Prediction of the Potential Efficacy of Dlx5 in Immunotherapy for Hypopharyngeal Cancer through Integrated Bulk and Single-Cell RNA Sequencing

Prediction of the Potential Efficacy of Dlx5 in Immunotherapy for Hypopharyngeal Cancer through Integrated Bulk and Single-Cell RNA Sequencing | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Prediction of the Potential Efficacy of Dlx5 in Immunotherapy for Hypopharyngeal Cancer through Integrated Bulk and Single-Cell RNA Sequencing Jiang yao, Li Lianhe, Liang Jing This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4617116/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 Background : Immunotherapy, as a personalized treatment strategy, has displayed promising potential in the management of head and neck squamous cell carcinoma. Nevertheless, the heterogeneity and initial resistance of hypopharyngeal squamous cell carcinoma present new obstacles to treatment, highlighting the urgent need for identifying novel predictive biomarkers to develop more targeted and effective treatment approaches. Method : We employed the CIBERSORT algorithm, which quantifies immune cell composition, along with Weighted Gene Co-expression Network Analysis (WGCNA) to identify gene modules associated with tumor immune infiltration of CD4+ T cells. We integrated single-cell sequencing technology to complement each other, conducting bidirectional screening to narrow down molecular associations with tumors. By constructing Protein-Protein Interaction (PPI) networks and conducting clinical Kaplan-Meier analysis, we identified crucial hub genes. We calculated tumor mutation rates, immune checkpoint expression, chemokine factors, and their corresponding receptor correlations to predict the efficacy of immunotherapy targeting DLX5. The R package "oncopredict" was utilized to compute drug sensitivity for each sample, inferring potential chemotherapeutic drugs targeting DLX5. Finally, we explored the precancerous phenotype of DLX5 in the Fadu cell line. Result: Bulk RNA sequencing and single-cell RNA sequencing revealed that in hypopharyngeal squamous cell carcinoma, the prognostically associated EGFR and DLX5 genes are upregulated. Immunological analysis showed a higher mutation rate of DLX5, which is significantly positively correlated with immune checkpoints and chemokine factors. Most importantly, three small molecule compounds (BI.2536_1086, MN.64_1854, Ulixertinib_2047) were identified, which could be potential drugs for treating hypopharyngeal cancer patients. Finally, high expression of DLX5 promoted proliferation, invasion, and migration of hypopharyngeal cancer cells. Conclusion： The association of Dlx5 with CD4+ T cells in hypopharyngeal cancer correlates with the immunological characteristics of the disease and the potential efficacy of immune checkpoint inhibitor therapy. These results indicate that DLX5 might respond well to immunotherapy, shedding light on the role of Dlx5 in hypopharyngeal cancer, providing crucial insights and offering vital information for the development of personalized immunotherapeutic strategies. DLX5 tumor-infifiltrating CD4+T cells bulk RNA sequencing single-cell RNA sequencing hypopharyngeal carcinoma cell proliferation and Invasion Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Introduction Hypopharyngeal cancer, predominantly composed of squamous cells, is more commonly observed in males and is often associated with a history of smoking (90%) and alcohol abuse (50%)[1].The 5-year survival rate for this type of cancer ranges from only 25% to 40%[2],making it one of the most challenging cancers to prognose among head and neck cancers. Patients typically present with lymph node metastasis at diagnosis,and distant metastasis is more common compared to other cancers in the head and neck cancers, presenting significant challenges for treatment and outlook. The introduction of anti-PD-1 antibodies in comprehensive treatment regimens for head and neck squamous cell carcinoma (HNSCC) has shown promise in providing sustained responses and surviving benefits in recurrent and metastatic diseases previously treated with platinum-based drugs. However, despite these promising research outcomes, the overall response rates (ORRs) of Nivolumab and Pembrolizumab in platinum-refractory recurrent and metastatic HNSCC remains low at only 13%-18%[3]. Even more concerning is that the majority of patients exhibit primary resistance to treatment, with only a small percentage experiencing long-lasting durable responses. Given the apparent heterogeneity of hypopharyngeal squamous cell carcinoma within head and neck squamous cell carcinoma, the search for new potential immunotherapeutic biomarkers becomes particularly crucial. The significance of tumor immune microenvironment (TIME) in cancer development and treatment response highlights the importance of discerning tumor immune characteristics in different cancer patients [4].Among these, tumor-infiltrating CD4+ T cells constitute a crucial component of the hypopharyngeal cancer TIME, playing a key role in recognizing and killing tumor cells. A study involving 278 patients with head and neck squamous cell carcinoma (HNSCC) found that higher levels of CD4+ T cell infiltration were linked to better overall survival (OS) and disease-specific survival (DSS) (p = .003 and p = .004, respectively) [5]. Therefore, pinpointing biomarkers connected to CD4+ T cell infiltration could assist in monitoring the response to immune therapy in hypopharyngeal cancer and identifying potential therapeutic targets. Bioinformatics advancements have facilitated the creation of numerous tools, with WGCNA being a popular choice for biomarker discovery. Focusing on hypopharyngeal cancer transcriptome data, we quantified the composition of immune cells using deconvolution algorithms and identified important modules and key genes associated with CD4+ T cell infiltration levels through WGCNA. Simultaneously, by integrating single-cell RNA sequencing data, we identified genes associated with CD4+ T cells in the tumor microenvironment of hypopharyngeal squamous cell carcinoma. The core principle of single-cell RNA sequencing (scRNA-seq) technology involves isolating individual cells, amplifying trace RNA quantities, and utilizing high-throughput sequencing to capture the gene expression profiles of each cell at a single-cell level[6].. Compared to traditional bulk sequencing methods, single-cell sequencing enables precise analysis of gene expression in each cell, accurate differentiation of cell populations, and comprehensive retention of information on tumor cell heterogeneity[7],[8]. In summary, by combining analysis of both bulk and single-cell RNA sequencing data with WGCNA co-expression networks, we have pinpointed DLX5 as a promising biomarker for immunotherapy. Previous investigations have highlighted DLX5's importance in skeletal development, while recent findings have linked it to the promotion of cell proliferation through upregulating MYC promoter activity in tumors[9]. Our study further confirms the association of DLX5 overexpression with carcinogenesis, suggesting its potential as a therapeutic target for hypopharyngeal cancer treatment. Materials and methods Patients and datasets We collected cancer tissue samples from 12 patients with hypopharyngeal cancer who received treatment at Chaoyang Central Hospital from June 2019 to March 2023, as well as normal hypopharyngeal mucosal tissue samples from 6 non-tumor patients. The patients were followed up until February 2024, with no distant metastasis or recurrence observed before surgery. Postoperative pathology confirmed hypopharyngeal squamous cell carcinoma. For detailed clinical and pathological characteristics, please refer to Table S1. The study's design roadmap is illustrated in Figure S1. This study has been approved by the Ethics Committee of Chaoyang Central Hospital. We downloaded RNA sequencing (RNA-seq) data from 546 samples of head and neck squamous cell carcinoma from the UCSC database (https://xenabrowser.net/), including 502 cancer tissue samples and 44 adjacent normal tissue samples, along with relevant clinical sion Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/), comprising 4 normal samples and 31 individual tumor samples of hypopharyngeal cancer, as well as single-cell sequencing data GSE227156, containing cancer cell and lymph node metastatic cancer cell samples from five hypopharyngeal cancer patients. Details are shown in Table 1. Furthermore, we utilized the GSCA website (http://bioinfo.life.hust.edu.cn/GSCA) to assess tumor mutations. TABLE 1 summary of the data sets utilized in this research and their features Dataset Database Platform Sample GSE2379 GEO GPL-91 11 cases of HPS GPL-8300 20 cases of HPS and 4 controls GSE227156 GEO 10x 5 cases of HPS and 5 case of HPC-LNM Immune checkpoint Literature 79 chemokine and receptor Literature 72 UCSC UCSC 502 cases of HPC and 44 controls Geo，Gene Expression Omnibus, HPS, Hypopharyngeal Cancer, LMHSCC , HNSCC，Head and Neck Squamous Cell Carcinoma, HPC-LNM, Hypopharyngeal cancer with lymph node metastasis Immune checkpoint, chemokine factor, and receptor-related genes. From references [10]-[13], a total of 79 immune checkpoint genes (ICGs) and 72 chemokine-related genes and their receptors (Table S3) were identified. These include 46 immune chemokines, which belong to the CC chemokine subfamily, CXC chemokine subfamily, XC chemokine subfamily, and CX3C chemokine subfamily, as well as 26 corresponding immune chemokine receptors. RNA extraction and sequencing The RNA extraction and sequencing process for the 18 specimens from Chaoyang Central Hospital proceeded as follows: Initially, around 100 milligrams of tumor tissue from each frozen tube were carefully excised with sterile scissors and placed into 2-milliliter centrifuge tubes that were free from ribonuclease (RNase) contamination. After that,2 steel beads and 1000 microliters of tissue lysis buffer were added, and the tubes underwent homogenization using a high-throughput tissue homogenizer operating at a frequency of 40 hertz for 180 seconds. Upon completion of the homogenization step, the tubes were removed, and the homogenate was collected using a 1-milliliter pipette tip that was RNase-free, and then transferred to new 1.5-milliliter centrifuge tubes also devoid of RNase. The samples were centrifuged at 12000 revolutions per minute for 2 minutes at 4 degrees Celsius. The supernatant was once again collected using an RNase-free pipette tip and transferred to new RNase-free 1.5-milliliter centrifuge tubes in preparation for total RNA extraction. The total RNA extraction process was carried out using the Kangwei Century Ultrapure RNA Kit (DNase I) with the catalog number CW0597S. Gene models and reference genome annotation files were obtained from the National Center for Biotechnology Information (NCBI) website at https://www.ncbi.nlm.nih.gov/. Subsequently, an index of the reference genome was generated using HISAT2 v2.0.5,and high-quality sequences were aligned to the reference genome. Following this, low-expression genes were filtered out, and redundant genes were normalized using the limma package. estimation of tumor immune-infiltrating cell type scores. We utilized the CIBERSORT algorithm and the LM22 gene set[14] available on the CIBERSORT website to conduct deconvolution analysis on the transcriptomes of individual samples using linear support vector regression. This method allowed us to estimate the involvement of different immune cell subtypes in the overall immune infiltration. By identifying the abundance of immune cells, we assessed the immune infiltration scores in patients with hypopharyngeal cancer. construction of weighted gene co-expression networks Weighted Gene Co-expression Network Analysis [15]is a method for network modular analysis used to examine correlated patterns of gene expression among different samples. It helps detect modules with highly coordinated variations and investigate their associations with relevant phenotypes, thus revealing potential biomarker genes or therapeutic targets. In our research, genes were ranked based on their expression standard deviation and the top 50% were chosen for WGCNA analysis. The pickSoftThreshold function was utilized to determine the power parameter ensuring network connections followed a scale-free distribution. Gene adjacency was converted into topological overlap to assess their connectivity in the network. Hierarchical clustering was then conducted using TOM dissimilarity to group genes with similar expression into modules, with a minimum module size of 50 genes. By computing dissimilarities between module characteristic genes, a suitable cutting line was identified to consolidate some modules. Additionally,400 genes were randomly selected for network visualization, facilitating the display of characteristic gene networks [16]. Identifying differential genes associated with CD4+ T cells DEGs were identified based on specific criteria: |log2 fold change (FC)| > 1 and p 1,p < 0.05,and downregulated DEGs as log FC < -1,p < 0.05. Volcano plots of DEGs were then created using the 'Pheatmap' and 'ggplot2' R packages. The DEGs selected were cross-referenced with the blue gene module linked to memory CD4 + T cell subsets, and the resulting DEGs related to CD4 + T cells were visualized using the “Venndiagram” package. Cell types and subtypes of 10× scRNA-Seq data We conducted single-cell RNA sequencing analysis of the GSE227156 dataset retrieved from the GEO website, which consists of samples from five patients diagnosed with hypopharyngeal squamous cell carcinoma. The visualization analysis process commenced by transforming 17,599 cells into a Seurat object using the “Seurat” R package[17]. Subsequently, we conducted quality control by filtering out low-quality cells and assessing mitochondrial and red blood cell gene expression as a percentage of total gene expression. Then, the Harmony algorithm was utilized for both principal component analysis (PCA) and non-linear dimensional reduction. We selected the top 35 principal components from the PCA for subsequent clustering analysis. Non-linear dimensional reduction was further performed using t-SNE and UMAP techniques. Single-cell consensus clustering was then employed for unsupervised clustering to identify distinct cell clusters. The initial clustering results were visualized using the t-SNE and UMAP packages. To identify marker genes for each cluster, we manually annotated genes based on references from “the Cell Marker database” website [18]and research conducted by Guan R and others[19]. Gene Set Enrichment Analysis (GSEA) enrichment analysis Gene Set Enrichment Analysis (GSEA) [20] is used to examine how genes from a predefined gene set are distributed within a ranked gene set linked to specific traits or conditions (using the Hallmark gene set from the Molecular Signatures Database, MSigDB). The goal is to pinpoint significantly enriched pathways among these ranked gene lists. Cell line and cell culture environment The FaDu cell line originates from human hypopharyngeal carcinoma and was acquired from the American Type Culture Collection (ATCC), situated in Manassas,Virginia,USA. These cells are grown in a humidified atmosphere at 37°C with 5% CO2,utilizing Dulbecco's Modified Eagle Medium (DMEM) supplemented with 10% fetal bovine serum (FBS),streptomycin (100 µg/ml),and penicillin (100 units/ml). Constructing siRNA knockdown and DLX5 overexpression. Stable passage 2 or more of FADU cells were seeded into a 12-well culture plate at a density of 5×10^5 cells per well, with a total of 7 wells. When the cell confluence reached 70%, transfection was performed according to the manufacturer's instructions using Lipofectamine™ 2000 Transfection Reagent (Thermo Fisher Scientific). The transfection included seven groups: siRNA-NC-FAM, siRNA-NC, siRNA-1, siRNA-2, siRNA-3, pCDNA3.1 empty vector, and pCDNA3.1-DLX5 overexpression. The sequences of siRNAs were as follows: siRNA-1: 5′-AAGCUUAUGCCGACUAUAGCUACTT-3′, siRNA-2: 5′-GAAGUGACCGAGCCCGAGGUGTT-3′, siRNA-3: 5′-UUCGUAAACCCAGGACUAUUUAUTT-3′, siRNA-NC: 5′-UUCUCCGAACGUGUCACGUTT-3′. siRNA-NC-FAM: 5′-UUCUCCGAACGUGUCACGUTT-3′. The transfection efficiency of the overexpression plasmid was evaluated using qPCR, and the optimal siRNA interference sequence was selected. Quantitative real‑time polymerase chain reaction (qRT‑PCR) The total RNA was isolated using the Kangwei Century Ultrapure RNA Kit (DNase I) and then converted to complementary DNA (cDNA) using the Transgen Reverse Transcription Kit. Real-time quantitative PCR (relative quantification) was performed using the SYBR Green I method on a LightCycler96 instrument (Roche, Switzerland). The qPCR reagent kit utilized was the TransStart Green qPCR SuperMix, with primer sequences synthesized by Shanghai Universal Biotechnology in China. The primer sequences were as follows: DLX5 forward primer: 5'-GCCAAAGCTTATGCCGACTA-3', reverse primer: 5'-GGGCTCGGTCACTTCTTTCT-3'; human GAPDH forward primer: 5'-GAAGGTGAAGGTCGGAGTCAA-3', reverse primer: 5'-CTGGAAGATGGTGATGGGATTT-3'. GAPDH was used as the internal control. The data were analyzed using the 2-Ct method, with each gene analyzed in triplicate. qPCR was employed to evaluate the mRNA overexpression level of the DLX5 gene and to determine the most effective siRNA sequence for interference. CCK8 assay The impact of the DLX5 gene on FADU cell proliferation was assessed using the Cell Counting Kit-8 (CCK-8) assay. Initially, FADU cells were seeded into a 96-well cell culture plate at a density of 3.1×10^4 cells per well, totaling 24 wells (4 groups × 6 wells). Subsequently, two sets of cell plates were prepared for CCK8 analysis at 24 and 48 hours post-transfection. The study included four groups: siRNA-NC, siRNA-1, pCDNA3.1 empty vector, and pCDNA3.1-DLX5 overexpression. At 0, 24, and 48 hours post-transfection, 10 μL of CCK8 solution was added to each well, followed by a 1-hour incubation period. The optical density (OD450) of each well was then measured using a microplate reader to assess cell proliferation activity. Wound healing assay In order to evaluate cell migration capacity, a wound healing experiment was conducted. FADU cells from four groups (siRNA-NC, siRNA-1, pCDNA3.1 empty vector, and pCDNA3.1-DLX5 overexpression) were cultured in 6-well plates. Once the cells reached 70% confluence, wounds were induced in the middle of the cell layer using a 10 μL pipette tip. The cell plates were then placed in a 37°C, 5% CO2 incubator for further incubation. Images were captured at 0 h, 24 h, and 48 h post-scratching, and each experiment was repeated thrice. Transwell invasion assay The Transwell assay is commonly used to evaluate the migration and invasion capabilities of cells. Initially, FADU cells were prepared at a concentration of 2×10^5/ml after digestion. Subsequently, a mixture of 100 μL of cells and 100 μL of transfection complex was added to each upper chamber of the Transwell. Following 48 hours of incubation, the culture plate was removed, and the upper culture medium was discarded. The cells were then fixed, stained, and three random fields were selected from each membrane for photography under an inverted microscope (100×) and saved. This experiment was repeated three times. Both the Transwell invasion and migration assays followed the same process, except that the upper layer of the Transwell chamber's PET membrane was uniformly coated with Matrigel matrix gel. Statistical analysis We used RStudio (version 4.3.2) to perform analysis on bulk RNA sequencing data with WGCNA and CIBERSORT packages, and for single-cell RNA sequencing data analysis, we utilized the Seurat R package. The optimal cutoff value was determined to divide patients into high and low expression groups. The “oncoPredict” R package [21]was used to evaluate drug sensitivity for each sample and suggest potential targeted therapies. Inter-group differences were assessed using Wilcoxon test and Student's t-test, with statistical significance set at a p-value below 0.05. RESULTS Differential Gene Expression in CD4+ T Cells Investigating the tumor immune cell infiltration microenvironment in hypopharyngeal cancer, we utilized the CIBERSORT algorithm to estimate the relative proportions of 22 immune cell types. The dominant frequencies of memory B cells, macrophages M0 and M1 types, activated mast cells, monocytes, resting natural killer cells, plasma cells, T cells CD4 memory resting cells, memory-activated T cells, CD4+ T cells, follicular helper T cells, and regulatory T cells (Tregs) are shown in Figure 1A. These proportions of immune cell subtypes served as feature data for Weighted Gene Co-expression Network Analysis (Figure 1D). Simultaneously, WGCNA was performed on the top 50% of genes based on standard deviation (n=9116). Setting the soft threshold power to 5 through the pickSoftThreshold function ensured that the gene network adhered to a scale-free distribution, with a scale-free topology model fit index of 0.9 achieved((Figure 1C). A hierarchical clustering tree was then generated using dynamic hybrid clustering. The resulting tree diagram depicted genes as individual leaves, with genes sharing similar expression data grouped together into branches to form gene modules. Modules with high similarity were merged at a cutoff of 0.25, resulting in the creation of 26 modules (Figure 1E，WGCNA Workflow Diagram depicted in Figure S2). The blue module comprised 724 genes, showing a strong correlation with CD4+ T cells (R2 = 0.75, P = 3e-04), while the blue-green module consisted of 278 genes, significantly linked to regulatory T cells (Tregs) (R2 = -0.81, P = 5e-05). To identify genes specifically associated with CD4+ T cells, sequencing count data from 12 hypopharyngeal cancer patients and 6 healthy controls (totaling 29,313 genes) were analyzed using the DESeq2 package for differential expression analysis. A total of 2,090 differentially expressed genes (DEGs) were identified, comprising 940 upregulated and 1,150 downregulated genes. The volcano plot (Figure 1B) illustrates the top 20 DEGs. After intersecting with the genes in the blue module, 195 differentially expressed genes associated with CD4+ T cells were identified. KEGG enrichment analysis of the differentially expressed genes mainly enriched in extracellular matrix structural constituents, integrin binding, protein polysaccharide binding, and Wnt receptor activity. Detailed results are provided in Figure S3 and Table S5. Single-cell overview of different cell types in hypopharyngeal carcinoma We performed single-cell gene expression analysis on hypopharyngeal cancer cells from five individuals in the GSE227156 dataset. After filtering out genes expressing red blood cells (<3%) and granulocytes (<10%), the datasets were merged and normalized. PCA dimensionality reduction was applied to 3000 highly variable genes, followed by batch correction using harmony to mitigate batch effects. During the data processing, no significant batch effects were observed（Figure S 4）. The resulting cell-gene matrix revealed an average of 1692 genes detected per cell. Utilizing umap/tsne clustering, we identified seven cell types: macrophages (Cluster 0), squamous epithelial carcinoma cells (Cluster 1), lymphocytes (Clusters 2 and 4), fibroblasts (Cluster 3), endothelial cells (Cluster 5), dendritic cells (Cluster 6), and epithelial cells (Cluster 7). Clusters were annotated manually using specific markers from the Cell Marker database and Durante et al.'s studies （Figure S 5）. Further analysis uncovered 75 differentially expressed genes associated with tumor-related CD4+ T cells within Cluster 2. PPI networks and Enrichment analysis of hub genes To explore the protein-protein interactions of differentially expressed genes associated with CD4+ T cells, we inputted 75 genes into the STRING database to analyze their interactions. Disconnected nodes were removed during network construction, using a default interaction score of 0.4. Subsequently, the interaction data was imported into Cytoscape, resulting in the generation of Figure 3A. Utilizing the CytoHubba plugin, we identified central hub genes within the PPI network, including EGFR, Dlx5, DSG2, TP63, DLX2, and TSLP. Further analysis via ssGSEA revealed that high EGFR expression levels were significantly associated with tumor protein polysaccharides (Figure 3B). Conversely, high DLX5 expression levels were predominantly linked to signaling pathways regulating stem cell pluripotency (Figure 3C). Identification of prognostic markers for hypopharyngeal cancer Based on clinical data collected from Chaoyang Central Hospital, Kaplan-Meier analysis was conducted on EGFR and Dlx5 genes (Figures 4A-B), revealing an association between elevated Dlx5 gene expression and poorer prognosis. To validate these findings, hypopharyngeal cancer microarray datasets (GSE2379) from the Gene Expression Omnibus (GEO) database were obtained, consisting of the GPL-91 and GPL-8300 platforms. Survival analysis results are detailed in Figures 4C-D-E-F. To address potential biases due to sample size limitations, the investigation was expanded to include a larger set of head and neck tumor samples. RNA sequencing data comprising 546 cases of head and neck squamous cell carcinoma from the UCSC database were accessed for validation, yielding a hazard ratio of 1.42 (95% confidence interval: 0.82-0.904) for EGFR and 0.73 (95% confidence interval: 0.6872-0.769) for Dlx5, as depicted in Figures 4G-H. Additionally, immunohistochemical (IHC) staining results from the Human Protein Atlas database were retrieved to further confirm the expression levels of EGFR and Dlx5 genes (Figures 4I-J). This combined version effectively integrates the key findings and methodological steps in a coherent manner. Correlation Analysis of Immune Checkpoint Immune checkpoints[22] are a class of immune-inhibitory molecules expressed on the surface of immune cells. They regulate the activation level of the immune system to prevent excessive activation, which could lead to autoimmune reactions. However, cancer cells exploit these immune checkpoints, particularly T cell negative regulatory mechanisms, to dampen the immune system's attack, enabling immune evasion. To counter this, inhibitors such as PD-1, PD-L1, and CTLA-4 have been developed to alleviate immune response constraints, thereby reactivating T cells to target tumor cells and enhance cancer treatment efficacy. Despite these advancements, the majority of patients do not experience significant benefits, with response rates typically ranging from 10% to 25% [23], even in approved therapeutic indications. Therefore, the future of immunotherapy for head and neck squamous cell carcinoma may lean towards combination therapies or strategies that enhance immune response rates by combining with other targeted drugs to overcome resistance to immune checkpoint blockade. In light of this, we assessed the correlation between the DLX5 gene and immune checkpoints (Figure 5, GSE227156 platform GPL-91), revealing significant associations between Dlx5 and the immune checkpoint BTN3A1 (R=0.71), CCD28 with BTN2A1 (R=0.72), and HLA-F with HLA-G (R=0.72). Thus, interventions targeting DLX5 offer a promising avenue to augment the effectiveness of immune-based therapies. Tumor Mutational Burden Tumor mutational burden (TMB) [24] indicates the quantity of mutations in a tumor. Mutated proteins form neoantigens, which are presented to T cells by antigen-presenting cells through major histocompatibility complex (MHC) proteins. This process allows T cells to identify and release perforins and granzymes to attack and eliminate mutated tumor cells. Increased mutations, leading to a higher TMB, enhance the chances of immune recognition and targeting of tumor cells, improving the effectiveness of immunotherapy. Studies by Chabanon et al. (2016) [24] and Rooney et al. (2015) [25]have demonstrated a positive link between high tumor mutational burden (TMB-H) and positive outcomes post-treatment with immune checkpoint inhibitors (ICI). Clinical data also reveals a significant association between TMB levels and responses to PD-1/PD-L1 inhibitors[26]. The GSCA website (GSCA, http://bioinfo.life.hust.edu.cn/GSCA) [27] integrates data on gene expression, mutations, drug sensitivity, and clinical information from four public sources across 33 cancer types. By using the genomic alteration module to visualize the mutation burden of core genes, Figure 6 illustrates mutation percentages for EGFR, FAM83B, Dlx5, DSG2, IL1RAP, and CXADR, which are 41%, 21%, 18%, 12%, 12%, and 6%, respectively. This highlights the relatively high immunogenicity of Dlx5. Correlation analysis of immune chemokines and receptors. The tumor microenvironment plays a pivotal role in mediating interactions between tumor cells and the immune system. Various immune cell populations are attracted to this environment by specific chemokine factors, influencing tumor progression and treatment responses significantly[28]. Thus, therapeutic strategies targeting both pro-tumor and anti-tumor chemokine-receptor signaling pathways, in conjunction with immunotherapy, offer promising clinical benefits for cancer patients. To bolster this hypothesis, we conducted a comprehensive analysis correlating key genes with chemokines and their receptors. We compared samples across three datasets: (i) 46 immune chemokines from different chemokine subfamilies, (ii) 26 corresponding immune chemokine receptors, and (iii) gene expression profiles of key genes. After logarithmically transforming gene expression data, we calculated distances between immune chemokines, receptors, and key genes using the Euclidean distance method. Subsequently, we utilized the linkET package in the R software to perform bias-corrected Mantel[29]correlation analysis. Our analysis revealed significant correlations between EGFR and immune chemokines and receptors, as well as a robust correlation between DSG2 and these immune factors. Notably, while DLX5 exhibited a significant correlation with chemokines, its relationship with receptors did not reach statistical significance. This suggests that DLX5 may regulate chemokine activity, while its interactions with receptors could be influenced by unexplored factors. Evaluating the Therapeutic Response To predict the response of hypopharyngeal cancer to chemotherapy, we utilized the oncoPredict R package to estimate chemotherapy response based on half-maximal inhibitory concentration (IC50) data from the Cancer Cell Line Encyclopedia (CCLE) database, available for hypopharyngeal cancer patients. In our study, patients were stratified into high and low expression groups based on DLX5 expression levels, leading to the identification of 25 small molecule compounds with significantly different responses (Table S5). Figure 8 illustrates the top three small molecule compounds with the most statistically significant differences: BI.2536_1086 (P = 0.00019, Figure 8A), MN.64_1854 (P = 0.00103, Figure 8C), and Ulixertinib_2047 (P = 0.0013, Figure 8E). Overexpression Dlx5 promotes cell proliferation and invasion We conducted a detailed investigation into the role of Dlx5 in hypopharyngeal squamous cell carcinoma by introducing pCDNA3.1-DLX5 and empty pCDNA3.1 vectors into the FaDu cell line. RT-qPCR confirmed the successful overexpression of DLX5, and we evaluated the impact of Dlx5 on hypopharyngeal cancer cell proliferation through CCK-8 assays. Our findings revealed that upon DLX5 overexpression, cell proliferation was boosted, with an increase of over 42% in proliferation activity noted after 48 hours of overexpression (Figure 9A). Additionally, the outcomes from transwell migration and scratch healing experiments indicated a notable rise in cell migration speed and improved cell healing capability in FaDu cells (Figure 9C-D). Moreover, in the transwell invasion assay, the overexpression of Dlx5 substantially heightened the invasive potential of the tumor cells (Figure 9E). To summarize, the upregulation of Dlx5 stimulates the proliferation, migration, and invasion of hypopharyngeal cancer cells. Knockdown of Dlx5 inhibits cell viability and cell proliferation In order to confirm the potential involvement of Dlx5 in hypopharyngeal cancer, we developed three siRNAs (siRNA-1, siRNA-2, siRNA-3) to silence Dlx5 expression in FaDu cells. PCR validation demonstrated the effective suppression of Dlx5 expression by siRNA-1, achieving an mRNA knockdown efficiency of 80% (Table S7). To assess the impact of Dlx5 on cell proliferation, invasion, and migration, we performed CCK-8, Transwell, and scratch healing assays on FaDu cells transfected with siRNA-1 and a negative control (NC). The CCK-8 results revealed that silencing DLX5 decreased cell proliferation capacity, showing a maximum reduction of around 30% (Figure 9A). Transwell experiments indicated that downregulating Dlx5 constrained the migration and invasion abilities of FaDu cells (Figure 9D-E). The scratch healing assay further corroborated the diminished migration capability of FaDu cells following reduced Dlx5 expression (Figure 8B). These results suggest that suppressing Dlx5 expression can hinder the proliferation, migration, and invasion of hypopharyngeal cancer cells. Discussion Immune checkpoint inhibitors have shown good tolerability and anti-tumor activity in the management of recurrent or metastatic squamous cell carcinoma, sparking a reassessment of treatment approaches for hypopharyngeal cancer[30]. As a subset of head and neck squamous cell carcinoma, hypopharyngeal cancer displays significant diversity and heterogeneity, with treatment outcomes falling short of expectations. Therefore, a comprehensive understanding of immune system dysregulation during disease evolution and progression, along with exploration of potential molecular mechanisms tied to immunity, is essential for pinpointing novel therapeutic targets to enhance precision treatment and boost patient outcomes. Advanced technologies like high-throughput bulk RNA sequencing and single-cell RNA sequencing have revolutionized cancer research by uncovering molecular pathways crucial to tumor initiation, advancement, and treatment response, thereby offering crucial support for personalized therapy and precision medicine. Bulk RNA sequencing relies on analyzing RNA mixtures from various tissues, including solid tumors, to capture the mean gene expression levels in cell populations and minimize individual cell expression variations. The evolution of bioinformatics tools, notably the CIBERSORT algorithm, facilitates extensive RNA mixture analyses by estimating the proportions of distinct cell types within complex cell populations, enabling the identification of cellular biomarkers and potential therapeutic targets. With the advancement of technology, various bioinformatics tools have been developed, with the CIBERSORT algorithm being a notable example. This algorithm allows for the analysis of RNA mixtures on a large scale to pinpoint cellular biomarkers and potential therapeutic targets. By calculating the proportions of different cell types within mixed populations, CIBERSORT enhances our knowledge of cellular composition and function, which is essential for advancing precision medicine. Despite the maturity of transcriptome sequencing technology, which provides copious amounts of genetic and transcriptomic data, it still struggles to fully capture the cellular diversity within tumors[31]. Single-cell RNA sequencing examines individual cells in suspension, accurately depicting the gene expression of each cell and allowing for precise differentiation and comparison of cell populations. The value of this technology lies in its ability to elucidate the characteristics and functions of individual cells, offering a crucial tool for understanding cellular diversity[32]. Nonetheless, a drawback of single-cell RNA sequencing is the possibility of different cells expressing similar genes, as well as the presence of other cell types in mixed populations, leading to complexity in data analysis and result uncertainty. To address these challenges, a harmonious approach leveraging both traditional and single-cell sequencing technologies is adopted to comprehensively grasp the distribution and functional attributes of cells within tumor tissues. This bidirectional screening and integrated analysis strategy offer more accurate and comprehensive support for studying tumor pathogenesis and devising effective treatment strategies. While screening for prognostic-related genes using protein-protein interaction networks and clinical data, we observed a common occurrence of elevated Epidermal Growth Factor Receptor (EGFR) expression in head and neck squamous cell carcinoma (HNSCC). This discovery further validates the choice of utilizing cetuximab to target EGFR in treating advanced head and neck tumors. On the other hand, DLX5, known as a bone transcription factor, has emerged as a significant player in the field of oncology. Therefore, our study is centered around DLX5. Previous studies have demonstrated that increased DLX5 levels can stimulate the proliferation of ovarian cancer cells by enhancing the IRS-2-AKT signaling pathway[33]. Similarly, in oral squamous cell carcinoma (OSCC), DLX5 regulates CCND1 to drive cancer advancement[34]. Another research focusing on pan-squamous cell carcinoma has revealed that DLX5/TP63 contributes significantly to the proliferation, migration, and anchorage-independent growth of squamous cells[35]. Importantly, past studies employing molecular docking techniques have indicated the potential anti-tumor effects of DLX5[36]. This study focused on exploring tumor-infiltrating immune cells immunotherapy to provide fresh perspectives in this area. The research revealed an association between Dlx5 and CD4+ T cells. Validation at the cellular level confirmed that an increase in Dlx5 levels enhances the proliferation, migration, and invasion of hypopharyngeal cancer cells, while reducing Dlx5 suppresses these activities. The study predicted treatment outcomes by analyzing factors like tumor mutational burden, immune checkpoint correlation, and immune chemokine receptor correlation. It suggests that targeting DLX5 could be a promising strategy for anti-tumor medications, and combining immunotherapy with immune checkpoint inhibitors may boost treatment efficacy. Furthermore, the study identified four potential small molecule compounds, including BI.2536_1086, MN.64_1854, and Ulixertinib_2047.BI.2536_1086 is a highly specific inhibitor of PLK1 (IC50=0.83 nM), causing cell cycle arrest during mitosis and stimulating apoptosis in cancer cells. Moreover, it impedes tumor growth in nude mice and leads to tumor shrinkage[37]. MN.64_1854, also referred to as MN-64, is a flavonoid compound recognized for its role as a tankyrase inhibitor. It modulates cellular activities like proliferation and elongation of telomeres by targeting TNKS1 and TNKS2[38]. Ulixertinib_2047 is a potent inhibitor of ERK1/2, which hinders the activation of ERK1/2 signaling pathways, disrupting processes such as proliferation, survival, and metastasis[39]. These results not only reinforce our research but also provide novel treatment possibilities for individuals with hypopharyngeal cancer. However, our study faced limitations stemming from the lack of transcriptomic data on hypopharyngeal cancer. This resulted in a shortage of clinical samples for validation, leading to suboptimal survival analysis results. To address this, we leveraged a large dataset of head and neck squamous cell carcinoma (HNSCC) samples that include hypopharyngeal cancer cases to verify survival outcomes. Moving forward, we aim to enhance the number of hypopharyngeal cancer samples, gather more sequencing data, and obtain additional clinical information to bolster validation efforts and facilitate deeper exploration. Despite integrating single-cell sequencing with bulk RNA sequencing data, these datasets are unable to distinguish distinct cellular populations within the tumor microenvironment. Therefore, further experimental analyses are essential to understand the role of CD4+ associated immune genes in hypopharyngeal cancer and the potential therapeutic benefits of targeting DLX5. Presently, experimental validation is restricted to verifying cell functions, necessitating further cellular and animal studies, along with clinical trials for validation purposes. Conclusion In this study, we used transcriptome sequencing data to identify prognosis-related genes associated with CD4+ T cells and integrated single-cell sequencing to locate the tumor-associated gene DLX5. By analyzing tumor mutation rates, immune checkpoint expression, chemokine factors, and their receptor correlations, we predicted the efficacy of Dlx5 immunotherapy and drugs sensitive to DLX5 targeting. In vitro experiments showed DLX5 promotes proliferation, migration, and invasion of Fadu cells. Declarations Acknowledgements The authors extend their heartfelt appreciation to the participants and staff of Chaoyang Central Hospital for their valuable contributions. Author contributions J.Y. conducted data analysis and drafted the manuscript under the guidance of L.L.H., who provided conceptual guidance and revised the content. L.J. was responsible for investigation and resource management.All authors have read and approved the final manuscript. Funding The Liaoning Natural Science Foundation Program (No2019-ZD-0901). Ethics approval and consent to participate The study has been approved by Medical Ethics Committee of Chaoyang Central Hospital.Committee Reference Number:EC-20231212-1041.This study obtained written informed consent from all participants before commencing. Participants voluntarily took part without any coercion and had fully understood the purpose, procedures, and potential risks and benefits of the study before signing the consent forms. The consent forms detailed the measures for the confidentiality of personal data and the rights of the participants to withdraw from the study at any time. Consent for publication Not applicable Competing interests The authors declare that they have no competing interests. Availability of data and materials The datasets generated and/or analyzed in this study are available at NCBI-GEO, UCXC, and supplementary materials. References Wycliffe N D, Grover R S, Kim P D, et al. Hypopharyngeal cancer[J]. Topics in magnetic resonance imaging, 2007, 18(4): 243–258. De Figueiredo B H, Fortpied C, Menis J, et al. Long-term update of the 24954 EORTC phase III trial on larynx preservation[J]. European Journal of Cancer, 2016, 65: 109–112. Oliva M, Spreafico A, Taberna M, et al. Immune biomarkers of response to immune-checkpoint inhibitors in head and neck squamous cell carcinoma[J]. Annals of oncology, 2019, 30(1): 57–67. Zhao S, Ji W, Shen Y, et al. 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Zhang X, Feng H, Li Z, et al. Application of weighted gene co-expression network analysis to identify key modules and hub genes in oral squamous cell carcinoma tumorigenesis[J]. OncoTargets and therapy, 2018: 6001–6021. Butler A, Hoffman P, Smibert P, et al. Integrating single-cell transcriptomic data across different conditions, technologies, and species[J]. Nature biotechnology, 2018, 36(5): 411–420. Zhang X, Lan Y, Xu J, et al. CellMarker: a manually curated resource of cell markers in human and mouse[J]. Nucleic acids research, 2019, 47(D1): D721-D728. Li C, Guan R, Li W, et al. Single-cell RNA sequencing reveals tumor immune microenvironment in human hypopharygeal squamous cell carcinoma and lymphatic[J]. Frontiers in Immunology, 14: 1168191. Subramanian A, Tamayo P, Mootha V K, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles[J]. Proceedings of the National Academy of Sciences, 2005, 102(43): 15545–15550. Maeser D, Gruener RF, Huang RS. oncoPredict: an R package for predicting in vivo or cancer patient drug response and biomarkers from cell line screening data. Brief Bioinform. 2021;22(6):bbab260. doi: 10.1093/bib/bbab260 Shen X, Zhao B. Efficacy of PD-1 or PD-L1 inhibitors and PD-L1 expression status in cancer: meta-analysis[J]. Bmj, 2018, 362. Schoenfeld A J, Hellmann M D. Acquired resistance to immune checkpoint inhibitors[J]. Cancer cell, 2020, 37(4): 443–455. Chabanon R M, Pedrero M, Lefebvre C, et al. Mutational landscape and sensitivity to immune checkpoint blockers[J]. Clinical Cancer Research, 2016, 22(17): 4309–4321. Rooney M S, Shukla S A, Wu C J, et al. Molecular and genetic properties of tumors associated with local immune cytolytic activity[J]. Cell, 2015, 160(1): 48–61. Chalmers Z R, Connelly C F, Fabrizio D, et al. Analysis of 100,000 human cancer genomes reveals the landscape of tumor mutational burden[J]. Genome medicine, 2017, 9: 1–14. Liu C J, Hu F F, Xie G Y, et al. GSCA: an integrated platform for gene set cancer analysis at genomic, pharmacogenomic and immunogenomic levels[J]. Briefings in Bioinformatics, 2023, 24(1): bbac558. Nagarsheth N, Wicha MS, Zou W. Chemokines in the cancer microenvironment and their relevance in cancer immunotherapy. Nat Rev Immunol. 2017;17(9):559–572. doi: 10.1038/nri.2017.49 . Epub 2017 May 30. PMID: 28555670; PMCID: PMC5731833. Dismantling the Mantel tests[J]. Methods in Ecology and Evolution, 2013, 4(4): 336–344. Seiwert T Y, Burtness B, Mehra R, et al. Safety and clinical activity of pembrolizumab for treatment of recurrent or metastatic squamous cell carcinoma of the head and neck (KEYNOTE-012): an open-label, multicentre, phase 1b trial[J]. The lancet oncology, 2016, 17(7): 956–965. Garraway L A, Lander E S. Lessons from the cancer genome[J]. Cell, 2013, 153(1): 17–37. Mints M, Tirosh I. Nasopharyngeal carcinoma joins the single-cell party[J]. Cancer Communications, 2020, 40(9): 453. Tan Y, Cheung M, Pei J, et al. Upregulation of DLX5 promotes ovarian cancer cell proliferation by enhancing IRS-2-AKT signaling[J]. Cancer research, 2010, 70(22): 9197–9206. Zhang J, Wu J, Chen Y, et al. Dlx5 promotes cancer progression through regulation of CCND1 in oral squamous cell carcinoma (OSCC)[J]. Biochemistry and Cell Biology, 2021, 99(4): 424–434. Huang Y, Yang Q, Zheng Y, et al. Activation of bivalent factor DLX5 cooperates with master regulator TP63 to promote squamous cell carcinoma[J]. Nucleic Acids Research, 2021, 49(16): 9246–9263. Timakhov R A, Fedichev P O, Vinnik A A, et al. Transcription factor Dlx5 as a new target for promising antitumor agents[J]. Acta Naturae (англоязычная версия), 2011, 3(3 (10)): 47–51. Steegmaier M, Hoffmann M, Baum A, et al. BI 2536, a potent and selective inhibitor of polo-like kinase 1, inhibits tumor growth in vivo. Curr Biol. 2007;17(4):316–322. doi: 10.1016/j.cub.2006.12.037 Narwal M, Koivunen J, Haikarainen T, et al. Discovery of tankyrase inhibiting flavones with increased potency and isoenzyme selectivity. J Med Chem. 2013;56(20):7880–7889. doi: 10.1021/jm401463y Ciombor K K, Strickler J H, Bekaii-Saab T S, et al. BRAF-mutated advanced colorectal cancer: A rapidly changing therapeutic landscape[J]. Journal of Clinical Oncology, 2022, 40(24): 2706. Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterial.docx Supplementary Information Figure S1. Analysis Workflow of this Study Figure S2. WGCNA Workflow Diagram, A Merge high-similarity dynamic modules at the 0.25 cut line. ,B Correlation plot between modules C Visualization of gene network (TOM plot) D By aggregating genes with strong correlations in the same module, different modules were obtained and are displayed in different colors。 Figure S3. Enrichment Analysis of Differentially Expressed Genes Using KEGG Pathways Figure S4. 3000 highly variable genes AND Harmony De-Batch Chart Figure S5. Identification of Cell Type-Specific Markers in Single-Cell Analysis of Six Cell Types Table S1. Clinical samples collected from Chaoyang Central Hospital. Table S2. GSE227156 of clinical information Table S3. 72 Immune checkpoint, chemokine factor, and receptor-related genes Table S4. Immune checkpoint genes and descriptions. Table S5 GO and KEGG enrichment tables of 195 differentially expressed genes. Table S6. Drugs with significant differences in IC50 values between High-expression group and low-expression group Table S7. DLX5 knockdown and overexpression efficiency 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-4617116","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":322054235,"identity":"503cadb1-f8b5-472a-8e38-8e919291685f","order_by":0,"name":"Jiang yao","email":"","orcid":"","institution":"China Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jiang","middleName":"","lastName":"yao","suffix":""},{"id":322054237,"identity":"2ea81e95-83dd-48c6-a184-5dfebb501561","order_by":1,"name":"Li Lianhe","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAApUlEQVRIiWNgGAWjYBACPnYehgMfKmx4+PkbiNTCxszDeHDGmTQZyRkHiNfCfJi35bCNQUMC0Vp4Dxyc2XCex4DhAOOHjzlEaeFLOPBxx20ec+YGZsmZ24hzmMHBmWdu81g2HADaSKyWw7xt53gMDiSQpuUAiVqAgZzMIznjYDNxfuFn7zH+8KHCzp6fv/ngh4/EaEECjA2kqR8Fo2AUjIJRgBsAAJy2NJPPSKP0AAAAAElFTkSuQmCC","orcid":"","institution":"Central Hospital of Chaoyang","correspondingAuthor":true,"prefix":"","firstName":"Li","middleName":"","lastName":"Lianhe","suffix":""},{"id":322054239,"identity":"40cb5e5a-56e7-4436-a402-dae3e6204d9f","order_by":2,"name":"Liang Jing","email":"","orcid":"","institution":"The First Affiliated Hospital of Jinzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Liang","middleName":"","lastName":"Jing","suffix":""}],"badges":[],"createdAt":"2024-06-21 11:29:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4617116/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4617116/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":60625100,"identity":"583a4000-9ebd-4315-9207-04f5917b1e51","added_by":"auto","created_at":"2024-07-18 22:20:21","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":306264,"visible":true,"origin":"","legend":"\u003cp\u003eImmune infiltration analysis and construction of weighted gene co-expression networks. (A) The relative abundance of 22 immune cell types in 12 cases of nasopharyngeal carcinoma. (B) Volcano plot of DEGs in 12 cases of nasopharyngeal carcinoma. (C) Histogram of frequency distribution when = 5; scale-free topology check when = 5. (D) Sample clustering plot and heatmap of immune cell features. (E) 26 modules revealed by the WGCNA. WGCNA, weighted gene co-expression network analysis.\u003c/p\u003e","description":"","filename":"floatimage1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4617116/v1/1f96a9ec86e00e637d71c45f.jpg"},{"id":60625104,"identity":"d9d75ccc-04e8-4263-8c6b-52869f96b01d","added_by":"auto","created_at":"2024-07-18 22:20:22","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":248619,"visible":true,"origin":"","legend":"\u003cp\u003eSingle-cell landscape and location analysis of nasopharyngeal carcinoma.\u003c/p\u003e\n\u003cp\u003e(A) t-SNE plots visualize cell type populations based on the expression of known marker genes.\u003c/p\u003e\n\u003cp\u003e(B) umpa plots visualized cell clusters\u003c/p\u003e\n\u003cp\u003e(C) distribution histogram of the identifified cell types\u003c/p\u003e\n\u003cp\u003e(D) Venn diagram showing differentially expressed genes associated with CD4+ T cells\u003c/p\u003e","description":"","filename":"floatimage2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4617116/v1/875b7d7825acdedde28dba33.jpg"},{"id":60625101,"identity":"045fc86f-2a18-4bbe-bd9c-884e177eabd6","added_by":"auto","created_at":"2024-07-18 22:20:22","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":128089,"visible":true,"origin":"","legend":"\u003cp\u003eProtein-protein interaction networks and Enrichment analysis of EGFR and DLX5\u003c/p\u003e\n\u003cp\u003e(A) CD4+ T cell-associated differential gene-protein interaction network\u003c/p\u003e\n\u003cp\u003e(B) Enrichment of ssGSEA for EGFR\u003c/p\u003e\n\u003cp\u003e(C) Enrichment of ssGSEA for DLX5\u003c/p\u003e","description":"","filename":"floatimage3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4617116/v1/31de9e5904b9e688978384f4.jpg"},{"id":60625103,"identity":"25f059a9-24d7-48d3-927e-e547a5107974","added_by":"auto","created_at":"2024-07-18 22:20:22","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":656224,"visible":true,"origin":"","legend":"\u003cp\u003eThe survival curve of prognostic markers for hypopharyngeal cancer, as well as the expression status of key genes detected in the HPA database. (A-B)Survival curves of DLX5 and EGFR:（C-D） Survival curves of DLX5 and EGFR based on data from the GSE2379 GPL-8300 platform.(E-F) Survival curves of DLX5 and EGFR based on data from the GSE2379 GPL-91 platform. (G-H) Survival curves of DLX5 and EGFR based on UCSCS data.(I) Typical immunohistochemical staining of EGFR in normal tissues; (J) Typical immunohistochemical staining of EGFR in HNSCC tissues; (K) Typical immunohistochemical staining of DLX5 in normal tissues; (L) Typical immunohistochemical staining of DLX5 in HNSCC tissues.\u003c/p\u003e","description":"","filename":"floatimage4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4617116/v1/ea5f94537fd39001edf66aaa.jpg"},{"id":60625109,"identity":"a788e47a-7e5f-4d61-a3c2-3f44d32ca7b4","added_by":"auto","created_at":"2024-07-18 22:20:22","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":183899,"visible":true,"origin":"","legend":"\u003cp\u003eThe correlation between DLX5 and immune checkpoints, as well as the interrelationships among immune checkpoints. Platform information for GSE227156: GPL-91.\u003c/p\u003e","description":"","filename":"floatimage5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4617116/v1/d89740e56dc8c2dd2e6339ae.jpg"},{"id":60625107,"identity":"32d920af-40d3-4f8c-9553-ee45d52cb29a","added_by":"auto","created_at":"2024-07-18 22:20:22","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":157181,"visible":true,"origin":"","legend":"\u003cp\u003eSummary of the mutation information with statistical calculations.\u003c/p\u003e\n\u003cp\u003e(A) \u0026nbsp;Different mutation types can be classified into categories. Missense mutations are the most common, with SNPs being more frequent than insertions or deletions. Among SNVs, \u0026nbsp;C\u0026gt;T and C\u0026gt;G mutations are common.\u003c/p\u003e\n\u003cp\u003e(B) The mutated genes ( rows, hub gene) in HNSCC samples exhibit significant mutations. Genes are sorted by mutation frequency, while samples are arranged to highlight mutual exclusivity among mutations. The right side displays the percentage of mutations, and the top indicates the total number of mutations. Color coding represents different mutation types.\u003c/p\u003e","description":"","filename":"floatimage6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4617116/v1/9095836d231c55c9f248cc61.jpg"},{"id":60625105,"identity":"1c96a44b-474a-4eb8-98eb-e8fddd589151","added_by":"auto","created_at":"2024-07-18 22:20:22","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":148664,"visible":true,"origin":"","legend":"\u003cp\u003ePairwise comparisons of hub genes are depicted using a color gradient to represent Spearman's correlation coefficients. Through partial (geographic distance-corrected) Mantel tests, chemokines and their respective receptors are associated with each hub gene. The width of edges corresponds to Mantel's r statistic for the respective distance correlations, and the color of edges indicates statistical significance.\u003c/p\u003e","description":"","filename":"floatimage7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4617116/v1/bf18bbc09e3b63c4bf33a2cd.jpg"},{"id":60625971,"identity":"ce398993-76c3-4c44-be2c-dd3c5a3af2a2","added_by":"auto","created_at":"2024-07-18 22:28:21","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":259415,"visible":true,"origin":"","legend":"\u003cp\u003eSelect chemotherapy drugs for the \u0026nbsp;\u0026nbsp;treatment of squamous cell carcinoma of the hypopharynx\u003c/p\u003e\n\u003cp\u003e(A),BI.2536_1086 (C), MN.64_1854 (E),Ulixertinib_2047 \u0026nbsp;In patients with high \u0026nbsp;\u0026nbsp;and low expression of DLX5 in hypopharyngeal cancer. The corresponding 3D \u0026nbsp;\u0026nbsp;structures of the top four small molecular compounds showing significant \u0026nbsp;\u0026nbsp;differential responses are shown in (B), (D),and(F), respectively.\u003c/p\u003e","description":"","filename":"floatimage8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4617116/v1/1eec10d06b314085fc78278d.jpg"},{"id":60625106,"identity":"7a820e55-9870-4380-acb6-6d44c813440d","added_by":"auto","created_at":"2024-07-18 22:20:22","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":408443,"visible":true,"origin":"","legend":"\u003cp\u003eThe pre-cancerous phenotype of DLX5 in Fadu cell line.\u003c/p\u003e\n\u003cp\u003e(A) Cell viability was determined by CCK-8 assay to assess cell proliferation in the FADU cell line transfected with siRNA-NC, siRNA-1, pCDNA3.1 empty vector, and pCDNA3.1-DLX5 overexpression at 24 and 48 hours post-transfection. (B-C) Wound healing experiments demonstrated the migration capacity of FADU cells transfected with siRNA-NC, siRNA-1, pCDNA3.1 empty vector, and pCDNA3.1-DLX5 overexpression. ***p \u0026lt; 0.001, analyzed by one-way analysis of variance (ANOVA). (D-E) Cell migration and invasion abilities were evaluated through the Transwell assay.\u003c/p\u003e","description":"","filename":"floatimage9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4617116/v1/b13abd289861380445d6037d.jpg"},{"id":60626212,"identity":"c2e7a243-6319-48dc-bc64-1569d34383a4","added_by":"auto","created_at":"2024-07-18 22:36:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3100161,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4617116/v1/e1532448-42fd-4983-817a-eb38f55e3027.pdf"},{"id":60625108,"identity":"3f17a36d-e67b-40f8-96fc-e20389be1850","added_by":"auto","created_at":"2024-07-18 22:20:22","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":2552527,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Information\u003c/p\u003e\n\u003cp\u003eFigure S1. Analysis Workflow of this Study\u003c/p\u003e\n\u003cp\u003eFigure S2. WGCNA Workflow Diagram, A Merge high-similarity dynamic modules at the 0.25 cut line. ,B Correlation plot between modules C Visualization of gene network (TOM plot) D By aggregating genes with strong correlations in the same module, different modules were obtained and are displayed in different colors。\u003c/p\u003e\n\u003cp\u003eFigure S3. Enrichment Analysis of Differentially Expressed Genes Using KEGG Pathways\u003c/p\u003e\n\u003cp\u003eFigure S4. 3000 highly variable genes AND Harmony De-Batch Chart\u003c/p\u003e\n\u003cp\u003eFigure S5. Identification of Cell Type-Specific Markers in Single-Cell Analysis of Six Cell Types\u003c/p\u003e\n\u003cp\u003eTable S1. Clinical samples collected from Chaoyang Central Hospital.\u003c/p\u003e\n\u003cp\u003eTable S2. GSE227156 of clinical information\u003c/p\u003e\n\u003cp\u003eTable S3. 72 Immune checkpoint, chemokine factor, and receptor-related genes\u003c/p\u003e\n\u003cp\u003eTable S4. Immune checkpoint genes and descriptions.\u003c/p\u003e\n\u003cp\u003eTable S5 GO and KEGG enrichment tables of 195 differentially expressed genes.\u003c/p\u003e\n\u003cp\u003eTable S6. Drugs with significant differences in IC50 values between High-expression group and low-expression group\u003c/p\u003e\n\u003cp\u003eTable S7. DLX5 knockdown and overexpression efficiency\u003c/p\u003e","description":"","filename":"SupplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-4617116/v1/9f91320748138a82c23816e9.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Prediction of the Potential Efficacy of Dlx5 in Immunotherapy for Hypopharyngeal Cancer through Integrated Bulk and Single-Cell RNA Sequencing","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHypopharyngeal cancer, predominantly composed of squamous cells, is more commonly observed in males and is often associated with a history of smoking (90%) and alcohol abuse (50%)[1].The 5-year survival rate for this type of cancer ranges from only 25% to 40%[2],making it one of the most challenging cancers to prognose among head and neck cancers. Patients typically present with lymph node metastasis at diagnosis,and distant metastasis is more common compared to other cancers in the head and neck cancers, \u0026nbsp; presenting significant challenges for treatment and outlook. The introduction of anti-PD-1 antibodies in comprehensive treatment regimens for head and neck squamous cell carcinoma (HNSCC) has shown promise in providing sustained responses and surviving benefits in recurrent and metastatic diseases previously treated with platinum-based drugs. However, despite these promising research outcomes, the overall response rates (ORRs) \u0026nbsp;of Nivolumab and Pembrolizumab in platinum-refractory recurrent and metastatic HNSCC remains low at only 13%-18%[3]. Even more concerning is that the majority of patients exhibit primary resistance to treatment, with only a small percentage experiencing long-lasting durable responses. Given the apparent heterogeneity of hypopharyngeal squamous cell carcinoma within head and neck squamous cell carcinoma, the search for new potential immunotherapeutic biomarkers becomes particularly crucial.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe significance of tumor immune microenvironment (TIME) in cancer development and treatment response highlights the importance of discerning tumor immune characteristics in different cancer patients\u0026nbsp;[4].Among these, tumor-infiltrating CD4+ T cells constitute a crucial component of the hypopharyngeal cancer TIME, playing a key role in recognizing and killing tumor cells. A study involving 278 patients with head and neck squamous cell carcinoma (HNSCC) found that higher levels of CD4+ T cell infiltration were linked to better overall survival (OS) and disease-specific survival (DSS) (p = .003 and p = .004, respectively)\u0026nbsp;[5]. Therefore, pinpointing biomarkers connected to CD4+ T cell infiltration could assist in monitoring the response to immune therapy in hypopharyngeal cancer and identifying potential therapeutic targets.\u003c/p\u003e\n\u003cp\u003eBioinformatics advancements have facilitated the creation of numerous tools, with WGCNA being a popular choice for biomarker discovery. Focusing on hypopharyngeal cancer transcriptome data, we quantified the composition of immune cells using deconvolution algorithms and identified important modules and key genes associated with CD4+ T cell infiltration levels through WGCNA. Simultaneously, by integrating single-cell RNA sequencing data, we identified genes associated with CD4+ T cells in the tumor microenvironment of hypopharyngeal squamous cell carcinoma. The core principle of single-cell RNA sequencing (scRNA-seq) technology involves isolating individual cells, amplifying trace RNA quantities, and utilizing high-throughput sequencing to capture the gene expression profiles of each cell at a single-cell level[6].. Compared to traditional bulk sequencing methods, single-cell sequencing enables precise analysis of gene expression in each cell, accurate differentiation of cell populations, and comprehensive retention of information on tumor cell heterogeneity[7],[8].\u003c/p\u003e\n\u003cp\u003eIn summary, by combining analysis of both bulk and single-cell RNA sequencing data with WGCNA co-expression networks, we have pinpointed DLX5 as a promising biomarker for immunotherapy. Previous investigations have highlighted DLX5\u0026apos;s importance in skeletal development, while recent findings have linked it to the promotion of cell proliferation through upregulating MYC promoter activity in tumors[9]. Our study further confirms the association of DLX5 overexpression with carcinogenesis, suggesting its potential as a therapeutic target for hypopharyngeal cancer treatment.\u003c/p\u003e"},{"header":"Materials and methods ","content":"\u003cp\u003e\u003cstrong\u003ePatients and datasets\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe collected cancer tissue samples from 12 patients with hypopharyngeal cancer who received treatment at Chaoyang Central Hospital from June 2019 to March 2023, as well as normal hypopharyngeal mucosal tissue samples from 6 non-tumor patients. The patients were followed up until February 2024, with no distant metastasis or recurrence observed before surgery. Postoperative pathology confirmed hypopharyngeal squamous cell carcinoma. For detailed clinical and pathological characteristics, please refer to\u0026nbsp;Table S1. The study\u0026apos;s design roadmap is illustrated in Figure S1.\u0026nbsp;This study has been approved by the Ethics Committee of Chaoyang Central Hospital.\u003c/p\u003e\n\u003cp\u003eWe downloaded RNA sequencing (RNA-seq) data from 546 samples of head and neck squamous cell carcinoma \u0026nbsp;from the UCSC database (https://xenabrowser.net/), including 502 cancer tissue samples and 44 adjacent normal tissue samples, along with relevant clinical sion Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/), comprising 4 normal samples and 31 individual tumor samples of hypopharyngeal cancer, as well as single-cell sequencing data GSE227156, containing cancer cell and lymph node metastatic cancer cell samples from five hypopharyngeal cancer patients. Details are shown in Table 1. Furthermore, we utilized the GSCA website (http://bioinfo.life.hust.edu.cn/GSCA) to assess tumor mutations.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTABLE 1 summary of the data sets utilized in this research and their features\u0026nbsp;\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003eDataset\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003eDatabase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003ePlatform\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003eSample\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\" rowspan=\"2\"\u003e\n \u003cp\u003eGSE2379\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" rowspan=\"2\"\u003e\n \u003cp\u003eGEO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003eGPL-91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003e11 cases of HPS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003eGPL-8300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003e20 cases of HPS and 4 controls\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003eGSE227156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003eGEO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003e10x\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003e5 cases of HPS and 5 case of\u0026nbsp;HPC-LNM\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003eImmune checkpoint\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003eLiterature\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003echemokine and receptor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003eLiterature\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003eUCSC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003eUCSC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003e502 cases of HPC and 44 controls\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eGeo，Gene Expression Omnibus, HPS, Hypopharyngeal Cancer, LMHSCC , HNSCC，Head and Neck Squamous Cell Carcinoma, \u0026nbsp;HPC-LNM, \u0026nbsp;Hypopharyngeal cancer with lymph node metastasis\u003c/p\u003e\n\u003ch2\u003eImmune checkpoint, chemokine factor, and receptor-related genes.\u003c/h2\u003e\n\u003cp\u003eFrom references\u0026nbsp;[10]-[13], a total of 79 immune checkpoint genes (ICGs) and 72 chemokine-related genes and their receptors (Table S3) were\u0026nbsp;identified. These include 46 immune chemokines, which belong to the CC chemokine subfamily, CXC chemokine subfamily, XC chemokine subfamily, and CX3C chemokine subfamily, as well as 26 corresponding immune chemokine receptors.\u003c/p\u003e\n\u003ch2\u003eRNA extraction and sequencing\u003c/h2\u003e\n\u003cp\u003eThe RNA extraction and sequencing process for the 18 specimens from Chaoyang Central Hospital proceeded as follows: Initially, around 100 milligrams of tumor tissue from each frozen tube were carefully excised with sterile scissors and placed into 2-milliliter centrifuge tubes that were free from ribonuclease (RNase) contamination. After that,2 steel beads and 1000 microliters of tissue lysis buffer were added, and the tubes underwent homogenization using a high-throughput tissue homogenizer operating at a frequency of 40 hertz for 180 seconds. Upon completion of the homogenization step, the tubes were removed, and the homogenate was collected using a 1-milliliter pipette tip that was RNase-free, and then\u0026nbsp;transferred\u0026nbsp;to new 1.5-milliliter centrifuge tubes also devoid of RNase. The samples were centrifuged at 12000 revolutions per minute for 2 minutes at 4 degrees Celsius. The supernatant was once again collected using an RNase-free pipette tip and\u0026nbsp;transferred\u0026nbsp;to new RNase-free 1.5-milliliter centrifuge tubes in preparation for total RNA extraction. The total RNA extraction process was carried out using the Kangwei Century Ultrapure RNA Kit (DNase I) with the catalog number CW0597S. Gene models and reference genome annotation files were obtained from the National Center for Biotechnology Information (NCBI) website at https://www.ncbi.nlm.nih.gov/. Subsequently, an index of the reference genome was generated using HISAT2 v2.0.5,and high-quality sequences were aligned to the reference genome. Following this, low-expression genes were filtered out, and redundant genes were normalized using the limma package.\u003c/p\u003e\n\u003ch1\u003eestimation of tumor immune-infiltrating cell type scores.\u003c/h1\u003e\n\u003cp\u003eWe utilized the CIBERSORT algorithm and the LM22 gene set[14]\u0026nbsp;available on the CIBERSORT website to conduct deconvolution analysis on the transcriptomes of individual samples using linear support vector regression. This method allowed us to estimate the involvement of different immune cell subtypes in the overall immune infiltration. By identifying the abundance of immune cells, we assessed the immune infiltration scores in patients with hypopharyngeal cancer.\u003c/p\u003e\n\u003ch2\u003econstruction of weighted gene co-expression networks\u003c/h2\u003e\n\u003cp\u003eWeighted Gene Co-expression Network Analysis \u0026nbsp;[15]is a method for network modular analysis used to examine correlated patterns of gene expression among different samples. \u0026nbsp;It helps detect modules with highly coordinated variations and investigate their associations with relevant phenotypes, thus revealing potential biomarker genes or therapeutic targets. \u0026nbsp;In our research, genes were ranked based on their expression standard deviation and the top 50% were chosen for WGCNA analysis. The pickSoftThreshold function was utilized to determine the power parameter ensuring network connections followed a scale-free distribution. Gene adjacency was converted into topological overlap to assess their connectivity in the network. Hierarchical clustering was then conducted using TOM dissimilarity to group genes with similar expression into modules, with a minimum module size of 50 genes. By computing dissimilarities between module characteristic genes, a suitable cutting line was identified to consolidate some modules. Additionally,400 genes were randomly selected for network visualization, facilitating the display of characteristic gene networks\u0026nbsp;[16].\u003c/p\u003e\n\u003ch2\u003eIdentifying differential genes associated with CD4+ T cells\u003c/h2\u003e\n\u003cp\u003eDEGs were identified based on specific criteria: |log2 fold change (FC)| \u0026gt; 1 and p \u0026lt; 0.05,with upregulated DEGs defined as log FC \u0026gt; 1,p \u0026lt; 0.05,and downregulated DEGs as log FC \u0026lt; -1,p \u0026lt; 0.05. Volcano plots of DEGs were then created using the \u0026apos;Pheatmap\u0026apos; and \u0026apos;ggplot2\u0026apos; R packages. The DEGs selected were cross-referenced with the blue gene module linked to memory CD4 + T cell subsets, and the resulting DEGs related to CD4 + T cells were visualized using the \u0026ldquo;Venndiagram\u0026rdquo; package.\u003c/p\u003e\n\u003ch2\u003eCell types and subtypes of 10\u0026times; scRNA-Seq data\u003c/h2\u003e\n\u003cp\u003eWe conducted single-cell RNA sequencing analysis of the GSE227156 dataset retrieved from the GEO website, which consists of samples from five patients diagnosed with hypopharyngeal squamous cell carcinoma. The visualization analysis process commenced by transforming 17,599 cells into a Seurat object using the \u0026ldquo;Seurat\u0026rdquo; R package[17]. Subsequently, we conducted quality control by filtering out low-quality cells and assessing mitochondrial and red blood cell gene expression as a percentage of total gene expression. Then, the Harmony algorithm was utilized for both principal component analysis (PCA) and non-linear dimensional reduction. We selected the top 35 principal components from the PCA for subsequent clustering analysis. Non-linear dimensional reduction was further performed using t-SNE and UMAP techniques. Single-cell consensus clustering \u0026nbsp;was then employed for unsupervised clustering to identify distinct cell clusters. The initial clustering results were visualized using the t-SNE and UMAP packages. To identify marker genes for each cluster, we manually annotated genes based on references from \u0026ldquo;the Cell Marker database\u0026rdquo; website\u0026nbsp;[18]and research conducted by Guan R and others[19].\u003c/p\u003e\n\u003ch2\u003eGene Set Enrichment Analysis (GSEA) enrichment analysis\u003c/h2\u003e\n\u003cp\u003eGene Set Enrichment Analysis (GSEA)\u0026nbsp;[20]\u0026nbsp;is used to examine how genes from a predefined gene set are distributed within a ranked gene set linked to specific traits or conditions (using the Hallmark gene set from the Molecular Signatures Database, MSigDB). The goal is to pinpoint significantly enriched pathways among these ranked gene lists.\u003c/p\u003e\n\u003ch2\u003eCell line and cell culture environment\u003c/h2\u003e\n\u003cp\u003eThe FaDu cell line originates from human hypopharyngeal carcinoma and was acquired from the American Type Culture Collection (ATCC), situated in Manassas,Virginia,USA. These cells are grown in a humidified atmosphere at 37\u0026deg;C with 5% CO2,utilizing Dulbecco\u0026apos;s Modified Eagle Medium (DMEM) supplemented with 10% fetal bovine serum (FBS),streptomycin (100 \u0026micro;g/ml),and penicillin (100 units/ml).\u003c/p\u003e\n\u003ch2\u003eConstructing siRNA knockdown and DLX5 overexpression.\u003c/h2\u003e\n\u003cp\u003eStable passage 2 or more of FADU cells were seeded into a 12-well culture plate at a density of 5\u0026times;10^5 cells per well, with a total of 7 wells. When the cell confluence reached 70%, transfection was performed according to the manufacturer\u0026apos;s instructions using Lipofectamine\u0026trade; 2000 Transfection Reagent (Thermo Fisher Scientific). The transfection included seven groups: siRNA-NC-FAM, siRNA-NC, siRNA-1, siRNA-2, siRNA-3, pCDNA3.1 empty vector, and pCDNA3.1-DLX5 overexpression. The sequences of siRNAs were as follows: siRNA-1: 5\u0026prime;-AAGCUUAUGCCGACUAUAGCUACTT-3\u0026prime;, siRNA-2: 5\u0026prime;-GAAGUGACCGAGCCCGAGGUGTT-3\u0026prime;, siRNA-3: 5\u0026prime;-UUCGUAAACCCAGGACUAUUUAUTT-3\u0026prime;, siRNA-NC: 5\u0026prime;-UUCUCCGAACGUGUCACGUTT-3\u0026prime;. siRNA-NC-FAM: 5\u0026prime;-UUCUCCGAACGUGUCACGUTT-3\u0026prime;. The transfection efficiency of the overexpression plasmid was evaluated using qPCR, and the optimal siRNA interference sequence was selected.\u003c/p\u003e\n\u003ch2\u003eQuantitative real‑time polymerase chain reaction (qRT‑PCR)\u003c/h2\u003e\n\u003cp\u003eThe total RNA was isolated using the Kangwei Century Ultrapure RNA Kit (DNase I) and then converted to complementary DNA (cDNA) using the Transgen Reverse Transcription Kit. Real-time quantitative PCR (relative quantification) was performed using the SYBR Green I method on a LightCycler96 instrument (Roche, Switzerland). The qPCR reagent kit utilized was the TransStart Green qPCR SuperMix, with primer sequences synthesized by Shanghai Universal Biotechnology in China. The primer sequences were as follows: DLX5 forward primer: 5\u0026apos;-GCCAAAGCTTATGCCGACTA-3\u0026apos;, reverse primer: 5\u0026apos;-GGGCTCGGTCACTTCTTTCT-3\u0026apos;; human GAPDH forward primer: 5\u0026apos;-GAAGGTGAAGGTCGGAGTCAA-3\u0026apos;, reverse primer: 5\u0026apos;-CTGGAAGATGGTGATGGGATTT-3\u0026apos;. GAPDH was used as the internal control. The data were analyzed using the 2-Ct method, with each gene analyzed in triplicate. qPCR was employed to evaluate the mRNA overexpression level of the DLX5 gene and to determine the most effective siRNA sequence for interference.\u003c/p\u003e\n\u003ch2\u003eCCK8 assay\u003c/h2\u003e\n\u003cp\u003eThe impact of the DLX5 gene on FADU cell proliferation was assessed using the Cell Counting Kit-8 (CCK-8) assay. Initially, FADU cells were seeded into a 96-well cell culture plate at a density of 3.1\u0026times;10^4 cells per well, totaling 24 wells (4 groups \u0026times; 6 wells). Subsequently, two sets of cell plates were prepared for CCK8 analysis at 24 and 48 hours post-transfection. The study included four groups: siRNA-NC, siRNA-1, pCDNA3.1 empty vector, and pCDNA3.1-DLX5 overexpression. At 0, 24, and 48 hours post-transfection, 10 \u0026mu;L of CCK8 solution was added to each well, followed by a 1-hour incubation period. The optical density (OD450) of each well was then measured using a microplate reader to assess cell proliferation activity.\u003c/p\u003e\n\u003ch2\u003eWound healing assay\u003c/h2\u003e\n\u003cp\u003eIn order to evaluate cell migration capacity, a wound healing experiment was conducted. FADU cells from four groups (siRNA-NC, siRNA-1, pCDNA3.1 empty vector, and pCDNA3.1-DLX5 overexpression) were cultured in 6-well plates. Once the cells reached 70% confluence, wounds were induced in the middle of the cell layer using a 10 \u0026mu;L pipette tip. The cell plates were then placed in a 37\u0026deg;C, 5% CO2 incubator for further incubation. Images were captured at 0 h, 24 h, and 48 h post-scratching, and each experiment was repeated thrice.\u003c/p\u003e\n\u003ch2\u003eTranswell invasion assay\u003c/h2\u003e\n\u003cp\u003eThe Transwell assay is commonly used to evaluate the migration and invasion capabilities of cells. Initially, FADU cells were prepared at a concentration of 2\u0026times;10^5/ml after digestion. Subsequently, a mixture of 100 \u0026mu;L of cells and 100 \u0026mu;L of transfection complex was added to each upper chamber of the Transwell. Following 48 hours of incubation, the culture plate was removed, and the upper culture medium was discarded. The cells were then fixed, stained, and three random fields were selected from each membrane for photography under an inverted microscope (100\u0026times;) and saved. This experiment was repeated three times. Both the Transwell invasion and migration assays followed the same process, except that the upper layer of the Transwell chamber\u0026apos;s PET membrane was uniformly coated with Matrigel matrix gel.\u003c/p\u003e\n\u003ch2\u003eStatistical analysis\u003c/h2\u003e\n\u003cp\u003eWe used RStudio (version 4.3.2) to perform analysis on bulk RNA sequencing data with WGCNA and CIBERSORT packages, and for single-cell RNA sequencing data analysis, we utilized the Seurat R package. The optimal cutoff value was determined to divide patients into high and low expression groups. The \u0026ldquo;oncoPredict\u0026rdquo; R package [21]was used to evaluate drug sensitivity for each sample and suggest potential targeted therapies. Inter-group differences were assessed using Wilcoxon test and Student\u0026apos;s t-test, with statistical significance set at a p-value below 0.05.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003eDifferential Gene Expression in CD4+ T Cells\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eInvestigating the tumor immune cell infiltration microenvironment in hypopharyngeal cancer, we utilized the CIBERSORT algorithm to estimate the relative proportions of 22 immune cell types. The dominant frequencies of memory B cells, macrophages M0 and M1 types, activated mast cells, monocytes, resting natural killer cells, plasma cells, T cells CD4 memory resting cells, memory-activated T cells, CD4+ T cells, follicular helper T cells, and regulatory T cells (Tregs) are shown in Figure 1A. These proportions of immune cell subtypes served as feature data for Weighted Gene Co-expression Network Analysis (Figure 1D). Simultaneously, WGCNA was performed on the top 50% of genes based on standard deviation (n=9116). Setting the soft threshold power to 5 through the pickSoftThreshold function ensured that the gene network adhered to a scale-free distribution, with a scale-free topology model fit index of 0.9 achieved((Figure 1C). A hierarchical clustering tree was then generated using dynamic hybrid clustering. The resulting tree diagram depicted genes as individual leaves, with genes sharing similar expression data grouped together into branches to form gene modules. Modules with high similarity were merged at a cutoff of 0.25, resulting in the creation of 26 modules (Figure 1E，WGCNA Workflow Diagram depicted in\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eFigure S2). The blue module comprised 724 genes, showing a strong correlation with CD4+ T cells (R2 = 0.75, P = 3e-04), while the blue-green module consisted of 278 genes, significantly linked to regulatory T cells (Tregs) (R2 = -0.81, P = 5e-05). To identify genes specifically associated with CD4+ T cells, sequencing count data from 12 hypopharyngeal cancer patients and 6 healthy controls (totaling 29,313 genes) were analyzed using the DESeq2 package for differential expression analysis. A total of 2,090 differentially expressed genes (DEGs) were identified, comprising 940 upregulated and 1,150 downregulated genes. The volcano plot (Figure 1B) illustrates the top 20 DEGs. After intersecting with the genes in the blue module, 195 differentially expressed genes associated with CD4+ T cells were identified. KEGG enrichment analysis of the differentially expressed genes mainly enriched in extracellular matrix structural constituents, integrin binding, protein polysaccharide binding, and Wnt receptor activity. Detailed results are provided in\u0026nbsp;Figure S3 and Table S5.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSingle-cell overview of different cell types in hypopharyngeal carcinoma \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe performed single-cell gene expression analysis on hypopharyngeal cancer cells from five individuals in the GSE227156 dataset. After filtering out genes expressing red blood cells (\u0026lt;3%) and granulocytes (\u0026lt;10%), the datasets were merged and normalized. PCA dimensionality reduction was applied to 3000 highly variable genes, followed by batch correction using harmony to mitigate batch effects. During the data processing, no significant batch effects were observed（Figure S 4）.\u003c/p\u003e\n\u003cp\u003eThe resulting cell-gene matrix revealed an average of 1692 genes detected per cell. Utilizing umap/tsne clustering, we identified seven cell types: macrophages (Cluster 0), squamous epithelial carcinoma cells (Cluster 1), lymphocytes (Clusters 2 and 4), fibroblasts (Cluster 3), endothelial cells (Cluster 5), dendritic cells (Cluster 6), and epithelial cells (Cluster 7). Clusters were annotated manually using specific markers from the Cell Marker database and Durante et al.\u0026apos;s studies （Figure S 5）. Further analysis uncovered 75 differentially expressed genes associated with tumor-related CD4+ T cells within Cluster 2.\u003c/p\u003e\n\u003cp\u003ePPI networks and Enrichment analysis of hub genes\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo explore the protein-protein interactions of differentially expressed genes associated with CD4+ T cells, we inputted 75 genes into the STRING database to analyze their interactions. Disconnected nodes were removed during network construction, using a default interaction score of 0.4. Subsequently, the interaction data was imported into Cytoscape, resulting in the generation of Figure 3A. Utilizing the CytoHubba plugin, we identified central hub genes within the PPI network, including EGFR, Dlx5, DSG2, TP63, DLX2, and TSLP. Further analysis via ssGSEA revealed that high EGFR expression levels were significantly associated with tumor protein polysaccharides (Figure 3B). Conversely, high DLX5 expression levels were predominantly linked to signaling pathways regulating stem cell pluripotency (Figure 3C).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIdentification of prognostic markers for hypopharyngeal cancer\u003c/p\u003e\n\u003cp\u003eBased on clinical data collected from Chaoyang Central Hospital, Kaplan-Meier analysis was conducted on EGFR and Dlx5 genes (Figures 4A-B), revealing an association between elevated Dlx5 gene expression and poorer prognosis. To validate these findings, hypopharyngeal cancer microarray datasets (GSE2379) from the Gene Expression Omnibus (GEO) database were obtained, consisting of the GPL-91 and GPL-8300 platforms. Survival analysis results are detailed in Figures 4C-D-E-F. To address potential biases due to sample size limitations, the investigation was expanded to include a larger set of head and neck tumor samples. RNA sequencing data comprising 546 cases of head and neck squamous cell carcinoma from the UCSC database were accessed for validation, yielding a hazard ratio of 1.42 (95% confidence interval: 0.82-0.904) for EGFR and 0.73 (95% confidence interval: 0.6872-0.769) for Dlx5, as depicted in Figures 4G-H. Additionally, immunohistochemical (IHC) staining results from the Human Protein Atlas database were retrieved to further confirm the expression levels of EGFR and Dlx5 genes (Figures 4I-J). This combined version effectively integrates the key findings and methodological steps in a coherent manner.\u003c/p\u003e\n\u003cp\u003eCorrelation Analysis of Immune Checkpoint\u003c/p\u003e\n\u003cp\u003eImmune checkpoints[22] are a class of immune-inhibitory molecules expressed on the surface of immune cells. They regulate the activation level of the immune system to prevent excessive activation, which could lead to autoimmune reactions. However, cancer cells exploit these immune checkpoints, particularly T cell negative regulatory mechanisms, to dampen the immune system\u0026apos;s attack, enabling immune evasion. To counter this, inhibitors such as PD-1, PD-L1, and CTLA-4 have been developed to alleviate immune response constraints, thereby reactivating T cells to target tumor cells and enhance cancer treatment efficacy. Despite these advancements, the majority of patients do not experience significant benefits, with response rates typically ranging from 10% to 25% [23], even in approved therapeutic indications. Therefore, the future of immunotherapy for head and neck squamous cell carcinoma may lean towards combination therapies or strategies that enhance immune response rates by combining with other targeted drugs to overcome resistance to immune checkpoint blockade. In light of this, we assessed the correlation between the DLX5 gene and immune checkpoints (Figure 5, GSE227156 platform GPL-91), revealing significant associations between Dlx5 and the immune checkpoint BTN3A1 (R=0.71), CCD28 with BTN2A1 (R=0.72), and HLA-F with HLA-G (R=0.72). Thus, interventions targeting DLX5 offer a promising avenue to augment the effectiveness of immune-based therapies.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTumor Mutational Burden\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTumor mutational burden (TMB) [24] indicates the quantity of mutations in a tumor. Mutated proteins form neoantigens, which are presented to T cells by antigen-presenting cells through major histocompatibility complex (MHC) proteins. This process allows T cells to identify and release perforins and granzymes to attack and eliminate mutated tumor cells. Increased mutations, leading to a higher TMB, enhance the chances of immune recognition and targeting of tumor cells, improving the effectiveness of immunotherapy. Studies by Chabanon et al. (2016) [24] and Rooney et al. (2015) [25]have demonstrated a positive link between high tumor mutational burden (TMB-H) and positive outcomes post-treatment with immune checkpoint inhibitors (ICI). Clinical data also reveals a significant association between TMB levels and responses to PD-1/PD-L1 inhibitors[26]. The GSCA website (GSCA, http://bioinfo.life.hust.edu.cn/GSCA) [27] integrates data on gene expression, mutations, drug sensitivity, and clinical information from four public sources across 33 cancer types. By using the genomic alteration module to visualize the mutation burden of core genes, Figure 6 illustrates mutation percentages for EGFR, FAM83B, Dlx5, DSG2, IL1RAP, and CXADR, which are 41%, 21%, 18%, 12%, 12%, and 6%, respectively. This highlights the relatively high immunogenicity of Dlx5.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorrelation analysis of immune chemokines and receptors.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe tumor microenvironment plays a pivotal role in mediating interactions between tumor cells and the immune system. Various immune cell populations are attracted to this environment by specific chemokine factors, influencing tumor progression and treatment responses significantly[28]. Thus, therapeutic strategies targeting both pro-tumor and anti-tumor chemokine-receptor signaling pathways, in conjunction with immunotherapy, offer promising clinical benefits for cancer patients. To bolster this hypothesis, we conducted a comprehensive analysis correlating key genes with chemokines and their receptors.\u003c/p\u003e\n\u003cp\u003eWe compared samples across three datasets: (i) 46 immune chemokines from different chemokine subfamilies, (ii) 26 corresponding immune chemokine receptors, and (iii) gene expression profiles of key genes. After logarithmically transforming gene expression data, we calculated distances between immune chemokines, receptors, and key genes using the Euclidean distance method. Subsequently, we utilized the linkET package in the R software to perform bias-corrected Mantel[29]correlation analysis.\u003c/p\u003e\n\u003cp\u003eOur analysis revealed significant correlations between EGFR and immune chemokines and receptors, as well as a robust correlation between DSG2 and these immune factors. Notably, while DLX5 exhibited a significant correlation with chemokines, its relationship with receptors did not reach statistical significance. This suggests that DLX5 may regulate chemokine activity, while its interactions with receptors could be influenced by unexplored factors.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEvaluating the Therapeutic Response\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo predict the response of hypopharyngeal cancer to chemotherapy, we utilized the oncoPredict R package to estimate chemotherapy response based on half-maximal inhibitory concentration (IC50) data from the Cancer Cell Line Encyclopedia (CCLE) database, available for hypopharyngeal cancer patients. In our study, patients were stratified into high and low expression groups based on DLX5 expression levels, leading to the identification of 25 small molecule compounds with significantly different responses (Table S5). Figure 8 illustrates the top three small molecule compounds with the most statistically significant differences: BI.2536_1086 (P = 0.00019, Figure 8A), MN.64_1854 (P = 0.00103, Figure 8C), and Ulixertinib_2047 (P = 0.0013, Figure 8E).\u003c/p\u003e\n\u003cp\u003eOverexpression Dlx5 promotes cell proliferation and invasion\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe conducted a detailed investigation into the role of Dlx5 in hypopharyngeal squamous cell carcinoma by introducing pCDNA3.1-DLX5 and empty pCDNA3.1 vectors into the FaDu cell line. RT-qPCR confirmed the successful overexpression of DLX5, and we evaluated the impact of Dlx5 on hypopharyngeal cancer cell proliferation through CCK-8 assays. Our findings revealed that upon DLX5 overexpression, cell proliferation was boosted, with an increase of over 42% in proliferation activity noted after 48 hours of overexpression (Figure 9A). Additionally, the outcomes from transwell migration and scratch healing experiments indicated a notable rise in cell migration speed and improved cell healing capability in FaDu cells (Figure 9C-D). Moreover, in the transwell invasion assay, the overexpression of Dlx5 substantially heightened the invasive potential of the tumor cells (Figure 9E). To summarize, the upregulation of Dlx5 stimulates the proliferation, migration, and invasion of hypopharyngeal cancer cells.\u003c/p\u003e\n\u003cp\u003eKnockdown of Dlx5 inhibits cell viability and cell proliferation\u003c/p\u003e\n\u003cp\u003eIn order to confirm the potential involvement of Dlx5 in hypopharyngeal cancer, we developed three siRNAs (siRNA-1, siRNA-2, siRNA-3) to silence Dlx5 expression in FaDu cells. PCR validation demonstrated the effective suppression of Dlx5 expression by siRNA-1, achieving an mRNA knockdown efficiency of 80% (Table S7). To assess the impact of Dlx5 on cell proliferation, invasion, and migration, we performed CCK-8, Transwell, and scratch healing assays on FaDu cells transfected with siRNA-1 and a negative control (NC). The CCK-8 results revealed that silencing DLX5 decreased cell proliferation capacity, showing a maximum reduction of around 30% (Figure 9A). Transwell experiments indicated that downregulating Dlx5 constrained the migration and invasion abilities of FaDu cells (Figure 9D-E). The scratch healing assay further corroborated the diminished migration capability of FaDu cells following reduced Dlx5 expression (Figure 8B). These results suggest that suppressing Dlx5 expression can hinder the proliferation, migration, and invasion of hypopharyngeal cancer cells.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eImmune checkpoint inhibitors have shown good tolerability and anti-tumor activity in the management of recurrent or metastatic squamous cell carcinoma, sparking a reassessment of treatment approaches for hypopharyngeal cancer[30]. As a subset of head and neck squamous cell carcinoma, hypopharyngeal cancer displays significant diversity and heterogeneity, with treatment outcomes falling short of expectations. Therefore, a comprehensive understanding of immune system dysregulation during disease evolution and progression, along with exploration of potential molecular mechanisms tied to immunity, is essential for pinpointing novel therapeutic targets to enhance precision treatment and boost patient outcomes. Advanced technologies like high-throughput bulk RNA sequencing and single-cell RNA sequencing have revolutionized cancer research by uncovering molecular pathways crucial to tumor initiation, advancement, and treatment response, thereby offering crucial support for personalized therapy and precision medicine.\u003c/p\u003e\n\u003cp\u003eBulk RNA sequencing relies on analyzing RNA mixtures from various tissues, including solid tumors, to capture the mean gene expression levels in cell populations and minimize individual cell expression variations. The evolution of bioinformatics tools, notably the CIBERSORT algorithm, facilitates extensive RNA mixture analyses by estimating the proportions of distinct cell types within complex cell populations, enabling the identification of cellular biomarkers and potential therapeutic targets.\u003c/p\u003e\n\u003cp\u003eWith the advancement of technology, various bioinformatics tools have been developed, with the CIBERSORT algorithm being a notable example. This algorithm allows for the analysis of RNA mixtures on a large scale to pinpoint cellular biomarkers and potential therapeutic targets. By calculating the proportions of different cell types within mixed populations, CIBERSORT enhances our knowledge of cellular composition and function, which is essential for advancing precision medicine. Despite the maturity of transcriptome sequencing technology, which provides copious amounts of genetic and transcriptomic data, it still struggles to fully capture the cellular diversity within tumors[31].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSingle-cell RNA sequencing examines individual cells in suspension, accurately depicting the gene expression of each cell and allowing for precise differentiation and comparison of cell populations. The value of this technology lies in its ability to elucidate the characteristics and functions of individual cells, offering a crucial tool for understanding cellular diversity[32]. Nonetheless, a drawback of single-cell RNA sequencing is the possibility of different cells expressing similar genes, as well as the presence of other cell types in mixed populations, leading to complexity in data analysis and result uncertainty. To address these challenges, a harmonious approach leveraging both traditional and single-cell sequencing technologies is adopted to comprehensively grasp the distribution and functional attributes of cells within tumor tissues. This bidirectional screening and integrated analysis strategy offer more accurate and comprehensive support for studying tumor pathogenesis and devising effective treatment strategies.\u003c/p\u003e\n\u003cp\u003eWhile screening for prognostic-related genes using protein-protein interaction networks and clinical data, we observed a common occurrence of elevated Epidermal Growth Factor Receptor (EGFR) expression in head and neck squamous cell carcinoma (HNSCC). This discovery further validates the choice of utilizing cetuximab to target EGFR in treating advanced head and neck tumors. On the other hand, DLX5, known as a bone transcription factor, has emerged as a significant player in the field of oncology. Therefore, our study is centered around DLX5. Previous studies have demonstrated that increased DLX5 levels can stimulate the proliferation of ovarian cancer cells by enhancing the IRS-2-AKT signaling pathway[33]. Similarly, in oral squamous cell carcinoma (OSCC), DLX5 regulates CCND1 to drive cancer advancement[34]. Another research focusing on pan-squamous cell carcinoma has revealed that DLX5/TP63 contributes significantly to the proliferation, migration, and anchorage-independent growth of squamous cells[35]. Importantly, past studies employing molecular docking techniques have indicated the potential anti-tumor effects of DLX5[36].\u003c/p\u003e\n\u003cp\u003eThis study focused on exploring tumor-infiltrating immune cells immunotherapy to provide fresh perspectives in this area. The research revealed an association between Dlx5 and CD4+ T cells. Validation at the cellular level confirmed that an increase in Dlx5 levels enhances the proliferation, migration, and invasion of hypopharyngeal cancer cells, while reducing Dlx5 suppresses these activities. The study predicted treatment outcomes by analyzing factors like tumor mutational burden, immune checkpoint correlation, and immune chemokine receptor correlation. It suggests that targeting DLX5 could be a promising strategy for anti-tumor medications, and combining immunotherapy with immune checkpoint inhibitors may boost treatment efficacy. Furthermore, the study identified four potential small molecule compounds, including BI.2536_1086, MN.64_1854, and Ulixertinib_2047.BI.2536_1086 is a highly specific inhibitor of PLK1 (IC50=0.83 nM), causing cell cycle arrest during mitosis and stimulating apoptosis in cancer cells. Moreover, it impedes tumor growth in nude mice and leads to tumor shrinkage[37]. MN.64_1854, also referred to as MN-64, is a flavonoid compound recognized for its role as a tankyrase inhibitor. It modulates cellular activities like proliferation and elongation of telomeres by targeting TNKS1 and TNKS2[38]. Ulixertinib_2047 is a potent inhibitor of ERK1/2, which hinders the activation of ERK1/2 signaling pathways, disrupting processes such as proliferation, survival, and metastasis[39]. These results not only reinforce our research but also provide novel treatment possibilities for individuals with hypopharyngeal cancer.\u003c/p\u003e\n\u003cp\u003eHowever, our study faced limitations stemming from the lack of transcriptomic data on hypopharyngeal cancer. This resulted in a shortage of clinical samples for validation, leading to suboptimal survival analysis results. To address this, we leveraged a large dataset of head and neck squamous cell carcinoma (HNSCC) samples that include hypopharyngeal cancer cases to verify survival outcomes. Moving forward, we aim to enhance the number of hypopharyngeal cancer samples, gather more sequencing data, and obtain additional clinical information to bolster validation efforts and facilitate deeper exploration. Despite integrating single-cell sequencing with bulk RNA sequencing data, these datasets are unable to distinguish distinct cellular populations within the tumor microenvironment. Therefore, further experimental analyses are essential to understand the role of CD4+ associated immune genes in hypopharyngeal cancer and the potential therapeutic benefits of targeting DLX5. Presently, experimental validation is restricted to verifying cell functions, necessitating further cellular and animal studies, along with clinical trials for validation purposes.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn this study, we used transcriptome sequencing data to identify prognosis-related genes associated with CD4+ T cells and integrated single-cell sequencing to locate the tumor-associated gene DLX5. By analyzing tumor mutation rates, immune checkpoint expression, chemokine factors, and their receptor correlations, we predicted the efficacy of Dlx5 immunotherapy and drugs sensitive to DLX5 targeting. In vitro experiments showed DLX5 promotes proliferation, migration, and invasion of Fadu cells.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors extend their heartfelt appreciation to the participants and staff of Chaoyang Central Hospital for their valuable contributions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;J.Y. conducted data analysis and drafted the manuscript under the guidance of L.L.H., who provided conceptual guidance and revised the content. L.J. was responsible for investigation and resource management.All authors have read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Liaoning Natural Science Foundation Program (No2019-ZD-0901).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study has been approved by\u0026nbsp;Medical Ethics Committee of Chaoyang Central Hospital.Committee Reference Number:EC-20231212-1041.This study obtained written informed consent from all participants before commencing. Participants voluntarily took part without any coercion and had fully understood the purpose, procedures, and potential risks and benefits of the study before signing the consent forms. The consent forms detailed the measures for the confidentiality of personal data and the rights of the participants to withdraw from the study at any time.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eAvailability of data and materials \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analyzed in this study are available at NCBI-GEO, UCXC, and supplementary materials.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWycliffe N D, Grover R S, Kim P D, et al. Hypopharyngeal cancer[J]. 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BRAF-mutated advanced colorectal cancer: A rapidly changing therapeutic landscape[J]. Journal of Clinical Oncology, 2022, 40(24): 2706.\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":"DLX5, tumor-infifiltrating CD4+T cells, bulk RNA sequencing, single-cell RNA sequencing, hypopharyngeal carcinoma, cell proliferation and Invasion","lastPublishedDoi":"10.21203/rs.3.rs-4617116/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4617116/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003eImmunotherapy, as a personalized treatment strategy, has displayed promising potential in the management of head and neck squamous cell carcinoma. Nevertheless, the heterogeneity and initial resistance of hypopharyngeal squamous cell carcinoma present new obstacles to treatment, highlighting the urgent need for identifying novel predictive biomarkers to develop more targeted and effective treatment approaches.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethod\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003eWe employed the CIBERSORT algorithm, which quantifies immune cell composition, along with Weighted Gene Co-expression Network Analysis (WGCNA) to identify gene modules associated with tumor immune infiltration of CD4+ T cells. We integrated single-cell sequencing technology to complement each other, conducting bidirectional screening to narrow down molecular associations with tumors. By constructing Protein-Protein Interaction (PPI) networks and conducting clinical Kaplan-Meier analysis, we identified crucial hub genes. We calculated tumor mutation rates, immune checkpoint expression, chemokine factors, and their corresponding receptor correlations to predict the efficacy of immunotherapy targeting DLX5. The R package \"oncopredict\" was utilized to compute drug sensitivity for each sample, inferring potential chemotherapeutic drugs targeting DLX5. Finally, we explored the precancerous phenotype of DLX5 in the Fadu cell line.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResult:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBulk RNA sequencing and single-cell RNA sequencing revealed that in hypopharyngeal squamous cell carcinoma, the prognostically associated EGFR and DLX5 genes are upregulated. Immunological analysis showed a higher mutation rate of DLX5, which is significantly positively correlated with immune checkpoints and chemokine factors. Most importantly, three small molecule compounds (BI.2536_1086, MN.64_1854, Ulixertinib_2047) were identified, which could be potential drugs for treating hypopharyngeal cancer patients. Finally, high expression of DLX5 promoted proliferation, invasion, and migration of hypopharyngeal cancer cells.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion：\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe association of Dlx5 with CD4+ T cells in hypopharyngeal cancer correlates with the immunological characteristics of the disease and the potential efficacy of immune checkpoint inhibitor therapy. These results indicate that DLX5 might respond well to immunotherapy, shedding light on the role of Dlx5 in hypopharyngeal cancer, providing crucial insights and offering vital information for the development of personalized immunotherapeutic strategies.\u003c/p\u003e","manuscriptTitle":"Prediction of the Potential Efficacy of Dlx5 in Immunotherapy for Hypopharyngeal Cancer through Integrated Bulk and Single-Cell RNA Sequencing","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-18 22:20:17","doi":"10.21203/rs.3.rs-4617116/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":"66c9c603-e5b6-4ef7-9429-0e9d02be8947","owner":[],"postedDate":"July 18th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-07-18T22:20:19+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-18 22:20:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4617116","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4617116","identity":"rs-4617116","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}