Comprehensive Bioinformatic Analysis for Identification of Crucial Genes and Signaling Transduction Pathways in Head and Neck Squamous Cell Carcinoma

Comprehensive Bioinformatic Analysis for Identification of Crucial Genes and Signaling Transduction Pathways 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 Article Comprehensive Bioinformatic Analysis for Identification of Crucial Genes and Signaling Transduction Pathways in Head and Neck Squamous Cell Carcinoma Jaehwan Cheon, Byoungjae Kim, Junhyoung Byun, Semyung Jung, Jaehyeong Kim, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3912796/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 Head and neck squamous cell carcinoma (HNSCC) is a prevalent global cancer, ranking sixth in incidence. Investigating the molecular basis of tumorigenesis and metastasis is crucial for early detection and effective treatment. Our study utilized three gene expression profile datasets (GSE6791, GSE29330, and GSE58911) to identify co-up or down-regulated differentially expressed genes (DEGs) between HNSCC tumor and normal tissue samples, associated primarily with processes like extracellular matrix (ECM) organization, proteolysis, ECM disassembly, and keratinization. A protein-protein interaction (PPI) network revealed eight hub genes, notably including the up-regulated SPP1 and down-regulated KRT78. Importantly, these hub genes demonstrated correlations with tumor grade, clinical individual cancer stage, and poor prognosis in patients with HNSCC. The comprehensive bioinformatics-driven investigation not only pinpointed co-DEGs but also illuminated associated pathways, providing valuable insights into the molecular mechanisms steering disease progression. These findings have substantial clinical potential, offering avenues for early diagnosis and the development of innovative therapeutic targets for individuals grappling with HNSCC. The identified genes and pathways contribute to a deeper understanding of the intricate molecular landscape of HNSCC, paving the way for more targeted and effective interventions in the clinical setting. Biological sciences/Molecular biology Biological sciences/Cancer Biological sciences/Cancer/Head and neck cancer Biological sciences/Cancer/Head and neck cancer/Oral cancer Biological sciences/Computational biology and bioinformatics Biological sciences/Computational biology and bioinformatics/Databases Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Head and neck squamous cell carcinoma (HNSCC) accounts for over 95% of malignancies in the head and neck region, making it the sixth most common cancer globally 1 – 3 . Each year, a staggering influx of approximately 890,000 new HNSCC cases arises, contributing to a toll of 450,000 deaths 4 . The advent of immune checkpoint inhibitors targeting programmed cell death protein-1 and programmed death-ligand 1 has revolutionized treatment outcomes for several malignancies. However, in the realm of HNSCC, despite substantial advancements and considerable investment in these therapies, their efficacy remains dishearteningly low 5 , 6 . This may be linked to the challenges of diagnosing HNSCC, as most patients present at advanced stages, leading to poor prognoses 7 . Additionally, the five-year overall survival rate for HNSCC remains below 40% 8 . Therefore, understanding the molecular mechanisms underlying HNSCC is crucial for developing early diagnostic methods and identifying therapeutic targets. Microarray-based bioinformatics analysis has gained increasing popularity in recent years for studying various diseases, including breast, lung, and pancreatic cancer. The aim is to unravel the complex molecular mechanisms underlying each disease and identify critical genes associated with them 9 , 10 , ultimately leading to the development of effective therapeutic targets selection and accurate diagnostic procedures. Despite several bioinformatics studies conducted on HNSCC, a comprehensive and integrative analysis remains absent. Therefore, conducting such analyses is essential to fill this knowledge gap and improve cancer treatment outcomes. In this study, we analyzed differentially expressed genes (DEGs) and co-DEGs in various datasets using Gene Ontology (GO) terminology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. Additionally, we constructed a protein-protein interaction (PPI) network from the co-DEGs to identify hub genes. Finally, we validated the mRNA and protein expression levels of hub genes and their association with the survival rate of patients with HNSCC. Materials and Methods 1. Microarray Data The National Center for Biotechnology Information Gene Expression Omnibus is a publicly accessible database at https://www.ncbi.nlm.nih.gov/geo. We searched for relevant keywords such as ‘head and neck cancer,’ ‘head and neck squamous cell carcinoma,’ and ‘HNSCC’ and identified three gene expression datasets available for download and analysis through GEO2R (https://www.ncbi.nlm.nih.gov/geo/info/geo2r.html) 11 . These datasets include GSE6791 (42 HNSCC tissues, 14 normal tissues), GSE29330 (13 HNSCC tissues, 12 normal tissues), GSE58911 (15 HNSCC tissues, 15 normal tissues). 2. Identification of DEGs and Visualizing the Data Using GEO2R, we analyzed DEGs between HNSCC and normal tissues across all three datasets. We identified DEGs within each dataset (GSE6791, GSE29330, and GSE58911) based on an adjusted p value 2. Volcano and heatmap visualizations for each DEG dataset were conducted using Hiplot (https://hiplot-academic.com). 12 3. GO and KEGG Pathway Analysis of Up and Down-regulated DEGs We utilized the Database for Annotation, Visualization, and Integrated Discovery (DAVID) web server (https://david.ncifcrf.gov) for gene list annotation and functional enrichment analysis 13 . Specifically, we performed GO and KEGG pathway analyses on both up- and down-regulated DEGs using version 7.0 of the DAVID database. 4. PPI Network Build Up on Up and Down-regulated DEGs for Hub Genes Identification STRING (https://string-db.org/) is a web-based database offering pre-computed protein association networks 14 . We used version 12.0 for our PPI network analysis to identify hub genes in both up- and down-regulated DEGs. 5. Analyzing the Hub Genes Expression and Their Impact on Survival Rate in Patients with Cancer The University of Alabama at Birmingham Cancer Data Analysis Portal (UALCAN) (http://ualcan.path.uab.edu/), is a valuable resource for interactive network analysis 15 . We used UALCAN to analyze hub genes in control and HNSCC tumor tissues, along with clinical data from the Cancer Genome Atlas (TCGA) and Clinical Proteomic Tumor Analysis Consortium (CPTAC). Additionally, immunohistochemistry data on protein expression was obtained from the Human Protein Atlas (HPA) (https://www.proteinatlas.org/) 16 . Finally, we employed TIMER 2.0 (http://timer.cistrome.org) to analyze the expression of hub genes and their impact on survival rate 17 . Results 1. Identification of DEGs in Three HNSCC Data Sets Using GEO2R, we analyzed three datasets (GSE6791, GSE29330, and GSE58911) and identified a total of 63,565 up-regulated and 79,091 down-regulated DEGs. Reciprocal volcano maps for each dataset illustrate the distribution of significantly altered genes (Figures 1A). Representative heatmaps showcase 20 DEGs in each dataset (Figures 1B). Notably, cross-analysis revealed 28 co-DEGs (eight up-regulated and 20 down-regulated), visualized in a Venn diagram (Figures 1 C and D). 2. GO and KEGG Pathway Analysis on Up-regulated DEGs To understand the functional implications of DEGs, we performed GO and KEGG pathway analyses for each dataset. As depicted in Figure 2A, in GSE6791, up-regulated DEGs were significantly enriched in pathways related to extracellular matrix (ECM) organization, innate immune response, defense response to viruses, and other related processes. GSE29330 also showed enrichment in pathways involving cell adhesion, collagen fibril organization, ECM organization, and other relevant processes. Notably, GSE58911 revealed associations with ECM organization, proteolysis, collagen catabolic process, and other relevant processes. KEGG pathway analysis further highlighted specific pathways enriched with up-regulated DEGs. GSE6791 exhibited significant enrichment in diabetic complications, amoebiasis, and ECM-receptor interaction pathways. Similarly, GSE29330 DEGs were enriched in protein digestion and absorption, ECM-receptor interaction, and advanced glycation endproducts-receptor for advanced glycation endproducts signaling pathways in diabetic complication. Interestingly, GSE58911 DEGs were primarily involved in interleukin (IL)-17 signaling pathway, rheumatoid arthritis, and lipid and atherosclerosis pathways (Figure 2B). 3. GO and KEGG Pathway Analysis on Down-regulated DEGs In GSE6791, prominent enriched GO terms were associated with proteolysis, keratinization, and epithelial cell differentiation. Similarly, GSE29330 down-regulated DEGs exhibited enrichment in GO terms related to immune response, adaptive immune response, and cell surface receptor signaling pathway. Notably, muscle contraction, keratinization, and sarcomere organization emerged as the most frequent GO terms for down-regulated DEGs in GSE58911 (Figure 3A). KEGG analysis further unveiled specific pathways enriched with down-regulated DEGs. As depicted in Figure 3B, GSE6791 showed significant enrichment in pathways related to drug metabolism involving cytochrome P450, taurine, and hypotaurine metabolism. Down-regulated DEGs in GSE29330 were concentrated in B cell receptor signaling, cell adhesion molecules, and hematopoietic cell lineage. Interestingly, GSE58911 down-regulated DEGs were primarily associated with hypertrophic and dilated cardiomyopathy, and motor proteins. 4. Identification of co-DEGs and GO and KEGG Pathway Analysis on those We next turned our attention to the overlapping co-DEGs across all three datasets and explored their shared functional roles. GO analysis revealed enrichment in terms related to ECM organization, proteolysis, and collagen catabolic processes (Figure 4A), suggesting a crucial role in ECM dynamics. Interestingly, KEGG pathway analysis highlighted significant enrichment in ECM-receptor interaction, rheumatoid arthritis, and IL-17 signaling pathway (Figure 4B). 5. PPI Network Construction of co-DEGs and Identification of Hub Genes To identify hub genes potentially driving HNSCC progression, we analyzed all 28 co-DEGs (eight up-regulated and 20 down-regulated) from GSE6791, GSE29330, and GSE58911 using the STRING database for PPI networks. We considered genes with connectivity exceeding six as hub genes, revealing the right promising candidates. Among these, cornulin ( CRNN ), matrix metalloproteinase (MMP) 1, and MMP3 exhibited the highest connectivity (seven), followed by keratin (KRT) 4, KRT78, MMP12, sciellin (SCEL) , and secreted phosphoprotein (SPP) 1 (connectivity of six) (Figure 4C). 6. Expression Level of Selected Hub Genes in Tumor of Patients with HNSCC Using the UALCAN database, we analyzed HNSCC data from TCGA and CPTAC. As expected, MMP1 , MMP3 , MMP12 , and SPP1 displayed significantly higher expression in tumor tissues compared to normal samples (Figure 5A). Conversely, CRNN , KRT4 , KRT78 , and SCEL expression were down-regulated in HNSCC tumors (Figure 5B). Further analysis revealed a striking correlation between tumor grade or individual cancer stages and hub gene expression. Notably, SPP1 expression increased markedly with higher tumor grades (Figure 6A), while KRT78 expression decreased progressively with advancing cancer stages (Figure 6B). Consistent with the mRNA data, protein expression analysis using the HPA confirmed significantly elevated levels of MMP1, MMP3, MMP12, and SPP1 in HNSCC tumors compared to normal tissues (Figure 7A). Conversely, protein levels of CRNN, KRT4, KRT78, and SCEL remained down-regulated (Figures 7B and 8A). 7. Survival Rate Related to Hub Genes Expression in Patients with Cancer Finally, we investigated the potential link between hub gene expression and patient survival rates. Interestingly, high SPP1 and low KRT78 expression levels were significantly associated with poorer overall survival outcomes (Figure 8B). Discussion HNSCC is the dominant subtype of head and neck cancer, representing over 90% of all cases 1 . Despite its high incidence and metastatic potential, early diagnosis remains challenging, and treatment options like immunotherapy lack consistent efficacy 5 . In light of these limitations, comprehensive research endeavors are crucial. In recent years, bioinformatics has offered a faster and more efficient approach to analyzing biological datasets, paving the way for novel insights 18 . In this study, we employed bioinformatics to analyze gene expression profiles from three datasets GSE6791 (42 HNSCC, 14 healthy controls), GSE29330 (13 HNSCC, 12 healthy controls), and GSE58911 (15 HNSCC, 15 healthy controls). Our analysis identified 28 co-DEGS in HNSCC, comprising eight up-regulated and 20 down-regulated genes. Furthermore, eight hub genes were identified, four up-regulated and four down-regulated. Notably, among the hub genes, up-regulated SPP1 and downregulated KRT78 expression significantly correlated with tumor grade, individual cancer stages, and poor survival in HNSCC patients. GO analysis revealed that co-DEGs were primarily enriched in terms related to ECM organization, proteolysis, collagen catabolic process, and ECM disassembly. KEGG pathway analysis further highlighted significant enrichment in ECM-receptor interaction, rheumatoid arthritis, and the IL-17 signaling pathway. This finding aligns with existing evidence associating ECM with tumor development and progression 19 – 21 . Li et al. reported that proteolytic ECM degradation, driven by elevated plasminogen activator urokinase levels, contributes significantly to HNSCC metastasis 22 . Similarly, Tanis et al. observed increased expression of ECM-related genes like MMPs, laminin, and collagen in oral squamous cell carcinoma (OSCC), another major HNSCC subtype, linking these genes to OSCC metastasis 23 . Notably, MMPs, known for their role in ECM degradation and protein up-regulation across various cancers 24 , have been shown to accelerate tumor metastasis and invasion in nasopharyngeal carcinoma, another HNSCC type, through activity regulation 25 . Regarding ECM formation, overexpression of ECM-composing proteins like collagen and hyaluronic acid has been linked to increased matrix stiffness in the tumor microenvironment 26 . This aligns with our GO analysis suggesting abnormal ECM organization and catabolic reactions might contribute to cancer progression. Our KEGG analysis further strengthens this notion by highlighting enrichment in ECM-receptor interaction consistent with Huang et al. ’s findings emphasizing the crucial role of membrane receptors in recognizing ECM components in cancer 26 . Additionally, the enrichment in the rheumatoid arthritis pathway aligns with studies demonstrating enhanced immune response to Epstein-Barr virus (EBV) infection in rheumatoid arthritis patients compared to healthy controls 27 . Given the association of EBV with both rheumatoid arthritis and HNSCC, potential similarities in immune responses between these diseases cannot be ruled out 28 . Further, clinical studies in OSCC have reported a higher frequency of T H 17 cells and a positive correlation between IL-17 expression and tumor budding 29 , 30 . Through PPI network analysis, we identified four up-regulated hub genes ( MMP1 , MMP3 , MMP12 , and SSP1) and four down-regulated genes ( CRNN , KRT4 , KRT78 , and SCEL). To determine their role in HNSCC, we conducted several analyses focusing on the relationship between hub gene expression and the disease. Our analysis of the TCGA HNSCC database revealed significantly elevated expression of MMP1 , MMP3 , MMP12 , and SPP1 in tumor tissues compared to normal tissues. This finding aligns with established knowledge in several other cancers, where MMPs have been implicated in poor prognosis. For example, Zhang et al. observed increased MMP1 genes and protein expression in HNSCC, with over-expression positively correlating with advanced tumor size and metastasis. Similar findings have been reported for MMP1 in colorectal cancer, where knockdown experiments confirmed its pro-tumorigenic role 31 , 32 . Additionally, a bioinformatic study mirrored our results by demonstrating MMP1 , MMP3 , and MMP12 up-regulation in colorectal cancer, highlighting the potential universal relevance of these MMPs in tumor progression 33 . This up-regulation might be related to the general tendencies of MMPs in cancer, where MMP1 is significantly and almost universally upregulated, and MMP3 and MMP12 show significant upregulation in at least 10 types of cancer 34 . Conversely, our analysis revealed significantly reduced expression of CRNN , KRT4 , KRT78 , and SCEL in HNSCC. Notably, this pattern was consistent across protein expression pattern data from both the CPTAC and HPA databases, further substantiating their potential involvement in the disease. CRNN, known for its tumor-suppressive functions in cell cycle regulation, is also down-regulated in other squamous cell carcinoma types, suggesting its broader role in epithelial malignancies 35 – 37 . Interestingly, CRNN down-regulation might contribute to KRT4 down-regulation, as CRNN acts as a keratinocyte proliferation marker 38 . Therefore, the co-downregulation of CRNN and KRT4 might synergistically promote the progression of squamous cell carcinomas like HNSCC. Regarding SCEL , identified as a precursor of the cornified envelope in keratinizing tissues, our finding of its down-regulation in HSCC aligns with observations in melanoma, where lower expression correlated with poor overall survival 39 , 40 . However, it’s worth noting that SCEL expression seems to vary depending on cancer type, as it has been found in gallbladder and pancreatic cancers 41 , 42 . Therefore, further investigations are needed to elucidate the context-specific roles of SCEL in different malignancies. Among the up-regulated hub genes, SPP1 , also known as OSTEOPONTIN , stood out for its strong association with tumor grade progression, particularly grade 2 (G2). This aligns with its established role in promoting tumor development and metastasis 43 . Feng et al. demonstrated that high SPP1 expression in HNSCC patients correlated with lymph node metastasis and macrophage infiltration, both of which contribute to cell proliferation and invasion of tumor 44 . Similarly, Cho et al. , showed that silencing SPP1 in non-small cell lung cancer decreased protein levels and inhibited tumor growth 45 . Notably, elevated SPP1 is also observed in colon, gastric, and lung cancers 43 , 46 , 47 . It's worth noting that tumor G2, characterized by intermediate growth and limited spread, might represent a tipping point for metastasis 48 , 49 . While both grade 1 and G2 are typically curable with treatment, targeting SPP1 , specifically at G2, could offer an effective therapeutic or diagnostic strategy. Among the down-regulated hub genes, KRT78 expression displayed a significant association with individual cancer stages. These findings resonate with the established function of keratins, the intermediate filament proteins forming the cytoskeleton. Keratins are categorized into types 1 and 2, based on their characteristics, with KRT78 classified as type 2, alongside KRT4 50 . Several studies have linked abnormal keratin expression to cancer progression 51 , in line with our GO analysis showing enriched keratinization in HNSCC co-DEGs. For instance, clinical studies report decreased keratin levels during the transition from normal to invasive HNSCC 52 , and down-regulation of both KRT4 and KRT13 in OSCC 53 . Additionally, co-expression of KRT4 , KRT13 , and KRT78 in the epithelium basal layer has been established 54 , further supporting our findings of co-downregulation of KRT4 and KRT78 in HNSCC. Interestingly Fortier et al. showed that loss of KRT8 and KRT18 in epithelial cells correlated with increased MMP2 and MMP9 activity, promoting collective cancer cell migration 55 . This parallels our observation of decreased KRT4 and KRT78 alongside increased MMP1 , MMP3 , and MMP12 expression in HNSCC. Conclusion Our study delved into the molecular mechanisms of HNSCC progression by integrating three microarray datasets from GEO2R using R software and bioinformatics tools. We narrowed down to ten up-regulated and ten down-regulated genes most likely relevant to the disease process. Further, cross-linking analysis yielded 28 candidate co-DEGs potentially implicated in HNSCC development. GO and KEGG pathways analyses revealed these co-DEGs to be primarily involved in ECM organization, disassembly, and proteolysis. These findings provide valuable insights into the biological underpinnings of HNSCC and establish a theoretical foundation for further research. Furthermore, we successfully constructed a PPI network of co-DEGs in HNSCC, identifying hub genes playing crucial roles in disease progression. By utilizing various databases, we validated the impact of hub gene up-regulation and down-regulation on HNSCC development. These results significantly enhance our understanding of HNSCC pathogenesis and the molecular mechanisms driving its occurrence and progression. Our research possesses significant clinical implications, paving the way for improved detection, treatment, and prevention of HNSCC. Notably, identified hub genes like SPP1 and KRT78 emerge as potential targets for effective therapeutic inventions. However, thorough clinical, pharmacological, and biological studies, including in vivo and in vitro experiments, are essential to definitively confirm the functions of these hub genes and their direct involvement in HNSCC progression. Declarations Acknowledgments This study was supported by a grant from Korea University, Seoul, Republic of Ko-rea (Grant No. K2314111) and the Basic Science Research Program of the National Research Foundation of Korea, funded by the Ministry of Science and Technology and the Ministry of Science, ICT and Future Planning (2017R1A2B2003575 and NRF-2020R1A2C1006398); the Ministry of Science and ICT, Korea, under the ICT Creative Consilience program (IITP-2023-2020-0-01819), supervised by the Institute for Information and Communications Technology Planning and Evaluation (IITP); and the Korea Health Technology R&D Project (HI17C0387, HR22C1302) through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health and Welfare. Author contributions J.C. and T.H.K played a guiding role in carrying out the studies, collecting data and drafting the manuscript. B.K., J.B., and S.J. helped to draft the manuscript. J.K., S.K., and J.S. were responsible for the finalization of the paper. Competing interests The authors declare no competing interests. Data availability statement All information produced or examined throughout this investigation is incorporated in this published article. Ethics declarations Not applicable. Consent for publication All authors agree with the publication of this original article. References Bhat, G. R., Hyole, R. G. & Li, J. Head and neck cancer: Current challenges and future perspectives. Advances in Cancer Research vol. 152 (Elsevier Inc., 2021). Cui, J., Zheng, L., Zhang, Y. & Xue, M. Bioinformatics analysis of DNMT1 expression and its role in head and neck squamous cell carcinoma prognosis. Sci. Rep. 11, 1–11 (2021). Johnson, D. E. et al. Head and neck squamous cell carcinoma. Nat. Rev. Dis. Prim. 6, (2020). Sung, H. et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA. Cancer J. 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Down-regulation of keratin 4 and keratin 13 expression in oral squamous cell carcinoma and epithelial dysplasia: A clue for histopathogenesis. Histopathology 58, 531–542 (2011). Langbein, L. et al. Localisation of keratin K78 in the basal layer and first suprabasal layers of stratified epithelia completes expression catalogue of type II keratins and provides new insights into sequential keratin expression. Cell Tissue Res. 363, 735–750 (2016). Fortier, A. M., Asselin, E. & Cadrin, M. Keratin 8 and 18 loss in epithelial cancer cells increases collective cell migration and cisplatin sensitivity through claudin1 up-regulation. J. Biol. Chem. 288, 11555–11571 (2013). 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3912796","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":272688312,"identity":"f5e20b6d-5d6d-4b6d-8154-624eda739587","order_by":0,"name":"Jaehwan Cheon","email":"","orcid":"","institution":"Korea University","correspondingAuthor":false,"prefix":"","firstName":"Jaehwan","middleName":"","lastName":"Cheon","suffix":""},{"id":272688313,"identity":"50c02e95-7bc9-4c34-a726-e5b947a35230","order_by":1,"name":"Byoungjae Kim","email":"","orcid":"","institution":"Korea University","correspondingAuthor":false,"prefix":"","firstName":"Byoungjae","middleName":"","lastName":"Kim","suffix":""},{"id":272688314,"identity":"f3759ed8-5ac9-45c9-82a6-87e2518d239d","order_by":2,"name":"Junhyoung Byun","email":"","orcid":"","institution":"Korea University","correspondingAuthor":false,"prefix":"","firstName":"Junhyoung","middleName":"","lastName":"Byun","suffix":""},{"id":272688315,"identity":"a9d877fd-a5e7-4b40-a92b-e92074cb9fb0","order_by":3,"name":"Semyung Jung","email":"","orcid":"","institution":"Korea University","correspondingAuthor":false,"prefix":"","firstName":"Semyung","middleName":"","lastName":"Jung","suffix":""},{"id":272688316,"identity":"c05206d2-7d79-4e71-9e9a-13da83b841df","order_by":4,"name":"Jaehyeong Kim","email":"","orcid":"","institution":"Korea University","correspondingAuthor":false,"prefix":"","firstName":"Jaehyeong","middleName":"","lastName":"Kim","suffix":""},{"id":272688317,"identity":"40556990-2f86-4836-95ed-ece5d97bc93d","order_by":5,"name":"Sooun Kwak","email":"","orcid":"","institution":"Korea University","correspondingAuthor":false,"prefix":"","firstName":"Sooun","middleName":"","lastName":"Kwak","suffix":""},{"id":272688318,"identity":"7712283b-e063-488d-9e02-d24ffc632d67","order_by":6,"name":"Jaemin Shin","email":"","orcid":"","institution":"Korea University","correspondingAuthor":false,"prefix":"","firstName":"Jaemin","middleName":"","lastName":"Shin","suffix":""},{"id":272688319,"identity":"03146c7a-3370-4cb0-b5e7-88fa7ecfa5da","order_by":7,"name":"Tae Hoon Kim","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyklEQVRIiWNgGAWjYDCCAxAqQYK9AUgZWBClhRGo1iBBggek2UCCFC0SCSAuEVr4jjc/f/Bxz588yZnPr274USDBwN/enYBXi+SZY4aNM54ZFEtL55Td7AE6TOLM2Q14tRjcyGFs5jlgkDhPOiftBg9Qi4FELrFaJM+k3fxDkpbZEuzHbhNlC8gvM2ccME6c2ZPDdlvGQIKHoF+AIfbgw4cDcokzjh9/dvPNHxs5/vZe/FqQAI8BmCRWOQiwPyBF9SgYBaNgFIwgAAD9Vk1VDZWtPAAAAABJRU5ErkJggg==","orcid":"","institution":"Korea University","correspondingAuthor":true,"prefix":"","firstName":"Tae","middleName":"Hoon","lastName":"Kim","suffix":""}],"badges":[],"createdAt":"2024-01-31 06:17:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3912796/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3912796/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":51184667,"identity":"0b9daa14-17b5-48f3-a9ce-5a25cb0b927d","added_by":"auto","created_at":"2024-02-15 15:49:47","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1490648,"visible":true,"origin":"","legend":"\u003cp\u003eVolcano maps and Hierarchical clustering heatmap of DEGs in three databases and identification of co-DEGs. Volcano map of the DEGs distribution in GSE6791, GSE29330, and GSE58911 (A). The representative 10 up and down-regulated DEGs in GSE6791, GSE29330, and GSE58911 (B). The red points in the volcano maps represent up-regulated genes that were screened based on fold change greater than or equal to 2.0 with a corrected \u003cem\u003ep-value\u003c/em\u003e of less than 0.05. The blue points in the volcano maps represent down-regulated genes that were screened based on fold change lesser than or equal to -2.0 and a corrected \u003cem\u003ep-value\u003c/em\u003e of less than 0.05. Black points indicate genes with no significant differences. Gene expression was visualized using color codes in the heatmap. Red indicates up-regulation, blue indicates down-regulation, and white indicates no significant change. Eight up-regulated-co-DEGs and twenty down-regulated-co-DEGs were identified by analyzing the cross-linking data in GSE6791, GSE29330, and GSE58911 using a Venn diagram (C and D).\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-3912796/v1/38aa2192ba612d02afb3f8ae.png"},{"id":51184666,"identity":"fc4df01e-17de-4b7c-890b-ef6293663f99","added_by":"auto","created_at":"2024-02-15 15:49:47","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1420648,"visible":true,"origin":"","legend":"\u003cp\u003eGO \u0026amp; KEGG analyses of up-regulated DEGs in three databases. GO analysis divided the up-regulated DEGs into several biological pathways based on their roles in GSE6791, GSE29330, and GSE58911, respectively (A). KEGG pathway analysis was used to categorize the up-regulated DEGs biochemical pathways based on their gene functions in GSE6791, GSE29330, and GSE58911, respectively (B).\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-3912796/v1/40def3c2ee4146dfbed0c6a1.png"},{"id":51184669,"identity":"fc7f899a-a7d0-4816-bd15-5bd0911b5a4c","added_by":"auto","created_at":"2024-02-15 15:49:48","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1325104,"visible":true,"origin":"","legend":"\u003cp\u003eGO \u0026amp; KEGG analyses of down-regulated DEGs in three databases. GO analysis divided the down-regulated DEGs into several biological pathways based on their roles in GSE6791, GSE29330, and GSE58911, respectively (A). KEGG pathway analysis was used to categorize the down-regulated DEGs biochemical pathways based on their gene functions in GSE6791, GSE29330, and GSE58911, respectively (B).\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-3912796/v1/ae886cf7ee4938dca5ae5334.png"},{"id":51184668,"identity":"420b0a4c-4cc4-4d84-9bf3-4a9c8d3dc879","added_by":"auto","created_at":"2024-02-15 15:49:48","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1563545,"visible":true,"origin":"","legend":"\u003cp\u003eGO \u0026amp; KEGG analyses of co-DEGs and PPI network on co-DEGs. GO analysis (A), KEGG pathway analysis of co-DEGs (B), and PPI network diagram on co-DEGs (C). GO and KEGG pathway analysis classify co-DEGs into some functional groups and biochemical pathways based on gene character. In the PPI network, circles represent genes and lines represent protein interactions. The results within the circles represent the protein structure. The line color represents the evidence of an interaction.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-3912796/v1/f0424e6e2ccde7362ff0b7d6.png"},{"id":51184673,"identity":"28fab548-bcdf-4894-ab72-52cfebb55b37","added_by":"auto","created_at":"2024-02-15 15:49:48","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":787446,"visible":true,"origin":"","legend":"\u003cp\u003eHub genes mRNA expression in patients with HNSCC. All up-regulated hub genes (A) and down-regulated hub genes (B). The mRNA expression of all hub genes was significantly altered in HNSCC primary tumors compared to normal tissues. *** \u003cem\u003ep\u003c/em\u003e \u0026lt;0.001, comparison between HNSCC and normal tissues.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-3912796/v1/72e0ea261183d2d8c260766d.png"},{"id":51184671,"identity":"d4c50c10-ae2c-4e94-92ec-dfc7dda4cc0b","added_by":"auto","created_at":"2024-02-15 15:49:48","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":936482,"visible":true,"origin":"","legend":"\u003cp\u003eHub genes mRNA expression based on individual cancer stage and tumor grade in HNSCC. The mRNA expression level of some hub genes based on tumor grade (A) and individual cancer stages (B). The mRNA expression of \u003cem\u003eKRT78\u003c/em\u003e and \u003cem\u003eSPP1\u003c/em\u003e significantly decreased and increased according to the tumor grade and individual cancer stage, respectively; * \u003cem\u003ep\u003c/em\u003e \u0026lt;0.05, ** \u003cem\u003ep\u003c/em\u003e \u0026lt;0.01, *** \u003cem\u003ep\u003c/em\u003e \u0026lt;0.001, comparison between HNSCC and normal tissues.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-3912796/v1/a80603593d3992b10479352d.png"},{"id":51184676,"identity":"bbeff7e8-cffd-49b3-bed0-611ec4eea4f0","added_by":"auto","created_at":"2024-02-15 15:49:49","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":643554,"visible":true,"origin":"","legend":"\u003cp\u003eHub genes protein expression in patients with HNSCC. All hub protein of up-regulated genes (A) and down-regulated hub genes (B). The Protein expression of all hub genes was significantly altered in HNSCC primary tumors compared with normal tissues. MMP1, MMP3, MMP12, and SPP1 were up-regulated, while CRNN, KRT78, KRT4, and SCEL were downregulated in HNSCC, ***: \u003cem\u003ep\u003c/em\u003e \u0026lt;0.001, comparison between HNSCC and normal tissues.\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-3912796/v1/4d9ea768e64079c77f4cb6b5.png"},{"id":51184672,"identity":"d5270983-8018-4eed-8967-87c31dc377cd","added_by":"auto","created_at":"2024-02-15 15:49:48","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":4490665,"visible":true,"origin":"","legend":"\u003cp\u003eHub genes protein expression analysis through immunohistochemistry in HNSCC and normal tissue and survival rate analysis related to the selected hub genes in patients with HNSCC. IHC results from the HPA database showed lower staining intensities for CRNN, KRT78, KRT4, and SCEL in HNSCC tissues than in normal tissues (A). Survival rate according to \u003cem\u003eSPP1 \u003c/em\u003e(B) and \u003cem\u003eKRT78\u003c/em\u003e expression in patients with HNSCC (C). High \u003cem\u003eSPP1\u003c/em\u003e expression is associated with a decreased survival rate while low \u003cem\u003eKRT78\u003c/em\u003eexpression is associated with it.\u003c/p\u003e","description":"","filename":"Figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-3912796/v1/845590810b1c8e1de2b91f2f.png"},{"id":66035246,"identity":"26721b10-12bd-4233-b09b-1b2a5507ab76","added_by":"auto","created_at":"2024-10-07 04:32:43","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":14269201,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3912796/v1/7f565328-1b35-4f4a-a407-3e78be16fa3e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Comprehensive Bioinformatic Analysis for Identification of Crucial Genes and Signaling Transduction Pathways in Head and Neck Squamous Cell Carcinoma","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHead and neck squamous cell carcinoma (HNSCC) accounts for over 95% of malignancies in the head and neck region, making it the sixth most common cancer globally\u003csup\u003e\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Each year, a staggering influx of approximately 890,000 new HNSCC cases arises, contributing to a toll of 450,000 deaths\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. The advent of immune checkpoint inhibitors targeting programmed cell death protein-1 and programmed death-ligand 1 has revolutionized treatment outcomes for several malignancies. However, in the realm of HNSCC, despite substantial advancements and considerable investment in these therapies, their efficacy remains dishearteningly low\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. This may be linked to the challenges of diagnosing HNSCC, as most patients present at advanced stages, leading to poor prognoses\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Additionally, the five-year overall survival rate for HNSCC remains below 40%\u003csup\u003e8\u003c/sup\u003e. Therefore, understanding the molecular mechanisms underlying HNSCC is crucial for developing early diagnostic methods and identifying therapeutic targets.\u003c/p\u003e \u003cp\u003eMicroarray-based bioinformatics analysis has gained increasing popularity in recent years for studying various diseases, including breast, lung, and pancreatic cancer. The aim is to unravel the complex molecular mechanisms underlying each disease and identify critical genes associated with them\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e, ultimately leading to the development of effective therapeutic targets selection and accurate diagnostic procedures. Despite several bioinformatics studies conducted on HNSCC, a comprehensive and integrative analysis remains absent. Therefore, conducting such analyses is essential to fill this knowledge gap and improve cancer treatment outcomes.\u003c/p\u003e \u003cp\u003eIn this study, we analyzed differentially expressed genes (DEGs) and co-DEGs in various datasets using Gene Ontology (GO) terminology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. Additionally, we constructed a protein-protein interaction (PPI) network from the co-DEGs to identify hub genes. Finally, we validated the mRNA and protein expression levels of hub genes and their association with the survival rate of patients with HNSCC.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cem\u003e1. Microarray Data\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe National Center for Biotechnology Information Gene Expression Omnibus is a publicly accessible database at https://www.ncbi.nlm.nih.gov/geo. We searched for relevant keywords such as \u0026lsquo;head and neck cancer,\u0026rsquo; \u0026lsquo;head and neck squamous cell carcinoma,\u0026rsquo; and \u0026lsquo;HNSCC\u0026rsquo; and identified three gene expression datasets available for download and analysis through GEO2R (https://www.ncbi.nlm.nih.gov/geo/info/geo2r.html)\u003csup\u003e11\u003c/sup\u003e. These datasets include GSE6791 (42 HNSCC tissues, 14 normal tissues), GSE29330 (13 HNSCC tissues, 12 normal tissues), GSE58911 (15 HNSCC tissues, 15 normal tissues).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2. Identification of DEGs and Visualizing the Data\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eUsing GEO2R, we analyzed DEGs between HNSCC and normal tissues across all three datasets. We identified DEGs within each dataset (GSE6791, GSE29330, and GSE58911) based on an adjusted \u003cem\u003ep value\u003c/em\u003e \u0026lt;0.05, and a log fold change (log FC) threshold of |log FC| \u0026gt;2. Volcano and heatmap visualizations for each DEG dataset were conducted using Hiplot (https://hiplot-academic.com).\u003csup\u003e12\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e3. GO and KEGG Pathway Analysis of Up and Down-regulated DEGs\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe utilized the Database for Annotation, Visualization, and Integrated Discovery (DAVID) web server (https://david.ncifcrf.gov) for gene list annotation and functional enrichment analysis\u003csup\u003e13\u003c/sup\u003e. Specifically, we performed GO and KEGG pathway analyses on both up- and down-regulated DEGs using version 7.0 of the DAVID database.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e4. PPI Network Build Up on Up and Down-regulated DEGs for Hub Genes Identification\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eSTRING (https://string-db.org/) is a web-based database offering pre-computed protein association networks\u003csup\u003e14\u003c/sup\u003e. We used version 12.0 for our PPI network analysis to identify hub genes in both up- and down-regulated DEGs.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e5. Analyzing the Hub Genes Expression and Their Impact on Survival Rate in Patients with Cancer\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;The University of Alabama at Birmingham Cancer Data Analysis Portal (UALCAN) (http://ualcan.path.uab.edu/), is a valuable resource for interactive network analysis\u003csup\u003e15\u003c/sup\u003e. We used UALCAN to analyze hub genes in control and HNSCC tumor tissues, along with clinical data from the Cancer Genome Atlas (TCGA) and Clinical Proteomic Tumor Analysis Consortium (CPTAC). Additionally, immunohistochemistry data on protein expression was obtained from the Human Protein Atlas (HPA) (https://www.proteinatlas.org/)\u003csup\u003e16\u003c/sup\u003e. Finally, we employed TIMER 2.0 (http://timer.cistrome.org) to analyze the expression of hub genes and their impact on survival rate\u003csup\u003e17\u003c/sup\u003e.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cem\u003e1. Identification of DEGs in Three HNSCC Data Sets\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eUsing GEO2R, we analyzed three datasets (GSE6791, GSE29330, and GSE58911) and identified a total of 63,565 up-regulated and 79,091 down-regulated DEGs. Reciprocal volcano maps for each dataset illustrate the distribution of significantly altered genes (Figures 1A). Representative heatmaps showcase 20 DEGs in each dataset (Figures 1B). Notably, cross-analysis revealed 28 co-DEGs (eight up-regulated and 20 down-regulated), visualized in a Venn diagram (Figures 1 C and D).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2. GO and KEGG Pathway Analysis on Up-regulated DEGs\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTo understand the functional implications of DEGs, we performed GO and KEGG pathway analyses for each dataset. As depicted in Figure 2A, in GSE6791, up-regulated DEGs were significantly enriched in pathways related to extracellular matrix (ECM) organization, innate immune response, defense response to viruses, and other related processes. GSE29330 also showed enrichment in pathways involving cell adhesion, collagen fibril organization, ECM organization, and other relevant processes. Notably, GSE58911 revealed associations with ECM organization, proteolysis, collagen catabolic process, and other relevant processes.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eKEGG pathway analysis further highlighted specific pathways enriched with up-regulated DEGs. GSE6791 exhibited significant enrichment in diabetic complications, amoebiasis, and ECM-receptor interaction pathways. Similarly, GSE29330 DEGs were enriched in protein digestion and absorption, ECM-receptor interaction, and advanced glycation endproducts-receptor for advanced glycation endproducts signaling pathways in diabetic complication. Interestingly, GSE58911 DEGs were primarily involved in interleukin (IL)-17 signaling pathway, rheumatoid arthritis, and lipid and atherosclerosis pathways (Figure 2B).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e3. GO and KEGG Pathway Analysis on Down-regulated DEGs\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eIn GSE6791, prominent enriched GO terms were associated with proteolysis, keratinization, and epithelial cell differentiation. Similarly, GSE29330 down-regulated DEGs exhibited enrichment in GO terms related to immune response, adaptive immune response, and cell surface receptor signaling pathway. Notably, muscle contraction, keratinization, and sarcomere organization emerged as the most frequent GO terms for down-regulated DEGs in GSE58911 (Figure 3A).\u003c/p\u003e\n\u003cp\u003eKEGG analysis further unveiled specific pathways enriched with down-regulated DEGs. As depicted in Figure 3B, GSE6791 showed significant enrichment in pathways related to drug metabolism involving cytochrome P450, taurine, and hypotaurine metabolism. Down-regulated DEGs in GSE29330 were concentrated in B cell receptor signaling, cell adhesion molecules, and hematopoietic cell lineage. Interestingly, GSE58911 down-regulated DEGs were primarily associated with hypertrophic and dilated cardiomyopathy, and motor proteins.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e4. Identification of co-DEGs and GO and KEGG Pathway Analysis on those\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe next turned our attention to the overlapping co-DEGs across all three datasets and explored their shared functional roles. GO analysis revealed enrichment in terms related to ECM organization, proteolysis, and collagen catabolic processes (Figure 4A), suggesting a crucial role in ECM dynamics. Interestingly, KEGG pathway analysis highlighted significant enrichment in ECM-receptor interaction, rheumatoid arthritis, and IL-17 signaling pathway (Figure 4B).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e5. PPI Network Construction of co-DEGs and Identification of Hub Genes\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTo identify hub genes potentially driving HNSCC progression, we analyzed all 28 co-DEGs (eight up-regulated and 20 down-regulated) from GSE6791, GSE29330, and GSE58911 using the STRING database for PPI networks. We considered genes with connectivity exceeding six as hub genes, revealing the right promising candidates. Among these, \u003cem\u003ecornulin\u003c/em\u003e (\u003cem\u003eCRNN\u003c/em\u003e), \u003cem\u003ematrix metalloproteinase (MMP) 1,\u0026nbsp;\u003c/em\u003eand\u003cem\u003e\u0026nbsp;MMP3\u0026nbsp;\u003c/em\u003eexhibited the highest connectivity (seven), followed by keratin\u003cem\u003e\u0026nbsp;(KRT) 4, KRT78, MMP12, sciellin (SCEL)\u003c/em\u003e, and \u003cem\u003esecreted phosphoprotein (SPP)\u003c/em\u003e 1 (connectivity of six) (Figure 4C).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e6. Expression Level of Selected Hub Genes in Tumor of Patients with HNSCC\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eUsing the UALCAN database, we analyzed HNSCC data from TCGA and CPTAC. As expected, \u003cem\u003eMMP1\u003c/em\u003e, \u003cem\u003eMMP3\u003c/em\u003e, \u003cem\u003eMMP12\u003c/em\u003e, and \u003cem\u003eSPP1\u003c/em\u003e displayed significantly higher expression in tumor tissues compared to normal samples (Figure 5A). Conversely, \u003cem\u003eCRNN\u003c/em\u003e, \u003cem\u003eKRT4\u003c/em\u003e, \u003cem\u003eKRT78\u003c/em\u003e, and \u003cem\u003eSCEL\u003c/em\u003e expression were down-regulated in HNSCC tumors (Figure 5B). Further analysis revealed a striking correlation between tumor grade or individual cancer stages and hub gene expression. Notably, \u003cem\u003eSPP1\u003c/em\u003e expression increased markedly with higher tumor grades (Figure 6A), while \u003cem\u003eKRT78\u003c/em\u003e expression decreased progressively with advancing cancer stages (Figure 6B).\u003c/p\u003e\n\u003cp\u003eConsistent with the mRNA data, protein expression analysis using the HPA confirmed significantly elevated levels of MMP1, MMP3, MMP12, and SPP1 in HNSCC tumors compared to normal tissues (Figure 7A). Conversely, protein levels of CRNN, KRT4, KRT78, and SCEL remained down-regulated (Figures 7B and 8A).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e7. Survival Rate Related to Hub Genes Expression in Patients with Cancer\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFinally, we investigated the potential link between hub gene expression and patient survival rates. Interestingly, high SPP1 and low KRT78 expression levels were significantly associated with poorer overall survival outcomes (Figure 8B).\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eHNSCC is the dominant subtype of head and neck cancer, representing over 90% of all cases\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Despite its high incidence and metastatic potential, early diagnosis remains challenging, and treatment options like immunotherapy lack consistent efficacy\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. In light of these limitations, comprehensive research endeavors are crucial. In recent years, bioinformatics has offered a faster and more efficient approach to analyzing biological datasets, paving the way for novel insights\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. In this study, we employed bioinformatics to analyze gene expression profiles from three datasets GSE6791 (42 HNSCC, 14 healthy controls), GSE29330 (13 HNSCC, 12 healthy controls), and GSE58911 (15 HNSCC, 15 healthy controls). Our analysis identified 28 co-DEGS in HNSCC, comprising eight up-regulated and 20 down-regulated genes. Furthermore, eight hub genes were identified, four up-regulated and four down-regulated. Notably, among the hub genes, up-regulated \u003cem\u003eSPP1\u003c/em\u003e and downregulated \u003cem\u003eKRT78\u003c/em\u003e expression significantly correlated with tumor grade, individual cancer stages, and poor survival in HNSCC patients.\u003c/p\u003e \u003cp\u003eGO analysis revealed that co-DEGs were primarily enriched in terms related to ECM organization, proteolysis, collagen catabolic process, and ECM disassembly. KEGG pathway analysis further highlighted significant enrichment in ECM-receptor interaction, rheumatoid arthritis, and the IL-17 signaling pathway. This finding aligns with existing evidence associating ECM with tumor development and progression\u003csup\u003e\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Li \u003cem\u003eet al.\u003c/em\u003e reported that proteolytic ECM degradation, driven by elevated plasminogen activator urokinase levels, contributes significantly to HNSCC metastasis\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Similarly, Tanis \u003cem\u003eet al.\u003c/em\u003e observed increased expression of ECM-related genes like MMPs, laminin, and collagen in oral squamous cell carcinoma (OSCC), another major HNSCC subtype, linking these genes to OSCC metastasis\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Notably, MMPs, known for their role in ECM degradation and protein up-regulation across various cancers\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e, have been shown to accelerate tumor metastasis and invasion in nasopharyngeal carcinoma, another HNSCC type, through activity regulation\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Regarding ECM formation, overexpression of ECM-composing proteins like collagen and hyaluronic acid has been linked to increased matrix stiffness in the tumor microenvironment\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. This aligns with our GO analysis suggesting abnormal ECM organization and catabolic reactions might contribute to cancer progression.\u003c/p\u003e \u003cp\u003eOur KEGG analysis further strengthens this notion by highlighting enrichment in ECM-receptor interaction consistent with Huang \u003cem\u003eet al.\u003c/em\u003e\u0026rsquo;s findings emphasizing the crucial role of membrane receptors in recognizing ECM components in cancer\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Additionally, the enrichment in the rheumatoid arthritis pathway aligns with studies demonstrating enhanced immune response to Epstein-Barr virus (EBV) infection in rheumatoid arthritis patients compared to healthy controls\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Given the association of EBV with both rheumatoid arthritis and HNSCC, potential similarities in immune responses between these diseases cannot be ruled out\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Further, clinical studies in OSCC have reported a higher frequency of T\u003csub\u003eH\u003c/sub\u003e17 cells and a positive correlation between IL-17 expression and tumor budding\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e,\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThrough PPI network analysis, we identified four up-regulated hub genes (\u003cem\u003eMMP1\u003c/em\u003e, \u003cem\u003eMMP3\u003c/em\u003e, \u003cem\u003eMMP12\u003c/em\u003e, and \u003cem\u003eSSP1)\u003c/em\u003e and four down-regulated genes (\u003cem\u003eCRNN\u003c/em\u003e, \u003cem\u003eKRT4\u003c/em\u003e, \u003cem\u003eKRT78\u003c/em\u003e, and \u003cem\u003eSCEL).\u003c/em\u003e To determine their role in HNSCC, we conducted several analyses focusing on the relationship between hub gene expression and the disease. Our analysis of the TCGA HNSCC database revealed significantly elevated expression of \u003cem\u003eMMP1\u003c/em\u003e, \u003cem\u003eMMP3\u003c/em\u003e, \u003cem\u003eMMP12\u003c/em\u003e, and \u003cem\u003eSPP1\u003c/em\u003e in tumor tissues compared to normal tissues. This finding aligns with established knowledge in several other cancers, where \u003cem\u003eMMPs\u003c/em\u003e have been implicated in poor prognosis. For example, Zhang \u003cem\u003eet al.\u003c/em\u003e observed increased MMP1 genes and protein expression in HNSCC, with over-expression positively correlating with advanced tumor size and metastasis. Similar findings have been reported for \u003cem\u003eMMP1\u003c/em\u003e in colorectal cancer, where knockdown experiments confirmed its pro-tumorigenic role\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Additionally, a bioinformatic study mirrored our results by demonstrating \u003cem\u003eMMP1\u003c/em\u003e, \u003cem\u003eMMP3\u003c/em\u003e, and \u003cem\u003eMMP12\u003c/em\u003e up-regulation in colorectal cancer, highlighting the potential universal relevance of these MMPs in tumor progression\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. This up-regulation might be related to the general tendencies of \u003cem\u003eMMPs\u003c/em\u003e in cancer, where \u003cem\u003eMMP1\u003c/em\u003e is significantly and almost universally upregulated, and \u003cem\u003eMMP3\u003c/em\u003e and \u003cem\u003eMMP12\u003c/em\u003e show significant upregulation in at least 10 types of cancer\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eConversely, our analysis revealed significantly reduced expression of \u003cem\u003eCRNN\u003c/em\u003e, \u003cem\u003eKRT4\u003c/em\u003e, \u003cem\u003eKRT78\u003c/em\u003e, and \u003cem\u003eSCEL\u003c/em\u003e in HNSCC. Notably, this pattern was consistent across protein expression pattern data from both the CPTAC and HPA databases, further substantiating their potential involvement in the disease. CRNN, known for its tumor-suppressive functions in cell cycle regulation, is also down-regulated in other squamous cell carcinoma types, suggesting its broader role in epithelial malignancies\u003csup\u003e\u003cspan additionalcitationids=\"CR36\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. Interestingly, \u003cem\u003eCRNN\u003c/em\u003e down-regulation might contribute to \u003cem\u003eKRT4\u003c/em\u003e down-regulation, as \u003cem\u003eCRNN\u003c/em\u003e acts as a keratinocyte proliferation marker\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. Therefore, the co-downregulation of \u003cem\u003eCRNN\u003c/em\u003e and \u003cem\u003eKRT4\u003c/em\u003e might synergistically promote the progression of squamous cell carcinomas like HNSCC. Regarding \u003cem\u003eSCEL\u003c/em\u003e, identified as a precursor of the cornified envelope in keratinizing tissues, our finding of its down-regulation in HSCC aligns with observations in melanoma, where lower expression correlated with poor overall survival\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e,\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. However, it\u0026rsquo;s worth noting that \u003cem\u003eSCEL\u003c/em\u003e expression seems to vary depending on cancer type, as it has been found in gallbladder and pancreatic cancers\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e,\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. Therefore, further investigations are needed to elucidate the context-specific roles of \u003cem\u003eSCEL\u003c/em\u003e in different malignancies.\u003c/p\u003e \u003cp\u003eAmong the up-regulated hub genes, \u003cem\u003eSPP1\u003c/em\u003e, also known as \u003cem\u003eOSTEOPONTIN\u003c/em\u003e, stood out for its strong association with tumor grade progression, particularly grade 2 (G2). This aligns with its established role in promoting tumor development and metastasis\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. Feng \u003cem\u003eet al.\u003c/em\u003e demonstrated that high \u003cem\u003eSPP1\u003c/em\u003e expression in HNSCC patients correlated with lymph node metastasis and macrophage infiltration, both of which contribute to cell proliferation and invasion of tumor\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. Similarly, Cho \u003cem\u003eet al.\u003c/em\u003e, showed that silencing \u003cem\u003eSPP1\u003c/em\u003e in non-small cell lung cancer decreased protein levels and inhibited tumor growth\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. Notably, elevated \u003cem\u003eSPP1\u003c/em\u003e is also observed in colon, gastric, and lung cancers\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e,\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e,\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. It's worth noting that tumor G2, characterized by intermediate growth and limited spread, might represent a tipping point for metastasis\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e,\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e. While both grade 1 and G2 are typically curable with treatment, targeting \u003cem\u003eSPP1\u003c/em\u003e, specifically at G2, could offer an effective therapeutic or diagnostic strategy.\u003c/p\u003e \u003cp\u003eAmong the down-regulated hub genes, \u003cem\u003eKRT78\u003c/em\u003e expression displayed a significant association with individual cancer stages. These findings resonate with the established function of keratins, the intermediate filament proteins forming the cytoskeleton. Keratins are categorized into types 1 and 2, based on their characteristics, with \u003cem\u003eKRT78\u003c/em\u003e classified as type 2, alongside \u003cem\u003eKRT4\u003c/em\u003e\u003csup\u003e50\u003c/sup\u003e. Several studies have linked abnormal keratin expression to cancer progression\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e, in line with our GO analysis showing enriched keratinization in HNSCC co-DEGs. For instance, clinical studies report decreased keratin levels during the transition from normal to invasive HNSCC\u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e, and down-regulation of both \u003cem\u003eKRT4\u003c/em\u003e and \u003cem\u003eKRT13\u003c/em\u003e in OSCC\u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e. Additionally, co-expression of \u003cem\u003eKRT4\u003c/em\u003e, \u003cem\u003eKRT13\u003c/em\u003e, and \u003cem\u003eKRT78\u003c/em\u003e in the epithelium basal layer has been established\u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e, further supporting our findings of co-downregulation of \u003cem\u003eKRT4\u003c/em\u003e and \u003cem\u003eKRT78\u003c/em\u003e in HNSCC. Interestingly Fortier \u003cem\u003eet al.\u003c/em\u003e showed that loss of \u003cem\u003eKRT8\u003c/em\u003e and \u003cem\u003eKRT18\u003c/em\u003e in epithelial cells correlated with increased \u003cem\u003eMMP2\u003c/em\u003e and \u003cem\u003eMMP9\u003c/em\u003e activity, promoting collective cancer cell migration\u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e. This parallels our observation of decreased \u003cem\u003eKRT4\u003c/em\u003e and \u003cem\u003eKRT78\u003c/em\u003e alongside increased \u003cem\u003eMMP1\u003c/em\u003e, \u003cem\u003eMMP3\u003c/em\u003e, and \u003cem\u003eMMP12\u003c/em\u003e expression in HNSCC.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur study delved into the molecular mechanisms of HNSCC progression by integrating three microarray datasets from GEO2R using R software and bioinformatics tools. We narrowed down to ten up-regulated and ten down-regulated genes most likely relevant to the disease process. Further, cross-linking analysis yielded 28 candidate co-DEGs potentially implicated in HNSCC development. GO and KEGG pathways analyses revealed these co-DEGs to be primarily involved in ECM organization, disassembly, and proteolysis. These findings provide valuable insights into the biological underpinnings of HNSCC and establish a theoretical foundation for further research. Furthermore, we successfully constructed a PPI network of co-DEGs in HNSCC, identifying hub genes playing crucial roles in disease progression. By utilizing various databases, we validated the impact of hub gene up-regulation and down-regulation on HNSCC development. These results significantly enhance our understanding of HNSCC pathogenesis and the molecular mechanisms driving its occurrence and progression. Our research possesses significant clinical implications, paving the way for improved detection, treatment, and prevention of HNSCC. Notably, identified hub genes like \u003cem\u003eSPP1\u003c/em\u003e and \u003cem\u003eKRT78\u003c/em\u003e emerge as potential targets for effective therapeutic inventions. However, thorough clinical, pharmacological, and biological studies, including \u003cem\u003ein vivo\u003c/em\u003e and \u003cem\u003ein vitro\u003c/em\u003e experiments, are essential to definitively confirm the functions of these hub genes and their direct involvement in HNSCC progression.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by a grant from Korea University, Seoul, Republic of Ko-rea (Grant No. K2314111) and the Basic Science Research Program of the National Research Foundation of Korea, funded by the Ministry of Science and Technology and the Ministry of Science, ICT and Future Planning (2017R1A2B2003575 and NRF-2020R1A2C1006398); the Ministry of Science and ICT, Korea, under the ICT Creative Consilience program (IITP-2023-2020-0-01819), supervised by the Institute for Information and Communications Technology Planning and Evaluation (IITP); and the Korea Health Technology R\u0026amp;D Project (HI17C0387, HR22C1302) through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health and Welfare.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJ.C. and T.H.K played a guiding role in carrying out the studies, collecting data and drafting the manuscript. B.K., J.B., and S.J. helped to draft the manuscript. J.K., S.K., and J.S. were responsible for the finalization of the paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll information produced or examined throughout this investigation is incorporated in this published article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors agree with the publication of this original article.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eBhat, G. R., Hyole, R. G. \u0026amp; Li, J. 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Chem. 288, 11555\u0026ndash;11571 (2013).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-3912796/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3912796/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eHead and neck squamous cell carcinoma (HNSCC) is a prevalent global cancer, ranking sixth in incidence. Investigating the molecular basis of tumorigenesis and metastasis is crucial for early detection and effective treatment. Our study utilized three gene expression profile datasets (GSE6791, GSE29330, and GSE58911) to identify co-up or down-regulated differentially expressed genes (DEGs) between HNSCC tumor and normal tissue samples, associated primarily with processes like extracellular matrix (ECM) organization, proteolysis, ECM disassembly, and keratinization. A protein-protein interaction (PPI) network revealed eight hub genes, notably including the up-regulated SPP1 and down-regulated KRT78. Importantly, these hub genes demonstrated correlations with tumor grade, clinical individual cancer stage, and poor prognosis in patients with HNSCC. The comprehensive bioinformatics-driven investigation not only pinpointed co-DEGs but also illuminated associated pathways, providing valuable insights into the molecular mechanisms steering disease progression. These findings have substantial clinical potential, offering avenues for early diagnosis and the development of innovative therapeutic targets for individuals grappling with HNSCC. The identified genes and pathways contribute to a deeper understanding of the intricate molecular landscape of HNSCC, paving the way for more targeted and effective interventions in the clinical setting.\u003c/p\u003e","manuscriptTitle":"Comprehensive Bioinformatic Analysis for Identification of Crucial Genes and Signaling Transduction Pathways in Head and Neck Squamous Cell Carcinoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-15 15:49:43","doi":"10.21203/rs.3.rs-3912796/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":"dada9f99-851e-4f21-9a08-9f44053cfc4e","owner":[],"postedDate":"February 15th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":28743518,"name":"Biological sciences/Molecular biology"},{"id":28743519,"name":"Biological sciences/Cancer"},{"id":28743520,"name":"Biological sciences/Cancer/Head and neck cancer"},{"id":28743521,"name":"Biological sciences/Cancer/Head and neck cancer/Oral cancer"},{"id":28743522,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":28743523,"name":"Biological sciences/Computational biology and bioinformatics/Databases"}],"tags":[],"updatedAt":"2024-10-07T04:23:59+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-15 15:49:43","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3912796","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3912796","identity":"rs-3912796","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}