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.

Keywords: gene expression; lower-grade glioma; prognostic biomarker; signature; survival.

Figure 1

Development of the prognostic signature in the training dataset. (A) The survival-associated PCGs in Kaplan–Meier analysis were displayed as red dots in the scatter diagram. (B) Random forest supervised classification algorithm reduced the prognosis-associated PCGs to 11 PCGs. (C) The prognostic five-PCG signature was selected because its AUC was the largest (AUC = 0.739) among the 2¹¹−1 = 2,047 signatures.

Figure 2

Kaplan–Meier plots indicated that LGG patients could be classified into high- and low-risk groups according to the five-gene signature in the training (A) and test (B) datasets.

Figure 3

Risk score distribution, survival status, and PCG expression patterns for LGG patients in the training (A) and test (B) datasets.

Figure 4

Comparison of the survival predictive power of the signature with grade, age, and IDH mutation by ROC in the training (A) and test (B) sets. TimeROC analysis of survival predictive power for the signature, grade, age, and IDH mutation (C).

Figure 5

Radiotherapy stratification analysis. The five-PCG signature could further divide patients with radiotherapy (A) or patients without radiotherapy (B) into two groups with significantly different survival.

Figure 6

GO (A) and KEGG (B) functional enrichment analysis of the five PCGs in the signature.