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

ACCESS DATA

Publications

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

Similar Datasets

Project description:BackgroundSciatic nerve injury (SNI), which frequently occurs under the traumatic hip and hip fracture dislocation, induces serious complications such as motor and sensory loss, muscle atrophy, or even disabling. The present work aimed to determine the regulating factors and gene network related to the SNI pathology.MethodsSciatic nerve injury dataset GSE18803 with 24 samples was divided into adult group and neonate group. Weighted gene co-expression network analysis (WGCNA) was carried out to identify modules associated with SNI in the two groups. Moreover, differentially expressed genes (DEGs) were determined from every group, separately. Subsequently, co-expression network and protein-protein interaction (PPI) network were overlapped to identify hub genes, while functional enrichment and Reactome analysis were used for a comprehensive analysis of potential pathways. GSE30165 was used as the test set for investigating the hub gene involvement within SNI. Gene set enrichment analysis (GSEA) was performed separately using difference between samples and gene expression level as phenotype label to further prove SNI-related signaling pathways. In addition, immune infiltration analysis was accomplished by CIBERSORT. Finally, Drug-Gene Interaction database (DGIdb) was employed for predicting the possible therapeutic agents.Results14 SNI status modules and 97 DEGs were identified in adult group, while 15 modules and 21 DEGs in neonate group. A total of 12 hub genes was overlapping from co-expression and PPI network. After the results from both test and training sets were overlapped, we verified that the ten real hub genes showed remarkably up-regulation within SNI. According to functional enrichment of hub genes, the above genes participated in the immune effector process, inflammatory responses, the antigen processing and presentation, and the phagocytosis. GSEA also supported that gene sets with the highest significance were mostly related to the cytokine-cytokine receptor interaction. Analysis of hub genes possible related signaling pathways using gene expression level as phenotype label revealed an enrichment involved in Lysosome, Chemokine signaling pathway, and Neurotrophin signaling pathway. Immune infiltration analysis showed that Macrophages M2 and Regulatory T cells may participate in the development of SNI. At last, 25 drugs were screened from DGIdb to improve SNI treatment.ConclusionsThe gene expression network is determined in the present work based on the related regulating factors within SNI, which sheds more light on SNI pathology and offers the possible biomarkers and therapeutic targets in subsequent research.

Dataset Information

Gene correlation network analysis to identify regulatory factors in sepsis.

Publications

Gene correlation network analysis to identify regulatory factors in sepsis.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets