P014 Bioinformatic analysis of gene expression profiling data on shift work sleep disorder

Abstract Introduction Shift work sleep disorder (SWSD) is a circadian rhythm sleep disorder affecting individuals who work nontraditional hours, such as night shifts. The complexity of SWSD arises from the altered interactions between thousands of genes, making it challenging to prioritize disease mechanism, biomarkers, and drug targets. This study aims to identify the patterns of pathways and upstream regulators involved in SWSD using a transcriptome from isolated peripheral blood mononuclear cells (PBMCs) of hospital nurses. Method We conducted bioinformatic analyses on the GSE122541 dataset, which includes RNA samples from PBMCs collected from night-shift and day-shift nurses under controlled laboratory conditions. Total RNA was obtained from isolated PBMCs every three hours. Differentially expressed genes (DEGs) were identified by comparing the night-shift condition to the day-shift condition. These DEGs were subjected to pathway and biomarker analyses using bioinformatic tools. Results A total of 773 DEGs were identified when comparing night-shift to day-shift conditions. These DEGs were analyzed using ingenuity pathway analysis (IPA). The pathway analysis revealed several significantly decreased pathways, including interferon alpha/beta signaling, interferon signaling, and OAS antiviral response. The upstream regulator analysis included predicted inhibited upstream regulators, such as IFNA2 and IFNL1, supporting the inhibited pathways. The one of top diseases associated with the night-shift condition included cancer with highest score. Conclusion Despite the identification of numerous DEGs, the bioinformatic analysis highlighted significant inhibited pathways and upstream regulators between night-shift and day-shift conditions. Combining pathway analyses of microarray data with bioinformatic tools can provide insights into the pathogenic relevance of SWSD.