Abstract Background In several studies of head and neck squamous cell carcinoma (HNSC), the regulation of tumorigenesis and therapeutic sensitivity by pyroptosis has been observed. However, a systematic analysis of gasdermin family members (GSDMs, including GSDMA/B/C/D/E and PJVK), which are deterministic executors of pyroptosis, has not yet been reported in HNSC. Methods We performed comprehensive analyses of the expression profile, prognostic value, regulatory network, and immune infiltration modulation of GSDMs in HNSC on the basis of a computational approach and bioinformatic analysis of publicly available datasets. Results A total of 18.65 % (94/504) of HNSC patients harbored GSDM alterations, with the most dominant type being amplification. Compared with those in normal tissues, the mRNA and protein levels of GSDMs, especially GSDMD/E, were commonly elevated in HNSC (P < 0.05). Additionally, the expression of GSDMs differed significantly between the clinicopathological subgroups of HNSC patients. Overall survival of HNSC patients benefited from increased GSDMC expression (HR = 0.67, P = 0.0053) and decreased GSDME expression (HR = 1.42, P = 0.0140). Regulatory network analysis revealed several essential biological processes associated with GSDMs, including positive regulation of cytokine production involved in the immune response. Notably, almost all infiltrating immune cells and immune checkpoints were negatively correlated with GSDMA/C/E expression and positively related to GSDMB/D and PJVK expression. Conclusions We indicated the potential role of GSDMs (especially GSDME) in HNSC pathogenesis, progression and response to immunotherapy, providing important evidence for further prospective studies and molecular mechanism exploration. Keywords: Antitumor immunity, Gasdermin, Head and neck squamous cell carcinoma, Prognosis, Pyroptosis Highlights * • GSDMD/E were significantly overexpressed at both mRNA and protein levels. * • OS benefited from the increased GSDMC expression and decreased GSDME expression. * • Most infiltrating immune cells and immune checkpoints were correlated with GSDMs. * • GSDME has potential value as a therapeutic target for HNSC. 1. Introduction Head and neck cancers are the seventh most common malignancy originating from the mucosal epithelium of the oral cavity, oropharynx, hypopharynx and larynx, the majority (∼90 %) of which are head and neck squamous cell carcinomas (HNSCs) [[41]1]. After multimodal treatment with surgery, radiotherapy and cisplatin-based chemotherapy, 40–60 % of HNSC patients eventually experience disease recurrence and/or metastasis [[42]2,[43]3]. The efficacy of salvage therapy combining targeted therapy and immunotherapy remains suboptimal today, with a median overall survival (OS) of only 6–15 months [[44][4], [45][5], [46][6]]. Therefore, exploration of the potential oncogenic pathways of HNSC is imperative to identify new therapeutic targets. Pyroptosis is a newly discovered form of inflammatory programmed cell death caused by gasdermin family members (GSDMs) [[47]7,[48]8]. It involves cell rupture and cytolysis, resulting in the release of pro-inflammatory cytokines and immunogenic substances into the tumor microenvironment (TME) [[49][9], [50][10], [51][11], [52][12]]. In HNSC, several studies have constructed prognostic signatures of molecules associated with pyroptosis [[53][13], [54][14], [55][15], [56][16], [57][17], [58][18]]. Individual reports of HNSC suggest that pyroptosis contributes to sensitivity to radiotherapy [[59]19] and chemotherapy [[60]20,[61]21]. However, a systematic analysis of GSDMs (including GSDMA, GSDMB, GSDMC, GSDMD, GSDME/DFNA5 and pejvakin/PJVK/DFNB59), which are the real executors and determinants of pyroptosis, has not yet been reported in HNSC. In this work, we evaluated the expression profile, prognostic value, regulatory network, and immune infiltration modulation of GSDMs in HNSC via a computational approach and bioinformatic analysis of publicly available datasets ([62]Fig. 1). Overall, our findings demonstrated the important role of GSDMs (especially GSDME) in HNSC pathogenesis, progression and response to immunotherapy, providing ideas and clues for further mechanistic investigations and the development of GSDM-targeted antitumor therapies. Fig. 1. [63]Fig. 1 [64]Open in a new tab Flow diagram of the study. 2. Materials and methods 2.1. Expression profile GEPIA2 is a user-friendly interactive database for the analysis of RNA-seq data of tumor and normal samples from the TCGA and GTEx projects [[65]22]. It provides customizable analyses for differential expression, survival and similar gene detection. We compared the mRNA expression of GSDMs between HNSC tissues and adjacent normal tissues via the "Expression analysis-BoxPlot" module of GEPIA2. UALCAN is a comprehensive web server for analyzing cancer omics data that collects clinical and RNA-seq data from the TCGA [[66]23]. In this work, the mRNA expression of GSDMs in HNSC and normal tissues was explored according to sample type, individual cancer stage, patient gender and race, tumor grade, TP53 mutation status, and human papillomavirus (HPV) status. Moreover, the “Proteomics” module of UALCAN provides information on the protein expression of GSDMs in HNSC using CPTAC data. TISCH is a comprehensive online platform for the interactive exploration and visualization of single-cell transcriptome data in the TME [[67]24]. TISCH integrates a single-cell RNA-sequencing (scRNA-seq) atlas from 76 high-quality tumor datasets across 27 cancer types, mainly from GEO and ArrayExpress, processed through a standardized workflow for quality control, batch effect removal, clustering, differential expression, cell-type annotation, malignant cell classification, and functional enrichment. Our study utilized the HNSC dataset [68]GSE103322, encompassing 5902 single cells from 18 patients with oral cavity tumors, to investigate the mRNA expression of GSDMs in tumor cells. SpatialTME is a web-based portal that combines histological images with single-cell and spatial transcriptomics data to study the TME [[69]25]. Equipped with a computational pipeline, SpatialTME identifies differential gene expression, performs functional analyses, deconvolutes TME cell composition, and analyzes cell-cell interactions. It contains 296 spatial transcriptomics slides across 19 cancer types, offering a detailed view of the spatial TME. In our study, we leveraged the HNSC dataset [70]GSE181300, which includes primary tumor sections from four HNSC patients, to explore the spatial mRNA expression of GSDMs in tumor cells. HPA is an online open-access resource for investigating the expression and localization of specific human genes and proteins and contains more than just immunohistochemistry (IHC) expression data [[71]26]. Here, we obtained IHC images of GSDMs in HNSC tissues. 2.2. Prognostic value Kaplan–Meier Plotter is a public database containing information on the effects of 54,675 genes on survival outcomes for 21 cancer types from 10,461 cancer samples. The groups were stratified by automatically calculating the optimal cutoff value, and the hazard ratio (HR), corresponding 95 % confidence interval (CI) and log-rank P value were calculated. Here, the associations between the mRNA expression of GSDMs and OS and relapse-free survival (RFS) were clarified in HNSC patients via Kaplan–Meier Plotter. 2.3. Genetic alteration cBioPortal is a web portal for exploring, analyzing and visualizing cancer genomic data from the TCGA [[72]27]. We queried the genetic alteration profiles of GSDMs from a dataset containing 504 HNSC patients (TCGA, Firehose Legacy). The genetic alteration frequency and mutation features of GSDMs were visualized. In addition, we analyzed the relationships between the alteration status of GSDMs and OS and disease-free survival (DFS) in HNSC patients. 2.4. Regulatory network STRING is an online database for protein interaction analysis [[73]28]. Protein-protein interaction (PPI) network analysis via the STRING tool allows us to explore the potential interactions of proteins closely associated with GSDMs. Pathway enrichment analysis was then performed via Cytoscape and ClueGo. GeneMANIA is a resourceful database of gene information that enables functional analysis of gene lists via high-precision prediction algorithms [[74]29]. We performed molecular and functional enrichment analyses related to GSDMs via GeneMANIA. 2.5. Immune infiltration TISIDB, a powerful tool for evaluating tumor-immune system interactions, incorporates rich heterogeneous data from the TCGA [[75]30]. In this database, tumor immune infiltration is classified into subtypes C1-6, which are wound healing, interferon [IFN]-γ dominant, inflammatory, lymphocyte-depleted, immunologically quiet, and tumor growth factor [TGF]-β dominant. The correlation between GSDM expression and immune infiltration subtypes in HNSC was visualized via the “Subtype” module. TIMER estimates the abundance of infiltrating immune cells from gene expression profiles of TCGA cancer samples [[76]31]. Here, the correlation between GSDM expression and the abundance of major infiltrating immune cells in the TME of HNSC was visualized via scatter plots. Additionally, we investigated the associations of GSDMs with various immune cell markers in HNSC, sourced from similar studies [[77]32,[78]33] and the CellMarker 2.0 database [[79]34]. BEST is a valuable resource for exploring the biological significance of genes in various cancers [[80]35]. It facilitated us to explore the relationship between GSDMs and immune infiltration in the HNSC datasets [81]GSE117973, [82]GSE65858, [83]GSE75538, [84]GSE41613, [85]GSE42743, [86]GSE84713 and TCGA_HNSC with multiple algorithms (CIBERSORT, CIBERSORT_ABS, EPIC, ESTIMATE, MCPcounter, Quantiseq, TIMER, xCell). 3. Results The mRNA expression profiles of GSDMs in HNSC were investigated via GEPIA2, UALCAN, TISCH, and the SpatialTME databases. According to the GEPIA2 database, GSDMB/D/E mRNA was significantly overexpressed in HNSC tissues compared with normal tissues (P < 0.05) ([87]Fig. 2A). Analysis of the UALCAN database revealed that the mRNA expression of GSDMs (excluding GSDMA) was substantially higher in HNSC tissues than in normal tissues (P < 0.01) ([88]Fig. 2B). According to the results of both databases, the mRNA expression of GSDMD was the highest among the six GSDMs, followed by GSDME and GSDMC. Considering tissue heterogeneity, we analyzed the HNSC single-cell transcriptome dataset [89]GSE103322 via the TISCH database and the spatial transcriptome dataset [90]GSE181300 via the SpatialTME database, both of which revealed that the mRNA levels of GSDMC/D/E were generally upregulated in malignant cells ([91]Fig. 2C and D). Furthermore, significant differences in their transcriptional levels were commonly observed in HNSCs at different stages, genders, races, pathologic grades, TP53 mutation statuses, and HPV statuses (P < 0.05) ([92]Fig. S1). Fig. 2. [93]Fig. 2 [94]Open in a new tab Expression of GSDMs in HNSC. The mRNA expression of GSDMs in the (A) GEPIA2, (B) UALCAN, (C) TISCH, and (D) SpatialTME database. (E) Protein expression of GSDMs in the UALCAN database. (F) Representative immunohistochemistry images of GSDMs from HNSC tissues in the HPA database. ns, not significance, ∗P < 0.05, ∗∗P < 0.01, ∗∗∗P < 0.001. The protein expression levels of GSDMs in HNSC tissues were explored via the CPTAC and HPA databases. Compared with that in adjacent normal tissues, GSDMA/C/D/E protein expression was significantly elevated in HNSC tissues (P < 0.05) ([95]Fig. 2E). IHC revealed medium and high staining intensities of GSDMD and GSDME, respectively, while the protein levels of GSDMA/B/C were not detected or were low in HNSC tissues ([96]Fig. 2F). Protein expression data related to PJVK could not be retrieved from either database. Kaplan-Meier Plotter database was used to explore the prognostic value of GSDMs in HNSC patients by stratifying the transcript levels into high and low groups. The OS and RFS curves are presented in [97]Fig. 3. Elevated GSDMC predicted prolonged OS (HR = 0.67, P = 0.0053). Conversely, higher GSDME correlated with worse OS (HR = 1.42, P = 0.0140). However, no significant effect of GSDMs on RFS in patients with HNSC was observed. We subsequently focused on the correlation between GSDMC/E expression in different clinicopathological features and OS in HNSC patients. As shown in [98]Fig. S2, GSDMC overexpression was correlated with better OS in females, whites, and patients who were in stage 2, had a low mutation burden, and had a high neoantigen load (P < 0.05). GSDME overexpression was significantly associated with worse OS in males, whites and black/African-Americans, and patients who were in stages 1 and 4, had grade 4 disease, had a low mutation burden, and had a high neoantigen load (P < 0.05). Fig. 3. [99]Fig. 3 [100]Open in a new tab Relationships between the transcript levels of GSDMs and the OS and RFS of patients with head and neck squamous cell carcinoma via the Kaplan–Meier Plotter database. (A) GSDMA, (B) GSDMB, (C) GSDMC, (D) GSDMD, (E) GSDME, and (F) PJVK. Next, we investigated the genetic alterations of GSDMs and their correlation with the prognosis of HNSC patients via the cBioPortal database. Among the 504 HNSC patients included, 94 (18.65 %) had GSDM alterations. The most dominant type was amplification (16.07 %, 81 of 504 cases), followed by mutation (2.38 %, 12 of 504 cases) ([101]Fig. S3A). Among the six GSDMs, GSDMC and GSDMD had the greatest alteration frequency (11 %) ([102]Fig. S3B). However, we did not observe an association between the alteration status of GSDMs and OS or DFS in HNSC patients ([103]Figs. S3C–H). A PPI network analysis of GSDMs was carried out via the STRING database. As expected, GSDMs were closely associated with several inflammasome-related proteins (e.g., PYCARD, NLRP1, NLRP2, NLRP3, NLRP6, NLRP7, NLRP9, NLRC4, SCAF11 and NOD1) ([104]Fig. 4A). Pathway enrichment analysis revealed that GSDMs were mainly involved in the inflammasome complex (27.66 %), interleukin-1 production (25.53 %), cysteine-type endopeptidase activity involved in the apoptotic process (12.77 %), and peptidase regulator activity (10.64 %) ([105]Fig. 4B). They are also implicated in the positive regulation of cytokine production involved in the immune response ([106]Fig. 4C). The GeneMANIA database also revealed the functions of GSDMs and their associated molecules, which were primarily correlated with phosphatidylinositol bisphosphate binding, phosphatidylinositol phosphate binding, modified amino acid binding, phosphatidylinositol binding, phosphatidylglycerol binding, mannosidase activity, and protein demannosylation ([107]Fig. 4D). Fig. 4. [108]Fig. 4 [109]Open in a new tab Enrichment analysis of gasdermin-related proteins. (A) Protein-protein interaction network from the STRING database optimized with Cytoscape. (B–C) Pathway enrichment analysis via ClueGo. (D) Molecular and functional enrichment with the GeneMANIA database. According to the TISIDB database, the immune infiltrates of HNSC were predominantly types C1 (wound healing) and C2 (IFN-γ dominant) but not type C5 (immunologically quiet). The expression of GSDMs (excluding PJVK) markedly differed among the five immune subtypes (P < 0.01) ([110]Fig. 5A). Fig. 5. [111]Fig. 5 [112]Open in a new tab Correlation between GSDM expression and immune infiltration in HNSC. (A) Immune subtypes according to the TISIDB database. (B) Infiltrating immune cells via the TIMER database. (C) Infiltrating cell markers via the TIMER database. The relationships between GSDM expression and immune cell infiltration in HNSC, including B cells, CD8^+ T cells, CD4^+ T cells, macrophages, neutrophils and dendritic cells (DCs), were assessed via the TIMER database. As shown in [113]Fig. 5B, GSDMA was negatively correlated with all six infiltrating immune cells (P < 0.05). In addition, GSDMC expression was negatively correlated with B cell, CD8^+ T cell, macrophage and DC infiltration (P < 0.01). GSDME expression was negatively correlated with B-cell and CD8^+ T-cell infiltration (P < 0.001). In contrast, GSDMB and GSDMD were positively associated with CD8^+ T cell, CD4^+ T cell, macrophage, neutrophil and DC infiltration (P < 0.05). PJVK was also positively correlated with B-cell and macrophage infiltration (P < 0.05). We further explored the relationships between GSDM expression and a wide range of infiltrating immune cells in HNSC, including B cells, T cells [general], CD8^+ T cells, M1 macrophages, M2 macrophages, tumor-associated macrophages (TAMs), neutrophils, monocytes, DCs, natural killer (NK) cells, T-helper (Th)1 cells, Th2 cells, Th17 cells, follicular helper T (Tfh) cells, regulatory T (Treg) cells and exhausted T cells, via the TIMER database. Overall, almost all marker genes of immune cells were negatively correlated with GSDMA/C/E expression (P < 0.05) and positively correlated with GSDMB/D and PJVK expression (P < 0.05) ([114]Fig. 5C). In addition, GSDMs were closely associated with immune checkpoints (PD1, CTLA4, LAG3, TIM3 and TIGIT). The analysis of multiple HNSC datasets ([115]GSE117973, [116]GSE65858, [117]GSE75538, [118]GSE41613, [119]GSE42743, [120]GSE84713, and TCGA_HNSC) via the BEST database enhances the robustness of these findings from the TIMER database and their applicability to diverse patient populations ([121]Fig. 6). Fig. 6. [122]Fig. 6 [123]Open in a new tab Analysis of the impact of GSDMs on immune cell infiltration status in HNSC via the BEST database. 4. Discussion Given its potential role in tumor evolution and therapy, GSDM-mediated pyroptosis has attracted great interest from cancer researchers. To our knowledge, this is the first systematic analysis of GSDMs in terms of their expression profile, prognostic value, regulatory network and immune infiltration modulation in HNSC. Using a computational approach and bioinformatics analysis of publicly available datasets, we revealed that the mRNAs and proteins of GSDMs (especially GSDMD/E) were overexpressed in HNSC tissues and varied among different clinicopathological subgroups. Reduced GSDMC and elevated GSDME expression could predict shorter OS in HNSC patients. Furthermore, several essential pathways associated with GSDMs, including the inflammasome complex, interleukin-1 production, and positive regulation of cytokine production involved in immune response, have been identified. Notably, we demonstrated specific associations between GSDM expression and immune cell infiltration in the TME of HNSC. These findings initially demonstrated the potential role of GSDMs (especially GSDME) in HNSC pathogenesis, progression and response to immunotherapy, providing important evidence for further prospective studies and molecular mechanism exploration. According to current findings in cancer research, pyroptosis appears to be a dual process. Pyroptosis can rapidly cause a potent inflammatory response and remarkable tumor regression but promotes a tumor-supportive microenvironment [[124]10]. As a result, there is no intrinsic uniform pattern of GSDM expression in cancer. For example, GSDMs are deregulated in lung, gastric, breast and cervical cancers and can act as oncogenes or tumor suppressors to regulate proliferation, metastasis, treatment resistance and immune responses [[125]36,[126]37]. In this study, we showed that the mRNA and protein levels of GSDMs, especially GSDMD and GSDME, were commonly elevated in HNSC tissues. In addition, GSDMs were differentially expressed between multiple clinicopathological subgroups, indicating that GSDMs may be involved in the pathogenesis and progression of HNSC. We then attempted to determine the possible mechanisms underlying the overexpression of GSDMs in HNSC. Genetic alteration analyses suggested that high-frequency amplification may be the primary cause. However, the percentage of genetic alteration in GSDME was as low as 2.2 %, indicating the occurrence of transcriptional or post-translational modifications of GSDME in HNSC. Notably, increased GSDMC expression and decreased GSDME expression significantly favored OS in HNSC patients. Therefore, it is reasonable to speculate that among the six GSMDs, GSDME may be the most important oncogene for HNSC, with potential as a biomarker for prognosis evaluation. To date, two principal pathways of pyroptosis have been elucidated, i.e., GSDMD-induced pyroptosis involving inflammatory caspase-1 (classical pathway) or caspase-4/5/11 (non-classical pathway) [[127][38], [128][39], [129][40]]. Pyroptosis induced by GSDME via caspase-3 is the most widely recognized alternative pathway [[130]41]. Previous studies on HNSC have shown that caspase-3/GSDME-dependent pyroptosis can be activated by triptolide [[131]42], alantolactone [[132]43], and CXCR4-targeted nanotoxins [[133]44]. During the process of pyroptosis, the N-terminal domains of GSDMs (excluding PJVK) form pores on the cell membrane, resulting in the formation of large bubbles from the swollen cell membrane and the release of cellular contents. As expected, our PPI network and functional enrichment analyses of GSDMs support the essential role of inflammasomes and plasma membrane components in the process of pyroptosis. Notably, we found that GSDMs were closely associated with the positive regulation of cytokine production involved in the immune response, suggesting a potential role for GSDM-mediated pyroptosis in antitumor immunity. One of the key findings of this study is that we investigated the potential role of GSDM-mediated pyroptosis in recruiting and regulating immune cell infiltration in HNSC. This finding is consistent with studies by Wang et al. [[134]20] and Zi et al. [[135]21] reported that the stimulation of GSDME-mediated pyroptosis improved the antitumor immunity of chemotherapy in oral cancer. The therapeutic induction of pyroptosis works synergistically with immunotherapy to turn “cold” tumors “hot” [[136]12,[137]45,[138]46]. However, most immune cells have both antitumor and protumor effects [[139]47], which is consistent with the role of pyroptosis in tumors. Moreover, immune cell infiltration in the TME affects not only the biological behavior of tumors but also patient prognosis and immunotherapy efficacy [[140]48]. In this study, we found that almost all types of infiltrating immune cells were negatively correlated with GSDMA/C/E expression and positively correlated with GSDMB/D and PJVK expression. These close correlations indicate that GSDM-mediated pyroptosis may modulate immune cell infiltration and immune escape in HNSC. Furthermore, we observed close relationships between GSDMs and various immune checkpoints. Hence, elevated GSDMA/C/E expression and reduced GSDMB/D and PJVK expression may predict a suboptimal therapeutic effect of immune checkpoint inhibitors in HNSC patients. Collectively, these data provide a bioinformatics rationale for future applications of GSDM-targeted immunotherapy. Despite extensive efforts to investigate and integrate information from multiple available databases, several limitations of our study need to be noted. Microarray and sequencing data from different databases lack granularity and specificity and exhibit variation, which may lead to systematic bias. In addition, although bioinformatic analyses provided some vital insights into GSDMs in HNSC and supported our previous findings that GSDME plays a crucial role in pyroptosis and the progression of nasopharyngeal carcinoma [[141]19], further experiments are needed to confirm the impact of GSDMs on immune infiltration in HNSC. It is also important to consider confounding variables such as treatment regimens and comorbidities to enhance the precision of result interpretation. Moreover, no clinical trials have reported the safety and efficacy of GSDM inhibitors in HNSC. Thus, we do not have specific or complete case data available to determine the prognostic benefit of GSDM inhibitors in HNSC patients. Nevertheless, we elucidated a promising therapeutic strategy targeting GSDMs, especially GSDME, which may synergize with immunotherapy to improve survival in HNSC patients. 5. Conclusions In summary, this comprehensive analysis demonstrated the potential role of GSDMs (especially GSDME) in HNSC pathogenesis, progression and response to immunotherapy. Further prospective studies and molecular mechanism exploration are warranted to confirm our results. CRediT authorship contribution statement Huageng Huang: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing. Jingjing Ge: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Software, Supervision, Validation, Visualization, Writing – review & editing. Shunzhen Lu: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Software, Supervision, Validation, Visualization, Writing – review & editing. Xinyi Deng: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Software, Supervision, Validation, Visualization, Writing – review & editing. Ying Tian: Writing – review & editing. He Huang: Writing – review & editing. Zhao Wang: Writing – review & editing. Yuyi Yao: Writing – review & editing. Huangming Hong: Funding acquisition, Resources, Writing – review & editing. Tongyu Lin: Funding acquisition, Resources, Writing – review & editing. Ethics approval Not applicable. Data availability statement for this work All of the data from this study are publicly available online. Consent to participate Not applicable. Funding This work was supported by the National Natural Science Foundation of China (No. 82003196, 82270198); the Medical Science and Technology Foundation of Guangdong Province (No. A2021426); the Outstanding Young Scientific and Technological Talents Fund of Sichuan Province (No. 2022JDJQ0059); and the Cancer Innovative Research Program of Sun Yat-sen University Cancer Center (No. CIRP-SYSUCC-0022). Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgements