[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122458-en":3,"doc-seo-122458-105":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":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},122458,1374391974564,"Clementine","https://ap-avatar.wpscdn.com/avatar/14000253aa45c000a9e?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779874745381141002",8,"Research & Report","Multi-omics and machine learning identify novel biomarkers and therapeutic targets of COVID-19","COVID-19 continues to pose a persistent global health threat, making sensitive and specific biomarkers and immune-mechanism insights crucial for diagnosis, treatment, and prevention. An integrated multi-omics framework combines single-cell RNA sequencing, bulk RNA-seq, and proteomics with machine learning and molecular docking to predict key COVID-19 molecular markers. The study links gene and protein signals to CD8+ T cell abundance and proposes candidate diagnostic auxiliaries and therapeutic targets.","TYPE Original Research PUBLISHED 02 October 2025  \nDOI 10.3389/fimmu.2025.1671936  \nOPEN ACCESS  \nEDITED BY  \nRoberta Rizzo,  \nUniversity of Ferrara, Italy  \nREVIEWED BY  \nKe Lin,  \nFudan University, China Sudarson Sundarrajan, Cancyte Technologies, India  \n*CORRESPONDENCE  \nJi Wang  \n [doctorwang2009@126.com](doctorwang2009@126.com)[ ](doctorwang2009@126.com)Qi Wang  \n[wangqi710@126.com](wangqi710@126.com)  \n†These authors have contributed equally to this work and share first authorship  \nRECEIVED 23 July 2025  \nACCEPTED 15 September 2025  \nPUBLISHED 02 October 2025  \nCITATION  \nZhou Y, Fan P, Zhang H, Han S, Bai M, Wang Jand Wang Q (2025) Multi-omics and machine learning identify novel biomarkers and therapeutic targets of COVID-19 .  \nFront. Immunol. 16:1671936 .  \ndoi: 10.3389/fimmu.2025.1671936  \nCOPYRIGHT  \n© 2025 Zhou, Fan, Zhang, Han, Bai, Wang and Wang. 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.  \nMulti-omics and machine learning identify novel biomarkers and therapeutic targets of COVID-19  \nYumei Zhou 1†, Pengbei Fan 2†, Haiyun Zhang 1,3, Shuai Han 4, Minghua Bai 1, Ji Wang 1* and Qi Wang 1,5*  \n1 National Institute of Traditional Chinese Medicine (TCM) Constitution and Preventive Treatment of Disease, Wangqi Academy of Beijing University of Chinese Medicine, Beijing University of Chinese Medicine, Beijing, China, 2School of Traditional Chinese Medicine, Southern Medical University, Guangzhou, China, 3 Medical Laboratory Center, Dalian Municipal Women and Children’s Medical Center (Group), Dalian, China, 4 Laboratory Animal Center of Inner Mongolia Medical University, Hohhot, China, 5 Hubei Shizhen Laboratory, Hubei University of Chinese Medicine, Wuhan, China  \nIntroduction: COVID-19 has caused over 7 million deaths worldwide since its onset in 2019, and the virus remains a significant health threat. Identifying sensitive and specific biomarkers, along with elucidating immune-mediated mechanisms, is essential for improving the diagnosis, treatment, and prevention of COVID-19 . To predict key molecular markers of COVID-19 using an established multi-omics framework combined with machine learning models. Methods: We conducted an integrated analysis of single-cell RNA sequencing (scRNA-seq), bulk RNA sequencing, and proteomics data to identify critical biomarkers associated with COVID-19 . The multi-omics approach enabled the characterization of gene expression dynamics and alterations in immune cell subsets in COVID-19 patients. Machine learning techniques and molecular docking analyses were employed to identify biomarkers and therapeutic targets within the disease’s pathophysiological network.  \nResults: Principal component analysis effectively grouped samples based on clinical characteristics. Using random forest and SVM-RFE models, we identified clinical indicators capable of accurately distinguishing COVID-19 patients. Transcriptomic analysis, including scRNA-seq, highlighted the pivotal role of CD8+ T cells, and WGCNA identified related module genes. Proteomic analysis, integrated with machine learning, revealed 36 DEPs. Further investigation identified several genes associated with monocyte proportions. Correlation analysis showed that BTD, CFL1, PIGR, and SERPINA3 were strongly linked to CD8+ T cell abundance in COVID-19 patients. ROC curve analysis demonstrated that these genes could effectively distinguish between COVID-19 patients and healthy individuals. Concordant findings from both transcriptomic and proteomic levels support BTD, CFL1, PIGR, and SERPINA3 as potential auxiliary diagnostic markers . Finally, Alpha ","cbCaibXaCSAsC68e","https://ap.wps.com/l/cbCaibXaCSAsC68e","pdf",21937442,1,17,"English","en",105,"# Introduction\n# Methods\n# Results\n# Discussion","[{\"question\":\"What data types were integrated to identify COVID-19 biomarkers?\",\"answer\":\"The study integrated single-cell RNA sequencing, bulk RNA sequencing, and proteomics to discover biomarkers associated with COVID-19.\"},{\"question\":\"Which analysis strategy helped distinguish COVID-19 patients from controls?\",\"answer\":\"Random forest and SVM-RFE models were used with clinical indicators, while ROC curve analysis evaluated diagnostic performance for candidate genes.\"},{\"question\":\"What potential biomarkers and therapeutic targets were suggested?\",\"answer\":\"BTD, CFL1, PIGR, and SERPINA3 showed strong links to CD8+ T cell abundance and diagnostic value, and AlphaFold-based docking suggested these biomarkers could be candidate therapeutic targets.\"}]","Multi-omics and machine learning identify 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