Computational identification of cross-kingdom microRNA compatibility between Moringa oleifera miR156 and the human CDK4 transcript

Triple-negative breast cancer (TNBC) remains one of the most aggressive breast cancer subtypes and lacks durable targeted therapies. Dysregulation of cell-cycle control, particularly through CDK4/6 signaling, is a defining feature of TNBC biology (Garrido-Castro et al., 2019). Extracts of Moringa oleifera have repeatedly been shown to induce G1-phase arrest in breast cancer models, yet the molecular basis of this phenotype remains unclear (Al-Asmari et al., 2015) (Gaffar et al., 2019). Emerging work on cross-kingdom regulation has raised the possibility that plant-derived microRNAs may, under specific conditions, interact with mammalian transcripts (Zhang et al., 2012) (Chin et al., 2016). Sequence shuffling for the negative control was performed with set.seed(42) to ensure reproducibility. Additional visualisations (nucleotide alignment and thermodynamic analyses) were generated using Python 3 (matplotlib v3.7). Here, we performed a high-stringency computational screen of conserved Moringa microRNAs against 30 genes implicated in TNBC pathogenesis using local sequence alignment. We identify a predicted high-affinity interaction between mol-miR156 and the human CDK4 3' untranslated region (3'UTR), characterized by an uninterrupted 12-nucleotide complementary motif that exceeds canonical mammalian microRNA seed requirements. These findings support the hypothesis that conserved plant microRNAs may exhibit latent structural compatibility with oncogenic human transcripts. While physiological delivery and functional repression are not demonstrated here, this work establishes a molecular framework for future experimental investigation into cross-kingdom RNA interactions relevant to cancer cell-cycle regulation. Impact StatementA high-stringency computational screen identifies latent molecular compatibility between a conserved plant microRNA and the human CDK4 oncogene, establishing a testable framework for cross-kingdom RNA interference in triple-negative breast cancer.