Identification of a miRNA panel as a promising biomarker for diagnosis of glioblastoma patients using next-generation RNA sequencing
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ABSTRACT:
PROVIDER: PRJNA1022417 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR26218243.fastq.gz | Fastqsanger.gz | |||
| SRR26218244.fastq.gz | Fastqsanger.gz | |||
| SRR26218245.fastq.gz | Fastqsanger.gz | |||
| SRR26218246.fastq.gz | Fastqsanger.gz | |||
| SRR26218247.fastq.gz | Fastqsanger.gz |
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Project description:- Background & Aims: Considering the escalating prevalence of metabolic dysfunction-associated steatotic liver disease (MASLD) and MASLD-related fibrosis, accurate non-invasive biomarkers for diagnosis and staging of fibrosis are urgently needed. This study Aims to develop a blood-based biomarker panel for fibrosis detection in individuals with MASLD. - Approach & Results: Using a translational diet-induced LDLr-/-.Leiden MASLD mouse model, candidate biomarkers were identified focused on the mechanism of collagen deposition, by integrating hepatic gene expression and new extracellular matrix deposition, as detected by dynamic D2O-labeling. To translate these findings to humans, gene expression profiles and biomarkers were analyzed in liver biopsies and serum samples from 67 individuals with histologically characterized MASLD and variable degrees of fibrosis. This led to the selection of three biomarkers for a blood-based fibrosis biomarker panel: IGFBP7, SSc5D and Sema4D. The accuracy of the biomarker panel was tested in a separate cohort of 128 individuals with histologically characterized MASLD across different stages of fibrosis. A Light Gradient Boosting Machine (LGBM) model was applied to predict fibrosis stage in MASLD (F0/F1: AUC = 0.90; F2: AUC = 0.91; F3/F4: AUC = 0.87). - Conclusion & Discussion: Using a translational Approach to identify collagen turnover related proteins indicative of fibrosis, we developed an accurate blood-based biomarker panel to detect and stage hepatic fibrosis in individuals with MASLD.
2024-05-17 | GSE240729 | GEO
Project description:Purpose: Gastric cancer (GC) is one of the most common causes of cancer deaths worldwide; however, reliable and non-invasive screening methods for GC are not established. Therefore, we conducted this study to develop a biomarker for GC detection, consisting of urinary microRNAs (miRNAs). Experimental Design: We matched 306 participants by age and sex (153 pairs consisting of patients with GC and healthy controls [HCs]), then randomly divided them across three groups: (1) the discovery cohort (4 pairs); (2) the training cohort (95 pairs); (3) the validation cohort (54 pairs). Moreover, 64 participants (32 pairs) with serum samples were also enrolled in the serum cohort. Result: There were 22 urinary miRNAs with significantly aberrant expressions between the two groups in the discovery cohort. Upon multivariate analysis of the training cohort, urinary expression levels of miR-6807-5p and miR-6856-5p were significantly independent biomarkers for diagnosis of GC, in addition to Helicobacter pylori (H. pylori) status. A diagnostic panel that combined the aberrant miRNAs and H. pylori status distinguished between HC and GC samples with an area under the curve (AUC) = 0.736. In the validation cohort, urinary miR-6807-5p and miR-6856-5p showed significantly higher expression levels in the GC group, and the combination biomarker panel of miR-6807-5p, miR-6856-5p, and H. pylori status also showed excellent performance (AUC = 0.885). In addition, serum levels of miR-6807-5p and miR-6856-5p were significantly higher in the GC group. Conclusion: This novel biomarker panel enables early and non-invasive detection of GC.
2019-12-22 | GSE128720 | GEO
Project description:Applying Next Generation Sequencing technique we compared the miRNA expression pattern of tumor tissue sample of 6 GPs and peritumoral region of 6 lower grade (I-II) Glioma patients, serving as control group. To determine the difference on miRNA expresion level between GBM and control cases, we performed cluster analysis on the NGS dataset of 6 replicates for each of the two goups of samples with iDEP 96 software. In order to characterize the extent of up- or downregulation, log2FC values were calculated using the iDEP.96 web tool applying the DESeq2 algorithm. On the base of that 117 known miRNAs were identified to be differentially expressed using a threshold of false discovery rate (FDR) <0.05 and fold-change> 2 during the analysis. Among them, 35 miRNAs were upregulated (log2FC > 2) and 82 miRNAs were downregulated (log2FC < -2) with biological revelance in tissue samples comparing with the control samples. To validate our results obtained by NGS, five upregulated miRNAs: hsa-miR-196a-5p, hsa-miR-21-3p, hsa-miR-92b-5p, hsa-miR-10b-3p, hsa-miR-503-5p and three downregulated miRNAs: hsa-mir-383-5p, hsa-mir-490-3p, hsa-mir-1224-3p were chosen for RT-qPCR analysis. As the result of that hsa-miR-196a-5p, hsa-miR-21-3p, and hsa-miR-10b-3p was significantly upregulated while hsa-mir-383-5p and hsa-mir-490-3p was significantly downregulated, compared with those in the control samples. The other three miRNAs: hsa-miR-1224-3p, hsa-miR-92b-5p, hsa-miR-503-5p did not show significant difference between the control group and GPs.
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Project description:A multi biomarker panel of miRNA-lncRNA interaction a novel approach for early diagnosis of Acute coronary syndrome
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Project description:Background: Safe and sensitive methods for glioblastoma diagnosis and disease monitoring are urgently needed. Exosomes are nano-sized extracellular vesicles that contain molecules characteristic of their cell-of-origin, including microRNA. Exosomes released by glioblastoma tumors cross the blood-brain-barrier ino the peripheral circulation. Methods: Serum exosomal-microRNA isolated from IDH-wildtype glioblastoma (n=12) and IDH-mutant glioma grades II-III (n=10) were analyzed using small-RNA next generation sequencing and compared to age- and gender-matched healthy controls. Differentially expressed miRNAs (|fold change|³2 and p-value ≤ 0.05 in three statistical tests, Fischer-exact, t-test, and Wilcoxon) were identified and the predictive power of individual and subsets of miRNAs were tested using univariate (logistic regression) and multivariate (Random Forest) analyses. Additional glioblastoma sera (n=4) and independent sets of healthy (n=9) and non-glioma (n=10) controls were used to further test the predictive power of our glioblastoma miRNA signature. Results: Twenty-six miRNAs were differentially expressed in glioblastoma relative to healthy controls. Seven miRNAs (miR-182-5p, miR-328-3p, miR-339-5p, miR-340-5p, miR-485-3p, miR-486-5p and miR-543) were the most stable for classifying glioblastoma, achieving a predictive power of 91.7%. Strikingly, the combined expressions of miR-182-5p, miR-328-3p miR-485-3p miR-486-5p distinguished glioblastoma patients from controls with perfect accuracy. This miRNA panel was able to correctly classify all specimens in validation cohorts (n=23). An analogous approach was used to identify 23 dysregulated miRNAs in IDH-mutant gliomas, including a distinct subset of stable miRNAs for classifying patients with lower-grade IDH-mutant gliomas. Conclusions: Our serum exosomal-miRNA signature can accurately diagnose glioblastoma preoperatively. These findings have significant scope to revolutionize glioblastoma tumor diagnosis and disease monitoring. IMPORTANCE OF STUDY: There is a real need for accurate biomarkers that can measure glioblastoma disease activity and treatment response in a safe and timely manner. This study demonstrates that exosome-associated microRNAs have exceptional utility as blood-based biomarkers in glioma patients. This work also shows the potential for exosomal microRNA profiles to be used for glioma subtyping, grading and determining mutational states. The development of specific, sensitive and non-invasive screening tests would have significant clinical benefit by reducing costs of treatment monitoring, improving accessibility and quality-of-life measures. Moreover, such tests have the potential to provide objectively measured surrogate endpoints to allow clinical trial protocols to be more dynamic and adaptive.
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Project description:Pancreatic ductal adenocarcinoma (PDAC) suffers from lack of an effective diagnostic method, limiting improvement in patient survival. Although the FDA-approved biomarker CA19-9 has been used for detection and surveillance of PDAC, its clinical utility is generally regarded to be limited due to its suboptimal diagnostic performance. Liquid chromatography-mass spectrometry (LC-MS) has emerged as a burgeoning technology in clinical proteomics, and identified a plethora of protein biomarker candidates for PDAC. Yet few if any has successfully transitioned into clinical practice. This translational standstill is owed partly to the limited considerations of practical needs and perspectives of clinical implementation in the course of a biomarker pipeline, such as demonstrating analytical validity of proposed biomarkers, which is critical to turning research-grade assays to clinical-grade tests. Here, we developed a fit-for-purpose multi-marker panel for PDAC diagnosis by consolidating analytically robust biomarkers as well as employing a relatively simple LC-MS method. In the discovery phase, we systematically collected putative biomarkers from both in-house proteomics data and the large volume of literature documenting PDAC biomarkers. In the verification phase, we developed a multiple-reaction monitoring (MRM)-MS-based proteomic assay using surrogate peptides that passed stringent analytical validation criteria. Given the intended use of our panel (i.e., primary screening), we adopted a high-throughput protocol including short gradient (<10 min) and simple sample preparation (no depletion or enrichment steps). To further facilitate clinical implementation, we performed verification of our panel on serum samples, which are usually the preferred biospecimen available in clinical settings. We developed a stacking ensemble model based on our final panel of 12 protein biomarkers combined with CA19-9, which showed improved diagnostic performance compared to using CA19-9 alone (AUC 0.919 and 0.830, respectively). A large-scale clinical validation study is underway to demonstrate the clinical validity of our panel.
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