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. 2019 Apr 29;19:401. doi: 10.1186/s12885-019-5556-x

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© The Author(s). 2019

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

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Fig. 4 — Receiver operating characteristic (ROC) curves. a ROC curves for regression models discriminating EC from non-EC samples; RM1 (♦ - 8-marker model: ALDH1A1, CA9, CD44, Hepsin, Kallikrein 6, L1CAM, Midkine, TGM2), RM2 (△ - 5-marker model: ALDH1A1, CD44, EpCAM, Midkine, TGM2). b ROC curves for single makers discriminating EC from control samples (▲ - TGM2; ♦ - CD44; △ - EpCAM). c ROC curves for regression models discriminating EC from control samples, p-value for all AUCs was < 0.001 (♦ - EpCAM-TGM2; △ - EpCAM-TGM2-CD44; ▲ - TGM2-CD44; ■ - CD44-EpCAM). d ROC curve for regression model discriminating EC FIGO IA samples from control samples (▼ - CD44-TGM2). e ROC curve for regression model discriminating EC FIGO IA samples from FIGO IB - III samples (♦ - Mesothelin-G1). f ROC curve for regression model discriminating endometriosis samples from control samples (♦ - CD44-TGM2)