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Materials and methods: Use TCGA data with CIBERSORT to quantify immune cells and DESeq2 to identify differentially expressed genes; analyze TCMA microbial profiles with LEfSe to detect differential microbes across immune-cell-infiltration subgroups; apply Random Forest and deep neural network feature selection to build a prognosis model. Results: Define three immune subtypes and show the IRM model outperforms TNM for survival prediction, linking higher scores with worse prognosis. Low-risk patients show higher sensitivity to cisplatin and sorafenib.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & Report",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/document/multi-omics-analysis-of-immune-related-microbiome-and-prognostic-model-in-head-and-neck-squamous-cell-carcinoma/327461/",4,{"url":32,"name":10,"@type":35,"image":36,"author":41,"headline":10,"publisher":44,"fileFormat":47,"inLanguage":8,"description":12,"dateModified":48,"datePublished":49,"encodingFormat":47,"isAccessibleForFree":50,"interactionStatistic":51},"DigitalDocument",{"url":37,"@type":38,"width":39,"height":40},"https://docshare.wps.com/thumbnails/multi-omics-analysis-of-immune-related-microbiome-and-prognostic-model-in-head-and-neck-squamous-cell-carcinoma/327461.png","ImageObject",300,407,{"name":42,"@type":43},"Emma Wilson","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-10-29","2026-09-21",true,{"@type":52,"interactionType":53,"userInteractionCount":55},"InteractionCounter",{"@type":54},"ViewAction",8,{"@type":57,"mainEntity":58},"FAQPage",[59,65,69],{"name":60,"@type":61,"acceptedAnswer":62},"What is the main objective of the study?","Question",{"text":63,"@type":64},"The study aims to investigate transcriptional and microbial characteristics of immune phenotypes in head and neck cancer using a multi-omics approach.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"Which datasets and analytical methods are used?",{"text":68,"@type":64},"TCGA data are used with CIBERSORT for immune cell estimation and DESeq2 for differential gene expression. TCMA microbial profiles are analyzed with LEfSe, and prognosis modeling uses Random Forest and a deep neural network.",{"name":70,"@type":61,"acceptedAnswer":71},"How does the immune-related microbiome (IRM) model perform compared with TNM staging?",{"text":72,"@type":64},"The IRM model outperforms the TNM staging model in predicting survival, with higher IRM scores associated with poorer prognosis and demonstrated clinical utility.","https://schema.org",{"og:url":32,"og:type":75,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":77,"canonical":32},"index,follow",{"doc_id":79,"site_id":7},327461,1790217126,{"code":4,"msg":82,"data":83},"success",[84,88,92,96,101,106,111,114,119,122,126],{"id":22,"doc_module":4,"doc_module_name":25,"category_name":85,"show_sort_weight":86,"slug":87},"Story & Novel",90,"story-novel",{"id":26,"doc_module":4,"doc_module_name":25,"category_name":89,"show_sort_weight":90,"slug":91},"Literature",80,"literature",{"id":33,"doc_module":4,"doc_module_name":25,"category_name":93,"show_sort_weight":94,"slug":95},"Exam",70,"exam",{"id":97,"doc_module":4,"doc_module_name":25,"category_name":98,"show_sort_weight":99,"slug":100},5,"Comic",60,"comic",{"id":102,"doc_module":4,"doc_module_name":25,"category_name":103,"show_sort_weight":104,"slug":105},6,"Technology",50,"technology",{"id":107,"doc_module":4,"doc_module_name":25,"category_name":108,"show_sort_weight":109,"slug":110},7,"Healthcare",40,"healthcare",{"id":55,"doc_module":4,"doc_module_name":25,"category_name":29,"show_sort_weight":112,"slug":113},30,"research-report",{"id":115,"doc_module":4,"doc_module_name":25,"category_name":116,"show_sort_weight":117,"slug":118},9,"Religion & Spirituality",20,"religion-spirituality",{"id":117,"doc_module":4,"doc_module_name":25,"category_name":120,"show_sort_weight":117,"slug":121},"World Cup","world-cup",{"id":123,"doc_module":4,"doc_module_name":25,"category_name":124,"show_sort_weight":123,"slug":125},10,"Lifestyle","lifestyle",{"id":127,"doc_module":4,"doc_module_name":25,"category_name":128,"show_sort_weight":97,"slug":129},19,"General","general",{"code":4,"msg":82,"data":131},{"doc_id":79,"user_id":132,"nickname":42,"user_avatar":133,"doc_module":4,"category_id":55,"category_name":29,"doc_title":10,"doc_description":12,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":55,"is_deleted":4,"is_public":22,"is_downloadable":22,"audit_status":22,"page_count":139,"language":140,"language_code":8,"site_id":7,"html_lang":8,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":12,"update_tm":144,"read_time":112,"preview_page_count":4},3848291630094,"https://eur-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45","Clinical Oral Investigations (2024) 28:263  \n[https://doi.org/10.1007/s00784-024-05645-y](https://doi.org/10.1007/s00784-024-05645-y)  \nRESEARCH  \nMulti-omics analysis of immune-related microbiome and prognostic model in head and neck squamous cell carcinoma  \nYingqiao Liu1 · Haitao Lin1 · Weijun Zhong1 · Yudi Zeng1 · Guihai Zhou1 · Zhifeng Chen1 · Shi Huang2 · Leitao Zhang1 · Xiqiang Liu1  \nReceived: 19 February 2024 / Accepted: 1 April 2024 / Published online: 20 April 2024 © The Author(s) 2024  \nAbstract  \nObjectives The aim of our study is to explore the transcriptional and microbial characteristics of head and neck cancer’s immune phenotypes using a multi-omics approach.  \nMaterials and methods Employing TCGA data, we analyzed head and neck squamous cell carcinoma (HNSCC) immune cells with CIBERSORT and identified differentially expressed genes using DESeq2 . Microbial profiles, obtained from the TCMA database, were analyzed using LEfSe algorithm to identify differential microbes in immune cell infiltration (ICI) subgroups. Random Forest algorithm and deep neural network (DNN) were employed to select microbial features and developed a prognosis model.  \nResults We categorized HNSCC into three immune subtypes, finding ICI-2 with the worst prognosis and distinct microbial diversity. Our immune-related microbiome (IRM) model outperformed the TNM staging model in predicting survival, linking higher IRM model scores with poorer prognosis, and demonstrating clinical utility over TNM staging. Patients categorized as low-risk by the IRM model showed higher sensitivity to cisplatin and sorafenib treatments.  \nConclusions This study offers a comprehensive exploration of the ICI landscape in HNSCC. We provide a detailed scenario of immune regulation in HNSCC and report a correlation between differing ICI patterns, intratumor microbiome, and prognosis. This research aids in identifying prime candidates for optimizing treatment strategies in HNSCC.  \nClinical relevance This study revealed the microbial signatures associated with immunophenotyping of HNSCC and further found the microbial signatures associated with prognosis. The prognostic model based on IRM microbes is helpful for early prediction of patient prognosis and assisting clinical decision-making.  \nKeywords Head and neck squamous cell carcinoma · Immune subtype · Microbiome · Multi-omics · Prognosis  \nIntroduction  \nHead and neck cancer (HNC), ranking as the sixth most common cancer globally, accounts for approximately 30,000 fatalities each year [1] . The predominant  \n􀀍 Leitao Zhang [18665603765@163.com](18665603765@163.com)  \n􀀍 Xiqiang Liu [liuxiqiang@smu.edu.cn](liuxiqiang@smu.edu.cn)  \n1 Department of Oral and Maxillofacial Surgery, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China  \n2 Faculty of Dentistry, The University of Hong Kong, Hong Kong, Hong Kong SAR, China  \npathological subtype of HNC is squamous cell carcinoma [2] . In advanced cases of head and neck squamous cell carcinoma (HNSCC), the primary causes of mortality are local recurrence, cervical lymph node metastases, and resistance to conventional chemotherapy, which often leads to treatment failure. In HNSCC, the tumor microenvironment (TME) is composed of altered tumor cells, immune cells, and stromal cellular components [3] . Extensive research on TME has highlighted the pivotal role of tumor-infiltrating immune cells in tumor progression, recurrence, metastasis, and response to immunotherapy treatments [4] .  \nThe role of microbiota as a key regulator in immune cell activation, inflammation, and cancer progression has been increasingly recognized. Specifically, microbiota influences these biological processes through mechanisms involving nuclear factor kappa B (NF-κB), type I interferon, and  \ninflammasome activation [5] . Moreover, the local immune system’s interaction with gut microbiota plays a crucial role in modulating immune responses, tissue damage, and the development of ca","cbCailiqPkHBRVfN","https://ap.wps.com/l/cbCailiqPkHBRVfN","pdf",4653782,12,"English","# Abstract\n## Objectives\n## Materials and methods\n## Results\n## Conclusions\n## Clinical relevance\n# Introduction\n## Tumor microenvironment and immune regulation\n## Microbiota and cancer progression","[{\"question\":\"What is the main objective of the study?\",\"answer\":\"The study aims to investigate transcriptional and microbial characteristics of immune phenotypes in head and neck cancer using a multi-omics approach.\"},{\"question\":\"Which datasets and analytical methods are used?\",\"answer\":\"TCGA data are used with CIBERSORT for immune cell estimation and DESeq2 for differential gene expression. TCMA microbial profiles are analyzed with LEfSe, and prognosis modeling uses Random Forest and a deep neural network.\"},{\"question\":\"How does the immune-related microbiome (IRM) model perform compared with TNM staging?\",\"answer\":\"The IRM model outperforms the TNM staging model in predicting survival, with higher IRM scores associated with poorer prognosis and demonstrated clinical utility.\"}]","Multi-omics analysis of immune-related microbiome and prognostic model in head and neck squamous cell carcinoma | PDF",1789977250]