PANoptosis Clusters were identified using consensus clustering; (\u003cstrong\u003eB\u003c/strong\u003e,\u003cstrong\u003eC\u003c/strong\u003e) When k = 2 to 9, the average consistency within clusters and the area under the CDF curve; (\u003cstrong\u003eD\u003c/strong\u003e) Significant differences in OS between the three PANoptosis clusters; (\u003cstrong\u003eE\u003c/strong\u003e) PCA analysis showed that the composition of the three transcripts of PANoptosis clusters; (\u003cstrong\u003eF\u003c/strong\u003e) Heat map showing the distinct patterns of immune cell infiltration across the three identified PANoptosis clusters; *\u003cem\u003e p\u003c/em\u003e \u0026lt; 0.05, ** \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01, and ***\u003cem\u003e p\u003c/em\u003e \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5379601/v1/a819629de47801ae37ae609f.png"},{"id":71548442,"identity":"2d1d697e-b2a1-459c-aa4f-74ca3d6deaac","added_by":"auto","created_at":"2024-12-16 15:25:06","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":378791,"visible":true,"origin":"","legend":"\u003cp\u003eFunctional enrichment analysis of HNSCPAN_DEGs. (\u003cstrong\u003eA-C\u003c/strong\u003e) GSVA was used to compare the differences in pathway activity between the following pairs: PANoptosis Clusters A and B (A), PANoptosis Clusters B and C (B) PANoptosis Clusters A and C (C); (\u003cstrong\u003eD\u003c/strong\u003e)The Vene diagram showing differentially expressed genes (DEG) crossed between three PANoptosis Clusters; (\u003cstrong\u003eE\u003c/strong\u003e) The results of GO enrichment analyses based on the HNSCPAN_DEGs; (\u003cstrong\u003eF\u003c/strong\u003e) The results of KEGG enrichment analyses based on the HNSCPAN_DEGs.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5379601/v1/02ced4cacaeb52bd181decf8.png"},{"id":71548436,"identity":"1307ef37-f5e2-4b64-8e7c-3b8a63547122","added_by":"auto","created_at":"2024-12-16 15:25:00","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":174185,"visible":true,"origin":"","legend":"\u003cp\u003eEstablishment of the HNSCPAN-index prognostic model. (\u003cstrong\u003eA\u003c/strong\u003e) Coefficient trajectories of prognostically relevant differentially expressed genes; (\u003cstrong\u003eB\u003c/strong\u003e) LASSO Cox regression analysis conducted based on the minimum criterion; (\u003cstrong\u003eC\u003c/strong\u003e) Distribution of risk scores across PANoptosis clusters; (\u003cstrong\u003eD-G\u003c/strong\u003e) KM survival curves illustrates the prognostic relevance of the HNSCPAN-index across the TCGA overall cohort, as well as in the training, test, and GEO validation datasets; (\u003cstrong\u003eH\u003c/strong\u003e) Sankey diagrams depict the associations between HNSCPAN_DEG subtypes, risk stratification groups, and distinct clinical features.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-5379601/v1/1d192269c01cacf20fe21016.png"},{"id":71549150,"identity":"47d1b647-4fa1-40a9-ad43-a9ecef3e8a94","added_by":"auto","created_at":"2024-12-16 15:33:00","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":175260,"visible":true,"origin":"","legend":"\u003cp\u003ePrognostic assessment of the HNSCPAN-index model. (\u003cstrong\u003eA-B\u003c/strong\u003e) Univariate and multivariate Cox regression demonstrates HNSCPAN-index as an independent risk factor. (\u003cstrong\u003eC-D\u003c/strong\u003e) Kaplan-Meier analysis revealed significant survival differences between high- and low-risk groups in stages I-II and III-IV. (\u003cstrong\u003eE\u003c/strong\u003e) HNSCPAN-index demonstrated predictive AUC values for 1-, 3-, and 5-year overall survival in patients. (\u003cstrong\u003eF\u003c/strong\u003e) The AUC of HNSCPAN-index for the 3-year OS was greater than those of other prognostic factors. (\u003cstrong\u003eG\u003c/strong\u003e) The predictive accuracy of the risk score, assessed by the C-index, outperforms that of other prognostic indicators. (\u003cstrong\u003eH\u003c/strong\u003e) A nomogram for predicting prognosis of HNSCC patients.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-5379601/v1/119215835a62f614846d1864.png"},{"id":71548433,"identity":"e4581893-6b02-48ab-8df6-d5a5e9ae6133","added_by":"auto","created_at":"2024-12-16 15:25:00","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":342281,"visible":true,"origin":"","legend":"\u003cp\u003eThe HNSCPAN-index serves as a reliable predictor of the immune landscape, mutational landscape, and immunotherapy efficacy. (\u003cstrong\u003eA\u003c/strong\u003e) The CIBERSORT algorithm quantified 22 types of tumor-infiltrating immune cells in two risk groups. (\u003cstrong\u003eB\u003c/strong\u003e) The proportion of different immune cell infiltrations. (\u003cstrong\u003eC-D\u003c/strong\u003e) Waterfall plots illustrated the somatic mutation profiles of both risk groups. (\u003cstrong\u003eE\u003c/strong\u003e) Stromal-score, Immuno-score and Microenvironment-score in two groups. (\u003cstrong\u003eF\u003c/strong\u003e) Differences in immune checkpoint gene expression across different groups. (\u003cstrong\u003eG\u003c/strong\u003e) Comparison of TIDE scores between the two groups. (\u003cstrong\u003eH\u003c/strong\u003e) Correlation of risk scores with CR/PR and SD/PD was explored in the IMvigor210 cohort. (\u003cstrong\u003eI\u003c/strong\u003e) Comparison of OS between two risk groups in the IMvigor210 cohort.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-5379601/v1/c1845799fa2af02ae6d6b12b.png"},{"id":71549152,"identity":"6a55b446-3a60-4a60-9f37-82cc5f33c16b","added_by":"auto","created_at":"2024-12-16 15:33:00","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":89161,"visible":true,"origin":"","legend":"\u003cp\u003eHNSCPAN-index predicts the relevance of chemotherapy drug sensitivity. (\u003cstrong\u003eA–H\u003c/strong\u003e) Chemotherapy drugs to which the high-risk group is particularly sensitive compared to the low-risk group; (\u003cstrong\u003eH–O\u003c/strong\u003e) Chemotherapy drugs to which the low-risk group is sensitive.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-5379601/v1/67cd5489decd79671b9df9cf.png"},{"id":71548438,"identity":"8e6a5add-d85e-43f1-8182-cc081081fe09","added_by":"auto","created_at":"2024-12-16 15:25:00","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":695994,"visible":true,"origin":"","legend":"\u003cp\u003eDSCAM knockdown inhibits the proliferation and migration and promotes PANoptosis of HNSCC. (\u003cstrong\u003eA\u003c/strong\u003e) RT-qPCR analysis of DSCAM mRNA was conducted in human bronchial epithelial cells (16HBE) and HNSCC cell lines (CAL-27, HN30). (\u003cstrong\u003eB\u003c/strong\u003e) Notable overexpression of DSCAM protein was observed in HNSCC tumor tissues compared to normal tissues. (\u003cstrong\u003eC\u003c/strong\u003e, \u003cstrong\u003eD\u003c/strong\u003e) Assessment of the impact of si-DSCAM # 1 and si-DSCAM # 2 transfection on the proliferation of CAL-27 and HN30 cells utilizing the CCK-8 assay. (\u003cstrong\u003eE\u003c/strong\u003e, \u003cstrong\u003eF\u003c/strong\u003e) Following transfection, the migratory capacity of different groups of CAL-27 and HN30 cells was evaluated using a wound healing assay (0, 24, and 36 hours). (\u003cstrong\u003eG\u003c/strong\u003e) A Transwell assay was utilized to assess the migratory potential of transfected CAL-27 and HN30 cells, displaying representative images in the upper panel (original magnification ×200) and a histogram depicting the number of cells migrated in each group in the lower panel. (\u003cstrong\u003eH\u003c/strong\u003e) Western blot analysis of activated CASP1, activated GSDMD and activated GSDME; cleaved CASP3 and cleaved CASP8; pMLKL and tMLKL in CAL-27 cells transfected with si-NC, si-DSCAM #1 and si-DSCAM #2. GAPDH was used as the internal control. Data are expressed as mean±SD, n =3. *\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01, ***\u003cem\u003ep\u003c/em\u003e\u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-5379601/v1/d644e63c19e1ce4dc03f9ff2.png"},{"id":82589816,"identity":"74d883b2-92c7-4496-9999-9b41c4a0a7df","added_by":"auto","created_at":"2025-05-13 07:47:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3405013,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5379601/v1/9a2758be-b00e-4977-b866-c33725a466d3.pdf"},{"id":71549153,"identity":"e0787722-88b5-4704-90a1-abcf91ff8b2b","added_by":"auto","created_at":"2024-12-16 15:33:00","extension":"docx","order_by":11,"title":"","display":"","copyAsset":false,"role":"supplement","size":27547,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-5379601/v1/550b9c066924cd9e83a6ed13.docx"},{"id":71548441,"identity":"2f8409e0-416f-40d4-933c-3070d07ce20a","added_by":"auto","created_at":"2024-12-16 15:25:01","extension":"tif","order_by":12,"title":"","display":"","copyAsset":false,"role":"supplement","size":10589252,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfigure1.tif","url":"https://assets-eu.researchsquare.com/files/rs-5379601/v1/299021fc2e05875e58ff14a8.tif"}],"financialInterests":"No competing interests reported.","formattedTitle":"Identification and validation of PANoptosis-based HNSCPAN-index as a prognostic model for head and neck squamous cell carcinoma","fulltext":[{"header":"INTRODUCTION ","content":"\u003cp\u003eHead and Neck Squamous Cell Carcinoma (HNSCC) represent a heterogeneous group of malignancies, which are typically categorized according to their site of origin. These include oral cavity carcinoma, nasopharyngeal carcinoma, mucosal melanoma, hypopharyngeal carcinoma, sinonasal carcinoma, oropharyngeal carcinoma laryngeal carcinoma, salivary gland neoplasms, and other related subtypes [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In 2019, HNSCC accounted for \u0026gt;\u0026thinsp;870 000 new cases and 440 000 deaths worldwide [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. By 2022, China alone is projected to report 150 000 new cases and 80 000 deaths, with approximately 60% of patients presenting with locally or regionally advanced disease [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. HNSCC is often accompanied by severe pain, disfigurement, functional impairments, and profound psychosocial distress, all of which significantly diminish quality of life of the patient [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCell death can be orchestrated through various subprograms, among which PANoptosis emerges as a distinctive mode of inflammatory cell death characterized by the interplay among pyroptosis, apoptosis, and necroptotic pathways [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. This intricate process is orchestrated by the PANoptosome complex, a sophisticated assembly that integrates components from diverse cell death modalities[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Emerging evidence suggests that PANoptosis is triggered by various stimuli, including viral and bacterial infections, cytokine storms, and cancer. Several biomarkers implicated in PANoptosis, such as NLRP3, caspase-1 (CASP1), GSDMD (pyroptosis), ZBP1, CASP-3, CASP-8, (apoptosis), and RIPK3/RIPK1 (necroptosis), have demonstrated potential for cancer suppression [\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. HNSCC, a cancer associated with chronic inflammation, has been linked to upregulation of the NLRP3 inflammasome that is thought to drive tumor development and enhance the self-renewal capacity of cancer stem cells [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Recent studies on the regulation of cell death have provided new insights into the biological behaviour and precision treatment of HNSCC [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Moreover, interleukin-1β has been demonstrated to reduce cisplatin-induced apoptosis in caspase-3-mediated squamous carcinoma cells by inhibiting caspase-3 activity, thereby contributing to cisplatin resistance [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Treatment with the natural compound triptolide induces the Bad/Bax-caspase-3 cascade by suppressing mitochondrial HK-II expression and triggering GSDME-mediated pyroptosis in head and neck tumor cells [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Additionally, afatinib exerts antitumor effects on HNSCC by inducing apoptosis via mTORC1 inhibition [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. In summary, dysregulation of these cell death pathways in HNSCC not only contributes to tumor resistance but also highlights promising avenues for the development of targeted therapies.\u003c/p\u003e \u003cp\u003eHowever, studies investigating the role of PANoptosis in HNSCC are limited. We conducted co-clustering analyses to delineate three subgroups based on prognostically relevant PANoptosis genes. Subsequently, we examined the prognostic implications and immune cell infiltration within the tumor microenvironment (TME) of these subgroups and developed a PANoptosis risk score model for HNSCC (HNSCPAN-index). This index serves to stratify prognostic risk and predict responses to immunotherapy. Additionally, we validated drug responses across various HNSCPAN-index groups using public databases and explored the role and mechanism of the \u003cem\u003eDSCAM\u003c/em\u003e gene in HNSCC through \u003cem\u003ein vitro\u003c/em\u003e experiments. The workflow of this study is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. In summary, our findings offer significant clinical promise for advancing personalized decision-making in HNSCC immunotherapy.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData collection and processing\u003c/h2\u003e \u003cp\u003eRNA-Seq data for 44 non-cancerous samples, 522 HNSCC samples, and 528 HNSCC clinical data were downloaded from TCGA database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cancer.gov/tcga\u003c/span\u003e\u003cspan address=\"https://www.cancer.gov/tcga\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The clinical data included variables such as age, sex, TNM stage, and survival outcome. Somatic mutation data were retrieved separately from TCGA. Ensembl IDs were translated into an expression matrix profile using Perl, enabling the conversion of these identifiers into gene symbols. The validation dataset (GSE65858, n\u0026thinsp;=\u0026thinsp;270) was acquired from the GEO database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/geo/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The PANoptosis gene list was compiled from the literature (Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e) [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDifferential expression and survival analysis of PANoptosis genes in HNSCC\u003c/h3\u003e\n\u003cp\u003eTo evaluate the differentially expressed of PANoptosis genes between normal and HNSCC tissues, we conducted two sets of t-tests on the log2-transformed expression data. Box plots were generated using the \"ggpubr\" package. Overall survival (OS) was selected as the endpoint to elucidate the correlation between PANoptosis genes and prognostic outcomes.\u003c/p\u003e\n\u003ch3\u003eConsensus clustering of PANoptosis in molecular subtypes of HNSCC\u003c/h3\u003e\n\u003cp\u003eTo elucidate the characteristics of PANoptosis genes, we employed the \u0026ldquo;ConsensusClusterPlus\u0026rdquo; R package to stratify patients with HNSCC into three distinct clusters. Kaplan-Meier method was used to assess OS across three distinct clusters. Additionally, single-sample Gene Set Enrichment Analysis (ssGSEA) method was performed to compare immune cell infiltration in different clusters to understand differences in the immune microenvironment of different molecular subtypes. [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eIdentification of HNSCC differential genes associated with PANoptosis\u003c/h3\u003e\n\u003cp\u003eGene Set Variation Analysis (GSVA) was performed using gene expression profiles and phenotype classifications to assess the pertinent pathways and underlying molecular mechanisms [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Criteria for identifying significantly enriched pathways between the three distinct PANoptosis clusters included | logFC| \u0026gt; 0.1 and adj. \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Using the \"limma\" package, we performed differential expression gene analysis between PANoptosis molecular subtypes, with the criteria set at |logFC| \u0026gt; 0.585 and adj. \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 to identify significantly changed genes. The \u0026ldquo;clusterProfiler\u0026rdquo; package is employed to conduct the KEGG and GO enrichment analyses of DEGs [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eSurvival analysis and machine learning\u003c/h3\u003e\n\u003cp\u003eTo identify the prognostically relevant DEGs in the TCGA-HNSCC dataset, univariate Cox regression analyses were conducted, with DEGs serving as the independent variables. The entire dataset was randomly split into training and validation cohorts in an approximate 1:1 ratio. To refine the number of prognostically relevant DEGs, LASSO Cox regression analyses were conducted using the R \"glmnet\" package. The final prognostic risk score was subsequently constructed through multivariate Cox regression. The formula for the prognostic risk score is presented below:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:Risk\\:score\\:=\\:{\\sum\\:}_{i=1}^{n}bi*\\:Ei$$\u003c/div\u003e\u003c/div\u003e,\u003c/p\u003e \u003cp\u003ewhere \u003cem\u003en\u003c/em\u003e denotes the number of DEGs associated with prognosis within the risk model, \u003cem\u003eb\u003c/em\u003e represents the regression coefficients for multivariate Cox regression analysis, and \u003cem\u003eE\u003c/em\u003e indicates the expression levels of the risk-related DEGs. Risk difference analysis was performed to evaluate variations in risk scores across subgroups. Utilizing data from the TCGA and GEO databases, we classified patients into high-risk and low-risk groups based on the 50th percentile of their risk coefficients. Prognostic differences were analyzed using the R \"survival\" package, with long-rank tests determining statistical significance. Additionally, we developed a nomogram to predict OS for patients with HNSCC by integrating risk scores and clinical data, utilizing the R packages \"survival\" and \"rms\".\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003ePredicting the immune landscape, immunotherapy response, and chemotherapy sensitivity\u003c/h2\u003e \u003cp\u003eThe CIBERSORT algorithm was applied to estimate the relative proportions of immune cell subsets in the TCGA-HNSCC dataset by quantifying the relative abundance of RNA transcripts specific to various immune cell types [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Immuno-score, Stromal-score, and ESTIMATE score were computed using the ESTIMATE algorithm. TIDE score was used to predict tumor response to immune checkpoint inhibitors. Somatic mutations were evaluated utilizing the \"maftools\" R package. The TMB grade was estimated by dividing the number of non-synonymous mutations through the total genome size in megabases. Additionally, the chemotherapeutic drugs' IC50 was predicted using the \"prophytic\" R package. Data on immunotherapy cohorts are available from the public database IMvigor210 [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eValidation and functional characterization of the HNSCPAN-index\u003c/h3\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eCell culture and transfection\u003c/h2\u003e \u003cp\u003eHNSCC cell lines CAL-27 and HN30 were cultured in a complete DMEM medium supplemented with 10% fetal bovine serum (Gibco), while Human bronchial epithelial cells (16HBE) were cultured in complete RPMI-1640 medium. All cell lines were purchased from the Cell Bank of the Type Culture Collection of Chinese Academy of Sciences (Shanghai, China). Transfection for CAL-27 and HN30 cells was performed using Lipofectamine\u0026reg; 3000 (Invitrogen). Small interfering RNA (siRNA) was utilized to knock down the expression of the target gene. The siRNA sequence of DSCAM were as follows: si-1 5\u0026rsquo;-CGUGUUGGUUCAACCACAATT-3\u0026rsquo;, si-2 5\u0026rsquo;-GGACAAGCUGUCCGCUAATT-3\u0026rsquo;, and si-NC 5\u0026rsquo;-UUCUCCGAACGUGUCACGUTT-3\u0026rsquo;. HNSCC cell lines were treated with LPS and ATP to induce pyroptosis, following the protocol outlined in Ref [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eqRT-PCR\u003c/h2\u003e \u003cp\u003eUsing a 6-well plate, cells are washed twice with PBS and treated with 1 mL Trizol reagent (Invitrogen) per well. After pipetting and incubation, chloroform is added, shaken, and centrifuged to separate phases. The upper phase is collected, mixed with isopropanol to precipitate RNA, which is then washed with 75% ethanol, air-dried, and dissolved in nuclease-free water for quality assessment. Reverse transcription kit (Yeasen, Shanghai, China) is performed to generate cDNA, with GAPDH as the internal control for Real-time PCR, run in triplicate. Gene expression is analyzed via the 2\u003csup\u003e\u0026minus;△△Ct\u003c/sup\u003e method. Primer sequences are listed in Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eWestern blotting\u003c/h2\u003e \u003cp\u003eWestern blotting was performed as previously standard techniques [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Cells were washed with phosphate-buffered saline (PBS) and lysed on ice for 30 minutes in RIPA buffer supplemented with protease and phosphatase inhibitors. Following sonication and centrifugation, protein concentration was quantified using the BCA assay (Elabscience, Wuhan, China) at 595 nm. Proteins were denatured in 5\u0026times; loading buffer at 95\u0026deg;C for 10 minutes and then separated by SDS-PAGE. After transferring the proteins to a PVDF membrane at 300 mA for 90 minutes, the membranes were blocked with 5% skim milk and incubated overnight at 4\u0026deg;C with the primary antibody. The membranes were then incubated with a secondary antibody for one hour at room temperature. Detection was performed using enhanced chemiluminescence (ECL), and images were captured for further analysis. Antibody information is listed in Table S3.\u003c/p\u003e \u003cp\u003e \u003cb\u003eIn vitro\u003c/b\u003e \u003cb\u003eexperiments\u003c/b\u003e\u003c/p\u003e \u003cp\u003eAfter treating cells in a 96-well plate, a working solution was prepared by mixing CCK8 reagent (Elabscience, Wuhan, China) with complete medium at a 9:1 ratio, using 10 \u0026micro;L of CCK8 per well. Following the removal of the old medium, 100 \u0026micro;L of the CCK8 solution was added to each well, including control wells without cells, and incubated at 37\u0026deg;C in the dark for 2 hours. Absorbance at 450 nm was measured using a microplate reader, and cell viability was calculated according to the appropriate formula, with at least three replicates per group.\u003c/p\u003e \u003cp\u003eCell migration was evaluated using a wound-healing assay. HNSCC cells were seeded in 6-well plates. After the transfection procedure was accomplished, 200 \u0026micro;L pipette tip was employed to create a scratch in the center of each well. PBS was utilized for washing to remove the detached cells, and the remaining cells were cultivated under starvation conditions. Gap distances at 0, 24 and 36 hours were recorded using an inverted microscope.\u003c/p\u003e \u003cp\u003eCell invasion was further investigated using a transwell assay. First, thaw the ECM gel (Invitrogen) and dilute it with serum-free medium at a 1:8 ratio. Add the diluted solution to the upper chamber of the Transwell and incubate overnight at 37\u0026deg;C for gelation. Next, prepare a serum-free cell suspension, introduce approximately 2000 cells into the upper chamber, and add 500 \u0026micro;L of 10% FBS medium to the lower chamber. Finally, after 36 hours, wash the upper chamber, fix and stain the cells, and assess invasion capacity by counting them under a microscope.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eData analysis in this study utilized R software (version 4.2.1) and GraphPad Prism software (version 9.4.1). The Wilcoxon rank-sum test assessed mean differences between two groups, while the Kruskal-Wallis test evaluated differences among three or more groups. For survival analysis, the Kaplan-Meier method generated survival curves, with the log-rank test used to assess the significance of differences between these curves.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003eDifferential expression and survival analysis of PANoptosis genes in HNSCC\u003c/h2\u003e\n \u003cp\u003eThe PANoptosis gene set comprised 28 pyroptosis genes, 32 apoptosis genes, and 8 necrosis genes. We explored the differential expression of these 68 genes in samples from 528 patients with HNSCC. Among them, 59 genes (86.7%) exhibited differential expression between normal and tumor tissues (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA). Furthermore, univariate Cox regression analysis identified 14 genes (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eB), accounting for 20.5% of the total, that are significantly related with patient prognosis. Elevated expression levels of these genes appear to be correlated with poor clinical outcomes. These results provide the foundation for the further exploration of the role of PANoptosis genes in HNSCC.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003eConsensus clustering of PANoptosis genes in molecular subtypes of HNSCC\u003c/h2\u003e\n \u003cp\u003eBased on the expression profiles of PANoptosis genes, the cohort of 520 HNSCC samples was stratified into three distinct molecular clusters: A, B, and C, consisting of 147, 195, and 178 patients, respectively. The optimum number of clusters was determined to be K\u0026thinsp;=\u0026thinsp;3 (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA-C). To assess the reliability of the molecular subtypes, a KM survival analysis was performed, which showed significant differences in OS across subtypes. Notably, Cluster B exhibited markedly poorer OS outcomes than those of clusters A and C. (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eD). PCA analyses of the HNSCC population confirmed that patients with the three subtypes were unevenly distributed in the TCGA dataset. Heatmaps, generated using the ssGSEA algorithm, demonstrated the distribution of immune cell infiltration across the three clusters (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eE). Cluster C exhibited the highest degree of immune cell infiltration, correlating with a more favorable prognosis. Conversely, Clusters A and B that demonstrated poorer OS were characterized by lower levels of immune infiltration. In addition to antitumor immune cells, immunosuppression-associated myeloid-derived suppressor cells and CD8\u0026thinsp;+\u0026thinsp;T cells significantly infiltrated Cluster C (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eF). The enhanced prognosis observed in HNSCC tumors with high CD8\u0026thinsp;+\u0026thinsp;T cell infiltration may be attributed to the augmented effector function of these cells and their increased capacity for tumor cell eradication [\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e]. Differentially expressed genes identified by single-cell sequencing of HNSCC samples have been linked to distinct tumor subtypes [\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e]. Notably, certain genes such as the PANoptosis-related gene TNF were associated with immune activation and may exhibit context-specific responses within various TMEs. This observation implies that the degree of immune cell infiltration may reflect the expression levels of PANoptosis genes.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n \u003ch2\u003eFunctional analysis of differentially expressed genes associated with PANoptosis in HNSCC\u003c/h2\u003e\n \u003cp\u003eGSVA pathway analysis revealed that clusters A, B, and C were all enriched in immune response and apoptosis pathways. For example, PANoptosis Cluster C demonstrated significantly higher enrichment in pathways related to immune function compared to Clusters A and B. These pathways included the T cell receptor signaling pathway, primary immunodeficiency, antigen processing and presentation, the intestinal immune network for IgA production, natural killer cell-mediated cytotoxicity, and the NOD-like receptor signaling pathway. In contrast, PANoptosis Cluster B exhibited greater enrichment in RIG-I-like receptor signaling and apoptosis pathways than Cluster A (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eA\u0026ndash;C). Mapping the gene sets across the three clusters identified 179 DEGs associated with PANoptosis in HNSCC (HNSCPAN_DEGs) (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eD). Enrichment analysis of these DEGs revealed their involvement in immune system processes, inflammatory responses, and cell signaling. The principal biological process enriched among these DEGs was the cytokine-mediated signaling pathway, with chemokine-mediated signaling also playing a prominent role. In terms of cellular composition, DEGs were primarily enriched on the external side of the plasma membrane. Molecular functions analysis showed substantial enrichment in chemokine activity and receptor-ligand interactions. KEGG pathway enrichment analysis further demonstrated that HNSCPAN_DEGs were predominantly enriched in pathways such as NOD-like receptor, cytokine\u0026ndash;cytokine receptor interactions, JAK-STAT, TNF, and the AGE-RAGE signaling pathway in diabetic complications (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eE, F).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\n \u003ch2\u003eConstruction of the HNSCPAN-index prognostic model\u003c/h2\u003e\n \u003cp\u003eTo assess the effect of HNSCPAN_DEGs on survival outcomes, six gene features that exhibited a robust correlation with prognosis were identified by LASSO and univariate Cox regression analyses. These findings culminated in the construction of a PANoptosis risk score model based on HNSCPAN_DEGs, designated as the HNSCPAN-index (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eA, B). The HNSCPAN-index was calculated as follows: HNSCPAN-index\u0026thinsp;=\u0026thinsp;0.4198 * DSCAM\u0026thinsp;+\u0026thinsp;0.1734 * NT5E\u0026thinsp;+\u0026thinsp;0.1147 * CXCL1\u0026ndash;0.3343 * MIAT \u0026minus;\u0026thinsp;0.2405 * IL12RB2\u0026ndash;0.1253 * AKR1C3. Using the median risk score, patients with HNSCC were stratified into low-risk and high-risk cohorts. To validate the predictive accuracy of these prognostic characteristics, we randomly assigned 519 patients with complete survival data into training and test groups. Within the training set (n\u0026thinsp;=\u0026thinsp;260), patients were categorized into a high-risk group (n\u0026thinsp;=\u0026thinsp;130) and a low-risk group (n\u0026thinsp;=\u0026thinsp;130) based on the HNSCPAN-index. OS was significantly better in the low-risk group than in the high-risk group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eE); Similar outcomes were observed in the TCGA test (n\u0026thinsp;=\u0026thinsp;259) and GEO validation sets (GSE65858, n\u0026thinsp;=\u0026thinsp;270) (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eD\u0026ndash;F). To elucidate the relationships among the HNSCPAN_DEGs subtypes, risk groups, and individual clinical characteristics, we employed Sankey plots that indicated that Cluster B was predominantly represented in the high-risk group, whereas Clusters A and C were primarily associated with the low-risk group (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eH).\u003c/p\u003e\n \u003cp\u003eThe independent prognostic value of the HNSCPAN-index was further confirmed via both multivariate and univariate Cox regression analyses (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eA, B). Analysis of TNM staging revealed that patients classified as stage I and II were predominantly found in the low-risk group, while stages III and IV were primarily enriched in the high-risk group (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eC, D). ROC curve analysis was employed to validate the model\u0026apos;s specificity and sensitivity, yielding area under the curve (AUC) values for the HNSCPAN-index of 0.666, 0.667, and 0.642 for 1-, 3-, and 5-year overall survival, respectively, surpassing those of other factors (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eE, F). Moreover, the concordance index (C-index) indicated that the HNSCPAN-index exhibited superior predictive power compared to other clinical characteristics (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eG). We also constructed a nomogram integrating the risk scores with clinical factors, enhancing its applicability for predicting patient survival in clinical settings (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eH). In conclusion, the HNSCPAN-index we established demonstrates robust predictive accuracy for survival in HNSCC patients and serves as a valuable independent prognostic marker, supporting its potential role in guiding clinical decision-making.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\n \u003ch2\u003eAnalysis of the immune infiltration and mutational landscapes\u003c/h2\u003e\n \u003cp\u003ePrevious studies have indicated that PANoptosis modulates tumor mutation rates and immune infiltration[\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e]. To elucidate the immunological characteristics of the two risk groups defined by the HNSCPAN-index, we conducted immunological landscape analysis using the CIBERSORT and ESTIMATE algorithms. As illustrated in Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eA, the waterfall plot delineates the distribution of 22 immune cell types. Notably, we identified an upregulation of M0 and M2 macrophages, accompanied by a downregulation of M1 macrophages, CD8\u0026thinsp;+\u0026thinsp;T cells, follicular helper T cells, and memory-activated CD4\u0026thinsp;+\u0026thinsp;T cells in patients classified as high-risk (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eB). Subsequently, we evaluated Immune-Score, Stromal-Score, and Microenvironment-Score between the two groups. Our findings demonstrated substantial differences in both immune and Microenvironmental scores, with the low-risk group exhibiting higher scores than the high-risk group, while stromal scores showed no significant changes (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eE). These observations suggest that the high-risk group exhibits more susceptibility to immunosuppressive microenvironments.\u003c/p\u003e\n \u003cp\u003eImmune checkpoint inhibitors (ICIs) exert their anti-tumour effects by leveraging the patient\u0026rsquo;s immune system to suppress tumour growth[\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e]. In our analysis of immune checkpoint gene expression profiles between the two risk groups, we identified 16 genes exhibiting significant differential expression. Among these, BTNL2, TNFSF14, TNFSF18, CD276, CD200, VTCN1, NRP1, CD200R1, CD160, and ADORA2A were upregulated in the high-risk cohort. Conversely, LGALS9, PD-1 (PDCD1), LAG3, PD-L1 (CD274), TNFRSF14, and IDO1 levels were elevated in the low-risk group (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eF). Patients in the high-risk group exhibited elevated TIDE scores, suggesting an increased propensity for immune evasion and a diminished response to immune checkpoint therapies compared to those in the low-risk group (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eG). Further analysis of the IMvigor210 cohort corroborated these findings, revealing that the risk scores were significantly lower in the immunotherapy responder group (CR/PR) than non-responder group (SD/PD) (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eH). Following immunotherapy, OS was markedly improved in the low-risk group relative to the high-risk group (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002, Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eI), suggesting that patients with lower risk scores demonstrated a favorable response to immunotherapy.\u003c/p\u003e\n \u003cp\u003eWe subsequently elucidated the somatic mutation landscape across high-risk and low-risk groups. The 15 most frequently mutated genes included TP53, MUC16, TTN, FAT1, SYNE1, KMT2D, CDKN2A, CSMD3, NOTCH1, PIK3CA, USH2A, KMT2D, PCLO, DNAH5, FLG, and LRP1B. These mutations play a crucial role in cancer development and progression, particularly mutations in TP53, which exhibited a mutation rate of 75% in the high-risk group compared to 57% in the low-risk group (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eC, D). This significant disparity underscores the differences in mutation frequency between the two groups and offers vital insights into the biological characteristics of the tumors. Specifically, TP53 mutations, a key tumor suppressor gene, are closely linked to the malignancy of various cancers and patient prognosis, suggesting that individuals in the high-risk group may experience more severe disease progression [\u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e]. In summary, the HNSCPAN-index may emerge as a valuable biomarker for predicting responsiveness to immunotherapy.\u003c/p\u003e\n \u003cp\u003eThe HNSCPAN-index serves as a reliable predictor of the immune landscape, mutational landscape, and immunotherapy efficacy. (\u003cstrong\u003eA\u003c/strong\u003e) The CIBERSORT algorithm quantified 22 types of tumor-infiltrating immune cells in two risk groups. (\u003cstrong\u003eB\u003c/strong\u003e) The proportion of different immune cell infiltrations. (\u003cstrong\u003eC-D\u003c/strong\u003e) Waterfall plots illustrated the somatic mutation profiles of both risk groups. (\u003cstrong\u003eE\u003c/strong\u003e) Stromal-score, Immuno-score and Microenvironment-score in two groups. (\u003cstrong\u003eF\u003c/strong\u003e) Differences in immune checkpoint gene expression across different groups. (\u003cstrong\u003eG\u003c/strong\u003e) Comparison of TIDE scores between the two groups. (\u003cstrong\u003eH\u003c/strong\u003e) Correlation of risk scores with CR/PR and SD/PD was explored in the IMvigor210 cohort. (\u003cstrong\u003eI\u003c/strong\u003e) Comparison of OS between two risk groups in the IMvigor210 cohort.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\n \u003ch2\u003eHNSCPAN-index predicts HNSCC chemotherapy drug sensitivity\u003c/h2\u003e\n \u003cp\u003eICIs have demonstrated promising clinical outcomes in patients with HNSCC and elevated survival rates among treatment-responsive individuals[\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e]. However, the persistence of multidrug resistance is a primary impediment to ICI efficacy. Nonetheless, combination therapies, including chemotherapy, continue to serve as the cornerstones of treatment. This study aimed to assess the IC50 values for predicting the sensitivity of distinct HNSCC populations to various chemotherapeutic agents. Our findings revealed that the high-risk cohort displayed heightened sensitivity to numerous agents such as Dasatinib, Trametinib, Staurosporine, ERK_6604, SCH772984, PD0325901, VX-11e, and BI-2536 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003eA\u0026ndash;H). Conversely, the low-risk group exhibited sensitivity to Navitoclax, Daporinad, AMG-319, Vorinostat, Olaparib, Venetoclax, and Sorafenib (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003eI\u0026ndash;O).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\n \u003ch2\u003eIdentification of key molecule DSCAM on HNSCC\u003c/h2\u003e\n \u003cp\u003eUsing qRT-PCR, we observed that DSCAM mRNA was significantly overexpressed in HNSCC cell lines (CAL-27 and HN30) compared to 16HBE cells (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Fig. \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003eA). Immunohistochemical results from the Human Protein Atlas database revealed that DSCAM protein expression was higher in tumors than normal tissues (Fig. \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003eB). Knockdown of DSCAM in HNSCC cells was successfully achieved (Fig. \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003eC, D). CCK-8 assay results revealed that the DSCAM inhibition reduced the proliferative capacity of HNSCC cells (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Furthermore, transwell assays and wound healing indicated that DSCAM knockdown significantly suppressed HNSCC cell and invasion and migration (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Fig. \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003eE-G), indicating that DSCAM promotes HNSCC progression. To elucidate the impact of DSCAM interference on PANoptosis in HNSCC, we induced pyroptosis in CAL-27 cells using LPS and ATP and subsequently assessed the expression levels of cleaved CASP1 (p20). Our findings revealed that DSCAM downregulation facilitated CASP1 cleavage, leading to the activation of GSDMD and the generation of biologically active GSDMD-N fragments. These fragments induce pyroptosis through the formation of pores in the cell membrane. Furthermore, GSDME, another member of the Gasdermin family, is also recognized for its role in inducing pyroptosis under specific conditions. In DSCAM-knockdown CAL-27 cells, the cleavage of GSDME was significantly enhanced, indicating its potential involvement in pyroptosis. In addition to pyroptosis, DSCAM knockdown resulted in the upregulation of apoptosis, as evidenced by the cleavage of CASP8 (p18) and CASP3 (p17). DSCAM knockdown also increased MLKL phosphorylation, indicating the induction of necroptosis (original blots are presented in Supplementary Fig. 1). Collectively, our findings indicate that DSCAM knockdown not only promotes pyroptosis in CAL-27 cells but also underscores its pivotal role in the regulation of PANoptosis. These insights offer novel avenues for targeting DSCAM for the treatment of HNSCC (Fig. \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003eH).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eAlthough substantial progress has been made in the field of immunotherapy for advanced HNSCC, the overall response rate remains below 20% [\u003cspan additionalcitationids=\"CR35\" citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Combining immunotherapy with chemotherapy has demonstrated improved efficacy, yet its outcomes remain comparable to those of chemotherapy alone, with only marginal gains in survival rates over immunotherapy monotherapy [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] Patients with HNSCC continue to encounter formidable challenges in achieving optimal treatment outcomes. The marked heterogeneity and immunosuppressive nature of the TME, scarcity of reliable biomarkers, and resistance of tumor cells to radiotherapy and chemotherapy present major obstacles for effective treatment [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Personalized treatment strategies targeting the underlying mechanisms of cell death are urgently needed to improve therapeutic success.\u003c/p\u003e \u003cp\u003eRecently, PANoptosis, an emerging modality of cell death, has garnered increasing attention. This phenomenon integrates the intricate interplay between the pyroptosis, apoptosis, and necroptosis pathways, indicating remarkable potential for tumor suppression [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. PANoptosis not only regulates tumor growth but also enhances the immune response by influencing the TME [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Although certain molecular mechanisms related to PANoptosis in other types of cancers have been studied, research on its role in HNSCC remains relatively limited. This study constructed an HNSCPAN-index to reveal the potential role of PANoptosis in HNSCC and demonstrated its application in prognostic assessment, drug response prediction, and personalized therapy, offering new insights for future therapeutic strategies.\u003c/p\u003e \u003cp\u003eThree distinct PANoptosis clusters were identified in patients with HNSCC. PANoptosis Cluster C exhibited a markedly higher level of CD8\u0026thinsp;+\u0026thinsp;T cell infiltration in the TME compared to that of Clusters A and B. Furthermore, Cluster C exhibited greater enrichment in antigen processing and presentation pathways, consistent with previous findings that HNSCC tumors with robust CD8\u0026thinsp;+\u0026thinsp;T cell infiltration are associated with improved prognoses, likely due to enhanced effector cell function and increased tumor cell killing [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The expression profiles of these T cells may also predict the responses to checkpoint immunotherapy [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Single-cell sequencing of HNSCC further revealed distinct populations of cytotoxic CD8\u0026thinsp;+\u0026thinsp;T cells, including CD8\u0026thinsp;+\u0026thinsp;T and CD8\u0026thinsp;+\u0026thinsp;T exhausted cells, with differential expression of co-inhibitory receptors such as PD1 and CTLA4, along with other genes linked to T cell dysfunction. This suggests that immune checkpoint inhibitors (e.g., PD-1/PD-L1 or CTLA-4 inhibitors) may relieve T cell inhibition and enhance tumor cell killing in this subset of patients [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. This implies that modulation of these pathways in populations with high HNSCPAN-index scores may produce significant enhancement of HNSCC immunotherapy.\u003c/p\u003e \u003cp\u003eTo enhance prognostic prediction for patients with HNSCC, we identified DEGs across the three PANoptosis clusters that were significantly associated with prognosis. Based on these DEGs, we constructed an HNSCPAN-index that was validated for predictive accuracy, independence, and clinical applicability. Additionally, we developed a prognostic nomogram by integrating factors such as sex, tumor stage and age, thereby enabling individualized survival probability prediction. Consequently, the HNSCPAN-index emerged as a reliable and effective tool for predicting the HNSCC prognosis. We further evaluated its ability to predict the immune landscape and therapeutic response in patients with HNSCC. Patients classified as low-risk exhibited higher levels of M1 macrophages and CD8\u0026thinsp;+\u0026thinsp;T cells, correlating with better prognoses than that of those in the high-risk group, who possessed elevated levels of M0 macrophages and resting CD4\u0026thinsp;+\u0026thinsp;memory T cells, both of which contributed to immune suppression and tumor progression. These findings highlight the critical role of macrophages in HNSCC prognosis and suggest their potential as macrophage-targeted therapies. Notably, CD8\u0026thinsp;+\u0026thinsp;T cell and macrophage interactions in the HNSCC TME were characterized by predicted HAVCR2 (TIM-3) -LGALS9, CD274 (PD-L1)-PDCD1(PD-1) and TIGIT-NECTIN2 interactions that are likely key to tumor rejection [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. This implies that modulation of these pathways in populations with high HNSCPAN-index scores may produce significant enhancement of HNSCC immunotherapy. Previous studies have demonstrated co-localization of T cells with PD-L1\u0026thinsp;+\u0026thinsp;macrophages in inflammatory HNSCC lesions [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. This co-localization suggests that these cells may be jointly involved in the immune escape mechanism of tumor suppression through the immune checkpoint pathway. Therefore, as new ICR-targeting therapies are approved for the treatment of various malignancies, a better understanding of the primary cellular sources and expression patterns of ICR ligands in the HNSCC TME is of paramount importance [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Our findings suggest that low-risk patients, characterized by a high expression of immune checkpoint genes, may respond favorably to ICI therapy, as corroborated by the TIDE scores, IMvigor210 cohort data, and TMB analysis. Notably, patients with higher TMB, which is often associated with improved outcomes, were enriched in the low-risk group that also exhibited lower TIDE scores [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Furthermore, in patients with HNSCC receiving immunotherapy, the incidence of CR/PR was higher in the low-risk group, demonstrating that a lower risk score was closely associated with better clinical outcomes and greater responsiveness to immunotherapy.\u003c/p\u003e \u003cp\u003eGiven the potential impact of PANoptosis on HNSCC heterogeneity and associated clinical outcomes, we developed a prognostic model based on six PANoptosis-related DEGs to quantify the HNSCPAN-index. To validate our findings, we explored the role of DSCAM, a previously unstudied gene in cancer. DSCAM expression was significantly upregulated in HNSCC cell lines compared to that in normal endothelial cells, and knockdown of DSCAM markedly inhibited the proliferation and migration of HNSCC cells, suggesting that DSCAM acts as an oncogene in HNSCC and negatively affects patient prognosis. The precise relationship between DSCAM and PANoptosis remains unclear, thus necessitating further \u003cem\u003ein vitro\u003c/em\u003e and \u003cem\u003ein vivo\u003c/em\u003e studies to elucidate its underlying mechanisms.\u003c/p\u003e \u003cp\u003eThis study possesses certain limitations. Although we demonstrated the ability of the HNSCPAN-index to predict HNSCC prognosis and ICI therapy response, the study relied exclusively on data from the TCGA and GEO databases, potentially introducing biases in case selection and affecting the generalizability of the results. Therefore, large-scale clinical studies and prospective sample collection are required to confirm the robustness and clinical utility of the HNSCPAN-index. Furthermore, the risk score was based solely on gene expression levels without accounting for other factors, such as genetic mutations, that may influence patient outcomes.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, this study identified three distinct molecular subtypes of PANoptosis in patients with HNSCC, each associated with varying prognostic outcomes. The HNSCPAN-index, derived from these subtypes, emerged as a robust and independent predictor of prognosis, chemotherapeutic drug sensitivity, and immunotherapeutic responsiveness in HNSCC. Thus, the HNSCPAN-index possesses significant potential for refining risk stratification and advancing personalized immunotherapy for HNSCC, offering a strong theoretical framework for future research and clinical applications.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e Data of HNSC cohort were downloaded from TCGA database. Validation dataset of GSE65858 was acquired from the GEO database. Data of IMvigor210 cohort: http://research-pub.gene.com/IMvigor210CoreBiologies/.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;contributions\u0026nbsp;\u003c/strong\u003eFY conceived and designed the study, and FY, TQ, TX, ZM, and ZY participated in data collection, analysis, interpretation, cell experiments, and manuscript writing, review, and revision. LS supervised and monitored the data, participated in fund acquisition, reviewed and revised the manuscript. All authors reviewed and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the Clinical Medical Research Center for Otology in Hunan Province (2023SK4030).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e No administrative permissions or licenses were obtained to access the original data used in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eChow, L. Q. M. Head and Neck Cancer. \u003cem\u003eN Engl. J. 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Association of tumour mutational burden with outcomes in patients with advanced solid tumours treated with pembrolizumab: prospective biomarker analysis of the multicohort, open-label, phase 2 KEYNOTE-158 study. \u003cem\u003eLancet Oncol.\u003c/em\u003e \u003cb\u003e21\u003c/b\u003e, 1353\u0026ndash;1365 (2020).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"PANoptosis, Head and neck squamous cell carcinoma, prognosis, Cancer immunotherapy","lastPublishedDoi":"10.21203/rs.3.rs-5379601/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5379601/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePANoptosis, a recently characterized form of programmed cell death, remains incompletely understood in the context of Head and Neck Squamous Cell Carcinoma (HNSCC). In this study, we identified a prognostically relevant set of PANoptosis genes within The Cancer Genome Atlas (TCGA) database for HNSCC and uncovered three molecular subtypes based on their expression profiles. Each subtype exhibited distinct prognostic outcomes and immune cell infiltration patterns. To further elucidate the clinical relevance, we constructed a PANoptosis risk score model, termed the HNSCPAN-index, using least absolute shrinkage and selection operator (LASSO) Cox regression based on differentially expressed genes across the subtypes. Patients were stratified into high-risk and low-risk groups according to the HNSCPAN-index. The predictive power of the model was evaluated using Kaplan-Meier analysis, ROC, nomogram and validated using an external dataset. A lower HNSCPAN-index correlated with longer overall survival and enhanced immunotherapy responses, whereas a higher HNSCPAN-index indicated increased sensitivity to small-molecule targeted therapies. Moreover, the HNSCPAN-index demonstrated a strong correlation with chemotherapeutic drug sensitivity. Finally, DSCAM was identified as a key regulator in HNSCC, where silencing DSCAM expression enhanced cell death mediated by pyroptosis inducers. In conclusion, we constructed a risk model of PANoptosis in HNSCC and revealed its potential role in prognosis, TME, chemotherapy. These findings may provide a deeper understanding of PANoptosis in HNSCC and pave the way for the development of more personalized therapeutic strategies.\u003c/p\u003e","manuscriptTitle":"Identification and validation of PANoptosis-based HNSCPAN-index as a prognostic model for head and neck squamous cell carcinoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-16 15:24:55","doi":"10.21203/rs.3.rs-5379601/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c9773adb-aa6f-4ca9-b5d4-d0a145d245ce","owner":[],"postedDate":"December 16th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":40741362,"name":"Biological sciences/Cancer"},{"id":40741363,"name":"Biological sciences/Genetics"},{"id":40741364,"name":"Biological sciences/Immunology"},{"id":40741365,"name":"Biological sciences/Molecular biology"}],"tags":[],"updatedAt":"2025-05-13T07:38:53+00:00","versionOfRecord":[],"versionCreatedAt":"2024-12-16 15:24:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5379601","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5379601","identity":"rs-5379601","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}