[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127564-en":3,"doc-seo-127564-105":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},127564,687207020761,"Patrick","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Machine learning-based identification of glycosyltransferase-related mRNAs for improving outcomes and the anti-tumor therapeutic response of gliomas","Glycosyltransferases shape glycosylation and their dysregulation contributes to glioma carcinogenesis, progression, and immune evasion, yet their links to clinical outcomes and treatment response remain insufficiently characterized. Differentially expressed genes were analyzed using public expression databases, and 10 survival-oriented machine learning models were integrated into a glycosyltransferase-related mRNA signature (GRMS). GRMS stratified patients with robust, independent prognostic value and associated immune features.","TYPE Original Research PUBLISHED 16 August 2023  \nDOI 10.3389/fphar.2023.1200795  \nOPEN ACCESS  \nEDITED BY  \nChen Ling,  \nFudan University, China  \nREVIEWED BY  \nJiajia Chen,  \nFudan University, China Chen Zhong,  \nFudan University, China  \n*CORRESPONDENCE  \nWei Zhou,  \n [zhou_wei66@163.com](zhou_wei66@163.com)  \nRECEIVED 05 April 2023  \nACCEPTED 02 August 2023  \nPUBLISHED 16 August 2023  \nCITATION  \nZhang C and Zhou W (2023), Machine learning-based identification of glycosyltransferase-related mRNAs for improving outcomes and the anti-tumor therapeutic response of gliomas.  \nFront. Pharmacol. 14:1200795 .  \ndoi: 10.3389/fphar.2023.1200795  \nCOPYRIGHT  \n© 2023 Zhang and Zhou. This is an openaccess 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-based identification of glycosyltransferase-related mRNAs for improving outcomesand the anti-tumor therapeutic response of gliomas  \nChunyu Zhang 1 and Wei Zhou 2*  \n1School of Medicine, Tongji University, Shanghai, China, 2Department of Anesthesiology, Huzhou Central Hospital, The Affiliated Huzhou Hospital, Zhejiang University School of Medicine, Huzhou, Zhejiang, China  \nBackground: Glycosyltransferase participates in glycosylation modification, and glycosyltransferase alterations are involved in carcinogenesis, progression, and immune evasion, leading to poor outcomes. However, in-depth studies on the influence of glycosyltransferase on clinical outcomes and treatments are lacking.  \nMethods: The analysis of differentially expressed genes was performed using the Gene Expression Profiling Interactive Analysis 2 database. A total of 10 machine learning algorithms were introduced, namely, random survival forest, elastic network, least absolute shrinkage and selection operator, Ridge, stepwise Cox, CoxBoost, partial least squares regression for Cox, supervised principal components, generalized boosted regression modeling, and survival support vector machine. Gene Set Enrichment Analysis was performed to explore signaling pathways regulated by the signature. Cell-type identification by estimating relative subsets of RNA transcripts was used for estimating the fractions of immune cell types.  \nResults: Here, we analyzed the genomic and expressive alterations inglycosyltransferase-related genes in gliomas. A combination of 80 machine learning algorithms was introduced to establish the glycosyltransferase-related mRNA signature (GRMS) based on 2,030 glioma samples from The Cancer Genome Atlas Program, Chinese Glioma Genome Atlas, Rembrandt, Gravendeel, and Kamoun cohorts. The GRMS was identified as an independent hazardous factor for overall survival and exhibited stable and robust performance. Notably, gliomas in the high-GRMS subgroup exhibited abundant tumorinfiltrating lymphocytes and tumor mutation burden values, increased expressive levels of hepatitis A virus cellular receptor 2 and CD274, and improved progression-free survival when subjected to anti-tumor immunotherapy.  \nConclusion: The GRMS may act as a powerful and promising biomarker for improving the clinical prognosis of glioma patients.  \nKEYWORDS  \nglioma, glycosyltransferase, machine learning algorithms, prognosis, biomarker  \nFrontiers in Pharmacology 01 [frontiersin.org](frontiersin.org)  \nIntroduction  \nGlioma is the most frequently occurring type of brain cancer, and its most aggressive pathological form is known as brain glioblastoma (Louis et al., 2021) . Gliomas have significant heterogeneity, and recent research has reported the tools for further classification of gliomas (Carlson et al., 2021; Rui et al., 2023) . In clinical interventi","cbCaisYKPlABDQGu","https://ap.wps.com/l/cbCaisYKPlABDQGu","pdf",7161403,1,16,"English","en",105,"# Introduction\n## Background and clinical challenge\n# Methods\n## Differential gene analysis and machine learning models\n## Pathway analysis and immune cell estimation\n# Results\n## GRMS construction and prognostic performance\n## Immune and therapeutic response associations\n# Conclusion","[{\"question\":\"What problem does the study address about glycosyltransferase in glioma?\",\"answer\":\"It targets the lack of in-depth evidence connecting glycosyltransferase-related changes to clinical outcomes and anti-tumor treatment response in glioma.\"},{\"question\":\"How was the glycosyltransferase-related mRNA signature (GRMS) built?\",\"answer\":\"The work analyzed differentially expressed genes and integrated multiple machine learning algorithms to establish the GRMS based on 2,030 glioma samples from several cohorts.\"},{\"question\":\"What clinical relevance did GRMS show for glioma patients?\",\"answer\":\"GRMS acted as an independent hazardous factor for overall survival and showed stable performance, with the high-GRMS group displaying immune-related characteristics and improved progression-free survival under anti-tumor immunotherapy.\"}]","Machine learning-based identification of glycosyltransferase-related mRNAs for improving outcomes and the anti-tumor therapeutic response of gliomas | 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