Abstract
Glioblastoma (GBM) is one of the most common and aggressive primary adult brain tumors. Tumor heterogeneity poses a great challenge to the treatment of GBM, which is determined by both heterogeneous GBM cells and a complex tumor microenvironment. Single-cell RNA sequencing (scRNA-seq) enables the transcriptomes of great deal of individual cells to be assayed in an unbiased manner and has been applied in head and neck cancer, breast cancer, blood disease, and so on. In this study, based on the scRNA-seq results of infiltrating neoplastic cells in GBM, computational methods were applied to screen core biomarkers that can distinguish the discrepancy between GBM tumor and pericarcinomatous environment. The gene expression profiles of GBM from 2343 tumor cells and 1246 periphery cells were analyzed by maximum relevance minimum redundancy (mRMR). Upon further analysis of the feature lists yielded by the mRMR method, 31 important genes were extracted that may be essential biomarkers for GBM tumor cells. Besides, an optimal classification model using a support vector machine (SVM) algorithm as the classifier was also built. Our results provided insights of GBM mechanisms and may be useful for GBM diagnosis and therapy.