Yuan Jiaqi, Xu Peng, Ye Zheng, Liu Wenbin
Institute of Computational Science and Technology, Guangzhou University, Guangzhou, China.
School of Computer Science of Information Technology, Qiannan Normal University for Nationalities, Duyun, Guizhou, China.
PLoS One. 2025 Sep 9;20(9):e0322082. doi: 10.1371/journal.pone.0322082. eCollection 2025.
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 Visium ST datasets from nine cancers, STmiR identifies six pan-cancer conserved miRNAs (e.g., hsa-miR-21, hsa-let-7a) consistently ranked in the top 40 across malignancies, and uncovers cell-type-specific regulatory networks in fibroblasts, B cells, and malignant cells. A breast cancer case study demonstrates STmiR's utility in uncovering biologically relevant miRNA-target relationships and their association with key cancer pathways. By enabling 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.
微小RNA（miRNA）是癌症生物学中基因表达的关键调节因子，但由于当前空间转录组学（ST）技术的技术限制，它们在肿瘤微环境（TME）中的空间动态仍未得到充分探索。为了填补这一空白，我们提出了STmiR，这是一种基于XGBoost的新型框架，用于空间分辨的miRNA活性预测。STmiR将大量RNA测序数据（TCGA和CCLE）与空间转录组学图谱整合起来，以模拟非线性miRNA- mRNA相互作用，在四种主要癌症类型（乳腺癌、肺癌、卵巢癌、前列腺癌）中实现了高预测准确性（斯皮尔曼相关系数ρ>0.8），通过与独立空间转录组学数据集中实验测量的miRNA表达进行直接比较，进一步证实了其性能。应用于来自九种癌症的10X Visium ST数据集，STmiR识别出六种泛癌保守miRNA（如hsa-miR-21、hsa-let-7a），在所有恶性肿瘤中始终排名前40，并揭示了成纤维细胞、B细胞和恶性细胞中细胞类型特异性的调控网络。一项乳腺癌案例研究证明了STmiR在揭示生物学相关的miRNA-靶点关系及其与关键癌症通路关联方面的效用。通过实现miRNA活性的空间映射，STmiR提供了一种变革性工具，用于剖析癌症进展和TME重塑中miRNA介导的调控机制，对生物标志物发现和精准肿瘤学具有重要意义。
