{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T14:29:36Z","timestamp":1785421776922,"version":"3.56.0"},"reference-count":55,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T00:00:00Z","timestamp":1784764800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"crossref","award":["1P20GM104320"],"award-info":[{"award-number":["1P20GM104320"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"crossref"}]},{"award":["1P20GM104320"],"award-info":[{"award-number":["1P20GM104320"]}],"id":[{"id":"https:\/\/ror.org\/045p44t13","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Genes"],"abstract":"<jats:p>Background: MicroRNAs are key post-transcriptional regulators of gene expression and contribute to cancer progression, tumor heterogeneity, and context-dependent regulatory rewiring. However, most computational approaches rely on sequence-based target prediction or bulk expression association and are not designed to jointly model regulatory priors, expression context, and heterogeneous cancer states, particularly when matched single-cell microRNA\/mRNA co-profiling data are scarce. Methods: We developed a context-aware graph transformer framework for microRNA\u2013gene regulatory analysis across biological resolutions. The framework represents microRNAs, genes, and biological contexts as a heterogeneous graph, where contexts correspond to individual cells in single-cell data and tumor samples or subtype-defined profiles in bulk cohorts. Heterogeneous graph transformer learning generated regulatory embeddings, Bayesian topology optimization refined candidate microRNA\u2013gene interactions, and a dominance-based competition layer with Dominance Share scoring identified master regulators and cooperative target modules. Results: We applied miR-CellMap to matched single-cell miRNA\/mRNA co-sequencing data from K562 leukemia cells and paired bulk cancer datasets spanning pan-cancer and subtype-specific cohorts, including breast, colon, glioblastoma, lower-grade glioma, and ovarian cancer. The framework identified recurrent and dataset-specific miRNA regulatory programs, including regulators such as miR-186-5p, miR-214-3p, miR-27a-3p, and let-7 family members. Embedding-derived context analysis showed that predicted miRNA target programs were consistently closer to observed context-specific gene programs than random matched gene programs across all seven datasets. Dominance Share analysis further identified cooperative target modules and co-repressed target programs, supporting the use of miR-CellMap for interpretable cancer-focused miRNA regulatory discovery. Conclusions: This framework provides an interpretable strategy for mapping conserved, cancer-specific, and context-dependent microRNA\u2013gene regulatory programs across single-cell and bulk cancer datasets.<\/jats:p>","DOI":"10.3390\/genes17080846","type":"journal-article","created":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T17:13:17Z","timestamp":1784826797000},"page":"846","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Context-Aware Graph Transformer Framework for microRNA\u2013Gene Regulatory Inference Across Bulk Tumor and Single-Cell Cancer Data"],"prefix":"10.3390","volume":"17","author":[{"given":"Jane","family":"Ohia","sequence":"first","affiliation":[{"name":"Systems Biology and Biomedical Informatics Laboratory, School of Computing, University of Nebraska-Lincoln, Lincoln, NE 68588, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Juan","family":"Cui","sequence":"additional","affiliation":[{"name":"Systems Biology and Biomedical Informatics Laboratory, School of Computing, University of Nebraska-Lincoln, Lincoln, NE 68588, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,7,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1016\/j.cell.2009.01.002","article-title":"MicroRNAs: Target recognition and regulatory functions","volume":"136","author":"Bartel","year":"2009","journal-title":"Cell"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"351","DOI":"10.1146\/annurev-biochem-060308-103103","article-title":"Regulation of mRNA translation and stability by microRNAs","volume":"79","author":"Fabian","year":"2010","journal-title":"Annu. 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