{"title":"从轻度认知障碍到阿尔茨海默病快速进展的转录组学预测因子。","authors":"Yi-Long Huang, Tsung-Hsien Tsai, Zhao-Qing Shen, Yun-Hsuan Chan, Chih-Wei Tu, Chien-Yi Tung, Pei-Ning Wang, Ting-Fen Tsai","doi":"10.1186/s13195-024-01651-0","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Effective treatment for Alzheimer's disease (AD) remains an unmet need. Thus, identifying patients with mild cognitive impairment (MCI) who are at high-risk of progressing to AD is crucial for early intervention.</p><p><strong>Methods: </strong>Blood-based transcriptomics analyses were performed using a longitudinal study cohort to compare progressive MCI (P-MCI, n = 28), stable MCI (S-MCI, n = 39), and AD patients (n = 49). Statistical DESeq2 analysis and machine learning methods were employed to identify differentially expressed genes (DEGs) and develop prediction models.</p><p><strong>Results: </strong>We discovered a remarkable gender-specific difference in DEGs that distinguish P-MCI from S-MCI. Machine learning models achieved high accuracy in distinguishing P-MCI from S-MCI (AUC 0.93), AD from S-MCI (AUC 0.94), and AD from P-MCI (AUC 0.92). An 8-gene signature was identified for distinguishing P-MCI from S-MCI.</p><p><strong>Conclusions: </strong>Blood-based transcriptomic biomarker signatures show great utility in identifying high-risk MCI patients, with mitochondrial processes emerging as a crucial contributor to AD progression.</p>","PeriodicalId":7516,"journal":{"name":"Alzheimer's Research & Therapy","volume":"17 1","pages":"3"},"PeriodicalIF":7.9000,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11697870/pdf/","citationCount":"0","resultStr":"{\"title\":\"Transcriptomic predictors of rapid progression from mild cognitive impairment to Alzheimer's disease.\",\"authors\":\"Yi-Long Huang, Tsung-Hsien Tsai, Zhao-Qing Shen, Yun-Hsuan Chan, Chih-Wei Tu, Chien-Yi Tung, Pei-Ning Wang, Ting-Fen Tsai\",\"doi\":\"10.1186/s13195-024-01651-0\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Effective treatment for Alzheimer's disease (AD) remains an unmet need. Thus, identifying patients with mild cognitive impairment (MCI) who are at high-risk of progressing to AD is crucial for early intervention.</p><p><strong>Methods: </strong>Blood-based transcriptomics analyses were performed using a longitudinal study cohort to compare progressive MCI (P-MCI, n = 28), stable MCI (S-MCI, n = 39), and AD patients (n = 49). Statistical DESeq2 analysis and machine learning methods were employed to identify differentially expressed genes (DEGs) and develop prediction models.</p><p><strong>Results: </strong>We discovered a remarkable gender-specific difference in DEGs that distinguish P-MCI from S-MCI. Machine learning models achieved high accuracy in distinguishing P-MCI from S-MCI (AUC 0.93), AD from S-MCI (AUC 0.94), and AD from P-MCI (AUC 0.92). An 8-gene signature was identified for distinguishing P-MCI from S-MCI.</p><p><strong>Conclusions: </strong>Blood-based transcriptomic biomarker signatures show great utility in identifying high-risk MCI patients, with mitochondrial processes emerging as a crucial contributor to AD progression.</p>\",\"PeriodicalId\":7516,\"journal\":{\"name\":\"Alzheimer's Research & Therapy\",\"volume\":\"17 1\",\"pages\":\"3\"},\"PeriodicalIF\":7.9000,\"publicationDate\":\"2025-01-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11697870/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Alzheimer's Research & Therapy\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1186/s13195-024-01651-0\",\"RegionNum\":1,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"CLINICAL NEUROLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Alzheimer's Research & Therapy","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1186/s13195-024-01651-0","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CLINICAL NEUROLOGY","Score":null,"Total":0}
引用次数: 0
摘要
背景：阿尔茨海默病（AD）的有效治疗仍然是一个未满足的需求。因此，识别轻度认知障碍（MCI）患者进展为AD的高风险对于早期干预至关重要。方法：采用纵向研究队列进行基于血液的转录组学分析，比较进展型MCI （P-MCI, n = 28）、稳定期MCI （S-MCI, n = 39）和AD患者（n = 49）。采用统计DESeq2分析和机器学习方法鉴定差异表达基因（DEGs）并建立预测模型。结果：我们发现区分P-MCI和S-MCI的DEGs有显著的性别差异。机器学习模型在区分P-MCI和S-MCI (AUC 0.93)， AD和S-MCI （AUC 0.94）以及AD和P-MCI （AUC 0.92）方面取得了很高的准确性。鉴定出8个基因标记来区分P-MCI和S-MCI。结论：基于血液的转录组生物标志物特征在识别高风险MCI患者中显示出很大的效用，线粒体过程是AD进展的关键因素。
Transcriptomic predictors of rapid progression from mild cognitive impairment to Alzheimer's disease.
Background: Effective treatment for Alzheimer's disease (AD) remains an unmet need. Thus, identifying patients with mild cognitive impairment (MCI) who are at high-risk of progressing to AD is crucial for early intervention.
Methods: Blood-based transcriptomics analyses were performed using a longitudinal study cohort to compare progressive MCI (P-MCI, n = 28), stable MCI (S-MCI, n = 39), and AD patients (n = 49). Statistical DESeq2 analysis and machine learning methods were employed to identify differentially expressed genes (DEGs) and develop prediction models.
Results: We discovered a remarkable gender-specific difference in DEGs that distinguish P-MCI from S-MCI. Machine learning models achieved high accuracy in distinguishing P-MCI from S-MCI (AUC 0.93), AD from S-MCI (AUC 0.94), and AD from P-MCI (AUC 0.92). An 8-gene signature was identified for distinguishing P-MCI from S-MCI.
Conclusions: Blood-based transcriptomic biomarker signatures show great utility in identifying high-risk MCI patients, with mitochondrial processes emerging as a crucial contributor to AD progression.
期刊介绍：
Alzheimer's Research & Therapy is an international peer-reviewed journal that focuses on translational research into Alzheimer's disease and other neurodegenerative diseases. It publishes open-access basic research, clinical trials, drug discovery and development studies, and epidemiologic studies. The journal also includes reviews, viewpoints, commentaries, debates, and reports. All articles published in Alzheimer's Research & Therapy are included in several reputable databases such as CAS, Current contents, DOAJ, Embase, Journal Citation Reports/Science Edition, MEDLINE, PubMed, PubMed Central, Science Citation Index Expanded (Web of Science) and Scopus.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
