Establishment of a nomogram with EMP3 for predicting clinical outcomes in patients with glioma: A bi‐center study

Autor: Ling Yuan, Anke Zhang, Xiaying Han, Houshi Xu, Sheng Chen, Yi-Ke Chen, Yunjia Ni, Shiqi Gao, Yuanzhi Xu, Meiqing Lou, Junkun Jiang, Zeyu Zhang, Yibo Liu, Xiaotao Zhang, Jianmin Zhang
Rok vydání: 2021
Předmět:
Zdroj: CNS Neuroscience & Therapeutics
ISSN: 1755-5949
1755-5930
DOI: 10.1111/cns.13701
Popis: Aim To demonstrate the clinical value of epithelial membrane protein 3 (EMP3) with bioinformatic analysis and clinical data, and then to establish a practical nomogram predictive model with bicenter validation. Methods The data from CGGA and TCGA database were used to analyze the expression of EMP3 and its correlation with clinical prognosis. Then, we analyzed EMP3 expression in samples from 179 glioma patients from 2013 to 2017. Univariate and multivariate cox regression were used to predict the prognosis with multiple factors. Finally, a nomogram to predict poor outcomes was formulated. The accuracy and discrimination of nomograms were determined with ROC curve and calibration curve in training and validation cohorts. Results EMP3 was significantly higher in higher‐grade glioma and predicted poor prognosis. In multivariate analysis, high expression of EMP3 (HR = 2.842, 95% CI 1.984–4.071), WHO grade (HR = 1.991, 95% CI 1.235–3.212), and IDH1 mutant (HR = 0.503, 95% CI 0.344–0.737) were included. The nomogram was constructed based on the above features, which represented great predictive value in clinical outcomes. Conclusion This study demonstrated EMP3 as a novel predictor for clinical progression and clinical outcomes in glioma. Moreover, the nomogram with EMP3 expression represented a practical approach to provide individualized risk assessment for glioma patients.
The data from CGGA and TCGA database were used to analyze the expression of EMP3 and its correlation with clinical prognosis and immune infiltration. Then, we analyzed EMP3 expression by IHC staining in samples from 179 glioma patients from 2013 to 2017. Univariate and multivariate cox regression were used to predict the prognosis with multiple factors. Finally, a nomogram to predict poor outcomes was formulated. The accuracy and discrimination of nomograms were determined with ROC curve and calibration curve in training and validation cohorts.
Databáze: OpenAIRE
Externí odkaz:
Nepřihlášeným uživatelům se plný text nezobrazuje