Identification of Novel Prognostic Signature of Recurrent Low-Grade Glioma.

OBJECTIVES

The prognosis of patients with recurrent low-grade glioma (rLGG) varies greatly. Some patients can survive >10 years after recurrence, whereas other patients have <1 year of survival.

METHODS

To identify the related risk factors affecting the prognosis of patients with rLGG, we performed a series of bioinformatics analyses on RNA sequencing data of rLGG based on the Chinese Glioma Genome Altas database.

RESULTS

We constructed a 12-gene prognostic signature, dividing all the patients with rLGG into high- and low-risk subgroups. The result showed an excellent predictive effect in both the training cohort and the validation cohort using LASSO-Cox regression. Moreover, multivariate Cox analysis identified 4 independent prognostic factors of rLGG; among them, ZCWPW1 is identified as a high-value protective factor.

CONCLUSIONS

In all, this prognostic model displayed robust predictive capability for the overall survival of patients with rLGG, providing a new monitoring method for rLGG. The 4 independent prognostic factors, especially ZCWPW1, can be potential targets for rLGG, bringing new possibilities for the treatment of patients with rLGG.