Systematic review and meta-analysis of bulk RNAseq studies in human Alzheimer's disease brain tissue

对人类阿尔茨海默病脑组织中批量RNA测序研究的系统评价和荟萃分析

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Abstract

We systematically reviewed and meta-analyzed bulk RNA sequencing (RNAseq) studies comparing Alzheimer's disease (AD) patients to controls in human brain tissue. We searched PubMed, Web of Science, and Scopus for human brain bulk RNAseq studies, excluding re-analyses and studies limited to small RNAs or gene panels. We developed 10 criteria for quality assessment and performed a meta-analysis on three high-quality datasets. Of 3266 records, 24 qualified for the systematic review, and one study with three datasets qualified for the meta-analysis. The meta-analysis identified 571 differentially expressed genes (DEGs) in the temporal lobe and 189 in the frontal lobe, including CLU and GFAP. Pathway analysis suggested reactivation of developmental processes in the adult AD brain. Limited data availability constrained the meta-analysis. These findings underscore the need for rigorous methods in AD transcriptomic research to better identify transcriptomic changes and advance biomarker and therapeutic development. This review is registered in PROSPERO (CRD42023466522). HIGHLIGHTS: Comprehensive review: Conducted the first systematic review and meta-analysis of bulk RNA sequencing (RNAseq) studies comparing Alzheimer's disease (AD) patients with non-demented controls using primary human brain tissue. KEY FINDINGS: Identified 571 differentially expressed genes (DEGs) in the temporal lobe and 189 in the frontal lobe of patients with AD, revealing potential therapeutic targets. Pathway discovery: Highlighted key overlapping pathways such as "tube morphogenesis" and "neuroactive ligand-receptor interaction" that may play critical roles in AD. QUALITY ASSESSMENT: Emphasized the importance of methodological rigor in transcriptomic studies, including quality assessment tools to guide future research in AD. STUDY LIMITATION: Acknowledged limited access to complete data tables and lack of diversity in existing datasets, which constrained some of the analysis.

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