Development of a Prognostic Five-Gene Signature for Diffuse Lower-Grade Glioma Patients

Abstract

Background: Diffuse lower-grade gliomas (LGGs) are infiltrative and heterogeneous neoplasms. Gene signature including multiple protein-coding genes (PCGs) is widely used as a tumor marker. This study aimed to construct a multi-PCG signature to predict survival for LGG patients.Methods: LGG data including PCG expression profiles and clinical information were downloaded from The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA). Survival analysis, receiver operating characteristic (ROC) analysis, and random survival forest algorithm (RSFVH) were used to identify the prognostic PCG signature.Results: From the training (n = 524) and test (n = 431) datasets, a five-PCG signature which can classify LGG patients into low- or high-risk group with a significantly different overall survival (log rank P < 0.001) was screened out and validated. In terms of prognosis predictive performance, the five-PCG signature is stronger than other clinical variables and IDH mutation status. Moreover, the five-PCG signature could further divide radiotherapy patients into two different risk groups. GO and KEGG analysis found that PCGs in the prognostic five-PCG signature were mainly enriched in cell cycle, apoptosis, DNA replication pathways.Conclusions: The new five-PCG signature is a reliable prognostic marker for LGG patients and has a good prospect in clinical application.

Identification and validation of a 17-gene signature to improve the survival prediction of gliomas

“…LASSO regression and Cox regression analyses identified the best 17-gene signature, and the risk score of the 17-gene signature accurately predicted overall survival in glioma patients and could be considered an independent prognostic model. At the same time, the AUC value of the risk score reached 0.876 and the C-index value of the model was 0.859, which was much greater than the prognostic model constructed by the previous study, indicating that our 17-gene signature was more advantageous in precision (24)(25)(26). Aside from prognostic value, we found that the 17-gene signature was also related to tumour immunity and gene mutation.…”

Nanobodies targeting ABCC3 for immunotargeted applications in glioblastoma

“…The higher expression of ABCC3 in NK cells modulates their chemoresistance to TMZ in patients of malignant gliomas 43 . Recently, ABCC3 has been included in gene signatures ( ABCC3 , CD44 , TNFRSF1A , and MGMT 24 ; ABCC3 , SMC4 , EMP3 , WEE1 , and HIST1H2BK 44 ) that classify glioma patients in agreement with therapeutic response to chemotherapeutic agents, including TMZ. According to previous reports 20 , we found higher ABCC3 expression in patients with methylated MGMT promoter, correlating with a worse prognosis.…”

Nanobodies Targeting ABCC3 for Immunotargeted Applications in Glioblastoma

“…The higher expression of ABCC3 in NK cells modulates their chemoresistance to TMZ in patients of malignant gliomas [40]. Recently, ABCC3 has been included in gene signatures (ABCC3, CD44, TNFRSF1A, and MGMT [24]; ABCC3, SMC4, EMP3, WEE1, and HIST1H2BK [41]) that classify glioma patients in agreement with therapeutic response to chemotherapeutic agents, including TMZ. According to previous reports [20], we found higher ABCC3 expression in patients with methylated MGMT promoter, correlating with a worse prognosis.…”