J Cancer 2020; 11(13):3751-3761. doi:10.7150/jca.44034 This issue Cite
1. Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China
2. Hunan International Scientific and Technological Cooperation Base of Brain Tumor Research, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China
3. Xiangya Medical School, Central South University, Changsha, Hunan, 410008, China
*Authors contributed equally to this work.
Glioblastoma (GBM) is a common malignant brain tumor of the central nervous system with a poor prognosis. In order to identify the prognostic signatures of GBM, we screened differentially expressed genes (DEGs) that were based on a single-cell RNA sequencing (scRNA-seq) dataset. These genes characteristically represent the intra-tumor heterogenicity of glioblastoma. Moreover, we performed univariate analysis, log-rank test and multivariate Cox regression analyses to confirm a gene set that could be related to the overall survival (OS) among DEGs. Prognostic associated signatures (PAS) were utilized to construct a model for predicting OS in GBM patients. When considering either the training or the validation sets, time-dependent receiver operating characteristic (ROC) curves all indicated that our model displayed an excellent predictive ability. Additionally, we analyzed PAS at the single-cell level and found that the PAS score was associated with somatic mutations and clinical factors. Three factors, which included the PAS score, radiotherapy status, and age, were all used to establish a nomogram to predict the 6-month and 1-year survival probabilities. In conclusion, we constructed an optimal model that was derived from scRNA-seq to better predict the survival probability of GBM patients. These genes might also act as potential prognostic biomarkers and enable surgeons to develop individually therapeutic schedules and improve the prognosis of GBM patients.
Keywords: glioblastoma, single cell, differentially expressed genes, survival analysis, prognostic model