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. 2019 Apr 29;19(1):401.
doi: 10.1186/s12885-019-5556-x.

CD44, TGM2 and EpCAM as novel plasma markers in endometrial cancer diagnosis

Anna Torres  1   2   3   4 , Małgorzata Pac-Sosińska  5 , Krzysztof Wiktor  6 , Tomasz Paszkowski  7 , Ryszard Maciejewski  5 , Kamil Torres  5
Affiliations

CD44, TGM2 and EpCAM as novel plasma markers in endometrial cancer diagnosis

Anna Torres et al. BMC Cancer. .

Abstract

Background: Endometrial cancer (EC) is the most common malignancy of the female reproductive tract. Despite years of research, the accurate screening strategy is still not available in this disease and it is usually diagnosed only after the clinical signs are present. The recent technological advances in analytical methodologies enabled detection of multiple molecules in one, small sample of biological materials. Such approach was undertaken in the presented study.

Methods: Concentrations of aldehyde dehydrogenase 1 family, member A1 (ALDH1A1), carbonic anhydrase IX (CA9), CD44, epithelial cell adhesion molecule (EpCAM), hepsin, kallikrein-6, mesothelin, midkine, neural cell adhesion molecule L1 (L1CAM), and transglutaminase 2 (TGM2) were measured using MAGPIX®System in plasma samples of 45 EC, 20 healthy controls and 11 patients with endometriosis.

Results: Significantly increased concentration in EC as compared to healthy controls were found in case of CD44 (p < 0.001), EpCAM (p = 0.033) and TGM2 (p < 0.001). EpCAM and mesothelin concentrations differed based on FIGO stages. Regression analysis revealed marker panels with high accuracy in detection of EC. The highest AUC 0.937 was attributed to the 3-marker panel of CD44/TGM2/EpCAM (84% sensitivity, 100% specificity), FIGO IA samples were discriminated from more advanced stages of EC with the mesothelin/grade 1 model featuring AUC of 0.911 (95.24% sensitivity, 78.26% specificity).

Conclusions: Novel plasma biomarkers presenting good accuracy in diagnosing EC were found with TGM2 reported for the first time as plasma marker. It was also revealed that endometriosis may share similarities in the pattern of markers alterations characteristic for EC.

Keywords: CD44; Endometrial cancer; Endometriosis; EpCAM; Novel plasma markers; TGM2.

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Conflict of interest statement

Ethics approval and consent to participate

Medical University of Lublin Ethical Committee approved the study design (KE-0254/201/2008). Written, informed consent was obtained from each participant before entering the study.

Consent for publication

Not applicable

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Figures

Fig. 1
Fig. 1
Concentrations of biomarkers in plasma. a EpCAM concentration in EC vs. non-EC samples: median 44.99 (95% CI 34.80–49.08) vs. 28.67 (95% CI 23.93–35.79), (U = 489.50, Z = 2.2, p = 0.028). b TGM2 concentration in EC vs. non-EC samples: median 4808.36 (95% CI 3688.71–6224.69) vs. 1828.96 (95% CI 1574.94–3028.34), (U = 316, Z = 2.85, p = 0.004). c CA9 concentration in EC vs. control vs. endometriosis (ENDOM) samples. d CD44 concentration in EC vs. control vs. endometriosis samples. e EpCAM concentration in EC vs. control vs. endometriosis samples. f TGM2 concentration in EC vs. control samples vs. endometriosis samples. * p < 0.05, ** p < 0.01, *** p < 0.001
Fig. 2
Fig. 2
Comparison of biomarkers’ concentrations in endometrial cancer (EC), control, and endometriosis (ENDOM) plasma samples. a ALDH1A1. b L1CAM. c Hepsin. d Kallikrein-6. e Mesothelin. f Midkine. * p < 0.05, ** p < 0.01, *** p < 0.001
Fig. 3
Fig. 3
Concentrations of biomarkers in EC plasma samples depending on FIGO stage. a CD44 (t(3) = 16.10, p = 0.001, post-hoc p < 0.05). b EpCAM (t(3) = 12.06, p = 0.007, post-hoc p < 0.05). c Mesothelin (t(2) = 6.15, p = 0.046, post-hoc p < 0.05). d TGM2 (t(3) = 26.04, p < 0.001, post-hoc p < 0.05). * p < 0.05, ** p < 0.01, *** p < 0.001
Fig. 4
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)
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