Xiao-Xia Guo1, Jiao Su2, Xiao-Feng He3. 1. Department of Neurosurgery, Heping Hospital Affiliated to Changzhi Medical College, Changzhi, Shanxi, China. 2. Department of Biological Chemistry, Changzhi Medical College, Changzhi, Shanxi, China. 3. Department of Science and Education, Heping Hospital Affiliated to Changzhi Medical College, Changzhi, Shanxi, China.
Abstract
BACKGROUND: To identify independently prognostic gene panel in patients with glioblastoma (GBM). MATERIALS AND METHODS: The Cancer Genome Atlas (TCGA)-GBM was used as a training set and a test set. GSE13041 was used as a validation set. Survival associated differentially expression genes (DEGs), derived between GBM and normal brain tissue, was obtained using univariate Cox proportional hazards regression model and then was included in a least absolute shrinkage and selection operator penalized Cox proportional hazards regression model. Thus, a 4-gene prognostic panel was developed based on the risk score for each patient in that model. The prognostic role of the 4-gene panel was validated using univariate and multivariable Cox proportional hazards regression model. RESULTS: A total of 686 patients with GBM were included in our study; 724 DEGs was identified, 133 of which was significantly correlated with the overall survival (OS) of patients with GBM. A 4-gene panel including NMB, RTN1, GPC5, and epithelial membrane protein 3 (EMP3) was developed. Kaplan-Meier survival analysis suggested that patients in the 4-gene panel low risk group had significantly better OS than those in the 4-gene panel high risk group in the training set (hazard ratio [HR] = 0.3826; 95% confidence interval [CI]: 0.2751-0.532; P < 0.0001), test set (HR = 0.718; 95% CI: 0.5282-0.9759; P = 0.033) and the independent validation set (HR = 0.6898; 95% CI: 0.4872-0.9766; P = 0.035). Both univariate and multivariable Cox proportional hazards regression analysis suggested that the 4-gene panel was independent prognostic factor for GBM in the training set. CONCLUSION: We developed and validated 4-gene panel that was independently correlated with the survival of patients with GBM.
BACKGROUND: To identify independently prognostic gene panel in patients with glioblastoma (GBM). MATERIALS AND METHODS: The Cancer Genome Atlas (TCGA)-GBM was used as a training set and a test set. GSE13041 was used as a validation set. Survival associated differentially expression genes (DEGs), derived between GBM and normal brain tissue, was obtained using univariate Cox proportional hazards regression model and then was included in a least absolute shrinkage and selection operator penalized Cox proportional hazards regression model. Thus, a 4-gene prognostic panel was developed based on the risk score for each patient in that model. The prognostic role of the 4-gene panel was validated using univariate and multivariable Cox proportional hazards regression model. RESULTS: A total of 686 patients with GBM were included in our study; 724 DEGs was identified, 133 of which was significantly correlated with the overall survival (OS) of patients with GBM. A 4-gene panel including NMB, RTN1, GPC5, and epithelial membrane protein 3 (EMP3) was developed. Kaplan-Meier survival analysis suggested that patients in the 4-gene panel low risk group had significantly better OS than those in the 4-gene panel high risk group in the training set (hazard ratio [HR] = 0.3826; 95% confidence interval [CI]: 0.2751-0.532; P < 0.0001), test set (HR = 0.718; 95% CI: 0.5282-0.9759; P = 0.033) and the independent validation set (HR = 0.6898; 95% CI: 0.4872-0.9766; P = 0.035). Both univariate and multivariable Cox proportional hazards regression analysis suggested that the 4-gene panel was independent prognostic factor for GBM in the training set. CONCLUSION: We developed and validated 4-gene panel that was independently correlated with the survival of patients with GBM.
Authors: Terry W Moody; Lingaku Lee; Irene Ramos-Alvarez; Tatiana Iordanskaia; Samuel A Mantey; Robert T Jensen Journal: Front Endocrinol (Lausanne) Date: 2021-09-01 Impact factor: 5.555
Authors: Nitish Gulve; Chenhe Su; Zhong Deng; Samantha S Soldan; Olga Vladimirova; Jayamanna Wickramasinghe; Hongwu Zheng; Andrew V Kossenkov; Paul M Lieberman Journal: Nat Commun Date: 2022-08-26 Impact factor: 17.694