WISP2/CCN5 revealed as a potential diagnostic biomarker for endometriosis based on machine learning and single-cell transcriptomic analysis

Objective Endometriosis is a prevalent gynecological disease characterized by the ectopic growth of functional endometrial tissue outside the uterine cavity, affecting millions of women worldwide. Currently, the definitive diagnosis relies on invasive laparoscopy (the gold standard), with an average diagnostic delay of 7–10 years from symptom onset. Non-invasive biomarkers from blood or endometrial samples could enable early screening and reduce diagnostic time. Emerging technologies like single-cell sequencing and transcriptomics offer promising approaches for identifying highly specific biomarkers, advancing endometriosis research into the precision medicine era.

Using three machine learning algorithms, we selected four hub genes, among which WISP2/CCN5 was validated as a potential diagnostic biomarker. We discovered higher-than-normal gene expression of WISP2/CCN5 in the eutopic endometrium, and substantially higher expression in the ectopic endometrium compared with that in the eutopic endometrium.

Finally, through cell communication analysis, we found that elevated WISP2/CCN5 expression in stem cells within ectopic lesions may be mediated by the mitogen-activated protein kinase and Wnt signaling pathways, acting downstream of the fibroblast growth factor pathway.

The transition of endometrial tissue from normal to eutopic, and ultimately to ectopic, was found to coincide with progressively increased expression of WISP2/CCN5, which may serve as a biomarker of endometriosis. Similar content being viewed by others Data availability This study analyzed publicly available data sets. This study used data from public GEO data portal website (https://www.ncbi.nlm.nih.gov/geo/) Login id: (GSE120103 GSE7307 GSE6364 GSE7305 GSE23339 GSE25628 GSE213216).

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We thank Michelle Kahmeyer-Gabbe, PhD, from Liwen Bianji (Edanz) (http://www.liwenbianji.cn) for editing the English text of a draft of this manuscript. Funding Scientific research and technology development plan of Baise(20241541); Project to improveme basic research ability of young and middle-aged teachers of Guangxi Universities (2025KY0568); Natural Science Foundation of Guangxi (2025GXNSFHA069073;2025GXNSFHA069187). Author information Authors and Affiliations Contributions RW and HQ directed the study and provided experimental equipment, experimental platform and experimental guidance. SD analyzed all the data, completed all the experiments and wrote the paper; SL, WN, BW, GL, YH were responsible for collecting clinical samples. All the authors approved the manuscript for submission. Corresponding authors Ethics declarations Ethical approval and consent to participate All procedures were approved by the ethics committees of the Affiliated Hospital of Youjiang Medical College for Nationalities and Baise People’s Hospital. All studies were conducted in accordance with the relevant guidelines and regulations of the ethics committees of the two hospitals, and informed consent was obtained from all study subjects and/or their legal guardians.Ethical approval number: 2024042302. Consent to participate Not applicable. Consent for publication Not applicable. Competing interests The authors declare no competing interests. Additional information Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Electronic supplementary material Below is the link to the electronic supplementary material. Rights and permissions Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. About this article Cite this article Dou, S., Ling, S., Nong, W. et al. WISP2/CCN5 revealed as a potential diagnostic biomarker for endometriosis based on machine learning and single-cell transcriptomic analysis. Funct Integr Genomics 25, 131 (2025). https://doi.org/10.1007/s10142-025-01631-z Received: Revised: Accepted: Published: Version of record: DOI: https://doi.org/10.1007/s10142-025-01631-z