Emerging evidence suggests expression from human endogenous retrovirus (HERV) loci likely contributes to, or is a biomarker of, glioblastoma multiforme (GBM) disease progression. However, the relationship between HERV expression and GBM malignant phenotype is unclear. Applying several in silico analyses based on data from The Cancer Genome Atlas (TCGA), we derived a locus-specific HERV transcriptome for glioma that revealed 211 HERVs significantly dysregulated in the comparisons of GBM vs. normal brain (NB), GBM vs. low-grade glioma (LGG), and LGG vs. NB. Our analysis supported development of a unique HERV scoring algorithm that segregated GBM, LGG, and NB. Interestingly, lower HERV scores showed correlation with lower survival in GBM. However, HERV scores were less robust in predicting LGG survival or LGG progression to GBM. Functional prediction analysis linked the 211 HERV loci with 18 voltage-gated potassium channel genes. The functional link between dysregulated HERVs and specific potassium channel genes may contribute to better understanding of GBM pathogenesis, disease progression, and possibly drug resistance.