Molecular phenotype of high-grade endometrioid carcinoma of the endometrium

Buchynska, L G; Glushchenko, N M; Nesina, I P; Brieieva, O V; Iurchenko, N P

doi:10.32471/exp-oncology.2312-8852.vol-42-no-4.15450

Cited by 5 publications

(2 citation statements)

References 33 publications

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“…ECS is a biphasic endometrial carcinoma composed of malignant epithelial and mesenchymal differentiation 22 . EUC is a solid-pattern tumor lacking overt morphologic evidence of epithelial differentiation 23 .…”

Section: Introductionmentioning

confidence: 99%

Diagnosis subtype of endometrial carcinoma using a deep learning model based on histopathological images

Qi,

Li,

Guo

et al. 2023

Preprint

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The histological determination of endometrial carcinoma (EC) remains the keystone in the diagnostic process and helps patients receive appropriate and effective treatment. We focused on constructing a prognostic deep learning (DL) model, which was trained and validated using four independent cohorts of 4108 Hematoxylin and Eosin (H&E) stained images from 1027 patients with multiple magnifications. The model yields an averaged area under the receiver operating curve (AUROC) of 0.962 from the internal dataset, an AUROC of 0.878 on an external dataset from another medical center, and an AUROC of 0.930 on the different scanners dataset, and an AUROC of 0.926 on the poor quality slides dataset, respectively. The predictive value by DL was significant with the mutant P53 of EC (p<0.05). Furthermore, the predictive performance of the model was better than the primary and senior pathologist's prediction (AUC= 0.962, 0.722 and 0.868, respectively). The DL model can accurately highlight morphological characteristics in H&E images and provide indications into the diagnosis and stratification for the management.

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Section: Introductionmentioning

confidence: 99%

Diagnosis subtype of endometrial carcinoma using a deep learning model based on histopathological images

Qi,

Li,

Guo

et al. 2023

Preprint

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“…In 2020, over 400,000 new cases of ECa were reported worldwide [1]. The treatment of ECa patients remains an ongoing clinical challenge due to the variability of the disease and difficulties in determining the molecular subtypes of endometrial carcinomas with an aggressive phenotype [2][3][4]. Identifying molecular and biological features of tumor cells that have a prognostic value remains a focus of ECa research.…”

mentioning

confidence: 99%

Expression of Hepatocyte Growth Factor and C-Met Receptor in Stromal Fibroblasts and Tumor Cells of Endometrial Carcinoma

Buchynska¹,

Brieieva²,

Nespriadko³

2023

Exp. oncol.

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Background: HGF/c-Met is one of the main signaling pathways that ensure communication between epithelial cells and components of the tumor microenvironment determining the invasive and metastatic potential of many cancers. However, the significance of HGF and c-Met in endometrial carcinoma (ECa) progression remains unclear. Aim: To evaluate copy number variations as well as expression of the c-Met receptor and its ligand HGF in endometrial carcinomas considering the clinical and morphological characteristics of ECa. Materials and Methods: The study was conducted on ECa samples of 57 patients, among which 32 had lymph nodes and/or distant metastasis. The copy number of c-MET gene was estimated by qPCR. The expression of HGF and c-Met in tissue samples was determined by the immunohistochemical method. Results: Amplification of c-MET gene was detected in 10.5% of the ECa cases. In most carcinomas, a combined expression pattern of HGF and c-Met was established, in which co-expression of these markers was observed in tumor cells, and the content of HGF+ fibroblasts increased in the stroma. The expression of HGF in tumor cells was associated with the tumor differentiation grade and was higher in G3 ECa (p = 0.041). The number of HGF+ fibroblasts in the stromal component increased in the ECa cases with metastasis compared to the cases without metastasis (p = 0.032). The content of stromal c-Met+ fibroblasts was higher in deeply invasive carcinomas of patients with metastases than in tumors with invasion of < 1/2 myometrium (p = 0.035). Conclusion: Increased expression of HGF and c-Met in stromal fibroblasts of endometrial carcinomas is associated with metastasis in patients with ECa and deep invasion of the tumor into the myometrium, and can contribute to the aggressive course of the disease.

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The KRAS, ATR and CHEK1 expression levels in endometrial cancer are the risk factors predicting recurrence

Buchynska,

Gordiienko,

Glushchenko

et al. 2024

PLoS ONE

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Endometrial cancer is the most prevalent gynecologic malignancy with a high risk of recurrence. Local recurrence occurs in 7–20% of patients with treated stage I cancer within 3 years after primary treatment. In this study, we found significantly elevated mRNA expression levels of the oncoprotein KRAS, along with two replicative stress markers, ATR and CHEK1, in samples of endometrial carcinomas of endometrium (ECE) from patients with relapse. In contrast, mRNA expression levels of the studied genes were low and uniform in samples from patients without relapse. Elevated levels of KRAS protein and the phosphorylated form of ATR/CHEK1 were distinguishing features of recurrent ECE. A strong positive correlation was found between elevated mRNA and protein levels of the studied molecules. Elevated KRAS protein levels are characteristic of poorly differentiated (G3) endometrial carcinomas with deep myometrial invasion in patients without recurrence. In contrast, in patients with recurrence, higher protein levels of KRAS, pATR and pCHEK1 were observed in samples of G1-2 endometrial carcinomas, with statistically significant differences confirmed for pATR. High pCHEK1 protein levels are associated with deep tumor invasion in the myometrium among patients with recurrence. ROC analysis confirmed that evaluating the specificity and sensitivity of KRAS, pATR and pCHEK1 predicts recurrence development in patients with ECE. Our findings indicate that markers of replicative stress may play a significant role in ECE pathogenesis. Determining their levels in tumor samples after primary treatment could help define patients at high risk of recurrence and guide consequent courses of treatment.

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Molecular phenotype of high-grade endometrioid carcinoma of the endometrium

Cited by 5 publications

References 33 publications

Diagnosis subtype of endometrial carcinoma using a deep learning model based on histopathological images

Diagnosis subtype of endometrial carcinoma using a deep learning model based on histopathological images

Expression of Hepatocyte Growth Factor and C-Met Receptor in Stromal Fibroblasts and Tumor Cells of Endometrial Carcinoma

The KRAS, ATR and CHEK1 expression levels in endometrial cancer are the risk factors predicting recurrence

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