Gene correlation network analysis to identify regulatory factors in sepsis
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Journal of Translational Medicine Open Access
RESEARCH
Gene correlation network analysis to identify regulatory factors in sepsis Zhongheng Zhang1*† , Lin Chen2†, Ping Xu3†, Lifeng Xing1, Yucai Hong1 and Pengpeng Chen1
Abstract Background and objectives: Sepsis is a leading cause of mortality and morbidity in the intensive care unit. Regulatory mechanisms underlying the disease progression and prognosis are largely unknown. The study aimed to identify master regulators of mortality-related modules, providing potential therapeutic target for further translational experiments. Methods: The dataset GSE65682 from the Gene Expression Omnibus (GEO) database was utilized for bioinformatic analysis. Consensus weighted gene co-expression netwoek analysis (WGCNA) was performed to identify modules of sepsis. The module most significantly associated with mortality were further analyzed for the identification of master regulators of transcription factors and miRNA. Results: A total number of 682 subjects with various causes of sepsis were included for consensus WGCNA analysis, which identified 27 modules. The network was well preserved among different causes of sepsis. Two modules designated as black and light yellow module were found to be associated with mortality outcome. Key regulators of the black and light yellow modules were the transcription factor CEBPB (normalized enrichment score = 5.53) and ETV6 (NES = 6), respectively. The top 5 miRNA regulated the most number of genes were hsa-miR-335-5p (n = 59), hsamiR-26b-5p (n = 57), hsa-miR-16-5p (n = 44), hsa-miR-17-5p (n = 42), and hsa-miR-124-3p (n = 38). Clustering analysis in 2-dimension space derived from manifold learning identified two subclasses of sepsis, which showed significant association with survival in Cox proportional hazard model (p = 0.018). Conclusions: The present study showed that the black and light-yellow modules were significantly associated with mortality outcome. Master regulators of the module included transcription factor CEBPB and ETV6. miRNA-target interactions identified significantly enriched miRNA. Keywords: Sepsis; intensive care unit, Gene co-expression netwoek, Mortality Background Sepsis is defined as organ dysfunction syndrome caused by uncontrolled inflammatory response to infection. Sepsis is a leading cause of mortality in hospitalized patients [1, 2], and accounts for 30% of case fatality in hospitalized patients [3]. Despite the high mortality and morbidity, *Correspondence: [email protected] † Zhongheng Zhang, Lin Che and Ping Xu contributed equally to this work 1 Department of Emergency Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, No 3, East Qingchun Road, Hangzhou 310016, Zhejiang Province, China Full list of author information is available at the end of the article
few agents are proven to be effective for the treatment of sepsis. Thus, more regulatory factors need to be identified to provide potential targets for the design of effective therapeutic agents. Several studies have used
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