STmiR: A Novel XGBoost-based framework for spatially resolved miRNA activity prediction in cancer transcriptomics.

ABSTRACT: MicroRNAs (miRNAs) are critical regulators of gene expression in cancer biology, yet their spatial dynamics within tumor microenvironments (TMEs) remain underexplored due to technical limitations in current spatial transcriptomics (ST) technologies. To address this gap, we present STmiR, a novel XGBoost-based framework for spatially resolved miRNA activity prediction. STmiR integrates bulk RNA-seq data (TCGA and CCLE) with spatial transcriptomics profiles to model nonlinear miRNA-mRNA interactions, achieving high predictive accuracy (Spearman's ρ > 0.8) across four major cancer types (breast, lung, ovarian, prostate), with performance further confirmed through direct comparison with experimentally measured miRNA expression in an independent spatial transcriptomics dataset. Applied to 10X V

SUBMITTER: Yuan J 

PROVIDER: S-EPMC12419590 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature