{"paper_id":"d4946084-f147-40e9-9700-249bbd665066","body_text":"Abstract\nBackground\nEndometriosis is a heterogeneous gynecological disorder characterized by chronic pain, infertility, and substantial impairment of quality of life. Increasing evidence indicates that microRNAs (miRNAs) are key regulators of endometriosis pathogenesis through their effects on inflammation, angiogenesis, cell proliferation, fibrosis, and hormone-responsive pathways.\nMethods\nIn this review, we summarize the biological roles of miRNAs in endometriosis and discuss their emerging value as diagnostic biomarkers and therapeutic targets. We further examine major bioinformatics resources and analytical tools used in miRNA research, including databases, target prediction platforms, and expression profiling approaches, with emphasis on their relevance and limitations in the context of endometriosis. In addition, we review recent advances in machine learning and deep learning for miRNA identification, target prediction, regulatory network reconstruction, and miRNA–disease association modeling. Particular attention is given to multi-omics integration strategies, which may better capture the molecular heterogeneity of endometriosis and improve biologically informed stratification.\nResults\nThis review highlights the key roles of miRNAs in endometriosis-related inflammation, angiogenesis, proliferation, fibrosis, and hormone-responsive signaling, and summarizes their potential as non-invasive biomarkers and therapeutic targets. It also emphasizes the value of bioinformatics, machine learning, and multi-omics approaches in identifying clinically relevant miRNA signatures, while acknowledging current challenges in standardization, validation, and interpretability.\nConclusions\nFuture studies should prioritize standardized multicenter datasets, explainable artificial intelligence, and integrative multi-omics frameworks to develop robust and clinically applicable miRNA-based diagnostic and therapeutic strategies for endometriosis.\nSimilar content being viewed by others\nAbbreviations\n- ML:\n-\nMachine learning\n- DL:\n-\nDeep learning\n- pri-miRNAs:\n-\nPrimary miRNAs\n- pre-miRNAs:\n-\nPrecursor miRNAs\n- VEGF:\n-\nVascular endothelial growth factor\n- MDA:\n-\nmiRNA-disease association\n- CDS:\n-\nCoding regions\n- CAGE:\n-\nGene expression cap analysis\n- TSS:\n-\nTranscription start sites\n- TarBase:\n-\nTarget gene database\n- sRNA-seq:\n-\nSmall RNA sequencing\n- MOADLN:\n-\nMulti-omics attention deep learning networks\n- Autoencoder:\n-\nAutoencoder networks\n- GNN:\n-\nGraph neural networks\n- SPALP:\n-\nSparse Autoencoder and Multi-Layer Perceptron\n- SVM:\n-\nSupport vector machine\n- CNN:\n-\nConvolutional neural network\n- RNN:\n-\nRecurrent neural network\n- Bi-LSTM:\n-\nBidirectional long short-term memory\n- MLP:\n-\nMultilayer perceptron\nFunding\nThis work was supported by Foundation of Science and Technology Department of Liaoning Province (Grants Nos. 2023-MSLH-385 and 2025-MSLH-351), Fundamental Research Funds for the Liaoning Universities (Grant No. LJ212410146026).\nAuthor information\nAuthors and Affiliations\nCorresponding authors\nEthics declarations\nEthics approval and consent to participate\nNot applicable.\nConsent for publication\nNot applicable.\nConflict of interest\nThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\nAdditional information\nPublisher’s note\nSpringer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.\nRights and permissions\nOpen Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.\nAbout this article\nCite this article\nGuo, C., Liu, Q., Hai, D. et al. MicroRNAs in endometriosis: bioinformatics resources, machine learning strategies, and multi-omics perspectives. J Transl Med (2026). https://doi.org/10.1186/s12967-026-08310-y\nReceived:\nAccepted:\nPublished:\nDOI: https://doi.org/10.1186/s12967-026-08310-y","source_license":"CC0","license_restricted":false}