[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127923-en":3,"doc-seo-127923-105":31,"detail-sidebar-cat-0-en-105":93},{"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":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},127923,687207024478,"Liam","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Machine learning-based diagnostic model of lymphatics-associated genes for new therapeutic target analysis in intervertebral disc degeneration","Machine learning-based analysis identifies lymphatics-associated genes (LAGs) linked to intervertebral disc degeneration (IVDD), addressing the unclear molecular mechanisms behind lymphatic vessel distribution differences between normal and pathological discs. Gene Expression Omnibus datasets are quality-controlled, normalized, and merged for training with an external dataset used for validation. Differential LAGs are derived from multiple gene resources, then four algorithms construct diagnostic models. The diagnostic signatures are evaluated by ROC, nomogram, and decision curve analysis, with regulatory drug and ceRNA networks explored for candidate therapeutic targets.","TYPE Original Research PUBLISHED 04 December 2024 DOI 10.3389/fimmu.2024.1441028  \nOPEN ACCESS  \nEDITED BY  \nMusie Ghebremichael,  \nHarvard University, United States  \nREVIEWED BY  \nFeng Jiang,  \nFudan University, China Jun Jiang,  \nFudan University, China  \n*CORRESPONDENCE  \nPengjie Song  \n [675680048@qq.com](675680048@qq.com)[ ](675680048@qq.com)Haiyu Zhou  \n [zhouhy@lzu.edu.cn](zhouhy@lzu.edu.cn)  \n†These authors have contributed equally to this work  \nRECEIVED 31 May 2024  \nACCEPTED 11 November 2024  \nPUBLISHED 04 December 2024  \nCITATION  \nLin M, Li S, Wang Y, Zheng G, Hu F, Zhang Q, Song P and Zhou H (2024) Machine learningbased diagnostic model of lymphaticsassociated genes for new therapeutic target analysis in intervertebral disc degeneration. Front. Immunol. 15:1441028 .  \ndoi: 10.3389/fimmu.2024.1441028  \nCOPYRIGHT  \n© 2024 Lin, Li, Wang, Zheng, Hu, Zhang, Song 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-based diagnostic model of lymphaticsassociated genes for new therapeutic target analysis in intervertebral disc degeneration  \nMaoqiang Lin 1,2†, Shaolong Li 1†, Yabin Wang 1,2†, Guan Zheng 1,2, Fukang Hu 1,2, Qiang Zhang 1,2, Pengjie Song 1* and Haiyu Zhou 1,2*  \n1 Department of Orthopedics, The Second Hospital & Clinical Medical School, Lanzhou University, Lanzhou, Gansu, China, 2 Key Laboratory of Bone and Joint Disease Research of Gansu Province, Lanzhou, Gansu, China  \nBackground: Low back pain resulting from intervertebral disc degeneration (IVDD) represents a significant global social problem. There are notable differences in the distribution of lymphatic vessels (LV) in normal and pathological intervertebral discs. Nevertheless, the molecular mechanisms of lymphatics-associated genes (LAGs) in the development of IVDD remain unclear. An in-depth exploration of this area will help to reveal the biological and clinical significance of LAGs in IVDD and may lead to the search for new therapeutic targets for IVDD.  \nMethods: Data sets were obtained from the Gene Expression Omnibus (GEO) database. Following quality control and normalization, the datasets (GSE153761, GSE147383, and GSE124272) were merged to form the training set, with GSE150408 serving as the validation set. LAGs from GeneCards, MSigDB, Gene Ontology, and KEGG database. The Venn diagram was employed to identify differentially expressed lymphatic-associated genes (DELAGs) that were differentially expressed in the normal and IVDD groups. Subsequently, four machine learning algorithms (SVM-RFE, Random Forest, XGB, and GLM) were used to select the method to construct the diagnostic model. The receiver operating characteristic (ROC) curve, nomogram, and Decision Curve Analysis (DCA) were used to evaluate the model effect. In addition, we constructed a potential drug regulatory network and competitive endogenous RNA (ceRNA) network for key LAGs.  \nResults: A total of 15 differentially expressed LAGs were identified. By comparing four machine learning methods, the top five genes of importance in the XGB model (MET, HHIP, SPRY1, CSF1, TOX) were identified as lymphatics-associated gene diagnostic signatures. This signature was used to predict the diagnosis of IVDD with strong accuracy and an area under curve(AUC) value of0 .938. Furthermore, the diagnostic model was validated in an external dataset (GSE150408), with an AUC value of 0 .772. The nomogram and DCA further prove that the diagnosis model has good performance and predictive value. Additionally, drug regulatory networks and ceRNA networks were constructed, revealing potential t","cbCaipu00kI27Knk","https://ap.wps.com/l/cbCaipu00kI27Knk","pdf",6857936,5,1,16,"English","en",105,"# Background\n## Lymphatic-associated genes and IVDD relevance\n# Methods\n## Data processing and integration\n## Differential gene identification and feature selection\n## Model construction and evaluation\n## Regulatory network analysis\n# Results\n## Differential LAGs and diagnostic signatures\n## Validation and predictive performance\n## Nomogram/DCA and network findings\n# Conclusion","[{\"question\":\"What problem does the diagnostic model address in IVDD research?\",\"answer\":\"It targets IVDD-related low back pain by focusing on lymphatics-associated genes whose molecular roles in IVDD development remain unclear.\"},{\"question\":\"How were datasets used to build and validate the model?\",\"answer\":\"Multiple GEO datasets were merged after quality control and normalization to form the training set, while a separate GEO dataset was used as the validation set.\"},{\"question\":\"Which evaluation tools were used to assess model performance?\",\"answer\":\"ROC curves, a nomogram, and decision curve analysis (DCA) were used to evaluate diagnostic effectiveness and predictive value.\"}]","Machine learning-based diagnostic model of lymphatics-associated genes for new therapeutic target analysis in intervertebral disc degeneration | 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