Dataset Information

Development of a novel non-invasive biomarker panel for hepatic fibrosis in individuals with MASLD

ABSTRACT: - 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.

ORGANISM(S): Homo sapiens

PROVIDER: GSE240729 | GEO | 2024/05/17

REPOSITORIES: GEO

ACCESS DATA

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Project description:Differentially methylated oral squamous cell carcinoma (OSCC) biomarkers, identified in-vitro and validated in well-characterized surgical specimens, have shown poor clinical correlation in cohorts with different risk profiles. To overcome this lack of relevance we used the HumanMethylation27 BeadChip, publicly available methylation and expression array data, and Quantitative Methylation Specific PCR to uncover differential methylation in OSCC clinical samples with heterogeneous risk profiles. A two stage-design consisting of Discovery and Prevalence screens was used to identify differential promoter methylation and deregulated pathways in patients diagnosed with OSCC and head and neck squamous cell carcinoma. Promoter methylation of KIF1A (k=0.64), HOXA9 (k=0.60), NID2 (k=0.60), and EDNRB (k=0.60) had a moderate to substantial agreement with clinical diagnosis in the Discovery screen. HOXA9 had 68% sensitivity, 100% specificity and a 0.81 AUC. NID2 had 71% sensitivity, 100% specificity and a 0.79 AUC. In the Prevalence screen HOXA9 (k=0.82) and NID2 (k=0.80) had an almost perfect agreement with histologic diagnosis. HOXA9 had 85% sensitivity, 97% specificity and a 0.95 AUC. NID2 had 87% sensitivity, 95% specificity and a 0.91 AUC. A HOXA9 and NID2 gene panel had 94% sensitivity, 97% specificity and a 0.97 AUC. In saliva from OSCC cases and controls HOXA9 had 75% sensitivity, 53% specificity and a 0.75 AUC. NID2 had 87% sensitivity, 21% specificity and a 0.73 AUC. This Phase I Biomarker Development Trial identified a panel of differentially methylated genes in normal and OSCC clinical samples from patients with heterogeneous risk profiles. This panel may be useful for early detection and cancer prevention studies. The 12 samples analyzed comprise equal numbers of three tissue-types: Primary oral squamous cell carcinoma, Oral leukoplakia and Normal oral mucosa. Gender, age and smoking-status were approximately equally represented in these goups. Normal oral mucosa served as a control.

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.

Dataset Information

Development of a novel non-invasive biomarker panel for hepatic fibrosis in individuals with MASLD

Dataset's files

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets