[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127541-en":3,"doc-seo-127541-105":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},127541,687207017582,"Himbo","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Identification of diagnostic biomarkers in Alzheimer’s disease by integrated bioinformatic analysis and machine learning strategies","Alzheimer’s disease demands more effective diagnostic indicators and biomarkers that also mirror disease progression. This study integrates multiple AD frontal cortex datasets and applies integrated bioinformatic pathway analyses with machine-learning workflows to discover candidate biomarkers. Functional enrichment using GO, KEGG, and Reactome highlights immune response and oxidative stress pathways. TXNIP, EGR1, and IGFBP5 are selected and their diagnostic performance is validated across independent datasets using AUC metrics, including combined-biomarker models.","TYPE Original Research PUBLISHED 26 June 2023  \nDOI 10.3389/fnagi.2023.1169620  \nOPEN ACCESS  \nEDITED BY  \nJiehui Jiang,  \nShanghai University, China  \nREVIEWED BY  \nFiroz Akhter,  \nStony Brook University, United States Dhiraj,  \nNational Eye Institute (NIH), United States  \n*CORRESPONDENCE  \nShaoming Sang  \n [sangshaoming6202@126.com](sangshaoming6202@126.com)[ ](sangshaoming6202@126.com)Chunjiu Zhong  \n [zhongcj@163.com](zhongcj@163.com)  \n†These authors have contributed equally to this work  \nRECEIVED 19 February 2023  \nACCEPTED 08 June 2023  \nPUBLISHED 26 June 2023  \nCITATION  \nJin B, Cheng X, Fei G, Sang S and Zhong C (2023) Identification of diagnostic biomarkers in Alzheimer’s disease by integrated bioinformatic analysis and machine learning strategies.  \nFront. Aging Neurosci. 15:1169620 .  \ndoi: 10.3389/fnagi.2023.1169620  \nCOPYRIGHT  \n© 2023 Jin, Cheng, Fei, Sang and Zhong. 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.  \nIdentification of diagnostic biomarkers in Alzheimer’s disease by integrated bioinformatic analysis and machine learning strategies  \nBoru Jin1,2†, Xiaoqin Cheng1,2†, Guoqiang Fei1,2 , Shaoming Sang3* and Chunjiu Zhong1,2*  \n1 Department of Neurology, Zhongshan Hospital, Fudan University, Shanghai, China, 2 Collaborative Innovation Center for Brain Science, Fudan University, Shanghai, China, 3 Shanghai Raising Pharmaceutical Technology Co., Ltd. Shanghai, China  \nBackground: Alzheimer’s disease (AD) is the most prevalent form of dementia, and is becoming one of the most burdening and lethal diseases. More useful biomarkers for diagnosing AD and reflecting the disease progression are in need and of significance.  \nMethods: The integrated bioinformatic analysis combined with machine-learning strategies was applied for exploring crucial functional pathways and identifying diagnostic biomarkers of AD. Four datasets (GSE5281, GSE131617, GSE48350, and GSE84422) with samples of AD frontal cortex are integrated as experimental datasets, and another two datasets (GSE33000 and GSE44772) with samples of AD frontal cortex were used to perform validation analyses. Functional Correlation enrichment analyses were conducted based on Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and the Reactome database to reveal AD-associated biological functions and key pathways. Four models were employed to screen the potential diagnostic biomarkers, including one bioinformatic analysis of Weighted gene co-expression network analysis (WGCNA)and three machine-learning algorithms: Least absolute shrinkage and selection operator (LASSO), support vector machine-recursive feature elimination (SVM-RFE) and random forest (RF) analysis. The correlation analysis was performed to explore the correlation between the identified biomarkers with CDR scores and Braak staging.  \nResults: The pathways of the immune response and oxidative stress were identified as playing a crucial role during AD. Thioredoxin interacting protein (TXNIP), early growth response 1 (EGR1), and insulin-like growth factor binding protein 5 (IGFBP5) were screened as diagnostic markers of AD. The diagnostic efficacy of TXNIP, EGR1, and IGFBP5 was validated with corresponding AUCs of 0.857, 0.888, and 0.856 in dataset GSE33000, 0.867, 0.909, and 0.841 in dataset GSE44770 . And the AUCs of the combination of these three biomarkers as a diagnostic tool for AD were 0 .954 and 0 .938 in the two verification datasets.  \nFrontiers in Aging Neuroscience 01 [frontiersin.org](frontiersin.org)  \nConclusion: The pathways of immune response and oxidative stress can playa cru","cbCairKQgAwCHSLx","https://ap.wps.com/l/cbCairKQgAwCHSLx","pdf",1317073,1,11,"English","en",105,"# Background\n# Methods\n# Results\n# Conclusion","[{\"question\":\"What datasets and validation strategy were used to identify AD biomarkers?\",\"answer\":\"Four AD frontal cortex datasets were integrated for experimental analysis, and two additional frontal cortex datasets were used for validation. The study performs validation based on diagnostic performance metrics across these independent sets.\"},{\"question\":\"Which analytical methods were combined in the biomarker discovery workflow?\",\"answer\":\"The approach uses integrated bioinformatic pathway exploration with machine learning. Pathway analysis relies on GO, KEGG, and Reactome, while diagnostic marker screening uses WGCNA and three machine-learning algorithms: LASSO, SVM-RFE, and random forest.\"},{\"question\":\"Which biomarkers were identified as diagnostic markers for Alzheimer’s disease?\",\"answer\":\"TXNIP, EGR1, and IGFBP5 were screened as diagnostic markers. Their diagnostic efficacy was validated using AUC values across the validation datasets.\"}]","Identification of diagnostic biomarkers in Alzheimer’s disease by integrated bioinformatic analysis and machine learning strategies | PDF",1785939849,28,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"identification-of-diagnostic-biomarkers-in-alzheimers-disease-by-integrated-bioinformatic-analysis-and-machine-learning-strategies","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/identification-of-diagnostic-biomarkers-in-alzheimers-disease-by-integrated-bioinformatic-analysis-and-machine-learning-strategies/127541/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-22","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What datasets and validation strategy were used to identify AD biomarkers?","Question",{"text":76,"@type":77},"Four AD frontal cortex datasets were integrated for experimental analysis, and two additional frontal cortex datasets were used for validation. The study performs validation based on diagnostic performance metrics across these independent sets.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which analytical methods were combined in the biomarker discovery workflow?",{"text":81,"@type":77},"The approach uses integrated bioinformatic pathway exploration with machine learning. Pathway analysis relies on GO, KEGG, and Reactome, while diagnostic marker screening uses WGCNA and three machine-learning algorithms: LASSO, SVM-RFE, and random forest.",{"name":83,"@type":74,"acceptedAnswer":84},"Which biomarkers were identified as diagnostic markers for Alzheimer’s disease?",{"text":85,"@type":77},"TXNIP, EGR1, and IGFBP5 were screened as diagnostic markers. 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