Similar Datasets
Project description:Diagnosis potential of subarachnoid haemorrhage using miRNA signatures isolated plasma-derived extracellular vesicles
| PRJNA924332 | ENA
Project description:Brain injury resulting from hemorrhagic stroke is clinically challenging to manage and results in high rates of morbidity and mortality. The pathophysiology of brain damage resulting from aneurysmal subarachnoid hemorrhage (aSAH) is largely unknown, and methods to treat and monitor patients are variable with no meaningful correlations to patient outcome. Prediction of patient risk for serious neurological complications is currently a significant clinical obstacle. An extracellular RNA (exRNA) biomarker to predict onset and severity of brain damage would improve patient outcomes. We sequenced plasma and CSF samples from adult patients with SAH. Samples were collected from post bleed day 1 to day 7. Total exRNA was isolated from each sample. In addition, we prepared a subset of 140 CSF samples, isolating the RNA contained within extracellular vesicles and vesicle-depleted biofluid.
2018-10-27 | GSE121868 | GEO
Project description:Investigation of gene expression profiles in blood of previous aSAH patients, aiming to gain insight into the pathogenesis of IA and aSAH, and to make a first step towards improvement of aSAH risk prediction. The results indicate that no gene expression differences are present in blood of previous aSAH patients compared to controls, besides one differentially co-expressed gene network without a clear relevant biological function. We collected peripheral blood of 119 patients with aSAH at least two years prior, and 118 controls. We determined gene expression profiles using Illumina HumanHT-12v4 BeadChips. After quality control, we divided the dataset in a discovery (2/3) and replication set (1/3), identified differentially expressed genes, and applied (co-)differential co-expression to identify disease-related gene networks.
2015-09-24 | E-GEOD-73378 | biostudies-arrayexpress
Project description:The accurate diagnosis and clinical management of traumatic brain injury (TBI) is currently limited by the lack of accessible molecular biomarkers that reflect the pathophysiology of this heterogeneous disease. To address this challenge, we developed a microchip diagnostic that can characterize TBI more comprehensively using the RNA found in brain-derived extracellular vesicles (EVs). Our approach measures a panel of EV miRNAs, processed with machine learning algorithms to capture the state of the injured and recovering brain. Our diagnostic combines surface marker-specific nanomagnetic isolation of brain-derived EVs, biomarker discovery using RNA sequencing, and machine learning processing of the EV miRNA cargo to minimally invasively measure the state of TBI. We achieved an accuracy of 99% identifying the signature of injured vs. sham control mice using an independent blinded test set (N = 77), where the injured group consists of heterogeneous populations (injury intensity, elapsed time since injury) to model the variability present in clinical samples. Moreover, we successfully predicted the intensity of the injury, the elapsed time since injury, and the presence of a prior injury using independent blinded test sets (N = 82). We demonstrated the translatability in a blinded test set by identifying TBI patients from healthy controls (AUC = 0.9, N = 60). This approach, which can detect signatures of injury that persist across a variety of injury types and individual responses to injury, more accurately reflects the heterogeneity of human TBI injury and recovery than conventional diagnostics, opening new opportunities to improve treatment of traumatic brain injuries.
| S-EPMC6334845 | biostudies-literature
Project description:Investigation of gene expression profiles in blood of previous aSAH patients, aiming to gain insight into the pathogenesis of IA and aSAH, and to make a first step towards improvement of aSAH risk prediction. The results indicate that no gene expression differences are present in blood of previous aSAH patients compared to controls, besides one differentially co-expressed gene network without a clear relevant biological function.
2015-09-24 | GSE73378 | GEO
Project description:tic disorders (TDs) are a series of childhood neuropsychiatric disorders characterized by invol-untary motor and/or vocal tics and commonly comorbid with several other psychopathological and/or behavioral disorders. In the present study, we aimed to identify serum small extracellular vesicles (sEVs) derived microRNAs (miRNAs) from the serum samples of children for Tic Disorders Diagnosis
2024-07-15 | GSE208318 | GEO
Project description:Approximately one-third of aneurysmal subarachnoid hemorrhage (aSAH) patients develop delayed cerebral vasospasm (DCV) 3-10 days after aneurysm rupture resulting in additional, permanent neurologic disability. Currently, no validated biomarker is available to determine the risk of DCV in aSAH patients. MicroRNAs (miRNAs) have been implicated in virtually all human diseases, including aSAH, and are found in extracellular biofluids including plasma and cerebrospinal fluid (CSF). We used a custom designed TaqMan Low Density Array miRNA panel to examine the levels of 47 selected brain and vasculature injury related miRNAs in CSF and plasma specimens collected from 31 patients with or without DCV at 3 days and 7 days after aSAH, as well as from 8 healthy controls. The analysis of the first 18-patient cohort revealed a striking differential expression pattern of the selected miRNAs in CSF and plasma of aSAH patients with DCV from those without DCV. Importantly, this differential expression was observed at the early time point (3 days after aSAH), before DCV event occurs. Seven miRNAs were identified as reliable DCV risk predictors along with a prediction model constructed based on an array of additional 19 miRNAs on the panel. These chosen miRNAs were then used to predict the risk of DCV in a separate, testing cohort of 15 patients. The accuracy of DCV risk prediction in the testing cohort reached 87%. The study demonstrates that our novel designed miRNA panel is an effective predictor of DCV risk and has strong applications in clinical management of aSAH patients.
2021-06-02 | GSE165608 | GEO
Project description:Introduction: Prostate cancer (PCa), one of the most prevalent malignancies affecting men worldwide, presents significant challenges in terms of early detection, risk stratification, and active surveillance. In recent years, liquid biopsies have emerged as a promising non-invasive approach to complement or even replace traditional tissue biopsies. Extracellular vesicles (EVs), nanosized membranous structures released by various cells into body fluids, have gained substantial attention as a source of cancer biomarkers due to their ability to encapsulate and transport a wide range of biological molecules, including RNA. In this study, we aimed to validate 15 potential RNA biomarkers, identified in a previous EV RNA sequencing study, using droplet digital PCR. Methods: The candidate biomarkers were tested in plasma and urinary EVs collected before and after radical prostatectomy from 30 PCa patients and their diagnostic potential was evaluated in a test cohort consisting of 20 benign prostate hyperplasia (BPH) and 20 PCa patients' plasma and urinary EVs. Next, the results were validated in an independent cohort of plasma EVs from 31 PCa and 31 BPH patients. Results: We found that the levels of NKX3-1 (p = 0.0008) in plasma EVs, and tRF-Phe-GAA-3b (p < 0.0001) tRF-Lys-CTT-5c (p < 0.0327), piR-28004 (p = 0.0081) and miR-375-3p (p < 0.0001) in urinary EVs significantly decreased after radical prostatectomy suggesting that the main tissue source of these RNAs is prostate and/or PCa. Two mRNA biomarkers-GLO1 and NKX3-1 showed promising diagnostic potential in distinguishing between PCa and BPH with AUC of 0.68 and 0.82, respectively, in the test cohort and AUC of 0.73 and 0.65, respectively, in the validation cohort, when tested in plasma EVs. Combining these markers in a biomarker model yielded AUC of 0.85 and 0.71 in the test and validation cohorts, respectively. Although the PSA levels in the blood could not distinguish PCa from BPH in our cohort, adding PSA to the mRNA biomarker model increased AUC from 0.71 to 0.76. Conclusion: This study identified two novel EV-enclosed RNA biomarkers-NKX3-1 and GLO1-for the detection of PCa, and highlights the complementary nature of GLO1, NKX3-1 and PSA as combined biomarkers in liquid biopsies of PCa.
| S-EPMC10720733 | biostudies-literature
Project description:No residual disease after debulking Surgery (R0 resection) is the most critical independent prognostic factor for advanced ovarian cancer (AOC). Therefore, it is of paramount importance to preoperative estimate the likelihood of R0 resection for choosing the best therapeutic strategy. Our study aimed to develop a non-invasive and reliable detection method for AOC patients with a high risk of residual disease. An integrated plasma small extracellular vesicles (sEVs) microRNA profiling was generated by RNA sequencing in AOC patients with no residual disease patients (R0) and residual disease(non-R0). We identified and validated a logistic model based on plasma sEVs miRNAs to predict residual disease in AOC patients.
2023-04-01 | GSE166310 | GEO
Project description:No residual disease after debulking Surgery (R0 resection) is the most critical independent prognostic factor for advanced ovarian cancer (AOC). Therefore, it is of paramount importance to preoperatively estimate the likelihood of R0 resection for choosing the best therapeutic strategy. Our study aimed to develop a non-invasive and reliable detection method for AOC patients with a high risk of residual disease. An integrated plasma small extracellular vesicles (sEVs) microRNA profiling was generated by RNA sequencing in AOC patients with no residual disease patients (R0) and residual disease (non-R0). We identified and validated a logistic model based on plasma sEVs miRNAs to predict residual disease in AOC patients.
2024-01-02 | GSE223126 | GEO