Survival nomogram for medulloblastoma and multi-center external validation cohort

Background: Medulloblastoma (MB) is a highly malignant neuroepithelial tumor occurring in the central nervous system. The objective of this study was to establish an effective prognostic nomogram to predict the overall survival (OS) of MB patients. Materials and methods: The nomogram was developed using data from a retrospective cohort of 280 medulloblastoma patients (aged 3-18 years) identified from Beijing Tiantan Hospital between 2016 and 2021 as the training cohort. To validate the performance of the nomogram, collaborations were formed with eight leading pediatric oncology centers across different regions of China. A total of 162 medulloblastoma patients meeting the inclusion criteria were enrolled from these collaborating centers. Cox regression analysis, best subsets regression, and Lasso regression were employed to select independent prognostic factors. The nomogram's prognostic effectiveness for overall survival was assessed using the concordance index, receiver operating characteristic curve, and calibration curve. Results: In the training cohort, the selected variables through COX regression, best subsets regression, and Lasso regression, along with their clinical significance, included age, molecular subtype, histological type, radiotherapy, chemotherapy, metastasis, and hydrocephalus. The internally and externally validated C-indexes were 0.907 and 0.793, respectively. Calibration curves demonstrated the precise prediction of 1-, 3-, and 5-year OS for MB patients using the nomogram. Conclusion: This study developed a nomogram that incorporates clinical and molecular factors to predict OS prognosis in medulloblastoma patients. The nomogram exhibited improved predictive accuracy compared to previous studies and demonstrated good performance in the external validation cohort. By considering multiple factors, clinicians can utilize this nomogram as a valuable tool for individualized prognosis prediction and treatment decision-making in medulloblastoma patients.

Establishment of a novel survival assessment and prediction model for advanced gastric cancer patients receiving immunotherapy

Currently, there are only a few risk assessment tools that provide predictions of survival duration for patients with gastric cancer (GC) receiving immunotherapy. The purpose of the present study was to develop and validate a nomogram that uses statistical data to predict survival and make risk assessments for patients with advanced staged GC. A total of 1,013 patients consisting of a development cohort (n=501) and validation cohort (n=512) collected during the time interval between January 2018 and June 2022 were included in the present study. The analysis consisted of the discrimination index, calibration plots and decision curve of the nomogram model. A total of 167 (33.33%) patients from the development cohort, and 158 (30.85%) from the validation cohort died during the observation period. The median overall survival (OS) of female patients was higher at 980 days (95% CI, 613-NA) compared with that of male patients, which was 748 days (95% CI, 597-NA; P=0.24). The median survival of patients with domestic immunotherapy was 789 (95% CI, 597-NA) days, which was lower compared with the imported immunotherapy group who had a median OS of 980 days (95% CI, 582-NA; P=0.22). A total of four independent predictors, age (HR=1.012; P=0.0245), histological grade (HR=1.395; P=0.016), immunotherapy cycles (HR=0.932; P=0.028) and line of first immunotherapy (HR=1.693; P=0.0003), were identified. The C-index was 0.64 and 0.67 for the development and validation cohorts, respectively. Patients who received more cycles of immunotherapy as the first-line treatment with highly differentiated tumor led to increase in the survival time of the patients. Thus, this nomogram could be used to determine the benefit of immunotherapies on patients at various stages of treatment of GC.

A universal co-expression gene network and prognostic model for hepatic-biliary-pancreatic cancers identified by integrative analyses

Hepatic, biliary and pancreatic cancers are a diverse set of malignancies with poor prognoses. It is possible that common molecular mechanisms are involved in the carcinogenesis of these cancers. Here, we identified LINC01537 and seven protein-coding genes by integrative analysis of transcriptomes of mRNAs, microRNAs and long non-coding RNAs from cholangiocarcinoma, hepatocellular carcinoma and pancreatic adenocarcinoma cohorts in TCGA. A predictive model constructed from seven biomarkers was established to successfully predict the survival rate of patients, which was then further verified in external cohorts. Additionally, patients with high-risk scores in our model were prone to epithelial-mesenchymal transition. Finally, activation of the biomarker PDE2A significantly attenuated migration and epithelial-mesenchymal transition in the HepG2 liver cancer cell line.

External Validation of a Nomogram and Risk Grouping System for Predicting Individual Prognosis of Patients With Medulloblastoma

Background: Medulloblastoma (MB) is one of the most malignant neuroepithelial tumors in the central nervous system. This study aimed to establish an effective prognostic nomogram and risk grouping system for predicting overall survival (OS) of patients with MB.

Materials and Methods: The nomogram was constructed based on data from the database of Surveillance, Epidemiology, and End Results (SEER). This database consisted of 2,824 patients with medulloblastoma and was used as the training cohort. The data of another additional 161 patients treated at the Sun Yat-sen University Cancer Center (SYSUCC) were used as the external validation cohort. Cox regression analysis was used to select independent prognostic factors. Concordance index (C-index) and calibration curve were used to predict the prognostic effect of the nomogram for overall survival.

Results: In the training cohort, Cox regression analyses showed that the prognostic factors included histopathology, surgery, radiotherapy, chemotherapy, tumor size, dissemination, and age at diagnosis. The internal and external validated C-indexes were 0.681 and 0.644, respectively. Calibration curves showed that the nomogram was able to predict 1-, 3-, and 5-year OS for patients with MB precisely. Using the training cohort, a risk grouping system was built, which could perfectly classify patients into four risk nomogroups with a 5-year survival rate of 83.9%, 76.5%, 64.5%, and 46.8%, respectively.

Conclusion: We built and validated a nomogram and risk grouping system that can provide individual prediction of OS and distinguish MB patients from different risk groups. This nomogram and risk grouping system could help clinicians making better treatment plan and prognostic assessment.