Identification of potential biomarkers related to glioma survival by gene expression profile analysis

Abstract

Background: Recent studies have proposed several gene signatures as biomarkers for different grades of gliomas from various perspectives. However, most of these genes can only be used appropriately for patients with specific grades of gliomas.

Methods: In this study, we aimed to identify survival-relevant genes shared between glioblastoma multiforme (GBM) and lower-grade glioma (LGG), which could be used as potential biomarkers to classify patients into different risk groups. Cox proportional hazard regression model (Cox model) was used to extract relative genes, and effectiveness of genes was estimated against random forest regression. Finally, risk models were constructed with logistic regression.

Results: We identified 104 key genes that were shared between GBM and LGG, which could be significantly correlated with patients' survival based on next-generation sequencing data obtained from The Cancer Genome Atlas for gene expression analysis. The effectiveness of these genes in the survival prediction of GBM and LGG was evaluated, and the average receiver operating characteristic curve (ROC) area under the curve values ranged from 0.7 to 0.8. Gene set enrichment analysis revealed that these genes were involved in eight significant pathways and 23 molecular functions. Moreover, the expressions of ten (CTSZ, EFEMP2, ITGA5, KDELR2, MDK, MICALL2, MAP 2 K3, PLAUR, SERPINE1, and SOCS3) of these genes were significantly higher in GBM than in LGG, and comparing their expression levels to those of the proposed control genes (TBP, IPO8, and SDHA) could have the potential capability to classify patients into high- and low- risk groups, which differ significantly in the overall survival. Signatures of candidate genes were validated, by multiple microarray datasets from Gene Expression Omnibus, to increase the robustness of using these potential prognostic factors. In both the GBM and LGG cohort study, most of the patients in the high-risk group had the IDH1 wild-type gene, and those in the low-risk group had IDH1 mutations. Moreover, most of the high-risk patients with LGG possessed a 1p/19q-noncodeletion.

Conclusion: In this study, we identified survival relevant genes which were shared between GBM and LGG, and those enabled to classify patients into high- and low-risk groups based on expression level analysis. Both the risk groups could be correlated with the well-known genetic variants, thus suggesting their potential prognostic value in clinical application.

Keywords: Biomarkers; Gene signature; High-grade glioma; Low-grade glioma (LGG); Prognosis.

Fig. 1

OS curve of various histological subtypes of gliomas (TCGA samples). LGG was divided into three subtypes: astrocytoma, oligoastrocytoma, and oligodendroglioma. X-axis: patients’ OS duration (days); Y-axis: patients’ survival rate

Fig. 2

System workflow. The left-hand-side figure was to identify the shared survival-related genes from LGG and GBM. The gene set was scaled down against a series analysis method. Then, the importance of candidate genes was proved through the performance estimation of survival predictors. The right-hand-side figure shows the extraction of survival-relevant biomarker representatives from these genes, which could be used in clinical practice

Fig. 3

Performance estimation of survival prediction models using the 104-gene group (TCGA samples). The X-axis represents two survival prediction models that were constructed with the 104 survival-related genes; one model was constructed for GBM and the other, for LGG. The Y-axis represents the distribution of AUC values after 1000 repetitions of 5-fold cross-validation

Fig. 4

Heatmap view of the unsupervised clustering of 670 patients with glioma with expression profiles of the 104-gene group (TCGA samples). In the heatmap, the Y-axis represents the 104 genes and the X-axis represents patients with glioma. The expression levels from low to high are represented as a color gradient from green to red, respectively. There are three color bars of the heatmap utilizes different colors to represent IDH status (wild type and mutation), risk group (high/low), and patients with LGG and GBM

Fig. 5

Patients with GBM were divided into high- and low-risk groups identified based on ten genes (TCGA samples). X-axis: patients’ OS duration (days); Y-axis: patients’ survival rate. Log rank test between high-risk (n = 135) and low-risk (n = 19) groups was significant difference (p < 0.01)

Fig. 6

Patients with LGG were divided into high- and low-risk groups identified based on ten genes (TCGA samples). X-axis: patients’ OS duration (days); Y-axis: patients’ survival rate. Log rank test between high-risk (n = 121) and low-risk (n = 395) groups was significant difference (p < 0.01)

Fig. 7

Patients with GBM and LGG were divided into high- and low-risk groups identified based on ten genes (GEO samples). X-axis: patients’ OS duration (days); Y-axis: patients’ survival rate. a GBM, log rank test between high-risk (n = 142) and low-risk (n = 17) groups was significant difference (p < 0.01). b LGG, log rank test between high-risk (n = 25) and low-risk (n = 84) groups was significant difference (p < 0.01)

Fig. 8

High- and low-risk groups of patients with LGG having wild-type IDH1 identified based on ten genes (TCGA samples). X-axis: patients’ OS duration (days); Y-axis: patients’ survival rate. Both risk groups could not be separated well; log rank test, p = 0.248 was not statistically significant

Fig. 9

High- and low-risk groups of patients with LGG having mutant-type IDH1 identified based on ten genes (TCGA samples). X-axis: patients’ OS duration (days); Y-axis: patients’ survival rate. Log rank test between high-risk (n = 47) and low-risk (n = 372) groups was significant difference (p < 0.05)

Fig. 10

Expression levels of ten genes decreased from GBM to LGGs (TCGA samples). All patients with glioma (n = 247) were dead (121 patients with GBM and 126 patients with LGG). The X-axis represents the patients’ OS duration and the Y-axis represents their gene expression levels (FPKM). The X-axis labels from left to right are: “Before median overall survival (OS) of GBM,” “After median OS of GBM,” “Before median OS of LGG,” and “After median OS of LGG”