The World Health Organization (WHO) notes gliomas as the most frequent intrinsic tumors of the CNS and classifies gliomas into multiple histological types. Among these, the most invasive type of brain cancer histology is Glioblastoma Multiforme (GBM). GBM is an extremely fast-growing type of malignant brain tumor seen in more than 15% of all patients with primary malignant brain tumors. The treatment of GBM poses a substantial clinical challenge due to tumor heterogeneity, ineffectiveness of surgery/radiotherapy, and chemoresistance. Prognostic biomarkers will enable clinicians to identify patients with a more aggressive tumor evolution. To identify novel biomarkers in GBM, gene expression datasets of metastatic brain cancer were obtained from The Cancer Genome Atlas (TCGA), a publicly-available collection of genomic data relating to the molecular and clinical basis of cancer. Genes from patient files were retrieved and analyzed for oncogenic capacity, metastatic contribution, and potential for prognostic signature. Using the differentially expressed genes RRN3P2, RPL7AP64, ACTR3C, and RPL7AP30, survival curves were generated using Kaplan Meier test to explore the prognostic value of the select risk signature. Further, immune infiltration analyses were performed to assess the immune infiltrates of six tumor-immune infiltrating cells (TIIC) subsets. Gene Ontology and KEGG were used to identify the correlated molecular pathways of the target genes and present possible therapeutic direction. This study explores the prognostic value of RRN3P2, RPL7AP64, ACTR3C, and RPL7AP30 genes in GBM, and provides new insight into the therapeutic value of the correlated molecular pathways of the set of genes. Further, this model is expected to provide guidance for clinical evaluation of the prognosis of GBM patients with overexpression of the aforementioned genes.