[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127511-en":3,"doc-seo-127511-105":30,"detail-sidebar-cat-0-en-105":92},{"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":20,"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},127511,13056712833777,"Logic","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Clustering and machine learning-based integration identify cancer-associated fibroblasts genes’ signature in head and neck squamous cell carcinoma","Clustering and machine learning-based analyses are used to dissect cancer-associated fibroblasts (CAFs) in head and neck squamous cell carcinoma (HNSCC), where CAF infiltration drives tumor progression yet targeted CAF trials have yielded limited success. The study defines two CAF gene expression patterns, quantifies enrichment with ssGSEA, and evaluates mechanisms using multiple analytic approaches. An integrated multi-algorithm framework builds a stable risk model to stratify prognosis and highlights immune suppression, pathway activation, and potential targets.","TYPE Original Research PUBLISHED 30 March 2023  \nDOI 10.3389/fgene.2023.1111816  \nOPEN ACCESS  \nEDITED BY  \nMinglun Li,  \nLMU Munich University Hospital, Germany  \nREVIEWED BY  \nDenggang Fu,  \nIndiana University, United States Gengming Cai,  \nFujian Medical University, China  \n*CORRESPONDENCE  \nGuolin Tan,  \n [guolintan@csu.edu.cn](guolintan@csu.edu.cn)  \nSPECIALTY SECTION  \nThis article was submitted to Cancer Genetics and Oncogenomics, a section of the journal  \nFrontiers in Genetics  \nRECEIVED 30 November 2022  \nACCEPTED 16 March 2023  \nPUBLISHED 30 March 2023  \nCITATION  \nWang Q, Zhao Y, Wang F and Tan G (2023), Clustering and machine learningbased integration identify cancer associated fibroblasts genes’ signature in head and neck squamous cell carcinoma. Front. Genet. 14:1111816 .  \ndoi: 10.3389/fgene.2023.1111816  \nCOPYRIGHT  \n© 2023 Wang, Zhao, Wang and Tan. 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.  \nClustering and machine learning-based integration identify cancer associated fibroblasts genes’ signature in head and neck squamous cell carcinoma  \nQiwei Wang 1, Yinan Zhao 2, Fang Wang 3 and Guolin Tan 4*  \n1Department of Otolaryngology Head and Neck Surgery, Third Xiangya Hospital, Central South University, Changsha, Hunan, China, 2Xiangya School of Nursing, Central South University, Changsha, Hunan, China, 3Department of Otorhinolaryngology/Head and Neck Surgery, University Hospital Rechts der Isar, Technical University of Munich, Munich, Bavaria, Germany, 4Third Xiangya Hospital, Central South University, Changsha, China  \nBackground: A hallmark signature of the tumor microenvironment in head and neck squamous cell carcinoma (HNSCC) is abundantly infiltration of cancerassociated fibroblasts (CAFs), which facilitate HNSCC progression. However, some clinical trials showed targeted CAFs ended in failure, even accelerated cancer progression. Therefore, comprehensive exploration of CAFs should solve the shortcoming and facilitate the CAFs targeted therapies for HNSCC.  \nMethods: In this study, we identified two CAFs gene expression patterns and performed the single-sample gene set enrichment analysis (ssGSEA) to quantify the expression and construct score system. We used multi-methods to reveal the potential mechanisms of CAFs carcinogenesis progression. Finally, we integrated 10 machine learning algorithms and 107 algorithm combinations to construct most accurate and stable risk model. The machine learning algorithms contained random survival forest (RSF), elastic network (Enet), Lasso, Ridge, stepwise Cox, CoxBoost, partial least squares regression for Cox (plsRcox), supervised principal components (SuperPC), generalised boosted regression modelling (GBM), and survival support vector machine (survival-SVM) .  \nResults: There are two clusters present with distinct CAFs genes pattern. Compared to the low CafS group, the high CafS group was associated with significant immunosuppression, poor prognosis, and increased prospect of HPV negative. Patients with high CafS also underwent the abundant enrichment of carcinogenic signaling pathways such as angiogenesis, epithelial mesenchymal transition, and coagulation. The MDK and NAMPT ligand–receptor cellular crosstalk between the cancer associated fibroblasts and other cell clusters may mechanistically cause immune escape. Moreover, the random survival forest prognostic model that was developed from 107 machine learning algorithm combinations could most accurately classify HNSCC patients.  \nConclusion: We revealed that CAFs would cause the activation of some carcinogenesis pathways suc","cbCaissHKQ3uTZO9","https://ap.wps.com/l/cbCaissHKQ3uTZO9","pdf",4733871,1,14,"English","en",105,"# Background\n# Methods\n# Results\n# Conclusion\n# Introduction\n## Key points and rationale\n## Research gap in CAF-targeted therapy","[{\"question\":\"What problem does this study address in HNSCC CAF research?\",\"answer\":\"It addresses why targeted CAF therapies have often failed despite the known role of CAF infiltration in promoting HNSCC progression, by seeking a more comprehensive CAF characterization using gene signatures.\"},{\"question\":\"How were CAF gene expression patterns and scores constructed?\",\"answer\":\"The study identifies two CAF gene expression patterns and applies single-sample gene set enrichment analysis (ssGSEA) to quantify expression and construct a scoring system.\"},{\"question\":\"What key findings relate CAFs to prognosis and tumor biology?\",\"answer\":\"The high-score CAF group shows immunosuppression, poor prognosis, enrichment of carcinogenic pathways, and a prognostic model that accurately classifies HNSCC patients using many algorithm combinations.\"}]","Clustering and machine learning-based integration identify cancer-associated fibroblasts genes’ signature in head and neck squamous cell carcinoma | 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