Hao-Jie Xu1, Shi-Zhu Lin1, Kai Shi1, Jin-Jia Qiu1, Jia-Min Hu1, Zeng-Gui Yu2, Dong-Sheng Dai2, Na Zhang1, Min Liang1, Hong-da Cai1, Kai Zeng3, Xiao-Dan Wu4. 1. Department of Anesthesiology, The First Affiliated Hospital, Fujian Medical University, Fuzhou, 350005, China. 2. Department of Anesthesiology, Fujian Provincial Hospital, Shengli Clinical Medical College of Fujian Medical University, Fujian Medical University, Fuzhou, 350001, Fujian, China. 3. Department of Anesthesiology, The First Affiliated Hospital, Fujian Medical University, Fuzhou, 350005, China. fymzk6822@163.com. 4. Department of Anesthesiology, Fujian Provincial Hospital, Shengli Clinical Medical College of Fujian Medical University, Fujian Medical University, Fuzhou, 350001, Fujian, China. wxiaodan@sina.com.
Abstract
BACKGROUND: Sleep deprivation (SD) has become a serious concern worldwide. This study aimed to identify key modules and candidate hub genes correlated with diseases caused by SD, using co-expression analysis. METHODS: The weighted gene co-expression network analysis was performed to construct a co-expression network of hub genes correlated with SD. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were performed to search for signaling pathways. The protein-protein interaction network analysis of central genes was performed to recognize the interactions among central genes. Molecular Complex Detection, a plugin in Cytoscape, was used to discover the hub gene clusters involved in SD. RESULTS: A total of 564 genes in the yellow module were identified based on the results of topological overlap measure-based clustering. The yellow module showed a pivotal correlation with SD. Six hub gene clusters prominently associated with SD were identified. Heat shock protein family and circadian clock genes among them may be the hub genes involved in SD. CONCLUSIONS: These genes and pathways might become therapeutic targets with clinical usefulness in the future.
BACKGROUND: Sleep deprivation (SD) has become a serious concern worldwide. This study aimed to identify key modules and candidate hub genes correlated with diseases caused by SD, using co-expression analysis. METHODS: The weighted gene co-expression network analysis was performed to construct a co-expression network of hub genes correlated with SD. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were performed to search for signaling pathways. The protein-protein interaction network analysis of central genes was performed to recognize the interactions among central genes. Molecular Complex Detection, a plugin in Cytoscape, was used to discover the hub gene clusters involved in SD. RESULTS: A total of 564 genes in the yellow module were identified based on the results of topological overlap measure-based clustering. The yellow module showed a pivotal correlation with SD. Six hub gene clusters prominently associated with SD were identified. Heat shock protein family and circadian clock genes among them may be the hub genes involved in SD. CONCLUSIONS: These genes and pathways might become therapeutic targets with clinical usefulness in the future.
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