[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125028-en":3,"doc-seo-125028-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},125028,8796095461564,"Liam","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Machine-learning derived identification of prognostic signature to forecast head and neck squamous cell carcinoma prognosis and drug response","Head and neck squamous cell carcinoma (HNSCC) is highly heterogeneous, with limited effectiveness of surgery alone and a continued need for reliable biomarkers. A machine-learning derived prognostic model (MLDPM) was built to predict outcomes and stratify risk, using multiple survival-oriented algorithms, time-dependent ROC and Kaplan–Meier evaluation, and validation via nomogram, calibration, and Cox analyses. Differential expression, immune profiling, GSEA, and drug sensitivity IC50 predictions linked high-risk biology to immune escape and altered therapeutic response.","TYPE Original Research PUBLISHED 19 December 2024 DOI 10.3389/fimmu.2024.1469895  \nOPEN ACCESS  \nEDITED BY  \nYongyan Wu,  \nLonggang Otolaryngology Hospital, China  \nREVIEWED BY  \nRuo Wang,  \nShanghai Jiao Tong University, China Feng Jiang,  \nFudan University, China  \n*CORRESPONDENCE  \nLiu-Qing Zhou  \n [2013xh0823@hust.edu.cn](2013xh0823@hust.edu.cn)[ ](2013xh0823@hust.edu.cn)Tao Zhou  \n [entzt2013@sina.cn](entzt2013@sina.cn)  \n†These authors have contributed equally to this work  \nRECEIVED 24 July 2024  \nACCEPTED 29 November 2024  \nPUBLISHED 19 December 2024  \nCITATION  \nLi S-Z, Sun H-Y, Tian Y, Zhou L-Q and Zhou T (2024) Machine-learning derived identification of prognostic signature to forecast head and neck squamous cell carcinoma prognosis and drug response. Front. Immunol. 15:1469895 .  \ndoi: 10.3389/fimmu.2024.1469895  \nCOPYRIGHT  \n© 2024 Li, Sun, Tian, Zhou and Zhou. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nMachine-learning derived identification of prognostic signature to forecast head and neck squamous cell carcinoma prognosis and drug response  \nSha-Zhou Li 1†, Hai-Ying Sun 1†, Yuan Tian2†, Liu-Qing Zhou 1* and Tao Zhou 1*  \n1 Department of Otorhinolaryngology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China, 2 Department of Geriatrics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China  \nIntroduction: Head and neck squamous cell carcinoma (HNSCC), a highly heterogeneous malignancy is often associated with unfavorable prognosis. Due to its unique anatomical position and the absence of effective early inspection methods, surgical intervention alone is frequently inadequate for achieving complete remission. Therefore, the identification of reliable biomarker is crucial to enhance the accuracy of screening and treatment strategies for HNSCC.  \nMethod: To develop and identify a machine learning-derived prognostic model (MLDPM) for HNSCC, ten machine learning algorithms, namely CoxBoost, elastic network (Enet), generalized boosted regression modeling (GBM), Lasso, Ridge, partial least squares regression for Cox (plsRcox), random survival forest (RSF), stepwise Cox, supervised principal components (SuperPC), and survival support vector machine (survival-SVM), along with 81 algorithm combinations were utilized. Time-dependent receiver operating characteristics (ROC) curves and Kaplan-Meier analysis can effectively assess the model’s predictive performance. Validation was performed through a nomogram, calibration curves, univariate and multivariate Cox analysis. Further analyses included immunological profiling and gene set enrichment analyses (GSEA). Additionally, the prediction of 50% inhibitory concentration (IC50) of potential drugs between groups was determined.  \nResults: From analyses in the HNSCC tissues and normal tissues, we found 536 differentially expressed genes (DEGs) . Subsequent univariate-cox regression analysis narrowed this list to 18 genes. A robust risk model, outperforming other clinical signatures, was then constructed using machine learning techniques. The MLDPM indicated that high-risk scores showed a greater propensity for immune escape and reduced survival rates. Dasatinib and 7 medicine showed the superior sensitivity to the high-risk NHSCC, which had potential to the clinical.  \nFrontiers in Immunology 01 [frontiersin.org](frontiersin.org)  \nConclusions: The construction of MLDPM effectively eliminated artificial bias by utilizing 101 algorithm combinations. This model demonstrated hig","cbCaijsf3emBeeus","https://ap.wps.com/l/cbCaijsf3emBeeus","pdf",9912640,1,11,"English","en",105,"# Introduction\n## Immunotherapy challenges\n# Method\n## Machine-learning prognostic model construction\n## Statistical validation and analyses\n# Results\n## Differential genes and risk model performance\n## Immune escape and drug sensitivity\n# Conclusions\n## Personalized treatment and therapeutic targets","[{\"question\":\"What problem does the study address for HNSCC?\",\"answer\":\"HNSCC shows poor prognosis and strong heterogeneity, and surgery alone often cannot achieve complete remission. Reliable biomarkers are needed to improve screening, treatment planning, and therapeutic targeting.\"},{\"question\":\"How was the machine-learning prognostic model (MLDPM) constructed and validated?\",\"answer\":\"Ten machine-learning survival algorithms and 81 algorithm combinations were used. Predictive performance was assessed with time-dependent ROC and Kaplan–Meier analysis, and validation used a nomogram, calibration curves, and univariate/multivariate Cox analyses.\"},{\"question\":\"What biological and therapeutic findings were associated with high-risk patients?\",\"answer\":\"High-risk scores were linked to greater immune escape tendencies and reduced survival. The study also evaluated drug sensitivity by comparing IC50 values between groups, identifying dasatinib and additional medicines with superior sensitivity in the high-risk group.\"}]","Machine-learning derived identification of prognostic signature to forecast head and neck squamous cell carcinoma prognosis and drug response | PDF",1785896246,28,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-derived-identification-of-prognostic-signature-to-forecast-head-and-neck-squamous-cell-carcinoma-prognosis-and-drug-response","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-derived-identification-of-prognostic-signature-to-forecast-head-and-neck-squamous-cell-carcinoma-prognosis-and-drug-response/125028/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the study address for HNSCC?","Question",{"text":75,"@type":76},"HNSCC shows poor prognosis and strong heterogeneity, and surgery alone often cannot achieve complete remission. Reliable biomarkers are needed to improve screening, treatment planning, and therapeutic targeting.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How was the machine-learning prognostic model (MLDPM) constructed and validated?",{"text":80,"@type":76},"Ten machine-learning survival algorithms and 81 algorithm combinations were used. Predictive performance was assessed with time-dependent ROC and Kaplan–Meier analysis, and validation used a nomogram, calibration curves, and univariate/multivariate Cox analyses.",{"name":82,"@type":73,"acceptedAnswer":83},"What biological and therapeutic findings were associated with high-risk patients?",{"text":84,"@type":76},"High-risk scores were linked to greater immune escape tendencies and reduced survival. The study also evaluated drug sensitivity by comparing IC50 values between groups, identifying dasatinib and additional medicines with superior sensitivity in the high-risk group.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]