mirCCC: Repression-aware graph learning for miRNA-mediated cell-cell communication inference

Authors

Chen, Y., Cui, J., Zhang, S., Liu, E., Xie, L., Feng, C., Chen, M.

Abstract

Cell-cell communication analyses usually focus on protein ligands and receptors and therefore miss the extracellular vesicle-mediated transfer of microRNAs, an important route of signalling in cancer. Here, we show that microRNA-mediated communication can be inferred from standard single-cell RNA sequencing by detecting coordinated decreases in the expression of validated miRNA target genes. We developed mirCCC, a computational framework that estimates cell-specific microRNA activity, models cellular sending and receiving capacities for extracellular vesicle transfer, and learns microRNA-resolved communication graphs from transcriptomic data. In synthetic benchmarks with strong confounding signals, mirCCC improved, whereas all comparison methods declined. Applied to a human colorectal cancer atlas, mirCCC recovered known colorectal cancer-associated microRNAs and identified stromal- and myeloid-to-epithelial communication converging on a plasticity program linked to TGF-{beta} and Wnt/{beta}-catenin signalling. These results provide a practical route for studying extracellular vesicle-mediated communication in existing single-cell atlases.

Preprint server: bioRxiv
The authors list and abstract were imported from bioRxiv on 02 Jul 2026.

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