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

Verschuren, Lars; Mak, Anne Linde; van Koppen, Arianne; Özsezen, Serdar; Difrancesco, Sonia; Caspers, Martien P. M.; Snabel, Jessica; van der Meer, David; van Dijk, Anne-Marieke; Rashu, Elias Badal; Nabilou, Puria; Werge, Mikkel Parsberg; van Son, Koen; Kleemann, Robert; Kiliaan, Amanda J.; Hazebroek, Eric J.; Boonstra, André; Brouwer, Willem P.; Doukas, Michail; Gupta, Saurabh; Kluft, Cornelis; Nieuwdorp, Max; Verheij, Joanne; Gluud, Lise Lotte; Holleboom, Adriaan G.; Tushuizen, Maarten E.; Hanemaaijer, Roeland

doi:10.1038/s41467-024-48956-0

Cited by 8 publications

References 50 publications

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Blood-based biomarkers for early frailty are sex-specific: validation of a combined in silico prediction and data-driven approach

de Jong,

Caspers,

Dulos

et al. 2024

GeroScience

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Frailty is characterized by loss of physical function and is preferably diagnosed at an early stage (e.g., during pre-frailty). Unfortunately, sensitive tools that can aid early detection are lacking. Blood-based biomarkers, reflecting pathophysiological adaptations before physical symptoms become apparent, could be such tools. We identified candidate biomarkers using a mechanism-based computational approach which integrates a priori defined database-derived clinical biomarkers and skeletal muscle transcriptome data. Identified candidate biomarkers were used as input for a sex-specific correlation analysis, using individual gene expression data from female (n = 24) and male (n = 28) older adults (all 75 + years, ranging from fit to pre-frail) and three frailty-related physical parameters. Male and female groups were matched based on age, BMI, and Fried frailty index. The best correlating candidate biomarkers were evaluated, and selected biomarkers were measured in serum. In females, myostatin and galectin-1 and, in males, cathepsin B and thrombospondin-4 serum levels were significantly different between the physically weakest and fittest participants (all p < 0.05). Logistic regression confirmed the added value of these biomarkers in conjunction with age and BMI to predict whether the subjects belonged to the weaker or fittest group (AUC = 0.80 in females and AUC = 0.83 in males). In conclusion, both in silico and in vivo analyses revealed the sex-specificity of candidate biomarkers, and we identified a selection of potential biomarkers which could be used in a biomarker panel for early detection of frailty. Further investigation is needed to confirm these leads for early detection of frailty.

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Blood-based biomarkers for early frailty are sex-specific: validation of a combined in silico prediction and data-driven approach

de Jong,

Caspers,

Dulos

et al. 2024

GeroScience

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Identifying patient subgroups in MASLD and MASH-associated fibrosis: molecular profiles and implications for drug development

González Hernández,

Verschuren,

Caspers

et al. 2024

Sci Rep

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The incidence of MASLD and MASH-associated fibrosis is rapidly increasing worldwide. Drug therapy is hampered by large patient variability and partial representation of human MASH fibrosis in preclinical models. Here, we investigated the mechanisms underlying patient heterogeneity using a discovery dataset and validated in distinct human transcriptomic datasets, to improve patient stratification and translation into subgroup specific patterns. Patient stratification was performed using weighted gene co-expression network analysis (WGCNA) in a large public transcriptomic discovery dataset (n = 216). Differential expression analysis was performed using DESeq2 to obtain differentially expressed genes (DEGs). Ingenuity Pathway analysis was used for functional annotation. The discovery dataset showed relevant fibrosis-related mechanisms representative of disease heterogeneity. Biological complexity embedded in genes signature was used to stratify discovery dataset into six subgroups of various sizes. Of note, subgroup-specific DEGs show differences in directionality in canonical pathways (e.g. Collagen biosynthesis, cytokine signaling) across subgroups. Finally, a multiclass classification model was trained and validated in two datasets. In summary, our work shows a potential alternative for patient population stratification based on heterogeneity in MASLD-MASH mechanisms. Future research is warranted to further characterize patient subgroups and identify protein targets for virtual screening and/or in vitro validation in preclinical models.

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MegaMASLD: An interactive platform for exploring stratified transcriptomic signatures in MASLD progression

Cheng,

Chua,

Chan

et al. 2024

Preprint

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Liver transcriptomic data from patients with metabolic dysfunction-associated steatotic liver disease (MASLD) offers valuable resource for deciphering pathogenic molecular drivers. Here, we performed a Mega-analysis of MASLD Liver Transcriptomes (MegaMASLD) which reanalysed raw RNAseq data of over 800 livers in a standardized and integrative manner, aiming to unravel druggable molecular events in MASLD. Our analysis revealed a progressive transcriptomic shift predominantly associated with immunopathologies during MASLD progression. The differential transcriptomes produced a MASLD gene signature useful for quantitative assessment of MASLD severity but failed to faithfully recapitulate the exact histological staging. Instead, a histologic-independent unsupervised clustering analysis predicted a high-risk group prone to develop metabolic dysfunction-associated steatohepatitis (MASH), characterized by aberrant changes in humoral immune response and antibody repertoires. These findings were supported by another histologic-independent pseudotime analysis, which also identified several potentially targetable molecular switches, including FGFR, PDGFR, PAK, PRKG1 and CAMK kinase families, activated at various transitory phases of MASLD. The robust analysis has enabled risk stratification and deepened our understanding of the dynamic molecular events driving MASLD, thereby offering new options to enhance precision medicine of MASLD. An online web tool featuring MegaMASLD is available at https://bioanalytics-hs.shinyapps.io/MegaMASLD/.

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

Cited by 8 publications

References 50 publications

Blood-based biomarkers for early frailty are sex-specific: validation of a combined in silico prediction and data-driven approach

Blood-based biomarkers for early frailty are sex-specific: validation of a combined in silico prediction and data-driven approach

Identifying patient subgroups in MASLD and MASH-associated fibrosis: molecular profiles and implications for drug development

MegaMASLD: An interactive platform for exploring stratified transcriptomic signatures in MASLD progression

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