Asgharpour, A. | bioRxiv Science

bioRxiv2025

Hepatic isomiR landscaping reveals new biological insights into metabolic dysfunction in steatotic liver disease

Post-transcriptionally modified microRNA (miRNA), called isomiRs, expand the repertoire of transcripts that can leveraged for therapeutic targets and biological insights. However, the expression of isomiRs has not been characterized in metabolic dysfunction-associated steatotic liver disease (MASLD). Therefore, we assessed the isomiR expression profile in liver biopsies from 79 patients with MASLD and modeled their potential role in disease biology. MiRNAs represented 75% of the sequencing reads and over 65% of them were attributed to isomiRs, demonstrating their higher expression and diversity compared to canonically annotated miRNAs. Differential expression and machine-learning analyses were used to identify 173 isomiRs associated to MASLD severity and 58 isomiRs associated to fibrosis score. Candidate target mRNAs were identified for each isomiR based on sequence complementarity. Using matched mRNA sequencing data, and supported by data from an independent study, we proposed key dysregulated mRNA targets involved in a selection of 33 disease-associated pathways. Importantly, isomiRs offered novel and unique mRNA targets compared to the canonical miRNA, e.g. isomiR-122 targeting INSIG1 (insulin and cholesterol metabolism), and isomiR-21 targeting HMGCS2 and PPARA (PARR and TGF-beta signaling). Our work advances knowledge regarding the role of isomiRs in MASLD and lays a foundation for therapeutic targets identification. HighlightsOur results provide a comprehensive analysis of microRNA (isomiRs) in liver tissue. Machine learning identified isomiRs whose expression is associated with MASLD. Multi-omic analysis uncovered novel isomiR regulatory mechanisms involved in MASLD.

bioinformatics↗

bioRxiv2020

Unannotated small RNA clusters in circulating extracellular vesicles detect early stage liver cancer

BackgroundHepatocellular carcinoma (HCC) is among the deadliest malignancies and surveillance tools for early detection are suboptimal. Extracellular vesicles (EVs) have gained increasing scientific interest due to their involvement in tumor initiation and metastasis, however, most extracellular RNA (exRNA) biomarker studies are limited to annotated genomic regions. MethodsEVs were isolated with ultracentrifugation and nanoDLD and quality assessed by electron microscopy, immunoblotting, nanoparticle tracking, and deconvolution analysis. We performed genome-wide small exRNA sequencing, including unannotated transcripts. We identified small RNA clusters (smRCs) and delineated their key genomic features across biospecimens (blood, urine, tissue) and EV isolation techniques. A 3-smRC signature for early HCC detection was trained and validated in two independent cohorts. ResultsEV-derived smRCs were dominated by uncharacterized, unannotated small RNA and uniformly tiled across the genome with a consensus sequence of 20bp. A 3-smRC signature was significantly overexpressed in circulating EVs of HCC patients compared to controls at risk or patients with non-HCC malignancies (p<0.01, n=157). An independent validation in a phase 2 biomarker study revealed 86% sensitivity and 91% specificity for the detection of early HCC from controls at risk (i.e. cirrhosis or chronic liver disease, n=209) (positive predictive value (PPV): 89%, area under the ROC curve [AUC]: 0.87). The 3-smRC signature was independent of alpha-fetoprotein (p<0.0001) and a composite model yielded an increased AUC of 0.93 (sensitivity: 85%, specificity: 94%, PPV: 95%). ConclusionAn exRNA-based 3-smRC signature from plasma detects early stage HCC, which directly leads to the prospect of a minimally-invasive, blood-only, operator-independent surveillance biomarker. One sentence summaryWe employ a novel, data-driven approach to identify and characterize small RNA clusters from unannotated loci in extracellular vesicle-derived RNA across different cancer types, isolation techniques, and biofluids, facilitating discovery of a robust biomarker for detection of early stage liver cancer.

cancer biology↗