Non-Coding RNA

MicroRNA (miRNA) target prediction is often treated as a single problem, yet it encompasses two distinct computational tasks: identifying whether a miRNA can bind a particular target RNA binding site, and predicting the change in gene expression that follows from miRNA perturbation. Here, we trace the evolution of the field of miRNA target prediction in humans from early seed- and conservation-based algorithms, to deep learning models trained on large-scale interaction datasets. We note that early frameworks obscured binding site recognition and gene-level regulation by aggregating binding site-level scores into gene-level predictions, whereas newer experimental and computational advances have enabled a clearer separation of the tasks. Deep learning has substantially advanced miRNA binding site prediction, but progress in miRNA target gene prediction remains constrained by limited perturbation data, inconsistent data processing and annotations, heterogeneous definitions of regulatory response, uneven predictor coverage, and the absence of comprehensive benchmarks. We also highlight the importance of negative-example construction, which can introduce dataset-specific biases and inflate apparent model performance. We propose two complementary benchmarked community challenges: the prediction of miRNA–binding site interactions, and the genome-wide prediction of miRNA-mediated changes in target gene expression in defined biological contexts. Treating these as distinct computational tasks, while recognising their biological interdependence, would enable more meaningful evaluation of methodological advances and provide a stronger foundation for modelling miRNA-mediated regulatory networks.

Skin-derived extracellular nucleic acids—particularly non-coding RNAs (ncRNAs) such as miRNAs and lncRNAs—are a rapidly evolving frontier in molecular diagnostics. These molecules are present in skin tissue, dermal interstitial fluid, and sweat, whilst ncRNAs regulate essential processes including epidermal differentiation, wound healing, immune regulation, and tumourigenesis, positioning them as promising candidates for minimally invasive biomarker development across a broad spectrum of dermatological diseases. This narrative review summarises their biological functions and potential clinical applications. The available evidence provides a conceptual basis for an emerging cutaneous “surface liquid biopsy” framework, whilst highlighting the need for standardised sampling, analytical validation, and clinical evaluation before such approaches can be translated into clinical practice. Although ample disease-associated ncRNA signatures have been uncovered, many remain at exploratory or early translational stages, and the concordance of these ncRNA signatures with corresponding blood and tissue biomarkers needs to be established. Continued advances in sampling strategies, analytical technologies, and clinical validation are needed to translate these findings into clinically deployable platforms for dermatological diseases and, potentially, systemic diseases in the future.

Background/Objectives: Intestinal mucosal function relies on the integrated contribution of multiple cell lineages, including secretory cells. Long noncoding RNA H19 modulates many cell processes essential for human pathologies. Here, we investigated the role of H19 in development and function of secretory lineage cells and elucidated the implication of altered ATOH1 by H19 in dysfunction of the secretory cells. Methods: Studies were conducted in H19-transgenic (H19-Tg) and H19-knockout (H19^−/−) mice, intestinal organoids, and Caco-2 cells. Secretory lineage cells were examined by fluorescent immunostaining with their specific cell markers, including Lysozyme, Mucin2, DCLK1, and Chromogranin A. Intestinal mucosal growth was measured by BrdU incorporation, and intestinal permeability was detected by paracellular tracer flux assay using FITC-Dextran. Single cell RNA-sequence, quantitative real-time PCR, and Western immunoblotting analyses were used to monitor changes in gene expression. Results: Tissue-specific transgenic expression of H19 in the intestinal epithelium caused defects in Paneth, goblet, tuft, and enteroendocrine cells in mice, whereas targeted deletion of the H19 gene increased the function of secretory lineage cells. Locally increasing the levels of H19 in the epithelium impaired gut barrier function in H19-Tg mice, although it failed to alter mucosal growth. The intestinal mucosal tissues of H19-Tg mice exhibited a remarkable decrease in the levels of differentiation-associated transcription factors including ATOH1. Ectopically expressed ATOH1 in the H19-enriched intestinal organoids derived from H19-Tg mice rescued development of the secretory lineage cells. Mechanistically, H19 inhibited ATOH1 expression via inhibition of its translation. Conclusions: These findings indicate that H19 functions as a negative regulator of differentiation and function of secretory lineage cells in the intestinal mucosa at least partially via the inhibition of ATOH1 translation.