Dendritic cell-related hub genes in head-and-neck squamous cell carcinoma: implications for prognosis and immunotherapy

Introduction

In the challenging landscape of oncology, head-and-neck squamous cell carcinoma (HNSCC) remains an enigma, and innovative solutions are required (1-3). Despite recent therapeutic advances, many patients continue to live with unfavorable prognoses (4). The nuanced dynamics of the tumor immune microenvironment (TIME) emerge as a pivotal determinant in the progression of HNSCC (5,6). Dendritic cells (DCs) serve as sentinels of the immune system and orchestrate the responses to malignant cells (7,8). This emphasizes the multifaceted roles played by DCs within the HNSCC TIME; manipulation of such cells may revolutionize therapeutic paradigms (9).

DC antigen presentation and the effects of DCs on immune checkpoint modulation are of major significance in HNSCC (10). Elucidating the functional nuances of DC subsets would help tailor therapies to shift the immune equilibrium in the desired direction (11). Recent DC-based vaccines comprising patient-derived DCs enriched with tumor-specific antigens (12-14) elicit potent anti-tumor responses while minimizing adverse effects (15). This research frontier provides a beacon of hope in the challenging battle against this formidable cancer (16,17).

In recent years, gene expression signatures associated with cancer (including HNSCC) prognoses have been used to predict patient outcomes (18,19). Such signatures identify high-risk patient subgroups that require personalized therapeutic strategies. The prognostic implications aside, exploration of the molecular mechanisms underlying the role of DCs in HNSCC progression would facilitate therapeutic interventions (20,21). However, no reliably prognostic gene signatures for HNSCC are yet available, and we have only a limited comprehension of the genetic principles governing DC behavior.

In this study, we comprehensively investigated potential drug targets within the DC gene signature to address the gaps identified above. Employing a multi-omics approach and advanced computational analyses, novel molecules and signaling networks that serve as prospective therapeutic targets for HNSCC were revealed. The establishment of a robust DC gene signature model via high-throughput genomic profiling enables precise patient stratification. Ultimately, by unraveling the molecular mechanisms and genetic drivers associated with DCs, we contribute to the development of precision medicine to enhance the prognostic assessment and treatment outcomes of HNSCC patients. We present this article in accordance with the TRIPOD reporting checklist (available at https://tcr.amegroups.com/article/view/10.21037/tcr-23-2360/rc).

Methods

Acquisition of clinical and gene expression information

In January 2023, we used the Genomic Data Commons (GDC) portal to acquire clinical and gene expression data from a The Cancer Genome Atlas (TCGA)-HNSCC cohort comprising 500 individuals. At that time, we also obtained clinical and transcriptomic data from 270 HNSCC cases in the GSE65858 cohort from the Gene Expression Omnibus database. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013).

The abundance of DCs and the application of weighted gene co-expression network analysis (WGCNA)

Quantification of DC abundance in the TCGA-HNSCC as well as GSE65858 cohorts was performed using the “xCells” package of R software (22). We acquired five different DC abundance scores for each cohort. We used the “WGCNA” package of R software to define co-expression networks and to obtain multiple estimates of DC abundance and stromal scores (23).

Random survival forest analysis, least absolute shrinkage and selection operator (LASSO) Cox’s regression model, and survival analysis

We employed random survival forest analysis to identify significantly prognostic genes using the “randomForestSRC” and “randomSurvivalForest” packages of R software (24). The LASSO Cox’s regression method was utilized to construct a prognostic model for hub genes associated with DCs, employing the “glmnet” package within the R software (25). The optimal cutoff values of the LASSO risk scores in terms of overall survival (OS) were determined for both the TCGA-HNSCC and GSE65858 cohorts using the “max stat” approach with an abseps threshold of 0.01. Subsequently, Kaplan-Meier analyses were conducted using the “survival” packages of R software.

Differential gene expression analysis

Differential expression analysis of messenger RNAs was executed utilizing the “EdgeR” package within the R software (26).

Gene set enrichment analysis (GSEA)

We built a GSEA algorithm using the “clusterProfiler” package of R software. This revealed normalized enrichment scores and their statistical significance across various gene sets, including the ontology and Hallmark gene sets, together with Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway terms (27).

Somatic mutation analysis

In March 2023, we utilized cBioPortal to obtain the top 200 somatic mutations with the highest frequency in the TCGA-HNSCC cohort. We employed the Chi-squared test to assess significant mutation enrichment among the different prognostic groups.

Single-cell sequencing analysis

We leveraged the Tumor Immune Single-cell Hub 2 database (http://tisch.comp-genomics.org/) (28) to conduct an analysis of single-cell sequencing data from GSE150430 cohorts.

Prediction of responses to immunotherapy

Predictions of responses to immunotherapy within the TCGA-HNSCC and GSE65858 cohorts were derived using the TIDE (Tumor immune dysfunction and exclusion) available at http://tide.dfci.harvard.edu/.

Calculation of “xCell” scores

We used the “xCell” package of R software to compute immune, stromal, and microenvironment scores and to characterize 64 distinct cell subtypes in both the TCGA-HNSCC and GSE65858 cohorts.

ESTIMATE algorithm

To evaluate the immune system and microenvironment scores, we employed the ESTIMATE algorithm, a computational method implemented through the ESTIMATE R package.

Transcriptional regulators prediction

Lisa (epigenetic Landscape In Silico deletion Analysis and the second descendent of MARGE) was used to predict the transcriptional regulators of differentially expressed or co-expressed gene sets (29).

Laboratory experiments

For a comprehensive understanding of laboratory experiments, please refer to the Appendix 1 for detailed protocols regarding cell culture, quantitative real-time polymerase chain reaction (qRT-PCR), and Luciferase assay procedures.

Statistical analysis

All data analyses and figure generation were performed using R software (version 4.0.1). Results were considered statistically significant at a P value of less than 0.05.

Results

Identification of DC-related gene candidates by WGCNA

In both the TCGA-HNSCC and GSE65858 cohorts, we identified the top 10,000 genes based on their median absolute deviations and used them to construct the WGCNA network. In the TCGA-HNSCC cohort, we removed three evident outliers as determined by hierarchical clustering. In the GSE65858 cohort, no outlier was detected. In constructing a scale-free topology network, a soft thresholding power of 9 was applied to the TCGA-HNSCC cohort (yielding a scale-free R² of 0.93, a slope of −1.85, and a truncated R² of 1) (Figure S1A,S1B) and of 5 for the GSE65858 cohort (resulting in a scale-free R² of 0.95, a slope of −2.07, and a truncated R² of 0.98) (Figure S1C,S1D). In the TCGA-HNSCC cohort, 10 co-expression gene modules were clustered (Figure 1A,1B, table available at https://cdn.amegroups.cn/static/public/tcr-23-2360-1.xlsx). In the GSE65858 cohort, 10 co-expression gene modules were clustered (Figure 1C,1D, table available at https://cdn.amegroups.cn/static/public/tcr-23-2360-2.xlsx). Specifically, in the TCGA-HNSCC cohort, the yellow module, consisting of 345 genes, exhibited the strongest positive correlation with DC abundance, as detailed in Figure 2A and further elaborated in Figure S2A. Meanwhile, in the GSE65858 cohort, the blue module, comprising 1,676 genes, demonstrated the most pronounced positive correlation with DC abundance, as evidenced in Figure 2B and further detailed in Figure S2B.

Figure 1 WGCNA analysis in TCGA-HNSCC as well as GSE65858. Cluster dendrograms presenting similar expression pattern genes were grouped into WGCNA gene modules in TCGA-HNSCC (A) as well as GSE65858 (B). Eigengene adjacency heatmaps presenting the relationships among each gene module eigengenes in TCGA-HNSCC (C) and GSE65858 (D). WGCNA, weighted gene co-expression network analysis; TCGA, The Cancer Genome Atlas; HNSCC, head-and-neck squamous cell carcinoma; ME, module eigengene.

Figure 2 Construction of DC immune signature using WGCNA. Heatmaps depicting module-trait relationships showcase the correlations between each module eigengene and phenotypes in both TCGA-HNSCC (A) and GSE65858 (B). Enriched biological process, cellular component, and molecular function terms for the intersected DC immune genes are illustrated through Gene Ontology analyses (C). 222 DC immune-related candidates intersected in TCGA-HNSCC yellow module and GSE65858 blue module (D). WGCNA, weighted gene co-expression network analysis; TCGA, The Cancer Genome Atlas; HNSCC, head-and-neck squamous cell carcinoma; DC, dendritic cell; iDC, immature dendritic cell; aDC, activated dendritic cell; cDC, conventional dendritic cell; pDC, plasmacytoid dendritic cells; MHC, major histocompatibility complex; BP, biological process; CC, cellular component; MF, molecular function.

A collective of 222 genes was found to be present in both modules. Through Gene Ontology enrichment analysis, pronounced enrichment was observed in the biological process category for leukocyte cell-cell adhesion (GO:0007159), in the cellular component category for endocytic vesicle (GO:0030139), and in the molecular function category for immune receptor activity (GO:0140375) (Figure 2C).

A DC-based prognostic signature of HNSCC

In the TCGA-HNSCC cohort, a random survival forest analysis was employed to systematically rank the 222 hub gene candidates in terms of OS. The following eight genes exhibited variable importance cutoffs that exceeded 0.5: FGD3, ACP2, IGJ, INPP5D, CCR7, CPVL, CD27, and SPOCK2 (Figures 2D,3A). Utilizing a LASSO Cox’s regression model, we identified significant prognostic candidates for OS based on gene coefficients, including FGD3 =−0.473, ACP2 =0.186, IGJ =−0.043, INPP5D =−0.035, CCR7 =−0.214, CPVL =0.339, and SPOCK2 =−0.056 as illustrated in Figure 3B,3C. Based on the aforementioned coefficients, we deduced the presence of seven DC candidate genes associated with HNSCC prognosis. Among these, ACP2 and CPVL were linked to unfavorable OS, while FGD3, IGJ, INPP5D, CCR7, and SPOCK2 were associated with favorable OS. To further validate these findings, we conducted additional single-gene studies on GSE65858 cohort. Remarkably, our analysis confirmed that nearly all identified genes exhibited prognostic directions consistent with those observed in the TCGA-HNSCC cohort and possessed statistical significance (Figure S3).

Figure 3 Survival-related DC immune signature established by LASSO Cox model. The bar plot presented the random survival forest analysis identified 8 risk hub candidates, which were determined by the variable relative importance cutoff bigger than 0.5 (A). The LASSO Cox model identified the top 7 relevant survival-associated candidates within the DC immune repertoire for the OS in the TCGA-HNSCC cohort (B,C). Kaplan-Meier plots were generated for the 5-year OS in both low and high-risk subgroups for TCGA-HNSCC and GSE65858 (D,E). Subgroup stratification was determined by employing the best cutoff algorithm computed through the log-rank test. DC, dendritic cell; LASSO, least absolute shrinkage and selection operator; OS, overall survival; TCGA, The Cancer Genome Atlas; HNSCC, head-and-neck squamous cell carcinoma.

In both the TCGA-HNSCC (Figure 3D) and GSE65858 (Figure 3E) cohorts, we categorized all patients into high- and low-risk groups using the optimal risk score cutoff values determined by the LASSO Cox model. We then derived Spearman correlations between each of the seven candidate genes and DC abundance in both the TCGA-HNSCC (Figure S4A) and GSE65858 (Figure S4B) cohorts, each gene exhibited a positive correlation with overall DC cell and its subpopulation scores. Notably, within the DC cell subpopulations, aDC and pDC showed a higher correlation with these genes compared to other subpopulation scores.

The analysis of single-cell sequences in GSE150430 cohort (Figures 4,5A,5B) revealed that the two candidates associated with unfavorable OS, ACP2 and CPVL, exhibited heightened expression primarily in DCs and Mono/Macro cells. In contrast, the five candidates linked to favorable OS, namely FGD3, IGJ, INPP5D, CCR7, and SPOCK2, were predominantly enriched in B cells, CD4Tconv, Tprolif, CD8Tex, Treg, and Tprolif. Notably, the unfavorable OS candidates co-expressed prominently in DCs, while the five favorable OS candidates co-expressed significantly in subpopulations of T and B cells. Furthermore, in GSE150430, both ACP2 and CPVL demonstrated heightened expression specifically in DCs (table available at https://cdn.amegroups.cn/static/public/tcr-23-2360-3.xlsx).

Figure 4 Single cell sequencing analysis for prioritized candidates in GSE150430. UMAP plots visualized the expression of prioritized candidates in indicated cell types of GSE150430 (IGJ is the previous HGNC symbols for JCHAIN). NPC, nasopharyngeal carcinoma; DC, dendritic cell; pDC, plasmacytoid dendritic cell; UMAP, Uniform Manifold Approximation and Projection; IGJ, immunoglobulin J chain; HGNC, Human Genome Organisation Gene Nomenclature Committee; JCHAIN, Joining Chain of Multimeric IgA and IgM.

Figure 5 Indicated cell types co-expression pattern for prioritized candidates in GSE150430. Violin plots were used to visualize the expression of prioritized candidates in indicated cell types of GSE150430 (A), while uniform manifold approximation and projection plots depicted the co-expression pattern of unfavorable or favorable OS candidates in the specified cell types of GSE150430 (B). NPC, nasopharyngeal carcinoma; DC, dendritic cell; pDC, plasmacytoid dendritic cell; OS, overall survival.

We explored differential gene expression between the ACP2⁺ DC cells and ACP2⁻ DC cells. This analysis led to the identification of 208 differentially expressed genes, meeting stringent criteria: |log₂ fold change (FC)| ≥0.3 and false discovery rate (FDR) ≤0.05. Among these, 176 genes exhibited upregulation in the ACP2⁺ DC cells, while 32 genes displayed upregulation in the ACP2⁻ DC cells (Figure 6A). Based on the Lisa analysis, top 5 significant transcription factors (STAT1, SPI1, SMAD1, CEBPB, and IRF1) were identified (Figure 6B).

Figure 6 The role of STAT1 in the ACP2 expression regulation. (A) Additionally, the volcano plot highlights significant differentially expressed genes (|log₂fold change| ≥0.3 and FDR ≤0.05) between the ACP2⁺ DCs and ACP2⁻ DCs. (B) Based on the Lisa analysis, top 5 significant transcription factors (STAT1, SPI1, SMAD1, CEBPB, and IRF1) were identified. (C) qRT-PCR analysis reveals the mRNA levels of STAT1 (left panel) and ACP2 (right panel) following STAT1 overexpression in HEK293T cells, with statistical significance denoted as **P<0.001. (D) Dual-luciferase assay analysis investigates the impact of STAT1 on the transcriptional activity of the ACP2 gene promoter in HEK293T cells, with statistical significance denoted as ***P<0.0001. DC, dendritic cell; FDR, false discovery rate; qRT-PCR, quantitative reverse transcription polymerase chain reaction.

The association between STAT1 and ACP2 (the most crucial candidate among the three noteworthy prognostic DC candidates) has captured our attention. To confirm the correlation between STAT1 and ACP2 in cell line, we conducted qRT-PCR and dual-luciferase assays in HEK293T cells. The qRT-PCR analysis demonstrated elevated mRNA levels of both STAT1 and ACP2 following STAT1 overexpression in HEK293T cells (Figure 6C). Furthermore, the dual-luciferase assay revealed that the transcriptional activity of the ACP2 gene promoter could be enhanced by the binding of STAT1 protein to the promoter of the ACP2 gene in HEK293T cells (Figure 6D).

Consistent with expectations, an elevated risk score was correlated with unfavorable OS in the TCGA-HNSCC cohort (Figure 7A,7B). In the high-risk group, ACP2 and CPVL levels exhibited an increase, while in the low-risk group, FGD3, IGJ, INPP5D, CCR7, and SPOCK2 levels showed elevated expression (Figure 7C).

Figure 7 Risk phenotype of immune LASSO Cox model in TCGA-HNSCC. The upper dot plot illustrates the risk scores for low- and high-risk subgroups, arranged in ascending order of increasing risk score (A). In the middle dot plot, overall survival data for patients is presented in accordance with the escalating risk score (B). The bottom heatmap provides a summary of the expression patterns of the seven prioritized candidate genes within the TCGA-HNSCC cohort (C). Additionally, the volcano plot highlights significant differentially expressed genes (|log₂fold change| ≥1 and FDR ≤0.05) between high and low-risk subgroups in TCGA-HNSCC (D). The waterfall plot delineates notable somatic mutations observed in low-risk (left) and high-risk (right) TCGA-HNSCCs, focusing on the top 200 somatic mutations by frequency (E). LASSO, least absolute shrinkage and selection operator; TCGA, The Cancer Genome Atlas; HNSCC, head-and-neck squamous cell carcinoma; FDR, false discovery rate.

Differential gene expression, immune characteristics, and enrichment pathways among the LASSO risk clusters

Using the TCGA-HNSCC cohort, we explored differential gene expression between the low- and high-risk groups. This analysis led to the identification of 274 differentially expressed genes, meeting stringent criteria: |log₂FC| ≥1 and FDR ≤0.05. Among these, 175 genes exhibited upregulation in the high-risk group, while 99 genes displayed upregulation in the low-risk group (Figure 7D, table available at https://cdn.amegroups.cn/static/public/tcr-23-2360-4.xlsx).

The xCell immune characteristics of the various LASSO risk clusters differed in both the TCGA-HNSCC and GSE65858 cohorts. The immune system and microenvironment scores, assessed using the ESTIMATE algorithm, exhibited elevated values in the low-risk groups as opposed to the high-risk groups in both the TCGA-HNSCC cohort (Figure S5A) and the GSE65858 cohort (Figure S5B). This observation suggests a potentially more favorable immune landscape and microenvironment in the low-risk groups across both cohorts. However, of the 64 xCell subtypes, 34 were significantly elevated or reduced in the TCGA-HNSCC cohort and 23 in the GSE65858 cohort. Twenty-one cell subtypes, including DC populations (DC, aDC, cDC, and pDC; Figure S3A,S3B), B cell populations (B cells, plasma cells, class-switched memory B cells, memory B cells, and pro-B cells), T cell populations (CD4 Tem cells, CD4 memory T cells, CD4-naive T cells, CD8 T cells, CD8 Tcm cells, CD8 Tem and Tgd cells, and Tregs), basophils, mast cells, pre-adipocytes, and smooth muscle cells, overlapped in all significant subtypes of the TCGA-HNSCC as well as GSE65858 cohorts (all P<0.01). Only smooth muscle cell abundance was elevated in the high-risk groups (Tables S1,S2).

After risk clustering stratification, we analyzed somatic mutations in the high- as well as low-risk groups. Among the top 200 somatic mutations, we identified 17 with levels that differed significantly between these groups. TP53 and FAT1 mutations were significantly more common in the high-risk groups (Figure 7E).

Additionally, the GSEA was employed to elucidate the functional significance between high- and low-risk groups. Specifically, we utilized the Hallmark and KEGG datasets to explore the enrichment of gene sets related to biological pathways and hallmark processes. Notably, the high-risk group exhibited significant enrichment of the terms KEGG_RIBOSOME and HALLMARK_EPITHELIAL_MESENCHYMAL_TRANSITION, and the low-risk group exhibited significant enrichment of the terms KEGG_PRIMARY_IMMUNODEFICIENCY and HALLMARK_ALLOGRAFT_REJECTION (Figure 8A,8B).

Figure 8 GSEA analysis results for DEGs in low and high-risk groups in TCGA-HNSCC cohort. Dot plots depict the normalized enrichment score of significant Hallmark (A) and KEGG (B) pathways identified by the GSEA algorithm between low and high-risk clusters in the TCGA-HNSCC cohort. GSEA, gene set enrichment analysis; DEGs, differentially expressed genes; TCGA, The Cancer Genome Atlas; HNSCC, head and neck squamous cell carcinoma; KEGG, Kyoto Encyclopedia of Genes and Genomes.

Immunotherapy sensitivity in LASSO risk stratification groups

We next explored the predicted efficacies of established immunotherapies in the high- as well as low-risk groups. TIDE predicted markedly better responses to immunotherapy in the low-risk than high-risk groups in both the TCGA-HNSCC and GSE65858 cohorts (Figure 9A,9B).

Figure 9 Immunotherapy prediction for HNSCC cohorts with a high or low risk score. TIDE immunotherapy prediction analyses in TCGA-HNSCC and GSE65858 (A,B). HNSCC, head-and-neck squamous cell carcinoma; TIDE, Tumor Immune Dysfunction and Exclusion; TCGA, The Cancer Genome Atlas; CTL, cytotoxic T lymphocytes.

Also, we found notable elevations of four TIDE immunotherapy markers (CD8, CD274, IFNG, and Merck18) in the low-risk group and two markers (MDSC and CAF) in the high-risk group (Figure 10A,10B).

Figure 10 TIDE immunotherapy features analysis in TCGA-HNSCC and GSE65858. Box plots comparing 6 significant TIDE immunotherapy features (CD8, CD274, IFNG, Merck18, CAF and MDSC) between high- and low-risk groups in TCGA-HNSCC as well as GSE65858 (A,B). TIDE, Tumor Immune Dysfunction and Exclusion; TCGA, The Cancer Genome Atlas; HNSCC, head-and-neck squamous cell carcinoma.

Discussion

Our study unveils the critical role of DCs in HNSCC. Through rigorous analysis of clinical datasets, it identifies 222 DC-related candidate genes and pinpoints seven key prognostic genes, including ACP2 and CPVL. Differential gene expression analysis reveals 208 genes distinguishing ACP2⁺ and ACP2⁻ DC cells. Notable findings include top transcription factors like STAT1 and genomic associations such as TP53 and FAT1 mutations. The confirmed correlation between STAT1 and ACP2, along with insights into immunotherapeutic sensitivity variations among risk clusters, hints at the potential for a robust DC-based prognostic system. This research lays the groundwork for personalized therapeutic strategies, promising improved outcomes in the challenging landscape of HNSCC.

The intricate interplay among DCs, T cells, and B cells in terms of tumor antigen presentation is pivotal in terms of cancer prognosis (11,30-32). DCs capture and present tumor-specific antigens, thereby activating T cells, including the potent cytotoxic T cells that eliminate tumor cells (33). Concurrently, B cells are stimulated to produce antibodies that play vital roles in antigen neutralization (34). These strongly coordinated collaborations greatly influence cancer outcomes. When such delicate interactions are disrupted or become unbalanced, tumors elude immune system detection, and prognosis deteriorates (35). It is essential to understand and promote the intricate interplay among DCs, T cells, and B cells to enhance prognosis. Effective tumor antigen presentation and immune cell activation increase patient survival and treatment success (36). The immune characteristics of the LASSO CAF risk groups in both the TCGA-HNSCC and GSE65858 cohorts revealed intriguing patterns. The low-risk groups exhibited higher immune system and tumor microenvironment scores, suggesting that the immune milieu was favorable, which was substantiated by the significant involvements of various immune cells (DCs, B cells, and T cells) in both cohorts. The immune signatures were similar. Critically, the low-risk groups were anticipated to exhibit a more favorable response to immunotherapy, as evidenced by heightened levels of markers associated with immunotherapeutic efficacy. In contrast, the high-risk clusters displayed increased proportions of immunosuppressive cells of various types. Such findings underscore the clinical need to stratify patients based on risk profiles and strongly suggest that individuals at low risk may benefit more from immunotherapy. The development of personalized treatment strategies tailored to individual risk profiles is essential. Our study has predominantly focused on identifying and analyzing the correlations among DCs, T cells, and B cells in the context of HNSCC. The universal significance of DCs in capturing and presenting tumor antigens, orchestrating T cell responses, and influencing antibody production is a phenomenon observed not only in HNSCC but also across diverse cancer types such as breast, lung, and colorectal cancers (37-40). Disruptions in these immune interactions have been implicated in immune evasion and prognosis in a broader oncological spectrum. Our findings, particularly in low-risk groups, highlight the potential applicability beyond HNSCC, emphasizing the need to stratify patients based on risk profiles and tailor personalized treatment strategies across a variety of cancer types.

Rigorous random survival forest analysis revealed eight high-potential hub candidates that were strongly associated with ACP2 and CPVL expression in the LASSO Cox model and were thus associated with poor OS. Notably, single-cell database analysis revealed that both genes were prominently expressed in DCs. ACP2 (encoding acid phosphatase 2) and CPVL (encoding cysteine proteinase inhibitor, clade V) exert known functions in cellular processes and protease regulation, respectively (41). Although their precise roles in head-and-neck cancers and DCs remain unclear, they may function within TIMEs. ACP2 action in lysosomes may affect DC-mediated antigen presentation and immune responses within tumors (42). Likewise, the role of CPVL in terms of protease regulation may indirectly affect DC activities; proteases are critical in terms of DC migration and antigen processing (43). However, no clear connections between the activities of these genes and DC functions have yet been described in head-and-neck cancer. Further research is needed to explore whether and how ACP2 and CPVL shape TIMEs and might thus be valuable therapeutic targets. On the other hand, our study investigated the roles of FGD3, JCHAIN (previously known as IGJ), INPP5D, CCR7, and SPOCK2, particularly their association with favorable OS. Of notable significance are JCHAIN (joining chain of multimeric IgA and IgM) and CCR7 (C-C motif chemokine receptor 7). JCHAIN is a key peptide in immunoglobulin assembly, primarily functions in B cells and plasma cells to facilitate the secretion of polymeric IgM and IgA, enhancing immune responses at mucosal surfaces. Within the tumor immune microenvironment, the identified association between JCHAIN and favorable OS suggests its potential role in promoting immune responses against cancer (44,45). The chemokine receptor CCR7 is essential in directing immune cells like T cells and DCs to lymph nodes, which is critical for the enhancement of immune surveillance and the overall immune response. Our findings highlight CCR7’s integral role in predicting favorable OS, emphasizing its significance in orchestrating immune cell localization and modulating anti-tumor immune responses (46). Collectively, these observations underscore the potential of JCHAIN and CCR7 as promising markers for predicting positive outcomes and bolstering immune responses in the realm of cancer.

The analysis of differential gene expression in ACP2⁺ and ACP2⁻ DC cells revealed distinct transcriptional profiles, showing upregulation of 176 genes in ACP2⁺ cells and 32 in ACP2⁻ cells. Our investigation into the association between STAT1 and ACP2, a crucial prognostic DC candidate, was validated through qRT-PCR and dual-luciferase assays in HEK293T cells, establishing a positive correlation. The identified top transcription factors, including STAT1, imply a complex regulatory network influencing ACP2 expression. This discovery not only holds implications for targeted therapeutic strategies in HNSCC but also unveils a novel axis in DC biology that may influence the tumor microenvironment. While recognizing the limitation of the experiments being conducted in HEK293T cells rather than DCs, further exploration of the functional consequences and potential therapeutic targeting of this regulatory network is warranted. Such investigations will contribute to a more comprehensive understanding of the molecular dynamics within the tumor microenvironment, fostering advancements in therapeutic interventions for HNSCC.

TP53 and FAT1 are frequently mutated in high-risk cancer subgroups, and these mutations significantly influence the TIME. TP53 is often termed the “guardian of the genome”. Mutations that render TP53 dysfunctional trigger genomic instability and tumor evasion of immune surveillance (47), in turn affecting DC-mediated antigen presentation and thus compromising anti-tumor immune responses (48). FAT1 mutations have been correlated with increased tumor invasiveness and changes in the tumor microenvironment (49). Any role of FAT in DC interactions remains unclear, but changes in the tumor microenvironment could indirectly affect DC function and immune responses (50). However, no direct relationship between TP53 or FAT1 mutations and the DCs in head-and-neck cancers has yet been defined; the mutations may affect DC recruitment, maturation, and antigen presentation and thus patient prognosis. It is essential to understand the intricate interplay between genetic alterations and the TIME, including DCs. Such findings are poised to facilitate the development of targeted therapies aimed at improving the clinical outcomes of patients with HNSCC.

Our study has certain limitations. Large biological datasets may be affected by data quality and consistency issues given the various experimental methods employed by different research groups. Additionally, the inherent complexities of tumors associated with the many cell types and molecular interactions involved render bioinformatics analyses difficult; not all relevant biological processes may be captured. Also, validation of bioinformatics data in experimental or clinical settings can be both resource-intensive and technically challenging. Any bioinformatics data on cancers must be interpreted with caution.

Conclusions

Our study reveals the pivotal role of DCs in HNSCC. Analyzing clinical datasets identifies 222 DC-related genes, pinpointing seven prognostic genes, including ACP2 and CPVL. Differential gene expression unveils 208 genes distinguishing ACP2⁺ and ACP2⁻ DC cells. Notable findings include top transcription factors (e.g., STAT1) and genomic associations like TP53 and FAT1 mutations. Confirmed STAT1 and ACP2 correlation, along with insights into immunotherapeutic sensitivity, hints at a robust DC-based prognostic system, laying the groundwork for personalized therapeutic strategies.

Acknowledgments

We extend our heartfelt gratitude to the committed participants of the TCGA-HNSCC and GSE65858 cohorts. Their invaluable contributions of clinical data and mRNA expression datasets have been pivotal for our comprehensive analysis.

Funding: The study was supported by fundings from Wenzhou Municipal Science and Technology Bureau (Nos. Y2020942 and Y20210321).

Footnote

Reporting Checklist: The authors have completed the TRIPOD reporting checklist. Available at https://tcr.amegroups.com/article/view/10.21037/tcr-23-2360/rc

Peer Review File: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-23-2360/prf

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tcr.amegroups.com/article/view/10.21037/tcr-23-2360/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013).

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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