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dc.contributor.authorŁaniewski, P.
dc.contributor.authorCui, H.
dc.contributor.authorMahnert, N.D.
dc.contributor.authorMourad, J.
dc.contributor.authorBorst, M.P.
dc.contributor.authorWillmott, L.
dc.contributor.authorChase, D.M.
dc.contributor.authorRoe, D.J.
dc.contributor.authorHerbst-Kralovetz, M.M.
dc.identifier.citationŁaniewski, P., Cui, H., Mahnert, N.D. et al. Protein biomarkers in cervicovaginal lavages for detection of endometrial cancer. Biomark Res 10, 88 (2022).
dc.description.abstractBackground: Rates of endometrial cancer (EC) are increasing. For a definitive diagnosis, women undergo various time-consuming and painful medical procedures, such as endometrial biopsy with or without hysteroscopy, and dilation and curettage, which may create a barrier to early detection and treatment, particularly for women with inadequate healthcare access. Thus, there is a need to develop robust EC diagnostics based on non- or minimally-invasive sampling. The objective of this study was to quantify a broad range of immuno-oncology proteins in cervicovaginal lavage (CVL) samples and investigate these proteins as predictive diagnostic biomarkers for EC. Methods: One hundred ninety-two women undergoing hysterectomy for benign or malignant indications were enrolled in this cross-sectional study. Classification of women to four disease groups: benign conditions (n = 108), endometrial hyperplasia (n = 18), low-grade endometrioid carcinoma (n = 53) and other EC subtypes (n = 13) was based on histopathology of biopsy samples collected after the surgery. CVL samples were collected in the operating room during the standard-of-care hysterectomy procedure. Concentrations of 72 proteins in CVL samples were evaluated using multiplex immunoassays. Global protein profiles were assessed using principal component and hierarchical clustering analyses. The relationships between protein levels and disease groups and disease severity were determined using Spearman correlation, univariate and multivariate receiver operating characteristics, and logistic regression analyses. Results: Women with EC and benign conditions exhibited distinctive cervicovaginal protein profiles. Several proteins in CVL samples (e.g., an immune checkpoint protein, TIM-3, growth factors, VEGF, TGF-α, and an anti-inflammatory cytokine, IL-10) discriminated EC from benign conditions, particularly, when tested in combinations with CA19–9, CA125, eotaxin, G-CSF, IL-6, MCP-1, MDC, MCP-3 and TRAIL (sensitivity of 86.1% and specificity of 87.9%). Furthermore, specific biomarkers (e.g., TIM-3, VEGF, TGF-α, TRAIL, MCP-3, IL-15, PD-L2, SCF) associated with histopathological tumor characteristics, including histological type and grade, tumor size, presence and depth of myometrial invasion or mismatch repair protein status, implying their potential utility for disease prognosis or monitoring therapies. Conclusions: This proof-of-principle study demonstrated that cervicovaginal sampling coupled with multiplex immunoassay technology can offer a minimally to non-invasive method for EC detection. © 2022, The Author(s).
dc.publisherBioMed Central Ltd
dc.rights© The Author(s) 2022. Open Access. This article is licensed under a Creative Commons Attribution 4.0 International License, (
dc.subjectCervicovaginal microenvironment
dc.subjectEndometrial cancer
dc.subjectMinimally to non-invasive diagnostic
dc.subjectProtein biomarker
dc.subjectUterine cancer
dc.subjectWomen’s health
dc.titleProtein biomarkers in cervicovaginal lavages for detection of endometrial cancer
dc.contributor.departmentCollege of Medicine – Phoenix, University of Arizona
dc.contributor.departmentUA Cancer Center, University of Arizona
dc.identifier.journalBiomarker Research
dc.description.noteOpen access journal
dc.description.collectioninformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at
dc.eprint.versionFinal published version
dc.source.journaltitleBiomarker Research

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© The Author(s) 2022. Open Access. This article is licensed under a Creative Commons Attribution 4.0 International License, (
Except where otherwise noted, this item's license is described as © The Author(s) 2022. Open Access. This article is licensed under a Creative Commons Attribution 4.0 International License, (