Dataset Information

Gene correlation network analysis to identify regulatory factors in sepsis.

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), hsa-miR-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.

SUBMITTER: Zhang Z

PROVIDER: S-EPMC7545567 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

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Gene correlation network analysis to identify regulatory factors in sepsis.

Zhang Zhongheng Z Chen Lin L Xu Ping P Xing Lifeng L Hong Yucai Y Chen Pengpeng P

Journal of translational medicine 20201008 1

<h4>Background and objectives</h4>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.<h4>Methods</h4>The dataset GSE65682 from the Gene Expression Omnibus (GEO) database was utilized for bioinformatic analysis. Consensus weig ...[more]

PMID: 33032623

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Project description:Many human diseases including cancer are the result of perturbations to transcriptional regulatory networks that control context-specific expression of genes. A comparative approach across multiple cancer types is a powerful approach to illuminate the common and specific network features of this family of diseases. Recent efforts from The Cancer Genome Atlas (TCGA) have generated large collections of functional genomic data sets for multiple types of cancers. An emerging challenge is to devise computational approaches that systematically compare these genomic data sets across different cancer types that identify common and cancer-specific network components. We present a module- and network-based characterization of transcriptional patterns in six different cancers being studied in TCGA: breast, colon, rectal, kidney, ovarian, and endometrial. Our approach uses a recently developed regulatory network reconstruction algorithm, modular regulatory network learning with per gene information (MERLIN), within a stability selection framework to predict regulators for individual genes and gene modules. Our module-based analysis identifies a common theme of immune system processes in each cancer study, with modules statistically enriched for immune response processes as well as targets of key immune response regulators from the interferon regulatory factor (IRF) and signal transducer and activator of transcription (STAT) families. Comparison of the inferred regulatory networks from each cancer type identified a core regulatory network that included genes involved in chromatin remodeling, cell cycle, and immune response. Regulatory network hubs included genes with known roles in specific cancer types as well as genes with potentially novel roles in different cancer types. Overall, our integrated module and network analysis recapitulated known themes in cancer biology and additionally revealed novel regulatory hubs that suggest a complex interplay of immune response, cell cycle, and chromatin remodeling across multiple cancers.

Dataset Information

Gene correlation network analysis to identify regulatory factors in sepsis.

Publications

Gene correlation network analysis to identify regulatory factors in sepsis.

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