MicroRNAs in endometriosis: bioinformatics resources, machine learning strategies, and multi-omics perspectives

Cuishan Guo, Guo C, Qing Liu, Liu Q, Darong Hai, Hai D, Zhu X, Xingyu Zhu, Mo Z, Zefei Mo, YI WANG, Wang Y, Qi Zhao, Zhang C, Chiyuan Zhang
Journal of translational medicine · 2026 · vol. 24(1) · doi:10.1186/s12967-026-08310-y · PMID:42185901 · PMC13390208 · W7162310976
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This review summarizes the roles of microRNAs in endometriosis pathogenesis, their potential as biomarkers and therapeutics, and highlights the utility of bioinformatics, machine learning, and multi-omics approaches for advancing diagnostic and treatment strategies.

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This paper is a review that examines how microRNAs contribute to endometriosis pathogenesis by regulating inflammation, angiogenesis, cell proliferation, fibrosis, and hormone-responsive signaling, and it surveys their potential as diagnostic biomarkers and therapeutic targets. It summarizes bioinformatics resources and analytical tools for miRNA research (including target prediction, expression profiling, and regulatory-network reconstruction), alongside machine learning and deep learning methods for miRNA identification, target prediction, and miRNA–disease association modeling. The authors emphasize multi-omics integration strategies to address endometriosis molecular heterogeneity, while explicitly noting challenges related to standardization, validation, and interpretability. This paper is centrally about endometriosis — it focuses specifically on microRNAs and the computational/multi-omics approaches used to study their roles as biomarkers and targets.

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Abstract

BACKGROUND: Endometriosis 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. METHODS: In 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. RESULTS: This 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. CONCLUSIONS: Future 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.
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Abstract

Background Endometriosis 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.

Methods

In 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.

Results

This 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.

Conclusions

Future 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. Similar content being viewed by others Abbreviations - ML: - Machine learning - DL: - Deep learning - pri-miRNAs: - Primary miRNAs - pre-miRNAs: - Precursor miRNAs - VEGF: - Vascular endothelial growth factor - MDA: - miRNA-disease association - CDS: - Coding regions - CAGE: - Gene expression cap analysis - TSS: - Transcription start sites - TarBase: - Target gene database - sRNA-seq: - Small RNA sequencing - MOADLN: - Multi-omics attention deep learning networks - Autoencoder: - Autoencoder networks - GNN: - Graph neural networks - SPALP: - Sparse Autoencoder and Multi-Layer Perceptron - SVM: - Support vector machine - CNN: - Convolutional neural network - RNN: - Recurrent neural network - Bi-LSTM: - Bidirectional long short-term memory - MLP: - Multilayer perceptron Funding This 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). Author information Authors and Affiliations Corresponding authors Ethics declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Conflict of interest The 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. Additional information Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Rights and permissions Open 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/. About this article Cite this article Guo, 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 Received: Accepted: Published: DOI: https://doi.org/10.1186/s12967-026-08310-y

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endometriosisinfertility

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Computational Biology Computational Biology Computational Biology Computational Biology Computational Biology Computational Biology Computational Biology Computational Biology Computational Biology Computational Biology Computational Biology Computational Biology Computational Biology Computational Biology Computational Biology Computational Biology Computational Biology Computational Biology Computational Biology Computational Biology

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