[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122604-en":3,"doc-seo-122604-105":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},122604,1099513958607,"Jiven","https://ap-avatar.wpscdn.com/avatar/100002390cf8733938c?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778829742770036399",8,"Research & Report","Identification of m6A-related signature genes in esophageal squamous cell carcinoma by machine learning method","This original research constructs and validates an esophageal squamous cell carcinoma (ESCC) m6A regulator framework using machine learning on transcriptomic datasets. Using ESCC RNA-seq from 66 paired samples and TCGA-ESCA data, Random Forest and SVM identify two m6A regulators, YTHDF1 and HNRNPC, with increased expression in ESCC. A two-gene prognostic signature is then built and validated as an independent predictor. Western blot and immunohistochemistry confirm the bioinformatics findings and support evidence for risk stratification and target exploration.","TYPE Original Research PUBLISHED 17 January 2023  \nDOI 10.3389/fgene.2023.1079795  \nOPEN ACCESS  \nEDITED BY  \nYuanji Xu,  \nFujian Medical University Cancer Hospital, China  \nREVIEWED BY  \nPing ’An Ding,  \nFourth Hospital of Hebei Medical University, China  \nWenjing Zhu,  \nQingdao Municipal Hospital, China  \n*CORRESPONDENCE  \nLong-Qi Chen,  \n [drchenlq@scu.edu.cn](drchenlq@scu.edu.cn)  \nSPECIALTY SECTION  \nThis article was submitted to Cancer Genetics and Oncogenomics, a section of the journal  \nFrontiers in Genetics  \nRECEIVED 25 October 2022  \nACCEPTED 03 January 2023  \nPUBLISHED 17 January 2023  \nCITATION  \nShang Q-X, Kong W-L, Huang W-H, Xiao X, Hu W-P, Yang Y-S, Zhang H, Yang L, YuanY and Chen L-Q (2023), Identification of m6a-related signature genes in esophageal squamous cell carcinoma by machine learning method.  \nFront. Genet. 14:1079795 .  \ndoi: 10.3389/fgene.2023.1079795  \nCOPYRIGHT  \n© 2023 Shang, Kong, Huang, Xiao, Hu, Yang, Zhang, Yang, Yuan and Chen. This isan 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.  \nIdentification of m6a-related signature genes in esophageal squamous cell carcinoma by machine learning method  \nQi-Xin Shang, Wei-Li Kong, Wen-Hua Huang, Xin Xiao,  \nWei-Peng Hu, Yu-Shang Yang, Hanlu Zhang, Lin Yang, Yong Yuan and Long-Qi Chen*  \nWest China Hospital, Sichuan University, Chengdu, Sichuan, China  \nBackground: We aimed to construct and validate the esophageal squamous cell carcinoma (ESCC)-related m6A regulators by means of machine leaning.  \nMethods: We used ESCC RNA-seq data of 66 pairs of ESCC from West China Hospital of Sichuan University and the transcriptome data extracted from The Cancer Genome Atlas (TCGA)-ESCA database to find out the ESCC-related m6A regulators, during which, two machine learning approaches: RF (Random Forest) and SVM (Support Vector Machine) were employed to construct the model of ESCCrelated m6A regulators. Calibration curves, clinical decision curves, and clinical impact curves (CIC) were used to evaluate the predictive ability and best-effort ability of the model. Finally, western blot and immunohistochemistry staining were used to assess the expression of prognostic ESCC-related m6A regulators.  \nResults: 2 m6A regulators (YTHDF1 and HNRNPC) were found to be significantly increased in ESCC tissues after screening out through RF machine learning methods from our RNA-seq data and TCGA-ESCA database, respectively, and overlapping the results of the two clusters. A prognostic signature, consisting of YTHDF1 and HNRNPC, was constructed based on our RNA-seq data and validated on TCGAESCA database, which can serve as an independent prognostic predictor. Experimental validation including the western and immunohistochemistry staining were further successfully confirmed the results of bioinformatics analysis.  \nConclusion: We constructed prognostic ESCC-related m6A regulators and validated the model in clinical ESCC cohort as well as in ESCC tissues, which provides reasonable evidence and valuable resources for prognostic stratification and the study of potential targets for ESCC.  \nKEYWORDS  \nM6A, RNA methylation, esophageal squamous cell carcinoma, machine learning, experimental validation  \nAbbreviations: AUC, Area Under Curve; CIC, Clinical Impact Curves; EC, Esophageal cancer; ESCC/ESCA, Esophageal quamous cell carcinoma; HR, Hazard Ratio; HRP, Horseradish Peroxidase; HNRNPC, RNAbinding protein, is a member of the heterogeneous ribonucleoproteins C; IGF2BP1-3, Insulin-like growth factor 2 mRNA-binding proteins 1-3; m6A, N6-methyladenosine; METTL3, Methyltransferase-like 3; METTL14, Methylt","cbCaimKpMdVSmXH3","https://ap.wps.com/l/cbCaimKpMdVSmXH3","pdf",2999459,1,14,"English","en",105,"# Background\n# Methods\n## Machine learning model construction\n## Predictive evaluation\n## Experimental validation\n# Results\n# Conclusion","[{\"question\":\"What was the study goal regarding ESCC and m6A?\",\"answer\":\"To construct and validate ESCC-related m6A regulators and derive a prognostic m6A-based signature using machine learning.\"},{\"question\":\"Which machine learning methods and datasets were used?\",\"answer\":\"Random Forest (RF) and Support Vector Machine (SVM) were applied to ESCC RNA-seq data (66 paired samples) and TCGA-ESCA transcriptome data.\"},{\"question\":\"What did the study find about the prognostic model?\",\"answer\":\"YTHDF1 and HNRNPC formed a two-gene prognostic signature that showed predictive value and was validated on the TCGA-ESCA database.\"}]","Identification of m6A-related signature genes in esophageal squamous cell carcinoma by machine 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