Bioinformatics Analysis of Gene Expression Profiles for Diagnosing Sepsis and Risk Prediction in Patients with Sepsis
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE232753 | GEO | 2023/05/19
REPOSITORIES: GEO
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Project description:Bioinformatics Analysis of Gene Expression Profiles for Diagnosing Sepsis and Risk Prediction in Patients with Sepsis
| PRJNA973696 | ENA
Project description:In this project we performed a comprehensive exploration of monocyte molecular responses in a cohort of patients with septic shock via label-free shotgun proteomics. We enrolled adult (≥18 years old) patients with sepsis from community-acquired infections, diagnosed according to the Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3) criteria. Blood samples were obtained within the first 72 hours from the diagnosis of sepsis (sepsis phase) and on de day before ICU discharge (recovery phase). The Control group consisted of age matched healthy volunteers. We excluded subjects with AIDS, advanced cancer, hematological diseases, and pregnancy.
2021-09-07 | PXD023938 | Pride
Project description:To screen the circulating miRNAs which can be used to evaluate the diagnosis and prognosis of sepsis by gene microarray,and to predict its target gene and mechanism by bioinformatics analysis in sepsis. To screen the circulating miRNAs which can be used to evaluate the diagnosis and prognosis of sepsis by gene microarray,and to predict its target gene and mechanism by bioinformatics analysis in sepsis.
2020-07-19 | GSE101639 | GEO
Project description:Accuracy of sepsis prediction was obtained using cross-validation of gene expression data from 12 human spleen samples and from 16 mouse spleen samples. For blood studies, classifiers were constructed using data from a training data set of 26 microarrays. The error rate of the classifiers was estimated on seven de-identified microarrays, and then on a subsequent cross-validation for all 33 blood microarrays. Estimates of classification accuracy of sepsis in human spleen were 67.1%; in mouse spleen, 96%; and in mouse blood, 94.4% (all estimates were based on nested cross-validation). Lists of genes with substantial changes in expression between study and control groups were used to identify nine mouse common inflammatory response genes, six of which were mapped into a single pathway using contemporary pathway analysis tools. Experiment Overall Design: Exploratory studies were conducted using spleens from septic patients and from mice with abdominal sepsis. Seven patients with sepsis after injury were identified retrospectively and compared with six injured patients. C57BL/6 male mice were subjected to cecal ligation and puncture, or to IP lipopolysaccharide. Control mice had sham laparotomy or injection of IP saline, respectively. A sepsis classification model was created and tested on blood samples from septic mice.
2008-06-14 | E-GEOD-5663 | biostudies-arrayexpress
Project description:We investigated the individual phenotypic predisposition to developing uncomplicated infection or sepsis in a large cohort of non-infected patients undergoing major elective surgery. Whole blood RNA sequencing analysis was performed on preoperative samples taken from 267 patients. These comprised patients who developed postoperative infection with (n=77) or without (n=49) sepsis, non-infectious systemic inflammatory response (n=31), or an uncomplicated postoperative course (n=110). Machine learning classification models built on preoperative transcriptomic signatures predicted postoperative outcomes including sepsis.
2024-08-30 | GSE208581 | GEO
Project description:We investigated the individual phenotypic predisposition to developing uncomplicated infection or sepsis in a large cohort of non-infected patients undergoing major elective surgery. We built machine learning classification models on preoperative transcriptomic signatures to predict postoperative outcomes including sepsis. To test the predictive capability of these models for ongoing infection, whole blood RNA sequencing analysis on 61 independent patients with COVID-19 (10 mild, 51 severe cases) was performed.
2024-08-30 | GSE208587 | GEO
Project description:The peripheral whole-blood transcriptome analysis has been used to the identification of biomarkers in different clinical syndromes. The ability to simultaneously measure the gene expression levels of the whole transcriptome is providing a broader view of clinical syndromes such as sepsis. The aim of this study was to assess the gene expression profiles of post-surgical patients with septic shock compared with patients without sepsis.
2014-03-15 | E-MTAB-1548 | biostudies-arrayexpress
Project description:Accuracy of sepsis prediction was obtained using cross-validation of gene expression data from 12 human spleen samples and from 16 mouse spleen samples. For blood studies, classifiers were constructed using data from a training data set of 26 microarrays. The error rate of the classifiers was estimated on seven de-identified microarrays, and then on a subsequent cross-validation for all 33 blood microarrays. Estimates of classification accuracy of sepsis in human spleen were 67.1%; in mouse spleen, 96%; and in mouse blood, 94.4% (all estimates were based on nested cross-validation). Lists of genes with substantial changes in expression between study and control groups were used to identify nine mouse common inflammatory response genes, six of which were mapped into a single pathway using contemporary pathway analysis tools. Keywords: genomics, diagnosis, microarray, calprotectin
2006-08-29 | GSE5663 | GEO
Project description:Background: Severe septic syndromes deeply impair innate and adaptive immunity. While neutrophils represent the first line of defense against infection, little is known about their phenotype and functions during sepsis-induced immunosuppression. The objective of this study was thus to perform for the first time a global evaluation of neutrophil alterations in immunosuppressed septic patients based on phenotypic, functional and transcriptomic studies. In addition, the potential association of these parameters and deleterious outcomes was assessed. Methods: Peripheral blood was collected from 9 septic shock patients at D3-4 and D6-8 presenting with features of sepsis-induced immunosuppression and compared to 8 healthy controls. Results: In order to get on overview of potential neutrophil alterations after sepsis, we performed transcriptomic analyses on purified neutrophils from 9 septic shock patients and 8 healthy volunteers. For each time point, comparisons were made between patients and controls. Venn diagrams indicated that 364 up-regulated genes and 328 down-regulated genes were common between the two analyses (Supplementary Tables 1 and 2). Interestingly, most of the differentially expressed genes are involved in cell maturation (CD177), apoptosis (STK4, Caspase 8), cell recruitment and chemotaxis (CD44, TPST, MMP9, CREB1), and antimicrobial properties (ARG1, STOM, ADAM9, CD63, YKL40) of neutrophils. Conclusions: The aim of the current study was to perform an extensive investigation of neutrophil alterations during sepsis-induced immunosuppression through phenotypic, functional and transcriptomic studies. Notably, transcriptomic study on purified neutrophils revealed differentially expressed genes between septic patients and healthy volunteers.
2015-12-28 | GSE64457 | GEO
Project description:THREE ARE SOME TECH PROBLEMS WHICH WILL BE SOLVED SOON! Sepsis, the dysregulated host response to infection causing life-threatening organ dysfunction, is an unmet global health challenge. Here we apply high-throughput tandem mass spectrometry to delineate the plasma proteome for 2622 sepsis and comparator samples (non-infected critical illness, postoperative inflammation and healthy volunteers) involving 4553 liquid chromatography-mass spectrometry analyses in a single batch, at 100 samples/day. We show how this scale of data can establish shared and specific proteins, pathways and co-expression modules in sepsis, and be integrated with paired leukocyte transcriptomic data (n=837 samples) using matrix decomposition. We map the landscape of the host response in sepsis including changes over time, and identify features relating to etiology, clinical phenotypes and severity. This work reveals novel subphenotypes informative for sepsis response state, disease processes and outcome, highlights potential biomarkers, pathways and processes for drug targets, and advances a systems-based precision medicine approach to sepsis.
2024-06-06 | PXD039875 | Pride