Identifying Key Genes for Neurobehavioral Disorders Caused by Long-Term Sleep Deprivation

Background: Sleep deprivation (SD) is a common health problem that causes molecular, immune and neurological changes. The aim of this study was to identify key genes by analyzing gene expression changes induced by prolonged sleep deprivation and to explore their potential relationship with immune regulation and neurobehavioral disorders. Methods: Microarray data of sleep deprivation at different time points were obtained by obtaining them from the Gene Expression Omnibus (GEO) database. Differential expression analysis was performed on SD samples and control samples at different time points using the Limma package in R to screen for significantly differentially expressed genes (DEGs). The functions of DEGs and the biological pathways involved were explored by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. In addition, genes that were significantly differentially expressed at all time points were screened as key genes for SD by overlap analysis. Finally, immune scores and immune cell scores were calculated using ESTIMATE, CIBERSORT and single-sample gene set enrichment analysis (ssGSEA). The relationships between key genes and immune scores and immune-related pathways were explored by Spearman correlation analysis. Results: At each time point from t01 to t12, we identified 367, 438, 615, 311, 444, 939, 629, 526, 200, 760 and 1026 DEGs between SD and control samples, respectively. Notably, six genes (USP32P1, TREML1, PF4V1, GPR146, DEFA1B, and CLEC1B) were identified as the DEGs between the two types of samples across all time points. Among these, CLEC1B, GPR146, PF4V1, and TREML1 were significantly upregulated in SD samples, while DEFA1B and USP32P1 were significantly downregulated. These key genes are involved in critical biological processes, including DNA repair, KRAS signaling, and the PI3K-AKT signaling pathway. Furthermore, PF4V1, GPR146, TREML1, and CLEC1B exhibited a significant negative correlation with immune scores and were closely associated with various immune regulatory pathways, such as antigen processing and presentation, B cell receptor signaling, and T cell receptor signaling pathways. Conclusion: In summary, this study systematically investigated the dynamic changes in gene expression induced by SD and their underlying molecular mechanisms, identifying six key genes. These findings provide a crucial molecular foundation for the early diagnosis and personalized treatment of SD-related diseases.