- Open Access
Identification of potential biomarkers related to glioma survival by gene expression profile analysis
© The Author(s). 2019
- Received: 26 May 2018
- Accepted: 6 February 2019
- Published: 20 March 2019
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.
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.
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.
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.
- Low-grade glioma (LGG)
- High-grade glioma
- Gene signature
Glioma is a common type of primary central nervous system (CNS) tumor which arises from glial cells . Following the World Health Organization (WHO) classification in 2007, gliomas can be subdivided into grade II, grade III, and grade IV (glioblastoma multiforme, GBM), depending on the degree of aggressiveness [2, 3]. In “The Cancer Genome Atlas” (TCGA) database, grade II and III are classified as lower-grade glioma (LGG), and grade IV as GBM. Despite developments in therapies that include surgical resection, chemotherapy, and radiotherapy, the median survival and prognosis remain poor, particularly for glioblastoma patients [4, 5]. The median overall survival time (mOS) of GBM is approximately 1.25 years [5, 6], and that of LGG is 6.5–8 years [7, 8]. Thus, it is important to elucidate the survival events of glioma, which could potentially aid in the diagnosis and prognosis of glioma patients.
Patient survival time with regards to tumor progression is associated with various subtypes and grades of the tumor . The histological classification of tumor subtypes is important to guide treatment decisions, which are often combined with several clinical prognostic features. In neuro-oncological practice, however, no clear national consensus for adult glioma diagnosis has been reached and the diagnosis is subject to interobserver variation [9, 10]; only utilizing histological information in studying various types of gliomas is restricted. On the other hand, previous studies have shown that gene expression profiling provides an objective method to classify tumors [11, 12]; it is better to correlate gene expression profiling, rather than tumor histology, with prognosis . Moreover, it may even be utilized to predict patients’ prognosis from various points of view [14–29]. Comparing these gene lists published from 2004 to 2016, it is observed that the genes identified from various research groups are quite different. This observation indicates that glioma patients’ overall survival (OS) is correlated with many kinds of events caused by various expression profiles of multiple genes. Therefore, extraction of comprehensive survival-related genes associated with gliomas is required, and it is possible for researchers to carry out further relevant studies. In addition, most previous studies [14, 18–23, 25–27, 29] have only utilized microarray datasets, rather than different kinds of datasets such as next-generation sequencing (NGS) data, to screen expression profiles of genes might have unexpected data bias; generally, utilizing NGS to detect gene signatures might be more precise than array data.
In this study, we aimed to identify common genes correlated with the overall survival of gliomas following the association of their expression profiles and patients’ survival time. Candidate genes were extracted from GBM and LGG study cohorts after analysis of NGS datasets from TCGA and validation by microarray datasets from Gene Expression Omnibus (GEO). Of these survival-related genes, the critical ones, which were potential biomarkers, were further analyzed and filtered, and then used to construct the survival-relevant risk models for clinical application against gliomas.
Patients and gene expression datasets
Statistics of datasets from TCGA and GEO databases
GBM with surv. Info.
LGG with surv. Info.
Clinical and histological characteristics of patients with glioma
After exclusion of genes that were not expressed in all patients, 19,924 genes were eligible for further analysis of the LGG and GBM cohorts from TCGA projects. Gene expression analysis of microarrays belonging to the GBM and LGG populations from the GEO database were first normalized using the R function normalize.quantiles .
Analysis workflow of this study
Identification of significant survival-related genes
The Cox proportional hazards regression model (“Cox model” hereafter; survival analysis) was used to identify possible factors that might be associated with patients’ OS duration. In this study, univariate Cox regression analysis  was performed to assess the expression profiles of genes that might be significantly correlated with the survival time of patients with GBM or LGG. Subsequently, these putative survival-related genes were ranked and filtered by applying stringent criteria (hazard ratio [HR] > 1; Wald test, p < 0.01). Each extracted gene was consequently analyzed to evaluate the correlation of its expression level with various survival durations in patients. Here, the median OS (in days) of GBM and LGG groups would be set as an important time point for both groups, respectively, to separate patients into shorter or longer survival durations, to recognize that the expression levels of genes differed significantly between the survival durations (number of days to death less or more than the median OS). The Student’s t-test (p < 0.05) was conducted to select statistically significant candidate genes.
Building survival predictive models for patients with GBM and LGG
The predictive model for survival analysis in this study was built using randomForestSRC [33–35], a nonparametric machine learning method. Moreover, because it can combine the results of many survival trees, this model is arguably more objective than other methods. Accordingly, the expression profiles of candidate genes related to survival durations were used to construct survival predictors for GBM and LGG. To assess the performance of the predictors, 1000 repetitions of five-fold cross-validation were performed, 80% of the samples were employed as the training dataset to train the model, and the remaining 20% served as the validation dataset. Receiver operating characteristic curves (ROC) obtained from the 1000 iterations were evaluated using a boxplot with their area under curve (AUC) values. The performance could be used to realize the importance of these candidate genes to GBM and LGG.
Gene set enrichment analysis
Ingenuity pathway analysis (IPA) software (Qiagen), GeneAnalytics , and DAVID [37, 38] were used to analyze the biological roles and molecular functions of candidate genes identified from patients with glioma. Survival-related genes common to both LGG and GBM could be useful in realizing shared functions; the pathways in both study cohorts were related to patient survival.
In this study, multiple gene set enrichment analysis tools were applied to increase the consistency and accuracy of the results. The functions and pathways that the gene set was involved were identified using at least two kinds of tools.
Gene expression level analysis between GBM and LGG
The survival-related genes with varying expression levels in case of relative high-risk (GBM) and relative low-risk (LGG) of gliomas would be further analyzed and could be used as putative biomarkers. A previous study demonstrated that the following five endogenous control genes were not differentially expressed between the glioma and normal brain: TBP, IPO8, GAPDH, RPL13A, and SDHA . Therefore, the log2-fold changes in the expression of these survival-related genes relative to those of the control genes were calculated; there were p × q unique features (the signatures of genes were higher or lower than those of the control genes) for each patient, when p survival-related genes and q control genes were present. For each feature, the percentages of patients with high and low expression were calculated and screened. If a gene expression was both high (or low) in over 50% of patients with GBM and low (or high) in over 50% of those with LGG, compared with the expression of the control genes, the log2-fold change value was used as a feature in this study. Subsequently, features with different expression levels between GBM and LGG were retained as the candidates of risk descriptors. For instance, the survival-related gene TIMP1- which had high expression in 98% of patients with GBM but low expression in 60% of patients with LGG compared with the reference gene TBP - was retained. In addition to RNA-Seq datasets (TCGA), three distinct microarray datasets (GEO) were utilized to validate the consistency of various gene signatures in both classes of patients, in order to increase the data strength.
Survival risk relevant genes identification
The median OS days of GBM and LGG were notably different, implying that patients with GBM have a shorter survival time (relative to high-risk) and those with LGG have longer survival time (relative to low-risk). Under this assumption, survival-related genes were first filtered (using the method mentioned in the previous section) as possible descriptors to classify patients into risk groups. However, the effectiveness of these genes needed to be determined for further analysis using a statistical model. A logistic regression model (Y = X1 × β1 + X2 × β2 + … + Xn × βn + k) was applied to evaluate the importance of these features, namely the survival-related genes versus the control genes. Here, Y is the estimated value of glioma prognosis risk (GBM defined as 1, LGG defined as 0), X represents the value of the log2-fold change of each feature, β is the unknown coefficient, and k is the unknown constant. The Akaike information criterion (AIC) was utilized to evaluate the relative quality of all models, which were constructed with various combinations of features. While repeating the process (backward elimination) to construct the logistic regression model, features with low predictive value for glioma prognosis were excluded each time until the number of features that provided the smallest AIC values was reached. Consequently, these features would be capable of recognizing the survival risk of patients with GBM or LGG; thus, the expression level of those genes relative to that of the control genes could be correlated to patients’ survival.
Differentiation of patients into different risk groups
After the candidate features (from previous section: Survival risk relevant genes identification) had been identified, they could be used directly to create risk models for GBM and LGG. Logistic regression was applied to construct both risk models. Here, the outcome variable Y was the estimated GBM or LGG prognosis risk (patients can’t live over mOS are relative high risk and can live longer than mOS are low risk); for the GBM risk model, survival durations shorter than 450 days were defined as 1 and those longer than 450 days are defined as 0. Similarly, for the LGG risk model, survival durations shorter than 2700 days were defined as 1 and those longer than survival durations longer than 2700 days were defined as 0. The variable X would be substituted into the log2-fold change value of the candidate features. The other variables, such as β and k were then estimated with the R package generalized linear models (glm) function for GBM and LGG risk model, respectively. In addition, 1000 repetitions of the five-fold cross-validation were run to evaluate the GBM and LGG models, which were used to classify patients into different risk groups.
GBM and LGG shared key survival-related genes
In this study, using gene expression profiling, we identified 104 genes that were significantly correlated with OS in patients with GBM and those with LGG. After application of the stringent criteria to filter the putative survival-related genes using the Cox model, the expression signatures of 582 and 5461 genes were identified and correlated to OS in case of GBM (n = 152) and LGG (n = 511), respectively. Subsequently, 266 genes were obtained through the gene lists from both study cohorts. However, only 104 of these genes were also significantly differentially expressed (t-test, p < 0.05) before and after the median OS time in the GBM and LGG study cohorts; these 104 survival-relevant genes are listed in Additional file 1: Table S1.
Effectiveness estimation of 104 genes for GBM and LGG survival
Capability estimation of 104 key genes for GBM and LGG survival prediction
Pathway involvement and function category of survival-related genes
Pathways summarized from the enrichment analysis of the 104 survival-related genes
Inhibition of matrix metalloproteases
TNF signaling pathway
Hematopoietic cell lineage
Jak-STAT signaling pathway
Molecular and cellular functions summarized from the enrichment analysis of the 104 survival-related genes
Molecular and cellular function
Endodermal cell differentiation
Extracellular matrix organization
Cytokine-mediated signaling pathway
Heterotypic cell–cell adhesion
Adaptive immune response
Integrin-mediated signaling pathway
Positive regulation of cell-substrate adhesion
Substrate-adhesion-dependent cell spreading
Positive regulation of tyrosine phosphorylation of Stat3 protein
Collagen fibril organization
Negative regulation of JAK-STAT cascade
Skeletal system development
Positive regulation of T cell proliferation
Regulation of vesicle-mediated transport
positive regulation of T cell chemotaxis
Movement of cell or subcellular component
The candidate patients’ severity-relevant features
Candidate features have different expression level between GBM and LGG
MAP 2 K3/TBP
Effectiveness features for evaluating risks of patients with glioma
Based on the assumption that patients with GBM (n = 154) have a higher risk (short median OS) than those with LGG (n = 516) (longer median OS), the construction of a logistic regression model with various combinations of features was repeated. The ten smallest features, namely, CTSZ/IPO8, EFEMP2/IPO8, ITGA5/IPO8, KDELR2/SDHA, MDK/IPO8, MICALL2/TBP, MAP 2 K3/TBP, PLAUR/TBP, SERPINE1/TBP, and SOCS3/IPO8 were utilized to construct the risk model with the lowest AIC value which was 239.51. Therefore, utilizing the signatures of ten genes relative to the three control genes would have the capability to evaluate patient risks.
Patients’ risk distinguishable with ten gene signatures
In this study, we identified 104 common survival-related genes from patients with gliomas. The effectiveness of these genes was evaluated by constructing prediction models, and the AUC values were estimated to be approximately 0.7 and 0.8 for the GBM and LGG models, respectively, after 1000 iterations of 5-fold cross-validation. The heatmap (Fig. 4) has shown that expression profiles of these genes are associated with the IDH1 and risk status among patients with gliomas; most of patients with GBM are wild-type IDH1 and have short survival time (high risk), but patients with LGG are mutant-type IDH1 and survive long (low risk). Most of these genes were involved in cell-related signaling pathways that affect cellular proliferation, apoptosis, and angiogenesis. Moreover, of the 104 survival-related genes, 10 could potentially distinguish patients with GBM or LGG into high- and low-risk groups. The expression levels of these ten genes were higher and the survival duration was shorter in patients with high-grade glioma than in those with lower grade glioma.
Identification of survival-related genes in gliomas has been ongoing over the past decade. However, the gene lists identified by our study and the other various research groups [14–29] differ considerably; only 13 common genes (BMP2, CLIC1, EST, IGFBP2, LDHA, LGALS1, MET, MSN, TGALN2, TIMP1, TNC, UPP1, ZYX) could be identified in at least three studies. These differences may be attributed to two major factors. First, researchers have analyzed glioma datasets from various perspectives; for instance, some studies have discussed some aspects only in patients with high-grade gliomas [14, 20, 23–27, 29], or LGG [21, 28], or at specific checkpoints such as the mitotic spindle checkpoint  or ion channel . Second, studies have analyzed different types of datasets obtained from various high-throughput platforms such as microarray or next-generation data. Because of technical limitations, the expression profiles of the same genes detected from different datasets may be inconsistent. For instance, the sequencing data have higher stochastic variability than array data, which would result in a lack of reads in short or low abundance genes . On the other hand, microarray data of gene expression can be affected by probes’ cross-hybridization, nonspecific hybridization, redundancy, and annotation . Rather than NGS, microarray analysis has been selected as the initial screening method in most relevant studies. Recently, various research groups have started using NGS data (e.g., RNA-Seq) as the main analysis platform and microarray data as an adjuvant platform to verify results.
Accurate survival prediction through comprehensive indicators is vital for patients with glioma. However, the 104-gene group identified in this study would include parts of those indicators and was also crucial for survival prediction in both types of gliomas; its effectiveness in analyzing the GBM and LGG cohorts demonstrated that there other specific survival-related genes might exist. However, these 104 genes were the basic factors for patients’ survival in case of GBM and LGG, because the average AUC value under multiple times of simulation could reach 0.7–0.8. These genes could be used together with self-specific genes of each type of glioma, to elaborate the regulation networks in various mechanisms. In addition, recent studies have identified crucial glioma imaging features from magnetic resonance imaging (MRI) and have correlated them with patient survival [42, 43]. The association of imaging and genomic features could be realized and applied in the field of radiogenomics.
The expression level analysis of survival-related genes could have implications; high signatures of genes in patients would be indicative of shorter survival durations in contrast to low signatures of genes, where patients have a longer survival time. Moreover, most of these genes were highly expressed in GBM and the converse is true in case of LGG. However, it is difficult to set the cutoff values to indicate whether gene expression was high or low, because of individual differences. Therefore, the reference genes (called control genes in this study) would be the target of comparison for survival-relevant genes. Furthermore, in order to have objective indicators consequently, different datasets were used to validate the results from NGS and the minor effectiveness of genes, which was decided by the logistic regression model, was removed.
IDH1 and 1p/19q status of high- and low-risk groups of patients with LGG and those with GBM
IDH1 and 1p/19q status
Wild type and noncodel
Mutation and noncodel
Mutation and codel
The cancer hallmarks mapping of the ten genes
MSigDB (Hallmark) 
Hallmarks of cancer
sustaining proliferative signaling, activating invasion and metastasis
Epithelial mesenchymal transition
sustaining proliferative signaling, activating invasion and metastasis
Epithelial mesenchymal transition, Inflammatory response
activating invasion and metastasis
Estrogen response late, Apical junction
sustaining proliferative signaling, activating invasion and metastasis
sustaining proliferative signaling
MAP 2 K3
TNF-a signaling via NF-kB, PI3K/AKT/mTOR signaling, mTORC1 signaling, Heme metabolism
sustaining proliferative signaling
TNF-alpha signaling via NF-kB, Cholesterol homeostasis
activating invasion and metastasis, inducing angiogenesis
TNF- alpha signaling via NF-kB, Hypoxia, TGF-beta signaling, Complement, Epithelial mesenchymal transition, Inflammatory response, Xenobiotic metabolism, UV response down, Coagulation
TNF- alpha signaling via NF-kB, IL6/JAK/STAT3 signaling, Interferon gamma response
sustaining proliferative signaling, tumor-promoting inflammation
In summary, the 104 genes identified, which are common between patients with GBM and those with LGG, can be used as core genes related to patient survival. Of these, 10 genes (CTSZ, EFEMP2, ITGA5, KDELR2, MDK, MICALL2, MAP 2 K3, PLAUR, SERPINE1, and SOCS3) can potentially serve as indicators to classify patients with gliomas into different risk groups and could be used to estimate the prognosis of patients with gliomas. Moreover, the expression profiles of these potential biomarkers could be correlated to the molecular subtypes of patients, such as IDH1/2 mutation/wild type and chromosome 1p/19q codeletion/noncodeletion.
We thank Dr. Shiu-Wen Huang for mapping the potential biomarkers to the hallmarks of cancer.
Publication of this article was funded by a grant from Taipei Medical University Hospital (106TMUH-SP-02).
Availability of data and materials
The datasets used and analyzed during the current study available from published databases, such as TCGA projects (TCGA-LGG and TCGA-GBM) https://cancergenome.nih.gov/, level 3 RNA-Seq datasets and their clinical information and GEO microarray datasets (GSE16011, GSE4412, and GSE4271) https://www.ncbi.nlm.nih.gov/geo/.
About this supplement
This article has been published as part of BMC Medical Genomics Volume 11 Supplement 7, 2018: Selected articles from the 17th International Conference on Bioinformatics (InCoB 2018): medical genomics. The full contents of the supplement are available online at https://bmcmedgenomics.biomedcentral.com/articles/supplements/volume-11-supplement-7.
JBKH conceived and designed the experiments. JBKH performed the experiments. JBKH and GAL analyzed the data. JBKH wrote the manuscript with revision by TYL and THC. Data and results were interpreted by JBKH, and CYC. The study was supervised by CYC. All authors read and approved the final manuscript.
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The authors declare that they have no competing interests.
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