ArticlePeerJ2026
Integrated transcriptomic identification and validation reveal key autophagy-associated biomarkers in sleep deprivation.
Article in PeerJ, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Sleep deprivation (SD) is harmful to individuals, but its pathogenesis is not clarified. Objective: This study seeks to identify SD-linked autophagy genes Methods: Primary SD transcriptomic datasets (GSE33302 and GSE9442), derived from murine brain tissue, were retrieved from the Gene Expression Omnibus (GEO) database to identify differentially expressed genes (DEGs). Murine gene symbols were subsequently mapped to their human orthologs to enable downstream bioinformatic analyses and integration with the GeneCards database. The converted DEGs were intersected with autophagy-related genes (ARGs) obtained from GeneCards to identify autophagy-associated DEGs, which were then subjected to functional enrichment analyses. Candidate predictor genes were selected using machine learning (ML) algorithms. Their expression was rigorously validated in both internal and external datasets (GSE9441 and GSE3767), encompassing murine brain tissue and human peripheral blood samples, respectively. In parallel, an SD rat model was established by exposing male Sprague-Dawley rats to continuous sleep deprivation for seven consecutive days. Brain tissues from the prefrontal cortex and hippocampus were harvested, and the expression levels of rat orthologs of the candidate genes were quantified using reverse transcription-quantitative polymerase chain reaction (RT-qPCR). The diagnostic performance of the identified genes was evaluated through nomogram construction and receiver operating characteristic (ROC) curve analysis. In addition, the immune landscape associated with SD was inferred using single-sample gene set enrichment analysis (ssGSEA). The cellular distribution and functional roles of the candidate genes were further explored Results: Three significantly dysregulated predictor genes: Conclusion:
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