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bioRxiv · 10.1101/2025.03.18.644021

STmiR: A Novel XGBoost-Based Framework for Spatially Resolved miRNA Activity Prediction

Yuan, J.·Xu, P.·Liu, W.·Ye, Z.
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Abstract

MicroRNAs (miRNAs) are critical regulators of gene expression in cancer biology; however, their spatial dynamics within tumor microenvironments (TME) remain underexplored owing 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 predictions. STmiR integrates bulk RNA-seq data (TCGA and CCLE) with spatial transcriptomics profiles to model nonlinear miRNA-mRNA interactions, achieving a high predictive accuracy (Spearmans {rho} > 0.8) across four major cancer types (breast, lung, ovarian, and prostate). Applied to 10X Visium ST datasets from nine cancers, STmiR identified six pan-cancer conserved miRNAs (e.g., hsa-miR-21, hsa-let-7a) consistently ranked in the top 40 across malignancies, and uncovered cell-type-specific regulatory networks in fibroblasts, B cells, and malignant cells. A breast cancer case study validated the utility of STmiR by linking miR-205 to androgen receptor (AR) signaling and miR-200b to epithelial-mesenchymal transition (EMT). By enabling the spatial mapping of miRNA activity, STmiR provides a transformative tool to dissect miRNA-mediated regulatory mechanisms in cancer progression and TME remodeling, with implications for biomarker discovery and precision oncology. HighlightsO_LIFirst integration of XGBoost and spatial transcriptomics: STmiR pioneers a machine learning framework to predict miRNA activity in spatially heterogeneous tissues, overcoming limitations of linear correlation-based methods. C_LIO_LIHigh accuracy and generalizability: Demonstrates robust performance (Spearmans {rho} > 0.8) across four cancer types validated by independent datasets. C_LIO_LIPan-cancer conserved miRNAs: Six miRNAs (e.g., hsa-miR-21 and hsa-let-7a) were shared across nine cancers, implicating their roles in core oncogenic pathways. C_LI Key ContributionsO_LIMethodological innovation: Combines XGBoosts nonlinear modeling with spatial transcriptomics to resolve miRNA activity in multicellular contexts. C_LIO_LIBiological discovery: Uncovers the conserved and context-dependent roles of miRNAs in tumor progression and microenvironment crosstalk. C_LIO_LITranslational impact: Provides a computational platform for identifying spatially regulated therapeutic targets in cancer. C_LI

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Yuan, J., Xu, P., Liu, W., Ye, Z.. 2025-03-19. STmiR: A Novel XGBoost-Based Framework for Spatially Resolved miRNA Activity Prediction. https://doi.org/10.1101/2025.03.18.644021

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