Dataset Information

Bioinformatic gene analysis for potential biomarkers and therapeutic targets of atrial fibrillation-related stroke.

ABSTRACT: BACKGROUND:Atrial fibrillation (AF) is one of the most prevalent sustained arrhythmias, however, epidemiological data may understate its actual prevalence. Meanwhile, AF is considered to be a major cause of ischemic strokes due to irregular heart-rhythm, coexisting chronic vascular inflammation, and renal insufficiency, and blood stasis. We studied co-expressed genes to understand relationships between atrial fibrillation (AF) and stroke and reveal potential biomarkers and therapeutic targets of AF-related stroke. METHODS:AF-and stroke-related differentially expressed genes (DEGs) were identified via bioinformatic analysis Gene Expression Omnibus (GEO) datasets GSE79768 and GSE58294, respectively. Subsequently, extensive target prediction and network analyses methods were used to assess protein-protein interaction (PPI) networks, Gene Ontology (GO) terms and pathway enrichment for DEGs, and co-expressed DEGs coupled with corresponding predicted miRNAs involved in AF and stroke were assessed as well. RESULTS:We identified 489, 265, 518, and 592 DEGs in left atrial specimens and cardioembolic stroke blood samples at?

SUBMITTER: Zou R

PROVIDER: S-EPMC6375208 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature

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Bioinformatic gene analysis for potential biomarkers and therapeutic targets of atrial fibrillation-related stroke.

Zou Rongjun R Zhang Dingwen D Lv Lei L Shi Wanting W Song Zijiao Z Yi Bin B Lai Bingjia B Chen Qian Q Yang Songran S Hua Ping P

Journal of translational medicine 20190213 1

<h4>Background</h4>Atrial fibrillation (AF) is one of the most prevalent sustained arrhythmias, however, epidemiological data may understate its actual prevalence. Meanwhile, AF is considered to be a major cause of ischemic strokes due to irregular heart-rhythm, coexisting chronic vascular inflammation, and renal insufficiency, and blood stasis. We studied co-expressed genes to understand relationships between atrial fibrillation (AF) and stroke and reveal potential biomarkers and therapeutic ta ...[more]

PMID: 30760287

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Project description:BackgroundSepsis and septic shock are life-threatening diseases with high mortality rate in intensive care unit (ICU). Acute kidney injury (AKI) is a common complication of sepsis, and its occurrence is a poor prognostic sign to septic patients. We analyzed co-differentially expressed genes (co-DEGs) to explore relationships between septic shock and AKI and reveal potential biomarkers and therapeutic targets of septic-shock-associated AKI (SSAKI).MethodsTwo gene expression datasets (GSE30718 and GSE57065) were downloaded from the Gene Expression Omnibus (GEO). The GSE57065 dataset included 28 septic shock patients and 25 healthy volunteers and blood samples were collected within 0.5, 24 and 48 h after shock. Specimens of GSE30718 were collected from 26 patients with AKI and 11 control patents. AKI-DEGs and septic-shock-DEGs were identified using the two datasets. Subsequently, Gene Ontology (GO) functional analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, and protein-protein interaction (PPI) network analysis were performed to elucidate molecular mechanisms of DEGs. We also evaluated co-DEGs and corresponding predicted miRNAs involved in septic shock and AKI.ResultsWe identified 62 DEGs in AKI specimens and 888, 870, and 717 DEGs in septic shock blood samples within 0.5, 24 and 48 h, respectively. The hub genes of EGF and OLFM4 may be involved in AKI and QPCT, CKAP4, PRKCQ, PLAC8, PRC1, BCL9L, ATP11B, KLHL2, LDLRAP1, NDUFAF1, IFIT2, CSF1R, HGF, NRN1, GZMB, and STAT4 may be associated with septic shock. Besides, co-DEGs of VMP1, SLPI, PTX3, TIMP1, OLFM4, LCN2, and S100A9 coupled with corresponding predicted miRNAs, especially miR-29b-3p, miR-152-3p, and miR-223-3p may be regarded as promising targets for the diagnosis and treatment of SSAKI in the future.ConclusionsSeptic shock and AKI are related and VMP1, SLPI, PTX3, TIMP1, OLFM4, LCN2, and S100A9 genes are significantly associated with novel biomarkers involved in the occurrence and development of SSAKI.

Dataset Information

Bioinformatic gene analysis for potential biomarkers and therapeutic targets of atrial fibrillation-related stroke.

Publications

Bioinformatic gene analysis for potential biomarkers and therapeutic targets of atrial fibrillation-related stroke.

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