Effect of circRNA on barrier function and the potential value as a biomarker in sepsis
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ABSTRACT:
PROVIDER: PRJNA842247 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR19393907_1.fastq.gz | Fastqsanger.gz | |||
| SRR19393907_2.fastq.gz | Fastqsanger.gz | |||
| SRR19393908_1.fastq.gz | Fastqsanger.gz | |||
| SRR19393908_2.fastq.gz | Fastqsanger.gz | |||
| SRR19393909_1.fastq.gz | Fastqsanger.gz |
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Project description:Sepsis is one of the main causes of death in critically ill patients with high morbidity and mortality. Circular RNAs (CircRNAs) is aberrantly expressed in many diseases and exhibit roles. However, the expression profiles and functions of circRNAs in sepsis have not yet been fully understood. We then performed gene expression profiling analysis using data obtained from RNA-seq of 2 different cells at the same time point.
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Project description:High-throughput sequencing was used to detect the expression profile of circRNA in PTMC of lymph node metastasis in lateral cervical region, and RT-qPCR was used to detect the expression of circRNA in cancer tissues and normal tissues adjacent to cancer. The receiver operating characteristic (ROC) curve and the area under ROC curve (AUC) were used to evaluate whether CircRNAs could be used as a biomarker for the diagnosis of thyroid cancer. Bioinformatics was used to predict the PTMC-related ceRNA mechanism of lateral cervical lymph node metastasis.
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Project description:Introduction: Sepsis is a complex immunological response to infection characterized by early hyperinflammation followed by severe and protracted immunosuppression, suggesting that a multi-marker approach has the greatest clinical utility for early detection, within a clinical environment focused on SIRS differentiation. Pre-clinical research using an equine sepsis model identified a panel of gene expression biomarkers that define the early aberrant immune activation. Thus, the primary objective was to apply these gene expression biomarkers to distinguish patients with sepsis from those who had undergone major open surgery and had clinical outcomes consistent with systemic inflammation due to physical trauma and wound healing. Methods: This was a multi-centre, prospective clinical trial conducted across 4 tertiary critical care settings in Australia. Sepsis patients were recruited if they met the 1992 Consensus Statement criteria and had clinical evidence of systemic infection based on microbiology diagnoses (n=27). Participants in the post-surgical (PS) group were recruited pre-operatively and blood samples collected within 24 hours following surgery (n=38). Healthy controls (HC) included hospital staff with no known concurrent illnesses (n=20). Each participant had minimally 5ml of PAXgene blood collected for leucocyte RNA isolation and gene expression analyses. Affymetrix array and multiplex tandem (MT)-PCR studies were conducted to evaluate transcriptional profiles in circulating white blood cells applying a set of 42 molecular markers that had been identified a priori. A LogitBoost algorithm was used to create a machine learning diagnostic rule to predict sepsis outcomes. Results: Based on preliminary microarray analyses comparing HC and sepsis groups. A panel of 42-gene expression markers were identified that represented key innate and adaptive immune function, cell cycling, WBC differentiation, extracellular remodelling and immune modulation pathways. Comparisons against GEO data confirmed the definitive separation of the sepsis cohort. Quantitative PCR results suggest the capacity for this test to differentiate severe systemic inflammation from HC is 92%. AUC ROC curve findings demonstrated sepsis prediction within a mixed inflammatory population, was between 86 - 92%. Conclusions: This novel molecular biomarker test has a clinically relevant sensitivity and specificity profile, and has the capacity for early detection of sepsis via the monitoring of critical care patients. GEO Note: Data made available represents the preliminary microarray investigation performed on Human U133 Plus 2.0 GeneChips (Affymetrix), assaying 41 patient samples (Sepsis n=10, Post-Surgical n=11, Control n=20). This was a multi-centre, prospective clinical trial conducted across 4 tertiary critical care settings in Australia. Sepsis patients were recruited if they met the 1992 Consensus Statement criteria and had clinical evidence of systemic infection based on microbiology diagnoses (n=27). Participants in the post-surgical (PS) group were recruited pre-operatively and blood samples collected within 24 hours following surgery (n=38). Healthy controls (HC) included hospital staff with no known concurrent illnesses (n=20). Each participant had minimally 5ml of PAXgene blood collected for leucocyte RNA isolation and gene expression analyses. The GEO data represents the preliminary microarray investigation performed on Human U133 Plus 2.0 GeneChips (Affymetrix), assaying 41 patient samples (Sepsis n=10, Post-Surgical n=11, Control n=20).
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Project description:This work describes for the first time a different circular RNA (circRNAs) expression pattern between frail and robust individuals. Moreover, the level of some circRNAs is modulated after a physical intervention. These results suggest that they could be used as minimally invasive biomarkers of frailty. Frailty is an intermediate and reversible geriatric syndrome that often precedes dependency. Therefore, its identification is essential to prevent dependency. Diverse molecules have been proposed as biomarkers of frailty, but none of them has reached clinical practice. Recently, circular RNAs have emerged as new non-coding RNAs. Their regulatory role together with their high stability in biofluids makes them good candidates as biomarkers for several processes, but, to date, no studies have characterized circRNA expression in frailty. We studied RNA from leukocytes of 35 frail and 35 robust individuals. After RNA-Sequencing, circRNA detection was performed by CIRI2 and Circexplorer2 and differential expression analysis by DESeq2. Validation was performed by Quantitative-PCR. A Linear Discriminant Analysis was performed to determine the best circRNA combination to discriminate frails from robusts. Besides, CircRNA candidates were studied in 13 additional elder donors before and after a 3-month physical intervention. We found 89 differentially expressed circRNAs (p-value<0.05, FC>|1.5|) with frailty. The upregulation of hsa_circ_0007817, hsa_circ_0101802 and hsa_circ_0060527 in frail individuals was validated. The combination of hsa_circ_0079284, hsa_circ_0007817 and hsa_circ_0075737 levels showed a great biomarker value with a 95.9% probability of correctly classifying frail and robust individuals. Moreover, hsa_circ_0079284 levels decreased after physical intervention in concordance with an improvement in frailty scores.
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