MOLECULAR MEDICINE REPORTS 19: 1613-1621, 2019 A thirteen‑gene set efficiently predicts the prognosis of glioblastoma HUYIN YANG, LUHAO JIN and XIAOYANG SUN Department of Neurosurgery, Affiliated Huaian No. 1 People's Hospital of Nanjing Medical University, Huai'an, Jiangsu 223300, P.R. China Received January 21, 2018; Accepted September 6, 2018 DOI: 10.3892/mmr.2019.9801 Abstract. Glioblastoma multiforme (GBM) is the most Introduction common type of brain cancer; it usually recurs and patients have a short survival time. The present study aimed to construct Glioblastoma multiforme (GBM) is the most common and the a gene expression classifier and to screen key genes associated most invasive subtype of brain cancer; it is characterized by with GBM prognosis. GSE7696 microarray data set included symptoms that include personality changes, headaches, nausea samples from 10 recurrent GBM tissues, 70 primary GBM and unconsciousness (1,2). GBM originates from normal brain tissues and 4 normal brain tissues. Seed genes were identi- cells or low-grade astrocytoma, and may be induced by genetic fied by the ‘survival’ package in R and subjected to pathway disorders and radiation exposure (3,4). Clinical techniques for enrichment analysis. Prognostic genes were selected from the the treatment of GBM include surgery combined with radia- seed genes using the ‘rbsurv’ package in R, unsupervised hier- tion therapy or chemotherapy; however, the survival benefit archical clustering, survival analysis and enrichment analysis. is limited to ~12-15 months, or even shorter if the disease Multivariate survival analysis was performed for the prog- recurs (4). nostic genes, and the GBM data set from The Cancer Genome Gene therapy is a novel strategy for treating cancers (5). Atlas database was utilized to validate the prognostic genes. Transient receptor potential genes are overexpressed in GBM, Of the 1,785 seed genes analyzed, 13 prognostic feature genes, which promote the survival of patients (6,7). It has been including collagen type XXVIII α1 chain (COL28A1), PDS5 previously reported that low expression of B cell-specific cohesin-associated factor A (PDS5A), zinc‑finger DHHC‑type Moloney murine leukemia virus integration site 1 suppresses containing 2 (ZDHHC2), zinc-finger protein 24 (ZNF24), proliferation and promotes apoptosis of U251 GBM cells, and myosin VA (MYO5A) and myeloid/lymphoid or mixed-lineage enhances the chemosensitivity of these cells to cisplatin (8,9). leukemia translocated to 4 (MLLT4), were identified. These The expression level of epidermal growth factor-containing genes performed well on sample classification and prognostic fibulin extracellular matrix protein 1 EFEMP1( ) was asso- risk differentiation, and six pathways, including adherens junc- ciated with the survival of patients with GBM treated with tion, cyclic adenosine 3',5'-monophosphate signaling and Ras temozolomide (TMZ) (10); thus, EFEMP1 is considered a signaling pathways, were enriched for these feature genes. The target for overcoming TMZ-resistance in GBM. Enhancer of high‑risk group was slightly older compared with the low‑risk zeste homolog 2 (EZH2) overexpression was associated with group. The validation data set confirmed the prognostic value tumor grade and predicts short overall survival in patients of the 13 feature genes for GBM; of these, COL28A1, PDS5A, with GBM (11); thus, EZH2 may be a promising prognostic ZDHHC2, ZNF24, MYO5A and MLLT4 may be crucial. These factor and therapeutic target for patients. Additionally, HOX results may aid the understanding of the pathogenesis of GBM transcript antisense RNA (HOTAIR) overexpression was asso- and provide important clues for the development of novel diag- ciated with poor outcome in patients with GBM, and HOTAIR nostic markers or therapeutic targets. may be a therapeutic molecular target for this disease (12,13). O-6-Methylguanine-DNA methyltransferase (MGMT) meth- ylation status and mutations in the isocitrate dehydrogenase 1 (IDH1) gene are two known clinicopathological factors linked to overall survival of patients with GBM (14). MGMT Correspondence to: Dr Xiaoyang Sun, Department of methylation is significantly associated with the clinical prog- Neurosurgery, Affiliated Huaian No. 1 People's Hospital of Nanjing Medical University, 6 Beijing West Road, Huai'an, Jiangsu 223300, nosis of GBM (15); IDH1 is a prognostic marker of GBM, and P. R. Ch i na mutations in this gene diminish the malignant progression of E-mail: [email protected] glioma (16). In addition, high expression of interleukin‑13R mRNA is strongly associated with poor prognosis of Key words: glioblastoma, survival analysis, prognostic feature GBM (17). POZ/BTB and AT hook‑containing zinc‑finger genes, enrichment analysis, hierarchical clustering 1 is another prognostic marker of GBM; it is overexpressed in GBM-derived glioma-initiating stem cells, and is associ- ated with the characteristic stem cell capacity to grow as 1614 YANG et al: THIRTEEN‑GENE SET PREDICTS GLIOBLASTOMA PROGNOSIS neurospheres in vitro (18). Despite this collective knowledge, for the seed genes to explore their functions. The categories the genes involved in the prognosis of GBM have not been with P<0.05 were considered to indicate a statistically signifi- comprehensively reported. cant difference. In 2008, Murat et al (19) established the GSE7696 gene expression profile, and demonstrated that high expres- Screening of prognostic feature genes, unsupervised sion levels of epidermal growth factor receptor and stem hierarchical clustering and analysis of prognostic char- cell‑related ‘self‑renewal’ signature are involved in the resis- acteristics. Using the ‘rbsurv’ package in R (bioconductor. tance to concomitant chemoradiotherapy of GBM. In 2011, org/packages/release/bioc/html/rbsurv.html) (26), robust Lambiv et al (20) used the GSE7696 data set to explore the likelihood‑based survival modeling was constructed to action mechanism of tumor suppressor gene Wnt inhibitory identify prognostic feature genes (27). The samples were factor 1 (WIF1) in GBM models, and concluded that WIF1 may classified based on the expression profiles of the prognostic have a tumor suppressive role in GBM through senescence. feature genes using unsupervised hierarchical clustering (28). However, this data set has not been fully explored. Using Thereafter, the prognostic differences among the classi- the GSE7696 data, additional key genes associated with the fied samples were analyzed by the Kaplan-Meier survival prognosis of GBM were investigated using comprehensive analysis (29). The expression differences of the feature bioinformatics methods, such as survival analysis, enrichment genes between primary GBM and normal samples were analysis and hierarchical clustering. Results from the present analyzed; and the scatter plot of the gene expression levels study provided novel insights into the prognosis of GBM and were drawn using the corrplot package (https://cran.r‑project. may aid in the development of novel therapeutic approaches. org/web/packages/corrplot/vignettes/corrplot‑intro.html) in R (version 3.4.4). Materials and methods Functional and pathway enrichment analysis for the feature Data source. The GSE7696 microarray data set, based on genes. The Gene Ontology (GO) database (www.geneontology. the GPL570 [HG‑U133_Plus_2] Affymetrix Human Genome org) is used to predict potential functions of genes and their U133 Plus 2.0 Array (Thermo Fisher Scientific, Inc.) platform, products (30). The prognostic feature genes were examined along with the corresponding clinical information was down- with GO functional and KEGG pathway enrichment analysis loaded from the Gene Expression Omnibus database (www. by the ‘clusterProfiler’ package in R (bioconductor.org/pack- ncbi.nlm.nih.gov/geo). The data set included 10 recurrent GBM ages/release/bioc/html/clusterProfiler.html), with the threshold tissue samples (2 females and 8 males; mean age, 51.31 years), q-value <0.05 (31). 70 primary GBM tissue samples (19 females and 51 males; mean age, 48.07 years) and 4 normal brain tissue samples (sex Multivariate survival analysis. The prognostic feature genes and age information not available). All patients participated in underwent multivariate survival analysis to check their overall a phase II or randomized phase III trial (21,22), with informed influences on prognosis. In addition, the ‘survivalROC’ consent provided. The GSE7696 data set is available and the package in R (cran.r‑project.org/web/packages/surviv- study was approved by the local ethics committee (19). alROC/index.html) was used to draw the receiver operating characteristic (ROC) curve and calculate the area under the Selection of probes with expression changes in primary ROC curve (AUC) (32). Specifically, the survivalROC package GBM tissue samples. The normalized data of GSE7696 were was used to calculate the ‘true positive rate’ and ‘false posi- obtained, and the primary GBM tissue samples data were tive rate’ of each sample, and the differences between the selected to use in the present study. Firstly, the unloaded ‘true positive rate’ and ‘false positive rate’ for each sample probes were removed. Subsequently, the probes with varied were subsequently calculated. The sample with the smallest expressions among 70 different patients with primary GBM difference value was set as the cut-off in the multifactorial cox were identified by the following steps. Firstly, the variance regression analysis, and samples with a higher value than