A Nomogram Model Involving Immunohistochemical Markers for Predicting the Recurrence of Stage I-II Endometrial Cancer

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 prognostic value of P53 index in predicting the recurrence of early low-risk endometrial cancer

AIM

We aimed to clarify the clinical value of P53 index in patients with early low-risk endometrial cancer (EC) and find an optimal cut-off value of P53 index for predicting the recurrence of these patients.

METHODS

The clinicopathological data of 157 patients with early low-risk EC (stage 1A with grade 1 or 2 endometrioid EC) were analyzed. The optimal cut-off value of the P53 index was calculated by the receiver operating characteristic curve analysis and Youden index. Cox regression model was used to evaluate the independent prognostic predictors of recurrence of EC. Then all patients were divided into two groups according to the optimal cut-off value of the P53 index. Differences of the clinicopathological parameters between the two groups were compared.

RESULTS

Multivariate analysis showed age PR (p = 0.020) and P53 (p = 0.001) were independent prognostic factors for the recurrence of EC. The value of P53 index was found to be the optimal cut-off point of 17.5% in estimating the recurrence of EC. The 5-year recurrence-free survival rates of patients in the low P53 index group (<17.5%) and the high P53 index group (≥17.5%) were 94.6% and 65.4% (p < 0.001).

CONCLUSION

It has been revealed that the P53 index is a prognostic factor for recurrence in early low-risk EC. The optimal cut-off value of P53 index may contribute to the postoperative individualized treatment options for early low-risk EC patients.

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.

A Novel Hematological Inflammation-Nutrition Score (HINS) and Its Related Nomogram Model to Predict Survival Outcome in Advanced Gastric Cancer Patients Receiving First-Line Palliative Chemotherapy

Purpose

This study aims to construct a novel hematological inflammation-nutrition score (HINS) and investigate its prognostic value in patients with advanced gastric cancer (AGC). We investigated the risk stratification performance of HINS and developed a HINS-based nomogram model to predict overall survival by combining traditional predictors.

Patients and Methods

We conducted a retrospective study on 812 AGC patients who received first-line platinum- or fluoropyrimidine-containing chemotherapy at The First Affiliated Hospital of Zhengzhou University Hospital between 2014 and 2019. Patients were randomly divided into a training cohort (N=609) and a validation cohort (N=203). HINS (0-2) was constructed based on a pre-chemotherapy systemic immune-inflammation index (SII) and albumin (ALB). Prognostic factors were screened by univariate and multivariate COX proportional regression models. Significant factors were used to construct a nomogram model. Internal validation was performed by calibration curves, time-dependent receiver operating characteristics (ROC) curves, and decision curve analysis (DCA), evaluating its prediction consistency, discrimination ability, and clinical net benefit.

Results

HINS was constructed based on SII and ALB. HINS showed a better stratification ability than JCOG prognostic index, with significant differences between groups. Multivariate analysis showed that ECOG ≥1 (HR: 1.379; P=0.005), Stage IV (HR: 1.581; P <0.001), diffuse-type histology (HR: 1.586; P <0.001), number of metastases ≥2 (HR: 1.274; P=0.038), without prior gastrectomy (HR: 1.830; P <0.001), ALP ≥ULN (HR: 1.335; P=0.034), HINS (P <0.001) were independent factors of OS. We successfully established a HINS-based nomogram model that showed a strong discriminative ability, accuracy, and clinical utility in training and validation cohorts.

Conclusion

HINS shows a superior risk stratification ability, which might be a potential prognostic biomarker for AGC patients receiving palliative first-line palliative chemotherapy. The HINS-based nomogram model is a convenient and efficient tool for managing prognosis and follow-up treatments.

Development and validation of a nomogram for primary duodenal carcinoma: a multicenter, population-based study

Aim: This study aimed to develop a predictive model for patients with duodenal carcinoma. Methods: Duodenal carcinoma patients from the Surveillance, Epidemiology, and End Results database (2010-2015) and the First Affiliated Hospital of Nanchang University (2010-2021) were enrolled. A nomogram was constructed according to least absolute shrinkage and selection operator regression analysis, the Akaike information criterion approach and Cox regression analysis. Results: Five independent prognostic factors were significantly associated with the prognosis of the duodenal carcinoma patients. A nomogram was constructed with a C-index in the training and validation cohorts of 0.671 (95% CI: 0.578-0.716) and 0.662 (95% CI: 0.529-0.773), respectively. Conclusion: The established nomogram model provided visualization of the risk of each prognostic factor.

A Nomogram Model to Predict Recurrence of Non-Muscle Invasive Bladder Urothelial Carcinoma After Resection Based on Clinical Parameters and Immunohistochemical Markers

OBJECTIVE

This study aims to establish a nomogram model by combining traditional clinical parameters with immunohistochemical markers to predict the recurrence of non-muscle invasive bladder urothelial carcinoma (NMIBUC) after resection.

METHODS

In total, 504 patients were included in this study. Of these patients, 353 underwent transurethral resection of bladder tumor (TURBT) in the Yongchuan Hospital of Chongqing Medical University and were identified as a training cohort. Univariate and multivariate Cox regression analyses were used to determine the risk factors associated with recurrence in the training cohort and to establish a nomogram model. A total of 151 patients who were hospitalized in the Second Affiliated Hospital of Chongqing Medical University (validation cohort) were used for further validation. The calibration curve was generated for internal and external model validation. The clinical practicability of this model was further verified by comparing the consistency index (C-index) among various models.

RESULTS

The mean follow-up time of the training cohort was 45.6 months (range 4-90). In total, 146 patients relapsed in training cohort. After univariate analysis, multivariate analysis further confirmed tumor grade (p=.034), immediate postoperative instillation therapy (p=.025), Ki67 (p=.047), P53 (p=.038) and CK20 (p=.049) as independent risk factors for recurrence, and these factors were included in the nomogram model. The model more accurately predicted recurrence compared with other models based on the highest C-index of 0.82 (95% CI, 0.78-0.86) in the training cohort and 0.80 (95% CI, 0.77-0.83) in the validation cohort.

CONCLUSIONS

This proposed nomogram model based on traditional clinical parameters and immunohistochemical markers can more accurately predict postoperative recurrence in patients with NMIBUC.

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

Objective

Lymph node metastasis (LNM) is an important reference indicator for the prognosis of endometrial cancer (EC). Even in patients with early low-risk EC, many people still have LNM. The purpose of this study was to investigate the related factors influencing LNM in early-stage EC and determine the optimal positive threshold of immunohistochemical parameter Ki67 for predicting LNM, providing auxiliary reference indicators for clinical diagnosis and treatment.

Methods

The clinicopathological data of 651 patients with “apparent” early-stage EC who underwent standard surgical treatment were included. Univariate and multivariate logistics regression were used to analyze the correlation between each clinicopathological factor and LNM. Receiver operating characteristic curve (ROC curve) and Youden index were used to determine the optimal positive threshold of Ki67 for predicting LNM. Finally, correlation between Ki67 and various clinicopathological factors was analyzed, and the predictive value of each prognostic factor was compared.

Results

Multivariate analysis found that histologic grade (P=0.023), lymphatic vessel space invasion (LVSI) (P < 0.001), serological index Ca125 (P=0.002), immunohistochemical parameter Ki67 (P < 0.001), ER (P < 0.001) and P53 (P=0.001) were independent prognostic factors of LNM. ROC curve and Youden index showed that the optimal positive thresholds of Ki67 to predict LNM were 40%. Based on this, ROC curve showed that the area under the curve (AUC) of Ki67 (AUC=0.714) was larger than other single predictors, and Ki67 combined with other predictors can significantly increase the AUC value (AUC= 0.847 and 0.868, respectively).

Conclusion

Ki67 was an important predictor for predicting the LNM in early-stage EC and taking a positive percentage of about 40% can be used as the positive threshold of the immunohistochemical parameter Ki67. On this basis, Ki67 combined with other predictive indicators can significantly improve prediction performance and can be used for segmentally predicting LNM of early-stage EC.