Transcriptomic Profiling of MicroRNA and Non-Coding RNA from Whole Blood of African Americans with MASLD

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Metabolic dysfunction-associated steatotic liver disease (MASLD), formerly known as non-alcoholic fatty liver disease (NAFLD), is a growing health concern, yet the role of non-coding RNAs (ncRNAs), including microRNAs (miRNAs), in its pathogenesis remains poorly understood. In this pilot study, we aimed to identify significantly expressed miRNAs and ncRNAs and correlate transcriptomic patterns of the findings with previously identified coding gene expression profiles to explore potential regulatory mechanisms in MASLD. Participants were selected from an existing study population. We conducted transcriptomic profiling of miRNAs and other ncRNAs in whole-blood samples from African American (AA) individuals with MASLD and matched controls (n = 4 per group) as a discovery cohort. A subsequent qRT-PCR validation study was performed in 30 participants, including 14 individuals with MASLD and 16 controls. miRNA sequencing was performed by Zymo, USA, followed by miRNA extraction using the Zymo-Seq™ miRNA Library Kit. Differentially expressed miRNAs and ncRNAs were analyzed using Ingenuity Pathway Analysis (IPA) to identify associated biological pathways. A total of 1412 miRNAs and 5423 other ncRNAs were identified in this study. Among them, 35 miRNAs and 28 other ncRNAs exhibited significant differential expressions (fold-change cutoff 1.5, p < 0.05). miR-206 was consistently upregulated, whereas miR-1343-5p, miR-1299, miR-224-5p, and miR-193a-5p were downregulated across all samples. miR-206 upregulation and miR-185-3p/miR-224-5p/miR-218-5p downregulation were validated, associating with lipid metabolism impairment and hepatic fibrosis via the AMPK/TGF-β pathway, implicating ncRNA-mediated regulation. To our knowledge, this is the first whole-blood non-coding RNA transcriptomic study in AA MASLD, an under-represented population. This small-scale pilot study requires validation in large multi-ethnic cohorts to confirm generalizability.

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