Exploration of the Critical Roles and Molecular Mechanisms of KIF2C and GPX4 Genes in Head and Neck Squamous Cell Carcinoma

Exploration of the Critical Roles and Molecular Mechanisms of KIF2C and GPX4 Genes in Head and Neck Squamous Cell Carcinoma | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Exploration of the Critical Roles and Molecular Mechanisms of KIF2C and GPX4 Genes in Head and Neck Squamous Cell Carcinoma Xiangwei Ren, Dan Li, Yongbin Di, Yuan Lai, Ziying Ji, Tianke Li, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8729738/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 Head and neck squamous cell carcinoma (HNSCC) is a highly invasive and heterogeneous malignancy with poorly understood molecular mechanisms. This study aims to investigate the potential roles of ferroptosis-related core genes, KIF2C and GPX4, in HNSCC progression and their impact on patient prognosis through bioinformatics analysis. Methods This study utilized the HNSCC gene expression dataset (GSE10774) from the gene expression omnibus (GEO) database to identify differentially expressed genes (DEGs). Weighted gene co-expression network analysis (WGCNA), protein-protein interaction (PPI) network construction, and functional enrichment analysis (GO and KEGG) were performed to identify core genes associated with ferroptosis pathways. Survival analysis was conducted using the The Cancer Genome Atlas (TCGA) database, and heatmaps and comparative toxicogenomics database (CTD) analyses were used to validate the expression patterns and functions of key genes. RT-PCR was performed to verify the expression of hub genes. Results KIF2C and GPX4 were significantly overexpressed in HNSCC tissues and strongly associated with poor prognosis. Functional enrichment analysis revealed that these genes were primarily enriched in ferroptosis, P53 signaling pathways, and MAPK signaling pathways. Further analyses suggested that KIF2C may promote tumor progression by regulating cell division and anti-apoptotic pathways, while GPX4 enhances tumor cell survival by inhibiting ferroptosis. RT-PCR showed that the relative expression of hub genes was differently expressed in cancer cells. Conclusion The overexpression of KIF2C and GPX4 may represent critical molecular mechanisms underlying HNSCC progression and poor prognosis. This study provides new perspectives and potential targets for the diagnosis and targeted therapy of HNSCC. Future studies are required to validate their functions and mechanisms in vitro and in vivo. KIF2C GPX4 head and neck squamous cell carcinoma (HNSCC) ferroptosis molecular mechanisms Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 1 Introduction Head and neck cancer refers to a group of malignancies originating from the oral cavity, pharynx, nasal cavity, and associated structures, with head and neck squamous cell carcinoma (HNSCC) being the most common subtype, accounting for over 90% of cases(Bray et al. 2018 ). Despite significant advancements in surgical, radiotherapeutic, and chemotherapeutic approaches in recent years, the overall survival rate of HNSCC patients has shown limited improvement(Cramer et al. 2019 ; Rettig and D'Souza 2015 ). Studies suggest that its occurrence is influenced by various factors, including environmental exposures such as smoking, alcohol consumption, and HPV infection(Gillison et al. 2000 ; Hashibe et al. 2009 ; Marur and Forastiere 2016 ). Additionally, genetic factors, chromosomal abnormalities, and gene fusions also play critical roles in the onset and progression of the disease(Leemans et al. 2011 ; Seiwert et al. 2015 ; Stransky et al. 2011 ). Therefore, an in-depth exploration of the molecular mechanisms underlying HNSCC could provide new insights and strategies for its early diagnosis, personalized treatment, and prognosis evaluation. In recent years, the application of bioinformatics technologies in cancer research has become increasingly widespread. Through techniques such as gene expression profiling, differential gene screening, protein-protein interaction network construction, and pathway enrichment analysis, bioinformatics allows for efficient and systematic identification of disease-associated potential biomarkers and therapeutic targets(Cancer Genome Atlas Network 2012 ). The advantages of bioinformatics lie in its capacity to handle large datasets, achieve high reproducibility, and rapidly pinpoint core genes and pathways associated with specific diseases. In HNSCC research, bioinformatics not only aids in unraveling the molecular mechanisms of cancer development but also facilitates the discovery of novel therapeutic targets through large-scale genomic and transcriptomic data analysis. Ferroptosis is a form of cell death characterized by the accumulation of iron-dependent lipid peroxides, which is closely associated with the initiation, progression, and treatment resistance of various cancers(Dixon et al. 2012 ; Stockwell et al. 2017 ). Previous studies have suggested that key genes regulating the ferroptosis process may play crucial roles in tumor cell proliferation, apoptosis, and metabolic regulation(Chen et al. 2021 ; Hassannia et al. 2019 ; Li et al. 2020 ). Kinesin Family Member 2C (KIF2C) is a motor protein involved in cell division, and its overexpression in multiple cancers is closely linked to poor prognosis(Lucanus and Yip 2018 ). Glutathione peroxidase 4 (GPX4), a central regulator in the ferroptosis process, is crucial for the survival of tumor cells(Friedmann Angeli et al. 2014 ; Seibt et al. 2019 ). However, the specific relationship between KIF2C, GPX4, and HNSCC remains unclear. Therefore, this study aims to use bioinformatics techniques to comprehensively analyze differentially expressed genes (DEGs) between HNSCC and normal tissues, and identify core genes, KIF2C and GPX4, related to the ferroptosis pathway. The study further performs enrichment analysis and pathway analysis, validating the significant roles of KIF2C and GPX4 in HNSCC through public datasets. Additionally, basic cellular experiments will be conducted to verify their biological functions, with the goal of providing new perspectives and insights for the study of the molecular mechanisms underlying HNSCC. 2 Methods 2.1 HNSCC Dataset In this study, the head and neck cancer dataset GSE10774 was downloaded from the gene expression omnibus (GEO) database ( http://www.ncbi.nlm.nih.gov/geo/ ), which was generated from GPL6569. GSE10774 includes 10 head and neck cancer samples and 4 normal tissue samples, which were used to identify differentially expressed genes (DEGs) in HNSCC. 2.2 DEG Screening (Liang et al. 2024 ) The R package "limma" was used to summarize probes and perform background correction for the gene expression matrix of GSE10774. The Benjamini-Hochberg method was applied to adjust the original p-values. Fold change (FC) was calculated using the false discovery rate (FDR). The cutoff criteria for DEGs were p 1.5. A volcano plot was generated to visualize the DEGs. 2.3 GSEA (Deng and Thompson 2023 ) For Gene Set Enrichment Analysis (GSEA), we obtained the GSEA software (version 3.0) from the GSEA website (DOI: 10.1073/pnas.0506580102 , http://software.broadinstitute.org/gsea/index.jsp ). The samples were divided into two groups based on disease status and normal tissues. We downloaded the c2.cp.kegg.v7.4.symbols.gmt gene set from the Molecular Signatures Database (DOI: 10.1093/bioinformatics/btr260 , http://www.gsea-msigdb.org/gsea/downloads.jsp ) to evaluate related pathways and molecular mechanisms. Based on gene expression profiles and phenotype grouping, the minimum gene set was set to 5 and the maximum gene set to 5000. One thousand resampling iterations were performed, and a P-value < 0.05 and FDR < 0.25 were considered statistically significant. GO and KEGG analyses were also performed on the entire genome, following GSEA guidelines. 2.4 Functional Enrichment Analysis (Sun et al. 2022 ) Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis are computational methods used to assess gene functions and biological pathways. In this study, the list of DEGs identified by limma differential analysis was input into the KEGG REST API ( https://www.kegg.jp/kegg/rest/keggapi.html ) to retrieve the latest KEGG pathway gene annotations. Using this as a background, genes were mapped to the background set, and the R package clusterProfiler (version 3.14.3) was used for enrichment analysis to obtain gene set enrichment results. Additionally, the GO annotations from the R package org.Hs.eg.db (version 3.1.0) were used to map genes to the background set. The minimum gene set was set to 5 and the maximum gene set to 5000. P-value < 0.05 and FDR < 0.25 were considered statistically significant criteria. Moreover, the Metascape database ( http://metascape.org/gp/index.html ) was used for comprehensive gene list annotation and analysis, providing a visual export of the enrichment results for the identified DEGs. 2.5 Weighted Gene Co-expression Network Analysis (WGCNA) (Wang et al. 2024 ) First, we used the batch-corrected gene expression matrix from the HNSCC dataset GSE10774 and calculated the Median Absolute Deviation (MAD) for each gene. Genes with the lowest 50% MAD values were excluded. Outlier genes and samples were removed using the goodSamplesGenes function from the R package WGCNA. A scale-free co-expression network was constructed using WGCNA. Specifically, Pearson correlation matrices and average linkage methods were applied to all paired genes. The weighted adjacency matrix was constructed using the power function Amn=∣Cmn∣βA_{mn} = |C_{mn}|^\betaAmn=∣Cmn∣β, where CmnC_{mn}Cmn represents the Pearson correlation between gene mmm and gene nnn, and AmnA_{mn}Amn represents their adjacency. The parameter β\betaβ, a soft-thresholding power, was selected to emphasize strong correlations and reduce the effects of weak correlations and negative correlations. After choosing a power of 10, the adjacency matrix was transformed into a Topological Overlap Matrix (TOM), which measures network connectivity. Connectivity is defined as the sum of adjacencies between a gene and all other genes, providing the basis for network gene ratios. The corresponding dissimilarity metric (1-TOM) was calculated. Genes with similar expression profiles were classified into gene modules using average linkage hierarchical clustering based on TOM dissimilarity. The minimum module size was set to 30 genes, with a sensitivity setting of 3. Modules with dissimilarity less than 0.25 were merged. The grey module was designated for genes that could not be assigned to any module. 2.6 Protein-Protein Interaction (PPI) Network Construction and Analysis The Search Tool for the Retrieval of Interacting Genes (STRING) database ( http://string-db.org/ ) is designed to collect, score, and integrate all publicly available information on protein-protein interactions, supplemented by computational predictions. The list of differentially expressed genes (DEGs) was input into the STRING database to construct a predicted PPI network for core genes with a confidence score > 0.4. The Cytoscape software was used to provide biological network analysis and two-dimensional visualization. The PPI network obtained from the STRING database was visualized using Cytoscape, and core modules were identified using the MCODE plugin. Furthermore, three algorithms (MCC, DMNC, and MNC) were used to compute the top 10 genes with the highest correlation. The intersecting genes were visualized, and the core gene list was exported. 2.7 Survival Analysis Clinical survival data for HNSCC were obtained from the TCGA database. Using the R package maxstat (version 0.7–25), we calculated the optimal cutoff value for the RiskScore of core genes. The minimum sample size for grouping was set to > 25% of the total, and the maximum sample size for grouping was < 75%. Based on this cutoff value, patients were divided into high-risk (H) and low-risk (L) groups. The survfit function from the R package survival was used to analyze prognosis differences between the two groups. The log-rank test was applied to evaluate the statistical significance of survival differences between the groups. 2.8 Heatmap of Gene Expression The R package heatmap was used to create a heatmap of the expression levels of core genes identified in the PPI network within the HNSCC dataset GSE10774. This visualization highlights the differential expression of core genes between HNSCC and normal tissue samples. 2.9 CTD Analysis The Comparative Toxicogenomics Database (CTD) integrates extensive interaction data between chemicals, genes, functional phenotypes, and diseases, providing a valuable resource for studying disease-related environmental exposures and potential drug mechanisms. Core genes were input into the CTD website, and the most disease-relevant associations were identified. Radar charts illustrating the expression differences of each gene were created using Excel. 2.10 miRNA Analysis To identify miRNAs targeting core genes, we utilized miRNA prediction websites. In our study, the online database miRTarBase (mirtarbase.cuhk.edu.cn) was used to filter miRNAs regulating the core genes. 2.11 RT-PCR Isolate RNA Centrifuge at 12,000 rpm for 10min at 4℃, the white precipitate of the tube bottom was called RNA. The liquid was aspirated and the precipitate was washed by adding 1.5ml of 75% ethanol. Centrifuged at 12000rpm for 5min at 4℃. Aspirated clean the liquid, and the centrifuge tube was placed on the ultra-clean table for 3min. Add 15µl RNA of the dissolution solution to dissolve the RNA. Nanodrop 2000 was used to detect the concentration and purity of RNA. After the instrument blank was set to zero, 2.5µl of RNA solution to be tested was placed on the detection base, the sample arm was put down, and the absorbance value was detected using the software on the computer. First Strand cDNA Synthesis After thawing, mix and briefly centrifuge the components of the kit. Store on ice. Add the following reagents into a sterile, nuclease-free tube on ice in the indicated order: Template RNA 2µg, 5 × Reaction Buffer 4 µL, Gene-specific primer (1.0 µM) 2µL, Servicebio®RT Enzyme Mix 1µL, nuclease-freeWater to 20 µL, Total volume 20 µL. Optional. If the RNA template is GC-rich or contains secondary structures, mix gently, centrifuge briefly and incubate at 65°C for 5 min. Chill on ice, spin down and place the vial back on ice. Mix gently and centrifuge briefly. Incubate for 30 min at 50°C. Terminate the reaction by heating at 85°C for 5 seconds. Preparation of PCR Master Mix For each 15µL reaction, prepare the following reation mix: 2×Universal Blue SYBR Green qPCR Master Mix 7.5µl F/R Primers(2.5µM) 1.5µl cDNA 2.0µl Water Nuclease-Free 4.0µl PCR amplification Stage1 Stage2(40 cycles) Stage3(Melt Curve) 95℃, 30s 95℃, 15s Denaturation 65℃→95℃ Pre-denaturation 60℃, 30s Anealing/Extension The results of processing ΔΔCT method: A = CT (target gene, sample) - CT (internal standard gene, sample) B = CT (target gene, control) - CT (internal standard gene, control) K = A-B RNA Expression = 2 − K 3 Results 3.1 Differentially Expressed Gene (DEG) Analysis In this study, we identified DEGs from the gene expression matrix of GSE10774 using the predetermined cutoff criteria (p < 0.05). A total of 945 DEGs were identified through analysis with the R software, and a volcano plot was generated to visualize the results (Fig. 1 ). 3.2 Functional Enrichment Analysis 3.2.1 GO and KEGG Functional Enrichment Analysis of DEGs GO and KEGG analyses were performed on the identified DEGs. According to GO analysis, the DEGs were mainly enriched in the organic acid catabolic process, condensed chromosome centromeric region, and oxidoreductase activity (Figs. 2 A, B, C). For KEGG pathway analysis, the DEGs were primarily enriched in ferroptosis, MAPK signaling pathway, TNF signaling pathway, P53 signaling pathway, TGF-beta signaling pathway, and HIF-1 signaling pathway (Fig. 2 D). 3.2.2 GSEA To further explore potential enrichment items among non-differentially expressed genes and validate the DEG results, we conducted GSEA. The enrichment results from GSEA were highly consistent with the GO and KEGG results for DEGs, showing enrichment in the P53 signaling pathway, MAPK signaling pathway, and TGF-beta signaling pathway (Figs. 2 E, F, G, H). These findings further corroborate the results of GO and KEGG enrichment analyses. 3.2.3 Metascape Enrichment Analysis In the enrichment results from the Metascape database, GO terms included interleukin signaling, cancer-related pathways, and the PI3K-Akt signaling pathway (Fig. 3 A). Additionally, enrichment networks visualized by term coloring and p-value coloring were generated to illustrate the associations and confidence levels of each enriched term (Figs. 3 B, C, and Fig. 4 ). 3.3 WGCNA The selection of the soft-thresholding power is a critical step in the WGCNA. Network topology analysis was performed to determine the optimal soft-thresholding power, which was set to 12 (Fig. 5 A). A hierarchical clustering tree for all genes was constructed, generating a total of 24 modules (Fig. 5 B). The interactions between important modules were analyzed (Fig. 5 C), and a module-phenotype correlation heatmap was created (Fig. 5 D). Additionally, a scatter plot showing the correlation between the module’s gene significance (GS) and module membership (MM) was generated (Fig. 6 A). The correlation between the module feature vectors and gene expression was calculated to obtain the MM values. Based on the cutoff criterion (|MM| > 0.8), six highly connected gene modules were identified as hub modules, and the genes in these modules were determined as hub genes. A Venn diagram was created by intersecting the hub genes obtained from WGCNA with the DEGs identified earlier, resulting in an intersection of 470 DEGs (Fig. 6 B). These intersecting DEGs were used for subsequent PPI network construction. 3.4 Protein-Protein Interaction (PPI) Network Construction and Analysis The PPI network was constructed using the STRING online database and analyzed using Cytoscape software (Fig. 7 A). Core gene clusters were identified (Fig. 7 B), and the core genes within these clusters were recognized using three algorithms: MCC, DMNC, and MNC (Figs. 7 C, D, E). A Venn diagram was generated to obtain the intersection of the top 10 genes from each algorithm as the core genes (Fig. 7 F). In the end, four core genes were identified: AURKA, KIF2C, FOXM1, and GPX4. 3.5 Survival Analysis Survival data for head and neck cancer from the TCGA database were downloaded and used to construct a prognostic risk score chart. It was observed that as the risk score increased, the survival rate of patients significantly decreased. The survival time and rate of the low-risk group were notably higher than those of the high-risk group (Fig. 8 A). A heatmap of the expression levels of core genes (AURKA, KIF2C, FOXM1, GPX4) in the survival data showed that these core genes are risk factors. As the risk score increased, the expression of these genes showed an upward trend (Fig. 8 B). Furthermore, a box plot of the core genes' expression in HNSCC was generated (Fig. 8 C), with red representing cancer samples (T) and gray representing normal samples (N). The results show that the expression levels of the core genes (AURKA, KIF2C, FOXM1, GPX4) in HNSCC were significantly higher than those in normal tissues, suggesting that these genes may play important roles in the progression of these cancers. 3.6 Heatmap of Core Gene Expression The expression levels of core genes in the HNSCC dataset GSE10774 were visualized using a heatmap (Fig. 9 A). We found that core genes (KIF2C, GPX4) were highly expressed in HNSCC samples and exhibited low expression in normal samples, with significant differences. Based on these results, we hypothesize that core genes (KIF2C, GPX4) may play regulatory roles in HNSCC. 3.7 CTD Analysis In this study, we input the list of core genes into the CTD website to investigate diseases associated with the core genes, enhancing our understanding of gene-disease associations. It was found that the core genes (KIF2C, GPX4) were linked to head and neck squamous cell carcinoma, headaches, infiltrative tumors, leukoplakia, oral ulcers, and pain (Fig. 9 B). This result further supports the association between the core genes and HNSCC. 3.8 miRNA Prediction and Functional Annotation for Hub Genes In this study, we used the miRTarBase database to find relevant miRNAs for the hub genes to enhance our understanding of gene expression regulation (Table 1). We identified that the related miRNAs for KIF2C included hsa-miR-423-3p, hsa-miR-148b-3p, and hsa-miR-124-3p, while the related miRNAs for GPX4 included hsa-miR-26b-5p, hsa-miR-124-3p, and hsa-miR-182-5p. 3.9 The higher expression of hub genes in the HNSCC Compared with control group, the expression of KIF2C was higher in the HNSCC group (P < 0.05). Compared with control group, the expression of GPX4 was higher in the HNSCC group (P < 0.05). And there is no differently expression between control and HNSCC group about the AURKA and FOXM1 (Fig. 10 ). 4 Discussion HNSCC is a highly invasive and heterogeneous cancer, encompassing subtypes such as oral cancer, pharyngeal cancer, and laryngeal cancer(Bray et al. 2018 ; Marur and Forastiere 2016 ). Epidemiological data show that the incidence of HNSCC continues to rise globally, making it one of the most common types of cancer worldwide(Shield et al. 2017 ). The main risk factors for HNSCC include smoking, alcohol consumption, and human papillomavirus infection. HNSCC is characterized by high invasiveness and recurrence, and patients with advanced-stage disease have poor prognosis, with a five-year survival rate still unsatisfactory(Cramer et al. 2019 ). Clinically, HNSCC patients often present with symptoms such as ulcers, pain, difficulty swallowing, hoarseness, and masses, while pathological features typically include epithelial cell atypical proliferation, invasive growth, and lymph node metastasis(Vigneswaran and Williams 2014 ). Understanding the molecular mechanisms underlying HNSCC is of paramount importance for the development of targeted therapies. The main findings of this study show that the KIF2C and GPX4 genes are significantly overexpressed in HNSCC, and their expression levels are inversely correlated with patient prognosis. The high expression of KIF2C and GPX4 may play crucial roles in the progression of HNSCC by influencing key biological processes such as cell proliferation, anti-apoptotic mechanisms, and the regulation of oxidative stress. These findings suggest that KIF2C and GPX4 could serve as potential biomarkers for prognosis and therapeutic targets in HNSCC. KIF2C is a motor protein involved in cell division, playing a crucial role in cell proliferation and chromosome segregation(Wordeman et al. 2007 ). KIF2C regulates microtubule depolymerization to ensure correct chromosome separation during cell division, maintaining the stability of the cell cycle(Desai and Mitchison 1997 ). Recent studies have shown that KIF2C is highly expressed in various cancers, including breast cancer(Lucanus and Yip 2018 ), lung cancer(Bai et al. 2019 ), colorectal cancer(Ishikawa et al. 2008 ), and bladder cancer(Yang et al. 2020 ), with its high expression often associated with poor prognosis. Additionally, abnormal expression of KIF2C in gliomas is closely linked to cell cycle dysregulation and rapid tumor progression(Guan et al. 2022 ). In HNSCC-related research, the potential role of KIF2C has been increasingly highlighted. Bioinformatics analyses of KIF family proteins have revealed that high expression levels are typically associated with enhanced invasiveness and poor prognosis in various cancers(Zhang et al. 2022 ). Furthermore, literature suggests that KIF family proteins may play significant roles in tumor progression by regulating the cell cycle, promoting cell proliferation, and enhancing anti-apoptotic processes, particularly in cancers with high invasiveness. While the specific mechanism of KIF2C in HNSCC remains to be further validated, these studies suggest that KIF2C could serve as a potential biomarker for HNSCC progression and prognosis, with the potential for use as a target for therapeutic intervention. GPX4 is a key regulatory factor in the ferroptosis process, primarily acting by reducing lipid peroxides on the cell membrane, inhibiting lipid peroxidation and cell death(Yang et al. 2014 ). Under physiological conditions, GPX4 plays a critical role in protecting cells from oxidative damage, particularly in combating lipid oxidation induced by ferroptosis(Friedmann Angeli et al. 2014 ; Seibt et al. 2019 ). The role of GPX4 in various cancers has garnered increasing attention, as studies suggest that high expression of GPX4 is closely linked to enhanced antioxidant capacity in tumor cells. For example, in breast cancer and liver cancer, high expression of GPX4 is associated with increased tumor cell tolerance to oxidative stress and poor patient prognosis(Ding et al. 2021 ; He et al. 2021 ; Pan et al. 2024 ; Xu et al. 2023 ). Inhibition of GPX4 has been shown to induce ferroptosis, thereby suppressing tumor cell growth and proliferation, making GPX4 a potential target for anti-cancer therapy. In HNSCC, high expression of GPX4 plays a significant role in tumor cell survival and treatment resistance. One study demonstrated that inhibiting GPX4 function could induce ferroptosis in HNSCC cells and significantly enhance their sensitivity to EGFR-targeted therapies, such as cetuximab(Shin et al. 2018 ). Another study found that GPX4 inhibitors effectively induced lipid peroxidation and ferroptosis in HNSCC cells, thereby reducing tumor cell viability(Jehl et al. 2023 ). Although these studies did not directly investigate the relationship between high GPX4 expression and patient survival, they suggest that GPX4 enhances tumor cell survival by inhibiting ferroptosis, which may impact treatment outcomes and clinical prognosis. Therefore, GPX4 likely plays a key role in the progression and prognosis of HNSCC, and its high expression may be associated with treatment resistance and disease progression. The review of the literature aligns with the results of this study. High expression of the KIF2C and GPX4 genes in various cancers is generally associated with high invasiveness, rapid proliferation, and poor prognosis, which is consistent with our observations in HNSCC. KIF2C may enhance tumor invasiveness by promoting cell proliferation and anti-apoptotic pathways, while GPX4 protects tumor cells by inhibiting ferroptosis, allowing them to survive in oxidative stress environments. These mechanisms together contribute to the poorer prognosis observed in HNSCC patients with high expression of KIF2C and GPX4. However, there are some limitations in this study. First, functional validation experiments, such as overexpression or knockout of KIF2C and GPX4, were not conducted, so the specific mechanisms of these genes in the development of HNSCC remain unclear. Second, this study was based on bioinformatics analysis of publicly available databases, and lacks experimental validation using patient samples. Therefore, future research should combine in vitro and in vivo experiments, using gene editing techniques to further explore the functions of KIF2C and GPX4, providing a theoretical basis for targeted therapy in HNSCC. 5 Conclusion In summary, the high expression of KIF2C and GPX4 in head and neck cancer is closely associated with poor prognosis in patients. KIF2C, as a motor protein involved in cell division, may play a role in promoting tumor cell proliferation and enhancing anti-apoptotic capabilities. GPX4, as a key regulator of ferroptosis, may enhance tumor cell survival by inhibiting ferroptosis. Future research should further explore the molecular mechanisms of KIF2C and GPX4 in HNSCC, with the aim of providing new directions and targets for targeted therapy. Abbreviations Head and neck squamous cell carcinoma (HNSCC) gene expression omnibus (GEO) differentially expressed genes (DEGs) Weighted gene co expression network analysis (WGCNA) protein protein interaction (PPI) The Cancer Genome Atlas (TCGA) comparative toxicogenomics database (CTD) Kinesin Family Member 2C (KIF2C) Glutathione peroxidase 4 (GPX4) fold change (FC) false discovery rate (FDR) Gene Set Enrichment Analysis (GSEA) Gene Ontology (GO) Kyoto Encyclopedia of Genes and Genomes (KEGG) Median Absolute Deviation (MAD) Topological Overlap Matrix (TOM) Search Tool for the Retrieval of Interacting Genes (STRING) Declarations Data Availability The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request. Author Contribution Xiangwei Ren performed experiments mentioned in the paper, contributed to the work concept and design of the paper research. Dan Li and Yongbin Di collected data. Yuan Lai and Ziying Ji made statistical analysis of data, Tianke Li and Jing Wei drafted the manuscript. Xiangwei Ren revised the main content of the manuscript. All authors read and agree on the manuscript Conflicts of Interest The authors have no conflicts of interest to declare Ethical Approval statement The data in this article are from public databases and are exempt from ethical review. Funding No Acknowledgments None References Bai Y, Xiong L, Zhu M, Yang Z, Zhao J, Tang H (2019) Co-expression network analysis identified KIF2C in association with progression and prognosis in lung adenocarcinoma. 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Clin Cancer Res 21(3):632–641. https://doi.org/10.1158/1078-0432.CCR-13-3310 Shield KD, Ferlay J, Jemal A, Sankaranarayanan R, Chaturvedi AK, Bray F et al (2017) The global incidence of lip, oral cavity, and pharyngeal cancers by subsite in 2012. CA Cancer J Clin 67(1):51–64. https://doi.org/10.3322/caac.21384 Shin D, Kim EH, Lee J, Roh JL (2018) Nrf2 inhibition reverses resistance to GPX4 inhibitor-induced ferroptosis in head and neck cancer. Free Radic Biol Med 129:454–462. https://doi.org/10.1016/j.freeradbiomed.2018.10.426 Stockwell BR, Friedmann Angeli JP, Bayir H, Bush AI, Conrad M, Dixon SJ et al (2017) Ferroptosis: A Regulated Cell Death Nexus Linking Metabolism, Redox Biology, and Disease. Cell 171(2):273–285. https://doi.org/10.1016/j.cell.2017.09.021 Stransky N, Egloff AM, Tward AD, Kostic AD, Cibulskis K, Sivachenko A et al (2011) The mutational landscape of head and neck squamous cell carcinoma. Science 333(6046):1157–1160. https://doi.org/10.1126/science.1208130 Sun Q, Bai L, Zhu S, Cheng L, Xu Y, Cai YD et al (2022) Analysis of Lymphoma-Related Genes with Gene Ontology and Kyoto Encyclopedia of Genes and Genomes Enrichment. Biomed Res Int 2022):8503511. https://doi.org/10.1155/2022/8503511 Vigneswaran N, Williams MD (2014) Epidemiologic trends in head and neck cancer and aids in diagnosis. Oral Maxillofac Surg Clin North Am 26(2):123–141. https://doi.org/10.1016/j.coms.2014.01.001 Wang H, Cheng W, Hu P, Ling T, Hu C, Chen Y et al (2024) Integrative analysis identifies oxidative stress biomarkers in non-alcoholic fatty liver disease via machine learning and weighted gene co-expression network analysis. Front Immunol 15:1335112. https://doi.org/10.3389/fimmu.2024.1335112 Wordeman L, Wagenbach M, von Dassow G (2007) MCAK facilitates chromosome movement by promoting kinetochore microtubule turnover. J Cell Biol 179(5):869–879. https://doi.org/10.1083/jcb.200707120 Xu Z, Wang X, Sun W, Xu F, Kou H, Hu W et al (2023) RelB-activated GPX4 inhibits ferroptosis and confers tamoxifen resistance in breast cancer. Redox Biol 68:102952. https://doi.org/10.1016/j.redox.2023.102952 Yang C, Li Q, Chen X, Zhang Z, Mou Z, Ye F et al (2020) Circular RNA circRGNEF promotes bladder cancer progression via miR-548/KIF2C axis regulation. Aging 12(8):6865–6879. https://doi.org/10.18632/aging.103047 Yang WS, SriRamaratnam R, Welsch ME, Shimada K, Skouta R, Viswanathan VS et al (2014) Regulation of ferroptotic cancer cell death by GPX4. Cell 156(1–2):317–331. https://doi.org/10.1016/j.cell.2013.12.010 Zhang X, Li Y, Hu P, Xu L, Qiu H (2022) KIF2C is a Biomarker Correlated With Prognosis and Immunosuppressive Microenvironment in Human Tumors. Front Genet 13:891408. https://doi.org/10.3389/fgene.2022.891408 Table 1 Table.1 A summary of miRNAs that regulate hub genes Gene MIRNA 1 KIF2C hsa-miR-423-3p hsa-miR-148b-3p hsa-miR-124-3p 2 GPX4 hsa-miR-26b-5p hsa-miR-124-3p hsa-miR-182-5p Additional Declarations No competing interests reported. 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8729738","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":592888366,"identity":"0f2c369c-5b50-4375-9e27-d465f6d03dcb","order_by":0,"name":"Xiangwei Ren","email":"","orcid":"","institution":"The First Hospital of Hebei Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiangwei","middleName":"","lastName":"Ren","suffix":""},{"id":592888368,"identity":"4187806e-b7a4-4c07-ba41-04e3d0057e74","order_by":1,"name":"Dan Li","email":"","orcid":"","institution":"The First Hospital of Hebei Medical University","correspondingAuthor":false,"prefix":"","firstName":"Dan","middleName":"","lastName":"Li","suffix":""},{"id":592888371,"identity":"b4c593b9-6525-4acb-84ab-ee6cbe961bc7","order_by":2,"name":"Yongbin Di","email":"","orcid":"","institution":"The First Hospital of Hebei Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yongbin","middleName":"","lastName":"Di","suffix":""},{"id":592888373,"identity":"0eefd5a4-111f-4a48-b6a4-7950a1fe9749","order_by":3,"name":"Yuan Lai","email":"","orcid":"","institution":"The Fourth Hospital of Hebei Medical University.","correspondingAuthor":false,"prefix":"","firstName":"Yuan","middleName":"","lastName":"Lai","suffix":""},{"id":592888375,"identity":"5c9f0310-9f79-4774-8500-a07487a0b88c","order_by":4,"name":"Ziying Ji","email":"","orcid":"","institution":"The Fourth Hospital of Hebei Medical University.","correspondingAuthor":false,"prefix":"","firstName":"Ziying","middleName":"","lastName":"Ji","suffix":""},{"id":592888376,"identity":"98d52927-4565-42bf-adb9-588a75760748","order_by":5,"name":"Tianke Li","email":"","orcid":"","institution":"The Fourth Hospital of Hebei Medical University.","correspondingAuthor":false,"prefix":"","firstName":"Tianke","middleName":"","lastName":"Li","suffix":""},{"id":592888377,"identity":"efa38a3d-5d28-4390-b467-3c826a70b98f","order_by":6,"name":"Jing Wei","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5ElEQVRIiWNgGAWjYHCChAMMDGz1/AxnGB8kVNQQrYUvQbLhDLPBgzPHiLZJLsGggYdN8mELM2G1BjcSHh4u+GWWZ8B49lhFYgMbA397dwIhLQmHZ/alFZsznEu7kbhDhkHizNkNhLXw9hxj3NlwxuxG4hk2BgOJXKK0/GfccOCMWUFiGzORWnh+sCWCtDAQpUXyzAOgLQ1sxsBANpZIOHOMh6Bf+I7nJH/m+cMmxy9xxvDjj4oaOf72XvxaFA7wJDAwtgFZEgfAAjx4lYOAfAM7UOkfIIu/gaDiUTAKRsEoGKEAAI92Ve0KWbDeAAAAAElFTkSuQmCC","orcid":"","institution":"The First Hospital of Hebei Medical University","correspondingAuthor":true,"prefix":"","firstName":"Jing","middleName":"","lastName":"Wei","suffix":""}],"badges":[],"createdAt":"2026-01-29 09:51:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8729738/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8729738/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103200873,"identity":"a6c81e69-0167-4732-be83-2c7b9d0862e2","added_by":"auto","created_at":"2026-02-23 05:56:05","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1395920,"visible":true,"origin":"","legend":"\u003cp\u003eDifferential gene analysis. A total of 945 DEGs.\u003c/p\u003e","description":"","filename":"figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8729738/v1/b278b8f5c22627e1cbc5ea71.jpg"},{"id":103505099,"identity":"194e4afc-abc0-4873-9f5a-c978b31efddf","added_by":"auto","created_at":"2026-02-26 13:23:46","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":4344620,"visible":true,"origin":"","legend":"\u003cp\u003e(A-D) GOKEGG enrichment analysis of DEGs. (A) Biological process analysis. (B) Cellular component analysis. (C) Molecular function analysis. (D) KEGG enrichment analysis. (E-H) GSEA enrichment analysis of DEGs. (E) Biological process analysis. (F) Cellular component analysis. (G) Molecular function analysis. (H) KEGG enrichment analysis.\u003c/p\u003e","description":"","filename":"figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8729738/v1/f636d403aefe1467c74c74b4.jpg"},{"id":103200889,"identity":"adaab1b5-0563-4cd3-99a8-574265036f0c","added_by":"auto","created_at":"2026-02-23 05:56:14","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":3877089,"visible":true,"origin":"","legend":"\u003cp\u003eMetascape enrichment analysis. (A) Bar graph of enriched terms across input gene lists, colored by p-values. (B) Network of enriched terms: colored by cluster ID, where nodes that share the same cluster ID are typically close to each other. (C) Colored by p-value, where terms containing more genes tend to have a more significant p-value.\u003c/p\u003e","description":"","filename":"figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8729738/v1/9457f7cccfb94bc9907f192a.jpg"},{"id":103200886,"identity":"9427fca6-0717-45ae-91e9-5fe1ef3285aa","added_by":"auto","created_at":"2026-02-23 05:56:12","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":3307067,"visible":true,"origin":"","legend":"\u003cp\u003eProtein-protein interaction network. And MCODE components identified in the gene lists.\u003c/p\u003e","description":"","filename":"figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8729738/v1/d127aa3cd02f3fce3f991491.jpg"},{"id":103200891,"identity":"ef2ddc79-b167-49cc-900d-57c9f409cc36","added_by":"auto","created_at":"2026-02-23 05:56:14","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":5314485,"visible":true,"origin":"","legend":"\u003cp\u003eWGCNA. (A) β = 12,0.86. β = 12, 10.28. (B, C) The hierarchical clustering tree of all genes was constructed, and 24 important modules were generated. (D) The heat map of correlation between modules and phenotypes.\u003c/p\u003e","description":"","filename":"figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8729738/v1/72eace3f0869f92ea2283e20.jpg"},{"id":103200894,"identity":"28a9a660-ec4e-4da4-aee1-15d268d91cea","added_by":"auto","created_at":"2026-02-23 05:56:15","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":2593579,"visible":true,"origin":"","legend":"\u003cp\u003e(A) The scatter map of correlation between GS and MM of related hub genes. (B) The DEGs screened by WGCNA and DEGs was used to obtain Venn map 470 intersection genes were obtained.\u003c/p\u003e","description":"","filename":"figure6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8729738/v1/3d9e35aa77082d5a363f7c3b.jpg"},{"id":103200862,"identity":"bb5d9ec1-dbf6-4956-a0fd-298bccdeb783","added_by":"auto","created_at":"2026-02-23 05:56:02","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":6280741,"visible":true,"origin":"","legend":"\u003cp\u003eConstruction and analysis of protein-protein interaction (PPI) networks. (A) PPI network of DEGs. (B) CLUSTER was used to identify the central gene. (C) MCC was used to identify the central gene. (D) DMNC was used to identify the central gene. (E) MNC was used to identify the central gene. (F) Four core genes (AURKA, KIF2C, FOXM1, GPX4) were obtained by merging using Venn diagrams.\u003c/p\u003e","description":"","filename":"figure7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8729738/v1/49444cbe4e77a4de85b63af4.jpg"},{"id":103200895,"identity":"893af9cf-be6b-4450-be2f-33d1641c59ff","added_by":"auto","created_at":"2026-02-23 05:56:15","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":3030770,"visible":true,"origin":"","legend":"\u003cp\u003eSurvival analysis. (A) Effect of GSE10774 on survival time and survival in patients with head and neck cancer. (B) Heatmap of GSE10774 core gene expression inhead and neck cancer survival data. (C) Box plot of core genes in head and neck cancer.\u003c/p\u003e","description":"","filename":"figure8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8729738/v1/5db4268b8503843067b82883.jpg"},{"id":103200898,"identity":"b1c58364-ad3a-43e4-a645-2e6e76e79f87","added_by":"auto","created_at":"2026-02-23 05:56:16","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":1604317,"visible":true,"origin":"","legend":"\u003cp\u003e(A)The heatmap depicting the expression levels of differentially expressed genes related to ferroptosis in the merged matrix of GSE10774 datasets. (B) CTD analysis. Two core genes (KIF2C, GPX4) are closely related to the occurrence of head and neck squamous cell carcinoma, headache, invasive tumor, leukoplakia, oral ulcer and pain.\u003c/p\u003e","description":"","filename":"figure9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8729738/v1/ab0292c5e4c582b970ceaae9.jpg"},{"id":103200870,"identity":"a674ba53-841d-40d9-a203-af40f51f4b24","added_by":"auto","created_at":"2026-02-23 05:56:04","extension":"jpg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":516545,"visible":true,"origin":"","legend":"\u003cp\u003eThe expression of KIF2C, GPX4 via the RT-PCR.\u003c/p\u003e","description":"","filename":"figure10.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8729738/v1/823de02185151d5faccc2ea4.jpg"},{"id":104397869,"identity":"b46fc882-5523-498b-9791-a8d197449d6d","added_by":"auto","created_at":"2026-03-11 11:58:22","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":33246019,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8729738/v1/af32d585-60cc-4dea-a18b-c479c539e02d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Exploration of the Critical Roles and Molecular Mechanisms of KIF2C and GPX4 Genes in Head and Neck Squamous Cell Carcinoma","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eHead and neck cancer refers to a group of malignancies originating from the oral cavity, pharynx, nasal cavity, and associated structures, with head and neck squamous cell carcinoma (HNSCC) being the most common subtype, accounting for over 90% of cases(Bray et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Despite significant advancements in surgical, radiotherapeutic, and chemotherapeutic approaches in recent years, the overall survival rate of HNSCC patients has shown limited improvement(Cramer et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Rettig and D'Souza \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Studies suggest that its occurrence is influenced by various factors, including environmental exposures such as smoking, alcohol consumption, and HPV infection(Gillison et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Hashibe et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Marur and Forastiere \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Additionally, genetic factors, chromosomal abnormalities, and gene fusions also play critical roles in the onset and progression of the disease(Leemans et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Seiwert et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Stransky et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Therefore, an in-depth exploration of the molecular mechanisms underlying HNSCC could provide new insights and strategies for its early diagnosis, personalized treatment, and prognosis evaluation.\u003c/p\u003e \u003cp\u003eIn recent years, the application of bioinformatics technologies in cancer research has become increasingly widespread. Through techniques such as gene expression profiling, differential gene screening, protein-protein interaction network construction, and pathway enrichment analysis, bioinformatics allows for efficient and systematic identification of disease-associated potential biomarkers and therapeutic targets(Cancer Genome Atlas Network \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The advantages of bioinformatics lie in its capacity to handle large datasets, achieve high reproducibility, and rapidly pinpoint core genes and pathways associated with specific diseases. In HNSCC research, bioinformatics not only aids in unraveling the molecular mechanisms of cancer development but also facilitates the discovery of novel therapeutic targets through large-scale genomic and transcriptomic data analysis.\u003c/p\u003e \u003cp\u003eFerroptosis is a form of cell death characterized by the accumulation of iron-dependent lipid peroxides, which is closely associated with the initiation, progression, and treatment resistance of various cancers(Dixon et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Stockwell et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Previous studies have suggested that key genes regulating the ferroptosis process may play crucial roles in tumor cell proliferation, apoptosis, and metabolic regulation(Chen et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Hassannia et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Li et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Kinesin Family Member 2C (KIF2C) is a motor protein involved in cell division, and its overexpression in multiple cancers is closely linked to poor prognosis(Lucanus and Yip \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Glutathione peroxidase 4 (GPX4), a central regulator in the ferroptosis process, is crucial for the survival of tumor cells(Friedmann Angeli et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Seibt et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). However, the specific relationship between KIF2C, GPX4, and HNSCC remains unclear.\u003c/p\u003e \u003cp\u003eTherefore, this study aims to use bioinformatics techniques to comprehensively analyze differentially expressed genes (DEGs) between HNSCC and normal tissues, and identify core genes, KIF2C and GPX4, related to the ferroptosis pathway. The study further performs enrichment analysis and pathway analysis, validating the significant roles of KIF2C and GPX4 in HNSCC through public datasets. Additionally, basic cellular experiments will be conducted to verify their biological functions, with the goal of providing new perspectives and insights for the study of the molecular mechanisms underlying HNSCC.\u003c/p\u003e"},{"header":"2 Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 HNSCC Dataset\u003c/h2\u003e \u003cp\u003eIn this study, the head and neck cancer dataset GSE10774 was downloaded from the gene expression omnibus (GEO) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.ncbi.nlm.nih.gov/geo/\u003c/span\u003e\u003cspan address=\"http://www.ncbi.nlm.nih.gov/geo/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), which was generated from GPL6569. GSE10774 includes 10 head and neck cancer samples and 4 normal tissue samples, which were used to identify differentially expressed genes (DEGs) in HNSCC.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003e2.2 DEG Screening\u003c/b\u003e(Liang et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)\u003c/h2\u003e \u003cp\u003eThe R package \"limma\" was used to summarize probes and perform background correction for the gene expression matrix of GSE10774. The Benjamini-Hochberg method was applied to adjust the original p-values. Fold change (FC) was calculated using the false discovery rate (FDR). The cutoff criteria for DEGs were p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and FC\u0026thinsp;\u0026gt;\u0026thinsp;1.5. A volcano plot was generated to visualize the DEGs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003e2.3 GSEA\u003c/b\u003e(Deng and Thompson \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)\u003c/h2\u003e \u003cp\u003eFor Gene Set Enrichment Analysis (GSEA), we obtained the GSEA software (version 3.0) from the GSEA website (DOI: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1073/pnas.0506580102\u003c/span\u003e\u003cspan address=\"10.1073/pnas.0506580102\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://software.broadinstitute.org/gsea/index.jsp\u003c/span\u003e\u003cspan address=\"http://software.broadinstitute.org/gsea/index.jsp\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The samples were divided into two groups based on disease status and normal tissues. We downloaded the c2.cp.kegg.v7.4.symbols.gmt gene set from the Molecular Signatures Database (DOI: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/bioinformatics/btr260\u003c/span\u003e\u003cspan address=\"10.1093/bioinformatics/btr260\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.gsea-msigdb.org/gsea/downloads.jsp\u003c/span\u003e\u003cspan address=\"http://www.gsea-msigdb.org/gsea/downloads.jsp\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to evaluate related pathways and molecular mechanisms. Based on gene expression profiles and phenotype grouping, the minimum gene set was set to 5 and the maximum gene set to 5000. One thousand resampling iterations were performed, and a P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.25 were considered statistically significant. GO and KEGG analyses were also performed on the entire genome, following GSEA guidelines.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003e2.4 Functional Enrichment Analysis\u003c/b\u003e(Sun et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/h2\u003e \u003cp\u003eGene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis are computational methods used to assess gene functions and biological pathways. In this study, the list of DEGs identified by limma differential analysis was input into the KEGG REST API (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.kegg.jp/kegg/rest/keggapi.html\u003c/span\u003e\u003cspan address=\"https://www.kegg.jp/kegg/rest/keggapi.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to retrieve the latest KEGG pathway gene annotations. Using this as a background, genes were mapped to the background set, and the R package clusterProfiler (version 3.14.3) was used for enrichment analysis to obtain gene set enrichment results. Additionally, the GO annotations from the R package org.Hs.eg.db (version 3.1.0) were used to map genes to the background set. The minimum gene set was set to 5 and the maximum gene set to 5000. P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.25 were considered statistically significant criteria.\u003c/p\u003e \u003cp\u003eMoreover, the Metascape database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://metascape.org/gp/index.html\u003c/span\u003e\u003cspan address=\"http://metascape.org/gp/index.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used for comprehensive gene list annotation and analysis, providing a visual export of the enrichment results for the identified DEGs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003e2.5 Weighted Gene Co-expression Network Analysis (WGCNA)\u003c/b\u003e(Wang et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)\u003c/h2\u003e \u003cp\u003eFirst, we used the batch-corrected gene expression matrix from the HNSCC dataset GSE10774 and calculated the Median Absolute Deviation (MAD) for each gene. Genes with the lowest 50% MAD values were excluded. Outlier genes and samples were removed using the goodSamplesGenes function from the R package WGCNA. A scale-free co-expression network was constructed using WGCNA. Specifically, Pearson correlation matrices and average linkage methods were applied to all paired genes. The weighted adjacency matrix was constructed using the power function Amn=∣Cmn∣βA_{mn} = |C_{mn}|^\\betaAmn=∣Cmn∣β, where CmnC_{mn}Cmn represents the Pearson correlation between gene mmm and gene nnn, and AmnA_{mn}Amn represents their adjacency.\u003c/p\u003e \u003cp\u003eThe parameter β\\betaβ, a soft-thresholding power, was selected to emphasize strong correlations and reduce the effects of weak correlations and negative correlations. After choosing a power of 10, the adjacency matrix was transformed into a Topological Overlap Matrix (TOM), which measures network connectivity. Connectivity is defined as the sum of adjacencies between a gene and all other genes, providing the basis for network gene ratios. The corresponding dissimilarity metric (1-TOM) was calculated. Genes with similar expression profiles were classified into gene modules using average linkage hierarchical clustering based on TOM dissimilarity. The minimum module size was set to 30 genes, with a sensitivity setting of 3. Modules with dissimilarity less than 0.25 were merged. The grey module was designated for genes that could not be assigned to any module.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Protein-Protein Interaction (PPI) Network Construction and Analysis\u003c/h2\u003e \u003cp\u003eThe Search Tool for the Retrieval of Interacting Genes (STRING) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://string-db.org/\u003c/span\u003e\u003cspan address=\"http://string-db.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) is designed to collect, score, and integrate all publicly available information on protein-protein interactions, supplemented by computational predictions. The list of differentially expressed genes (DEGs) was input into the STRING database to construct a predicted PPI network for core genes with a confidence score\u0026thinsp;\u0026gt;\u0026thinsp;0.4.\u003c/p\u003e \u003cp\u003eThe Cytoscape software was used to provide biological network analysis and two-dimensional visualization. The PPI network obtained from the STRING database was visualized using Cytoscape, and core modules were identified using the MCODE plugin. Furthermore, three algorithms (MCC, DMNC, and MNC) were used to compute the top 10 genes with the highest correlation. The intersecting genes were visualized, and the core gene list was exported.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Survival Analysis\u003c/h2\u003e \u003cp\u003eClinical survival data for HNSCC were obtained from the TCGA database. Using the R package maxstat (version 0.7\u0026ndash;25), we calculated the optimal cutoff value for the RiskScore of core genes. The minimum sample size for grouping was set to \u0026gt;\u0026thinsp;25% of the total, and the maximum sample size for grouping was \u0026lt;\u0026thinsp;75%. Based on this cutoff value, patients were divided into high-risk (H) and low-risk (L) groups. The survfit function from the R package survival was used to analyze prognosis differences between the two groups. The log-rank test was applied to evaluate the statistical significance of survival differences between the groups.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8 Heatmap of Gene Expression\u003c/h2\u003e \u003cp\u003eThe R package heatmap was used to create a heatmap of the expression levels of core genes identified in the PPI network within the HNSCC dataset GSE10774. This visualization highlights the differential expression of core genes between HNSCC and normal tissue samples.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.9 CTD Analysis\u003c/h2\u003e \u003cp\u003eThe Comparative Toxicogenomics Database (CTD) integrates extensive interaction data between chemicals, genes, functional phenotypes, and diseases, providing a valuable resource for studying disease-related environmental exposures and potential drug mechanisms. Core genes were input into the CTD website, and the most disease-relevant associations were identified. Radar charts illustrating the expression differences of each gene were created using Excel.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.10 miRNA Analysis\u003c/h2\u003e \u003cp\u003eTo identify miRNAs targeting core genes, we utilized miRNA prediction websites. In our study, the online database miRTarBase (mirtarbase.cuhk.edu.cn) was used to filter miRNAs regulating the core genes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e2.11 RT-PCR\u003c/h2\u003e \u003cp\u003e \u003cb\u003eIsolate RNA\u003c/b\u003e \u003c/p\u003e \u003cp\u003eCentrifuge at 12,000 rpm for 10min at 4℃, the white precipitate of the tube bottom was called RNA.\u003c/p\u003e \u003cp\u003eThe liquid was aspirated and the precipitate was washed by adding 1.5ml of 75% ethanol.\u003c/p\u003e \u003cp\u003eCentrifuged at 12000rpm for 5min at 4℃.\u003c/p\u003e \u003cp\u003eAspirated clean the liquid, and the centrifuge tube was placed on the ultra-clean table for 3min.\u003c/p\u003e \u003cp\u003eAdd 15\u0026micro;l RNA of the dissolution solution to dissolve the RNA.\u003c/p\u003e \u003cp\u003eNanodrop 2000 was used to detect the concentration and purity of RNA. After the instrument blank was set to zero, 2.5\u0026micro;l of RNA solution to be tested was placed on the detection base, the sample arm was put down, and the absorbance value was detected using the software on the computer.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFirst Strand cDNA Synthesis\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAfter thawing, mix and briefly centrifuge the components of the kit. Store on ice.\u003c/p\u003e \u003cp\u003eAdd the following reagents into a sterile, nuclease-free tube on ice in the indicated order:\u003c/p\u003e \u003cp\u003eTemplate RNA 2\u0026micro;g, 5 \u0026times; Reaction Buffer 4 \u0026micro;L, Gene-specific primer (1.0 \u0026micro;M) 2\u0026micro;L, Servicebio\u0026reg;RT Enzyme Mix 1\u0026micro;L, nuclease-freeWater to 20 \u0026micro;L, Total volume 20 \u0026micro;L.\u003c/p\u003e \u003cp\u003eOptional. If the RNA template is GC-rich or contains secondary structures, mix gently, centrifuge briefly and incubate at 65\u0026deg;C for 5 min. Chill on ice, spin down and place the vial back on ice.\u003c/p\u003e \u003cp\u003eMix gently and centrifuge briefly.\u003c/p\u003e \u003cp\u003eIncubate for 30 min at 50\u0026deg;C.\u003c/p\u003e \u003cp\u003eTerminate the reaction by heating at 85\u0026deg;C for 5 seconds.\u003c/p\u003e \u003cp\u003e \u003cb\u003ePreparation of PCR Master Mix\u003c/b\u003e \u003c/p\u003e \u003cp\u003eFor each 15\u0026micro;L reaction, prepare the following reation mix:\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e2×Universal Blue SYBR Green qPCR Master Mix 7.5µl\u003c/h3\u003e\n\u003cp\u003eF/R Primers(2.5\u0026micro;M) 1.5\u0026micro;l\u003c/p\u003e \u003cp\u003ecDNA 2.0\u0026micro;l\u003c/p\u003e \u003cp\u003eWater Nuclease-Free 4.0\u0026micro;l\u003c/p\u003e \u003cp\u003ePCR amplification\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStage2(40 cycles)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStage3(Melt Curve)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e95℃, 30s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e95℃, 15s Denaturation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65℃\u0026rarr;95℃\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePre-denaturation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60℃, 30s Anealing/Extension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eThe results of processing\u003c/b\u003e \u003c/p\u003e \u003cp\u003eΔΔCT method:\u003c/p\u003e \u003cp\u003eA\u0026thinsp;=\u0026thinsp;CT (target gene, sample) - CT (internal standard gene, sample)\u003c/p\u003e \u003cp\u003eB\u0026thinsp;=\u0026thinsp;CT (target gene, control) - CT (internal standard gene, control)\u003c/p\u003e \u003cp\u003eK\u0026thinsp;=\u0026thinsp;A-B\u003c/p\u003e \u003cp\u003eRNA Expression\u0026thinsp;=\u0026thinsp;2\u003csup\u003e\u0026minus;\u0026thinsp;K\u003c/sup\u003e\u003c/p\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Differentially Expressed Gene (DEG) Analysis\u003c/h2\u003e \u003cp\u003eIn this study, we identified DEGs from the gene expression matrix of GSE10774 using the predetermined cutoff criteria (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). A total of 945 DEGs were identified through analysis with the R software, and a volcano plot was generated to visualize the results (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Functional Enrichment Analysis\u003c/h2\u003e \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1 GO and KEGG Functional Enrichment Analysis of DEGs\u003c/h2\u003e \u003cp\u003eGO and KEGG analyses were performed on the identified DEGs. According to GO analysis, the DEGs were mainly enriched in the organic acid catabolic process, condensed chromosome centromeric region, and oxidoreductase activity (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, B, C).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFor KEGG pathway analysis, the DEGs were primarily enriched in ferroptosis, MAPK signaling pathway, TNF signaling pathway, P53 signaling pathway, TGF-beta signaling pathway, and HIF-1 signaling pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003e3.2.2 GSEA\u003c/h2\u003e \u003cp\u003eTo further explore potential enrichment items among non-differentially expressed genes and validate the DEG results, we conducted GSEA. The enrichment results from GSEA were highly consistent with the GO and KEGG results for DEGs, showing enrichment in the P53 signaling pathway, MAPK signaling pathway, and TGF-beta signaling pathway (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE, F, G, H). These findings further corroborate the results of GO and KEGG enrichment analyses.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section3\"\u003e \u003ch2\u003e3.2.3 Metascape Enrichment Analysis\u003c/h2\u003e \u003cp\u003eIn the enrichment results from the Metascape database, GO terms included interleukin signaling, cancer-related pathways, and the PI3K-Akt signaling pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Additionally, enrichment networks visualized by term coloring and p-value coloring were generated to illustrate the associations and confidence levels of each enriched term (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB, C, and Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e3.3 WGCNA\u003c/h2\u003e \u003cp\u003eThe selection of the soft-thresholding power is a critical step in the WGCNA. Network topology analysis was performed to determine the optimal soft-thresholding power, which was set to 12 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). A hierarchical clustering tree for all genes was constructed, generating a total of 24 modules (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). The interactions between important modules were analyzed (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC), and a module-phenotype correlation heatmap was created (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). Additionally, a scatter plot showing the correlation between the module\u0026rsquo;s gene significance (GS) and module membership (MM) was generated (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). The correlation between the module feature vectors and gene expression was calculated to obtain the MM values. Based on the cutoff criterion (|MM| \u0026gt; 0.8), six highly connected gene modules were identified as hub modules, and the genes in these modules were determined as hub genes.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eA Venn diagram was created by intersecting the hub genes obtained from WGCNA with the DEGs identified earlier, resulting in an intersection of 470 DEGs (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). These intersecting DEGs were used for subsequent PPI network construction.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Protein-Protein Interaction (PPI) Network Construction and Analysis\u003c/h2\u003e \u003cp\u003eThe PPI network was constructed using the STRING online database and analyzed using Cytoscape software (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). Core gene clusters were identified (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB), and the core genes within these clusters were recognized using three algorithms: MCC, DMNC, and MNC (Figs.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC, D, E). A Venn diagram was generated to obtain the intersection of the top 10 genes from each algorithm as the core genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eF). In the end, four core genes were identified: AURKA, KIF2C, FOXM1, and GPX4.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Survival Analysis\u003c/h2\u003e \u003cp\u003eSurvival data for head and neck cancer from the TCGA database were downloaded and used to construct a prognostic risk score chart. It was observed that as the risk score increased, the survival rate of patients significantly decreased. The survival time and rate of the low-risk group were notably higher than those of the high-risk group (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eA heatmap of the expression levels of core genes (AURKA, KIF2C, FOXM1, GPX4) in the survival data showed that these core genes are risk factors. As the risk score increased, the expression of these genes showed an upward trend (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eB). Furthermore, a box plot of the core genes' expression in HNSCC was generated (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eC), with red representing cancer samples (T) and gray representing normal samples (N). The results show that the expression levels of the core genes (AURKA, KIF2C, FOXM1, GPX4) in HNSCC were significantly higher than those in normal tissues, suggesting that these genes may play important roles in the progression of these cancers.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Heatmap of Core Gene Expression\u003c/h2\u003e \u003cp\u003eThe expression levels of core genes in the HNSCC dataset GSE10774 were visualized using a heatmap (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eA). We found that core genes (KIF2C, GPX4) were highly expressed in HNSCC samples and exhibited low expression in normal samples, with significant differences. Based on these results, we hypothesize that core genes (KIF2C, GPX4) may play regulatory roles in HNSCC.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e3.7 CTD Analysis\u003c/h2\u003e \u003cp\u003eIn this study, we input the list of core genes into the CTD website to investigate diseases associated with the core genes, enhancing our understanding of gene-disease associations. It was found that the core genes (KIF2C, GPX4) were linked to head and neck squamous cell carcinoma, headaches, infiltrative tumors, leukoplakia, oral ulcers, and pain (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eB). This result further supports the association between the core genes and HNSCC.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003e3.8 miRNA Prediction and Functional Annotation for Hub Genes\u003c/h2\u003e \u003cp\u003eIn this study, we used the miRTarBase database to find relevant miRNAs for the hub genes to enhance our understanding of gene expression regulation (Table\u0026nbsp;1). We identified that the related miRNAs for KIF2C included hsa-miR-423-3p, hsa-miR-148b-3p, and hsa-miR-124-3p, while the related miRNAs for GPX4 included hsa-miR-26b-5p, hsa-miR-124-3p, and hsa-miR-182-5p.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section2\"\u003e \u003ch2\u003e3.9 The higher expression of hub genes in the HNSCC\u003c/h2\u003e \u003cp\u003eCompared with control group, the expression of KIF2C was higher in the HNSCC group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Compared with control group, the expression of GPX4 was higher in the HNSCC group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). And there is no differently expression between control and HNSCC group about the AURKA and FOXM1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eHNSCC is a highly invasive and heterogeneous cancer, encompassing subtypes such as oral cancer, pharyngeal cancer, and laryngeal cancer(Bray et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Marur and Forastiere \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Epidemiological data show that the incidence of HNSCC continues to rise globally, making it one of the most common types of cancer worldwide(Shield et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The main risk factors for HNSCC include smoking, alcohol consumption, and human papillomavirus infection. HNSCC is characterized by high invasiveness and recurrence, and patients with advanced-stage disease have poor prognosis, with a five-year survival rate still unsatisfactory(Cramer et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Clinically, HNSCC patients often present with symptoms such as ulcers, pain, difficulty swallowing, hoarseness, and masses, while pathological features typically include epithelial cell atypical proliferation, invasive growth, and lymph node metastasis(Vigneswaran and Williams \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Understanding the molecular mechanisms underlying HNSCC is of paramount importance for the development of targeted therapies.\u003c/p\u003e \u003cp\u003eThe main findings of this study show that the KIF2C and GPX4 genes are significantly overexpressed in HNSCC, and their expression levels are inversely correlated with patient prognosis. The high expression of KIF2C and GPX4 may play crucial roles in the progression of HNSCC by influencing key biological processes such as cell proliferation, anti-apoptotic mechanisms, and the regulation of oxidative stress. These findings suggest that KIF2C and GPX4 could serve as potential biomarkers for prognosis and therapeutic targets in HNSCC.\u003c/p\u003e \u003cp\u003eKIF2C is a motor protein involved in cell division, playing a crucial role in cell proliferation and chromosome segregation(Wordeman et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). KIF2C regulates microtubule depolymerization to ensure correct chromosome separation during cell division, maintaining the stability of the cell cycle(Desai and Mitchison \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). Recent studies have shown that KIF2C is highly expressed in various cancers, including breast cancer(Lucanus and Yip \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), lung cancer(Bai et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), colorectal cancer(Ishikawa et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), and bladder cancer(Yang et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), with its high expression often associated with poor prognosis. Additionally, abnormal expression of KIF2C in gliomas is closely linked to cell cycle dysregulation and rapid tumor progression(Guan et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn HNSCC-related research, the potential role of KIF2C has been increasingly highlighted. Bioinformatics analyses of KIF family proteins have revealed that high expression levels are typically associated with enhanced invasiveness and poor prognosis in various cancers(Zhang et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Furthermore, literature suggests that KIF family proteins may play significant roles in tumor progression by regulating the cell cycle, promoting cell proliferation, and enhancing anti-apoptotic processes, particularly in cancers with high invasiveness. While the specific mechanism of KIF2C in HNSCC remains to be further validated, these studies suggest that KIF2C could serve as a potential biomarker for HNSCC progression and prognosis, with the potential for use as a target for therapeutic intervention.\u003c/p\u003e \u003cp\u003eGPX4 is a key regulatory factor in the ferroptosis process, primarily acting by reducing lipid peroxides on the cell membrane, inhibiting lipid peroxidation and cell death(Yang et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Under physiological conditions, GPX4 plays a critical role in protecting cells from oxidative damage, particularly in combating lipid oxidation induced by ferroptosis(Friedmann Angeli et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Seibt et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The role of GPX4 in various cancers has garnered increasing attention, as studies suggest that high expression of GPX4 is closely linked to enhanced antioxidant capacity in tumor cells. For example, in breast cancer and liver cancer, high expression of GPX4 is associated with increased tumor cell tolerance to oxidative stress and poor patient prognosis(Ding et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; He et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Pan et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Xu et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Inhibition of GPX4 has been shown to induce ferroptosis, thereby suppressing tumor cell growth and proliferation, making GPX4 a potential target for anti-cancer therapy.\u003c/p\u003e \u003cp\u003eIn HNSCC, high expression of GPX4 plays a significant role in tumor cell survival and treatment resistance. One study demonstrated that inhibiting GPX4 function could induce ferroptosis in HNSCC cells and significantly enhance their sensitivity to EGFR-targeted therapies, such as cetuximab(Shin et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Another study found that GPX4 inhibitors effectively induced lipid peroxidation and ferroptosis in HNSCC cells, thereby reducing tumor cell viability(Jehl et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Although these studies did not directly investigate the relationship between high GPX4 expression and patient survival, they suggest that GPX4 enhances tumor cell survival by inhibiting ferroptosis, which may impact treatment outcomes and clinical prognosis. Therefore, GPX4 likely plays a key role in the progression and prognosis of HNSCC, and its high expression may be associated with treatment resistance and disease progression.\u003c/p\u003e \u003cp\u003eThe review of the literature aligns with the results of this study. High expression of the KIF2C and GPX4 genes in various cancers is generally associated with high invasiveness, rapid proliferation, and poor prognosis, which is consistent with our observations in HNSCC. KIF2C may enhance tumor invasiveness by promoting cell proliferation and anti-apoptotic pathways, while GPX4 protects tumor cells by inhibiting ferroptosis, allowing them to survive in oxidative stress environments. These mechanisms together contribute to the poorer prognosis observed in HNSCC patients with high expression of KIF2C and GPX4.\u003c/p\u003e \u003cp\u003eHowever, there are some limitations in this study. First, functional validation experiments, such as overexpression or knockout of KIF2C and GPX4, were not conducted, so the specific mechanisms of these genes in the development of HNSCC remain unclear. Second, this study was based on bioinformatics analysis of publicly available databases, and lacks experimental validation using patient samples. Therefore, future research should combine in vitro and in vivo experiments, using gene editing techniques to further explore the functions of KIF2C and GPX4, providing a theoretical basis for targeted therapy in HNSCC.\u003c/p\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eIn summary, the high expression of KIF2C and GPX4 in head and neck cancer is closely associated with poor prognosis in patients. KIF2C, as a motor protein involved in cell division, may play a role in promoting tumor cell proliferation and enhancing anti-apoptotic capabilities. GPX4, as a key regulator of ferroptosis, may enhance tumor cell survival by inhibiting ferroptosis. Future research should further explore the molecular mechanisms of KIF2C and GPX4 in HNSCC, with the aim of providing new directions and targets for targeted therapy.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHead and neck squamous cell carcinoma (HNSCC)\u003c/div\u003e \u003cdiv class=\"Description\"\u003e\u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003egene expression omnibus (GEO)\u003c/div\u003e \u003cdiv class=\"Description\"\u003e\u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003edifferentially expressed genes (DEGs)\u003c/div\u003e \u003cdiv class=\"Description\"\u003e\u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eWeighted gene co\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eexpression network analysis (WGCNA)\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eprotein\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eprotein interaction (PPI)\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eThe Cancer Genome Atlas (TCGA)\u003c/div\u003e \u003cdiv class=\"Description\"\u003e\u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ecomparative toxicogenomics database (CTD)\u003c/div\u003e \u003cdiv class=\"Description\"\u003e\u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eKinesin Family Member 2C (KIF2C)\u003c/div\u003e \u003cdiv class=\"Description\"\u003e\u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGlutathione peroxidase 4 (GPX4)\u003c/div\u003e \u003cdiv class=\"Description\"\u003e\u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003efold change (FC)\u003c/div\u003e \u003cdiv class=\"Description\"\u003e\u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003efalse discovery rate (FDR)\u003c/div\u003e \u003cdiv class=\"Description\"\u003e\u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGene Set Enrichment Analysis (GSEA)\u003c/div\u003e \u003cdiv class=\"Description\"\u003e\u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGene Ontology (GO)\u003c/div\u003e \u003cdiv class=\"Description\"\u003e\u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eKyoto Encyclopedia of Genes and Genomes (KEGG)\u003c/div\u003e \u003cdiv class=\"Description\"\u003e\u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMedian Absolute Deviation (MAD)\u003c/div\u003e \u003cdiv class=\"Description\"\u003e\u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTopological Overlap Matrix (TOM)\u003c/div\u003e \u003cdiv class=\"Description\"\u003e\u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSearch Tool for the Retrieval of Interacting Genes (STRING)\u003c/div\u003e \u003cdiv class=\"Description\"\u003e\u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXiangwei Ren performed experiments mentioned in the paper, contributed to the work concept and design of the paper research. Dan Li and Yongbin Di collected data. Yuan Lai and Ziying Ji made statistical analysis of data, Tianke Li and Jing Wei drafted the manuscript. Xiangwei Ren revised the main content of the manuscript. All authors read and agree on the manuscript\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no conflicts of interest to declare\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data in this article are from public databases and are exempt from ethical review.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBai Y, Xiong L, Zhu M, Yang Z, Zhao J, Tang H (2019) Co-expression network analysis identified KIF2C in association with progression and prognosis in lung adenocarcinoma. 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Front Genet 13:891408. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fgene.2022.891408\u003c/span\u003e\u003cspan address=\"10.3389/fgene.2022.891408\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Table 1","content":"\u003cp\u003e\u003cstrong\u003eTable.1 A summary of miRNAs that regulate hub genes\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" \u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGene\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 306px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMIRNA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 159px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eKIF2C\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003ehsa-miR-423-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 159px;\"\u003e\n \u003cp\u003ehsa-miR-148b-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 159px;\"\u003e\n \u003cp\u003ehsa-miR-124-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGPX4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003ehsa-miR-26b-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 159px;\"\u003e\n \u003cp\u003ehsa-miR-124-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 159px;\"\u003e\n \u003cp\u003ehsa-miR-182-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\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":"KIF2C, GPX4, head and neck squamous cell carcinoma (HNSCC), ferroptosis, molecular mechanisms","lastPublishedDoi":"10.21203/rs.3.rs-8729738/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8729738/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eHead and neck squamous cell carcinoma (HNSCC) is a highly invasive and heterogeneous malignancy with poorly understood molecular mechanisms. This study aims to investigate the potential roles of ferroptosis-related core genes, KIF2C and GPX4, in HNSCC progression and their impact on patient prognosis through bioinformatics analysis.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis study utilized the HNSCC gene expression dataset (GSE10774) from the gene expression omnibus (GEO) database to identify differentially expressed genes (DEGs). Weighted gene co-expression network analysis (WGCNA), protein-protein interaction (PPI) network construction, and functional enrichment analysis (GO and KEGG) were performed to identify core genes associated with ferroptosis pathways. Survival analysis was conducted using the The Cancer Genome Atlas (TCGA) database, and heatmaps and comparative toxicogenomics database (CTD) analyses were used to validate the expression patterns and functions of key genes. RT-PCR was performed to verify the expression of hub genes.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eKIF2C and GPX4 were significantly overexpressed in HNSCC tissues and strongly associated with poor prognosis. Functional enrichment analysis revealed that these genes were primarily enriched in ferroptosis, P53 signaling pathways, and MAPK signaling pathways. Further analyses suggested that KIF2C may promote tumor progression by regulating cell division and anti-apoptotic pathways, while GPX4 enhances tumor cell survival by inhibiting ferroptosis. RT-PCR showed that the relative expression of hub genes was differently expressed in cancer cells.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe overexpression of KIF2C and GPX4 may represent critical molecular mechanisms underlying HNSCC progression and poor prognosis. This study provides new perspectives and potential targets for the diagnosis and targeted therapy of HNSCC. Future studies are required to validate their functions and mechanisms in vitro and in vivo.\u003c/p\u003e","manuscriptTitle":"Exploration of the Critical Roles and Molecular Mechanisms of KIF2C and GPX4 Genes in Head and Neck Squamous Cell Carcinoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-23 05:54:53","doi":"10.21203/rs.3.rs-8729738/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":"2bcaa48c-be5f-41d2-ad9e-f58ce061b3d4","owner":[],"postedDate":"February 23rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-27T08:25:36+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-23 05:54:53","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8729738","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8729738","identity":"rs-8729738","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}