Analysis of Factors Related to Lymph Node Metastasis in Early-Stage Type 1 Endometrial Cancer: Verifying the Clinical Value of Positive Threshold of the Immunohistochemical Parameter Ki67

The prognostic value of co-expression of stemness markers CD44 and CD133 in endometrial cancer

Objective

The purpose of this study was to investigate the correlation between stemness markers (CD44 and CD133) and clinical pathological features, and to further explore the prognostic value of co-expression of CD44 & CD133 in endometrial cancer (EC).

Methods

Clinical data of stage I-III EC patients who underwent initial surgical treatment at two large tertiary medical centers from 2015 to 2020 were retrospectively collected. Cohen's kappa coefficient was used to show the consistency of the expression between CD44 and CD133. The correlation between co-expression of CD44 & CD133 and prognosis of EC patients was explored using univariate and multivariate Cox regression analysis. Then, the prognosis models for early-stage (stage I-II) EC patients were constructed. Finally, stratified analysis was performed for EC patients in high-intermediate-risk and high-risk groups, Kaplan-Meier analysis was used to compare the survival differences between patients with and without adjuvant therapy in different co-expression states (low expression, mixed expression, high expression) of CD44 & CD133.

Results

A total of 1168 EC patients were included in this study. The consistency of the expression between CD44 and CD133 was 70.5%, the kappa coefficient was 0.384. High expression of CD44 & CD133 was associated with early FIGO stage (P=0.017), superficial myometrial invasion (P=0.017), and negative lymphatic vessel space invasion (P=0.017). Cox regression analysis showed that the co-expression of CD44 & CD133 was significantly correlated with the prognosis of early-stage (stage I-II) patients (P=0.001 for recurrence and P=0.005 for death). Based on this, the nomogram models were successfully constructed to predict the prognosis of early-stage EC patients. Meanwhile, Kaplan-Meier analysis showed that patients with adjuvant therapy had a better overall prognosis than those without adjuvant therapy in high-intermediate-risk and high-risk groups. However, there was no statistically significant difference in survival between patients with and without adjuvant therapy in high expression of CD44 & CD133 group (P=0.681 for recurrence, P=0.621 for death).

Conclusion

High expression of CD44 & CD133 was closely related to the adverse prognosis of early-stage EC patients. Meanwhile, patients with high expression of CD44 & CD133 may not be able to achieve significant survival benefits from adjuvant therapy.

The value of machine learning in preoperative identification of lymph node metastasis status in endometrial cancer: a systematic review and meta-analysis

Background

The early identification of lymph node metastasis status in endometrial cancer (EC) is a serious challenge in clinical practice. Some investigators have introduced machine learning into the early identification of lymph node metastasis in EC patients. However, the predictive value of machine learning is controversial due to the diversity of models and modeling variables. To this end, we carried out this systematic review and meta-analysis to systematically discuss the value of machine learning for the early identification of lymph node metastasis in EC patients.

Methods

A systematic search was conducted in Pubmed, Cochrane, Embase, and Web of Science until March 12, 2023. PROBAST was used to assess the risk of bias in the included studies. In the process of meta-analysis, subgroup analysis was performed according to modeling variables (clinical features, radiomic features, and radiomic features combined with clinical features) and different types of models in various variables.

Results

This systematic review included 50 primary studies with a total of 103,752 EC patients, 12,579 of whom had positive lymph node metastasis. Meta-analysis showed that among the machine learning models constructed by the three categories of modeling variables, the best model was constructed by combining radiomic features with clinical features, with a pooled c-index of 0.907 (95%CI: 0.886-0.928) in the training set and 0.823 (95%CI: 0.757-0.890) in the validation set, and good sensitivity and specificity. The c-index of the machine learning model constructed based on clinical features alone was not inferior to that based on radiomic features only. In addition, logistic regression was found to be the main modeling method and has ideal predictive performance with different categories of modeling variables.

Conclusion

Although the model based on radiomic features combined with clinical features has the best predictive efficiency, there is no recognized specification for the application of radiomics at present. In addition, the logistic regression constructed by clinical features shows good sensitivity and specificity. In this context, large-sample studies covering different races are warranted to develop predictive nomograms based on clinical features, which can be widely applied in clinical practice.

Systematic review registration

https://www.crd.york.ac.uk/PROSPERO, identifier CRD42023420774.

Consistency of P53 immunohistochemical expression between preoperative biopsy and final surgical specimens of endometrial cancer

Objective

The aim of this study is to explore the consistency of P53 immunohistochemical expression between preoperative biopsy and final pathology in endometrial cancer (EC), and to predict the prognosis of patients based on the 4-tier P53 expression and classic clinicopathological parameters.

Methods

The medical data of patients with stage I-III EC who received preoperative biopsy and initial surgical treatment in two medical centers was retrospectively collected. The consistency of P53 immunohistochemistry expression between preoperative biopsy and final pathology was compared using Cohen's kappa coefficient and Sankey diagram, then 4-tier P53 expression was defined (P53wt/P53wt, P53abn/P53wt, P53wt/P53abn, and P53abn/P53abn). Univariate and multivariate Cox regression analysis was used to determine the correlation between 4-tier P53 expression and the prognosis of patients. On this basis, the nomogram models were established to predict the prognosis of patients by combining 4-layer P53 expression and classic clinicopathological parameters, then risk stratification was performed on patients.

Results

A total of 1186 patients were ultimately included in this study through inclusion and exclusion criteria. Overall, the consistency of P53 expression between preoperative biopsy and final pathology was 83.8%, with a kappa coefficient of 0.624. ROC curve suggested that the AUC of 4-tier P53 expression to predict the prognosis of patients was better than AUC of P53 expression in preoperative biopsy or final pathology alone. Univariate and multivariate Cox regression analysis suggested that 4-tier P53 expression was an independent influencing factor for recurrence and death. On this basis, the nomogram models based on 4-tier P53 expression and classical clinicopathological factors were successfully established. ROC curve suggested that the AUC (AUC for recurrence and death was 0.856 and 0.838, respectively) of the models was superior to the single 4-tier P53 expression or the single classical clinicopathological parameters, which could provide a better risk stratification for patients.

Conclusion

The expression of P53 immunohistochemistry had relatively good consistency between preoperative biopsy and final pathology of EC. Due to the discrepancy of P53 immunohistochemistry between preoperative biopsy and final pathology, the prognosis of patients can be better evaluated based on the 4-layer P53 expression and classic clinical pathological parameters.

Development and validation of a nomogram involving immunohistochemical markers for prediction of recurrence in early low-risk endometrial cancer

Background

The purpose of this study was to construct a nomogram based on classical parameters and immunohistochemical markers to predict the recurrence of early low-risk endometrial cancer patients.

Methods

A total of 998 patients with early low-risk endometrial cancer who underwent primary surgical treatment were enrolled (668 in the training cohort, 330 in the validation cohort). Prognostic factors identified by univariate and multivariate analysis in the training cohort were used to construct the nomogram. Prediction performance of the nomogram was evaluated using the calibration curve, concordance index (C-index), and the time-dependent receiver operating characteristic curve. The cumulative incidence curve was used to describe the prognosis of patients in high-risk and low-risk groups divided by the optimal risk threshold of the model.

Results

In the training cohort, grade ( P = 0.040), estrogen receptor ( P < 0.001), progesterone receptor ( P = 0.001), P53 ( P = 0.004), and Ki67 ( P = 0.002) were identified as independent risk factors of recurrence of early low-risk endometrial cancer, and were used to establish the nomogram. The calibration curve showed that the fitting degree of the model was good. The C-indexes of training and validation cohorts were 0.862 and 0. 827, respectively. Based on the optimal risk threshold of the nomogram, patients were split into a high-risk group and a low-risk group. The cumulative incidence curves showed that the prognosis of the high-risk group was far worse than that of the low-risk group ( P < 0.001).

Conclusion

This nomogram, with a combination of classical parameters and immunohistochemical markers, can effectively predict recurrence in early low-risk endometrial cancer patients.

Expression Profile and Molecular Basis of Cyclin-Dependent Kinases Regulatory Subunit 2 in Endometrial Carcinoma Detected by Diversified Methods

Purpose: Our purpose was to systematically appraise the clinicopathological significance and explore the molecular bases of CKS2 in endometrial carcinoma. Patients and Methods: We measured the clinicopathological significance of CKS2 using diverse methods of public RNA-seq, microarrays, and in-house tissue microarrays to investigate the molecular basis of CKS2 in endometrial carcinoma through upstream transcriptional analysis, immune infiltration correlation analysis, and co-expression analysis. Results: Both the analysis for public RNA-seq plus the microarray data and in-house tissue microarray confirmed the significant overexpression of CKS2 in a total of 1,021 endometrial carcinoma samples compared with 279 non-cancer endometrium samples (SMD = 2.10, 95% CI = 0.72-3.48). The upregulated CKS2 was significantly related to the lymph node metastasis and advanced clinical grade of endometrial carcinoma patients (p < 0.001). Mutation types such as amplification and mRNA occurred with high frequency in the CKS2 gene in endometrial carcinoma patients. A series of miRNAs and transcription factors, such as hsa-miR-26a, hsa-miR-130a, hsa-miR-30, E2F4, MAX, and GABPA, were predicted to regulate the transcription and expression of CKS2. Significant links were found between CKS2 expression and the infiltration level of B cells, CD4⁺ T cells, and neutrophils in endometrial carcinoma. CKS2-coexpressed genes were actively involved in pathways such as the mitotic cell cycle process, PID aurora B pathway, and prolactin signaling pathway. Conclusion: The overexpressed CKS2 showed positive correlations with the clinical progression of endometrial carcinoma and was associated with various cancer-related biological processes and pathways, showing potential as a promising clinical biomarker for endometrial carcinoma.