[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128119-en":3,"doc-seo-128119-105":31,"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":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},128119,3985741905716,"Rowan","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Identification and validation of pyroptosis-related genes in Alzheimer’s disease based on multi-transcriptome and machine learning","Alzheimer’s disease progression is driven by chronic neuroinflammation, in which pyroptosis—an inflammatory programmed cell death pathway—acts as an important pathogenic contributor. The study integrates multiple transcriptomes from GEO to profile pyroptosis-related genes (PRGs), then applies machine-learning and comprehensive bioinformatics analyses to identify hub genes associated with AD. Key gene expression patterns are validated using AD mouse data, and regulatory networks are inferred via time-series and correlation analyses, while immune infiltration and ROC performance support candidate prioritization. Seven feature genes were highlighted, with MDH1 and PKN2 showing strongest evidence and experimental validation potential.","TYPE Original Research PUBLISHED 14 May 2025  \nDOI 10.3389/fnagi.2025.1568337  \nOPEN ACCESS  \nEDITED BY  \nZhijia Xia,  \nChongqing Medical University, China  \nREVIEWED BY  \nMaria Francesca Manchinu,  \nNational Research Council (CNR), Italy Ziwei Yin,  \nCentral South University, China Sirui Fu,  \nZhuhai People’s Hospital, China  \n*CORRESPONDENCE  \nZhuoze Wu  \n [wzz@nsmc.edu.cn](wzz@nsmc.edu.cn)[ ](wzz@nsmc.edu.cn)Qi He  \n [heqiawyyncdq@163.com](heqiawyyncdq@163.com)  \n†These authors have contributed equally to this work and share first authorship  \nRECEIVED 29 January 2025  \nACCEPTED 29 April 2025  \nPUBLISHED 14 May 2025  \nCITATION  \nWang Y, Li Y, Zhou L, Yuan Y, Liu C, Zeng Z, Chen Y, He Q and Wu Z (2025) Identification and validation of pyroptosis-related genes in Alzheimer’s disease based on  \nmulti-transcriptome and machine learning. Front. Aging Neurosci. 17:1568337.  \ndoi: 10.3389/fnagi.2025.1568337  \nCOPYRIGHT  \n© 2025 Wang, Li, Zhou, Yuan, Liu, Zeng, Chen, He and Wu. This is an open-access article distributed under the terms of the  \nCreative 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 and validation of pyroptosis-related genes in Alzheimer’s disease based on multi-transcriptome and machine learning  \nYuntai Wang 1,2†, Yilin Li3†, Lu Zhou 2, Yihuan Yuan 2, Chuanfei Liu4, Zimeng Zeng 1, Yuanqi Chen 5, Qi He 1* and Zhuoze Wu 1*  \n1 Institute of Basic Medicine, North Sichuan Medical College, Nanchong, China, 2School of Clinical Medicine, North Sichuan Medical College, Nanchong, China, 3School of Integrated Traditional Chinese and Western Clinical Medicine, North Sichuan Medical College, Nanchong, China, 4School of Medical Imaging, North Sichuan Medical College, Nanchong, China, 5School of Nursing, North Sichuan Medical College, Nanchong, China  \nBackground: Alzheimer’s disease (AD) progression is characterized by persistent neuroinflammation, where pyroptosis—an inflammatory programmed cell death mechanism—has emerged as a key pathological contributor. However, the molecular mechanisms through which pyroptosis-related genes (PRGs) drive AD pathogenesis remain incompletely elucidated.  \nMethods: We integrated multiple transcriptomes of AD patients from the GEO database and analyzed the expression of PRGs in combined datasets. Machine learning algorithms and comprehensive bioinformatics analysis (including immune infiltration and receiver operating characteristic (ROC)) were applied to identify the hub genes. Additionally, we validated the expression patterns of these key genes using the expression data from AD mice and constructed potential regulatory networks through time series and correlation analysis.  \nResults: We identified 91 PRGs in AD using the weighted gene co-expression network analysis (WGCNA) and differentially expressed genes analysis. By application of the protein–protein interaction and machine learning algorithms, seven pyroptosis feature genes (CHMP2A, EGFR, FOXP3, HSP90B1, MDH1, METTL3, and PKN2) were identified. Crucially, MDH1 and PKN2 demonstrated superior performance in terms of immune cell infiltration, ROC curves, and experimental validation. Furthermore, we constructed the long non-coding RNA and mRNA (lncRNA-mRNA) regulatory network of these characteristic genes using the gene expression profiles from AD mice at varying ages, revealing the potential regulatory mechanism in AD.  \nConclusion: This study provides the first comprehensive characterization of pyroptosis-related molecular signatures in AD. Seven hub genes were identified, with particular emphasis on MDH1 and PKN2 . Their superior performances were validated through comprehensive bioinformatic anal","cbCaigntaX3cla1h","https://ap.wps.com/l/cbCaigntaX3cla1h","pdf",25859118,2,1,17,"English","en",105,"# Introduction\n## Pyroptosis and neuroinflammation in Alzheimer’s disease\n# Methods\n## Transcriptome integration and PRG expression profiling\n## Machine-learning and bioinformatics identification\n## Immune infiltration and ROC analysis\n## Experimental and time-series validation\n# Results\n## Identification of PRGs and WGCNA findings\n## Hub gene selection and feature genes\n## Performance of MDH1 and PKN2\n## lncRNA-mRNA regulatory network construction\n# Conclusion\n## Pyroptosis molecular signatures and therapeutic implications","[{\"question\":\"What is the study’s primary goal regarding pyroptosis in Alzheimer’s disease?\",\"answer\":\"To identify and validate pyroptosis-related genes (PRGs) that may drive Alzheimer’s disease pathogenesis, using multi-transcriptome integration and machine-learning-based hub gene discovery.\"},{\"question\":\"How were candidate hub genes determined in the research?\",\"answer\":\"Multiple transcriptomes from GEO were combined and PRG expression was analyzed, then machine-learning and comprehensive bioinformatics approaches (including immune infiltration and ROC analysis) were used to identify hub genes.\"},{\"question\":\"Which genes were highlighted as key pyroptosis feature genes, and what evidence supported them?\",\"answer\":\"Seven feature genes were identified, with MDH1 and PKN2 showing stronger performance across immune cell infiltration, ROC curves, and expression validation using AD mouse data, alongside regulatory network insights.\"}]","Identification and validation of pyroptosis-related genes in Alzheimer’s disease based on multi-transcriptome and machine learning | 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is the study’s primary goal regarding pyroptosis in Alzheimer’s disease?","Question",{"text":76,"@type":77},"To identify and validate pyroptosis-related genes (PRGs) that may drive Alzheimer’s disease pathogenesis, using multi-transcriptome integration and machine-learning-based hub gene discovery.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How were candidate hub genes determined in the research?",{"text":81,"@type":77},"Multiple transcriptomes from GEO were combined and PRG expression was analyzed, then machine-learning and comprehensive bioinformatics approaches (including immune infiltration and ROC analysis) were used to identify hub genes.",{"name":83,"@type":74,"acceptedAnswer":84},"Which genes were highlighted as key pyroptosis feature genes, and what evidence supported them?",{"text":85,"@type":77},"Seven feature genes were identified, with MDH1 and PKN2 showing stronger performance across immune cell infiltration, ROC curves, and expression validation using AD mouse 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