A novel gene signature based on five glioblastoma stem-like cell relevant genes predicts the survival of primary glioblastoma.

PURPOSE

Primary glioblastoma (pGBM) is the most common and lethal type of neoplasms in the central nervous system, while the existing biomarkers, lacking consideration on the stemness changes of GBM cells, are not specific enough to predict the complex prognosis respectively. We aimed to build a high-efficiency prediction gene signature related to GBM cell stemness and investigate its prognostic value in primary glioblastoma.

METHODS

Differentially expressed genes were screened in GSE23806 database. The selected genes were then verified by univariate Cox regression in 591 patients from four enormous independent databases, including the Chinese Glioma Genome Atlas (CGGA), TCGA, REMBRANDT and GSE16011. Finally, the intersected genes were included to build the gene signature. GO analysis and GSEA were carried out to explore the bioinformatic implication.

RESULTS

The novel five-gene signature was used to identify high- and low-risk groups in the four databases, and the high-risk group showed notably poorer prognosis (P < 0.05). Gene ontology (GO) terms including "immune response", "apoptotic process", and "angiogenesis" were picked out by GO analysis and GSEA, which revealed that the gene signature was highly possibly related to the stemness of GSCs and predicting the prognosis of GBM effectively.

CONCLUSION

We built a gene signature with five glioblastoma stem-like cell (GSC) relevant genes, and predicted the survival in four independent databases effectively, which is possibly related to the stemness of GSCs in pGBM. Several GO terms were investigated to be correlated to the signature. The signature can predict the prognosis of glioblastoma efficiently.