Identification of hub genes and regulatory factors of glioblastoma multiforme subgroups by RNA-seq data analysis

Abstract

Glioblastoma multiforme (GBM) is the most common malignant brain tumor. This study aimed to identify the hub genes and regulatory factors of GBM subgroups by RNA sequencing (RNA-seq) data analysis, in order to explore the possible mechanisms responsbile for the progression of GBM. The dataset RNASeqV2 was downloaded by TCGA-Assembler, containing 169 GBM and 5 normal samples. Gene expression was calculated by the reads per kilobase per million reads measurement, and nor malized with tag count comparison. Following subgroup classification by the non-negative matrix factorization, the differentially expressed genes (DEGs) were screened in 4 GBM subgroups using the method of significance analysis of microarrays. Functional enrichment analysis was performed by DAVID, and the protein-protein interaction (PPI) network was constructed based on the HPRD database. The subgroup-related microRNAs (miRNAs or miRs), transcription factors (TFs) and small molecule drugs were predicted with predefined criteria. A cohort of 19,515 DEGs between the GBM and control samples was screened, which were predominantly enriched in cell cycle- and immunoreaction-related pathways. In the PPI network, lymphocyte cytosolic protein 2 (LCP2), breast cancer 1 (BRCA1), specificity protein 1 (Sp1) and chromodomain-helicase-DNA-binding protein 3 (CHD3) were the hub nodes in subgroups 1–4, respectively. Paired box 5 (PAX5), adipocyte protein 2 (aP2), E2F transcription factor 1 (E2F1) and cAMP-response element-binding protein-1 (CREB1) were the specific TFs in subgroups 1–4, respectively. miR-147b, miR-770-5p, miR-220a and miR-1247 were the particular miRNAs in subgroups 1–4, respectively. Natalizumab was the predicted small molecule drug in subgroup 2. In conclusion, the molecular regulatory mechanisms of GBM pathogenesis were distinct in the different subgroups. Several crucial genes, TFs, miRNAs and small molecules in the different GBM subgroups were identified, which may be used as potential markers. However, further experimental validations may be required.

Introduction

Glioblastoma multiforme (GBM) is the most frequent and lethal malignant brain tumor (1). Based on the World Health Organization classification, GBM belongs to the grade IV astrocytoma group, which includes diffusely infiltrating astrocytomas that account for half of all primary brain gliomas (2). Despite the advanced detection techniques, such as spectroscopy and perfusion, and improved treatments including surgery and the combination of radiation with chemotherapy, the usual survival time of the majority of patients with GBM after diagnosis is approximately 1 year (3). The major causative factors are the high resistance of GBM to therapeutics and the high variability of the benefits of therapeutic drugs in different GBM patients (4).

To improve the poor prognosis, considerable investigations have been conducted to explore more effective therapeutics at the genetic and protein level. For instance, via the enzyme-linked immunosorbent assay (ELISA), the level of serum glial fibrillary acidic protein (GFAP) was found to be increased in GBM patients, and was thus proposed as a biomarker for the prognosis of GBM (5). Other candidate genes, such as epidermal growth factor receptor (EGFR) (6) and herpes simplex virus (HSV)-thymidine kinase (TK)/interleukin (IL)-2 (7) have been shown to be involved in the prognosis of GBM, and are considered as potential therapeutic markers. MicroRNAs (miRNAs or miRs) are small non-coding RNAs that play central roles in cancer development via the regulation of target genes. Several miRNAs, including miR-21 (8), miR-124 and miR-137 (9) have been verified to play significant roles in anti-apoptosis and in the inhibition of tumor cell proliferation during GBM progression. Several small molecules have been applied in the treatment of GBM, such as temsirolimus (CCI-779), which is an inhibitor of mammalian target of rapamycin (mTOR), and has been identified as a therapeutic target of GBM (10). Additionally, the use of the small molecule drug, vorinostat, has been shown to result in the increased expression of anti-proliferative genes, such as death receptor 5 (DR5), and in the decreased expression of anti-apoptotic genes, such as cyclin-dependent kinase (CDK)2. Via the mediation of these genes, vorinostat serves as another crucial therapeutic target of GBM (11). Recently, a novel small molecule, CG500354, has been reported to target the specific variant, PDE4D, and thus plays a significant role in the suppression of GBM (12).

Previous studies have used the technique of RNA sequencing (RNA-seq), an emerging deep sequencing method for transcriptome profiling and provides more accurate expression measurements (13), to explicate the molecular events in GBM (14). However, most of these studies focused on the function of fusion genes, such as neurofascin (NFASC)-neurotrophic tyrosine kinase receptor type 1 (NTRK1) (14) and RNA splicing-related genes, such as PHD finger protein 5A (PHF5A) (15). The exploration of therapeutic small drugs in GBM patients using RNA-seq, and focusing on different subgroups of individuals in particular, is limited. Considering that different mechanisms may be presented due to different individuals, in this study, we analyzed the RNA-seq data to identify differentially expressed genes (DEGs) in samples between patients with GBM and normal subjects. Furthermore, the non-negative matrix factorization (NMF) method was used to further identify the subgroups of GBM. NMF is a well-known approach that could reduce the dimensionality of a dataset and cluster the data samples (16). The method has been widely used in computational biology. Previous studies have utilized NMF to discover subgroups of patients with complex genetic diseases (16,17).

In this study, by functional enrichment analysis and protein-protein interaction (PPI) network analysis, the altered pathways and the interactions of DEGs in each subgroup were predicted. Furthermore, the miRNAs, transcription factors (TFs) and small molecule drugs related to the DEGs in the subgroups were also explored. All these bioinformatics methods were used in an aim to elucidate the comprehensive molecular mechanisms responsible for the development and progression of GBM, and to provide potential small molecule agents as therapeutic candidates for the treatment of patients with different subgroups of GBM.

Data collection and analysis

Data preprocessing and GBM subgroup classification

The dataset of RNASeqV2 was downloaded using The Cancer Genome Atlas (TCGA)-Assembler (18), consisting of 169 GBM samples and 5 normal samples. The reads per kilobase per million reads (RPKM) measurement were used to calculate gene expression, followed by normalization with the tag count comparison (TCC; http://www.bioconductor.org/packages/release/bioc/html/TCC.html) package (19). The expression value was complemented as '1' for gene loss in several samples, followed by the transformation by log2 to achieve an approximate normal distribution. Following normalization, we applied the NMF method to classify the subgroup of all the GBM samples, and selected the optimal 'k' value based on the cophenetic correlation coefficient (CCC) (20), whose value varies from 0–1. The classification is more stable if the 'k' value is closer to 1. Finally, the highest 'k' value of the CCC was selected as the optimal value.

Identification of DEGs

Following normalization and subgroup classification, the DEGs were screened out using the significance analysis of microarrays (SAM) method (21). The thresholds for DEG selection were a false discovery rate (FDR) <0.05 and |log2 fold change| >1. Top 500 DEGs were clustered using Euclidean distance and the hierarchical clustering method, which were executed by heatmap.2 function in the gplot R package (https://cran.r-project.org/web/packages/gplots/).

Pathway enrichment analysis

The Kyoto Encyclopedia of Genes and Genomes (KEGG; http://www.genome.jp/kegg/pathway.html) (22) and Reactome (http://www.reactome.org) (23) pathway enrichment analyses were carried out for the selected DEGs, using the Database for Annotation, Visualization and Integration Discovery (DAVID; http://david.abcc.Ncifcrf.gov/) online software (24), based on the hypergeometric distribution test. The cut-off value for significant terms was p<0.05.

Construction of PPI network and transcriptional regulatory network

The Human Protein Reference Database (HPRD; http://hprd.org/) is a database of curated proteomic information, such as protein annotation, PPI and protein subcellular localization (25). The Pearson correlation coefficient was calculated of two inter-played proteins basing on the miRNA expression profiles, and the proteins with the Pearson correlation coefficient >0.5 were extracted to establish the PPI network. A node in the PPI network serves as a protein, and the degree of a node reflects the number of the interacted proteins with this specific protein. Hub nodes were deemed as the prominent nodes with high degrees.

Prediction of miRNAs, TFs and small molecule drugs

The combinatorial gene regulatory networks (cGRNB; http://www.scbit.org/cgrnb/) is a web server for the establishment of two major network modules such as miRNA-perturbed gene expression (MPGE) datasets and the parallel miRNA/mRNA expression datasets, to provide the combinatorial regulations involving TFs, miRNAs and genes (21). Among these regulations, the miRNA-gene interactions were derived from the starBase (http://starbase.sysu.edu.cn/) database (26), under the criteria of 'microRNA read Num' >0 and 'biological complexity' (BC) >1.

The DrugBank (http://www.drugbank.ca/) database (27) was utilized to recognize potential pharmaceutical molecules and the drug targets. Only the drugs that approved by the Food and Drug Administration (FDA) were downloaded.

GBM subgroup-related miRNAs, TFs and drug selection

Integrating the regulatory information in the cGRNB database, the miRNA-target or TF-target interactions were extracted, while the small molecule drugs and the targets were identified in combination with the information on DrugBank. Subsequently, the overlapping significance between DEGs (gene set N) in a subgroup and target genes (gene set Mi) of selected miRNAs/TFs/drugs (i) were detected based on Fisher's exact test (28). The formula used was as follows:

$$\mathrm{P}=\left(\frac{\mathrm{a}+\mathrm{b}}{\mathrm{a}}\right)\left(\frac{\mathrm{c}+\mathrm{d}}{\mathrm{c}}\right)/\frac{\mathrm{a}}{(\mathrm{a}+\mathrm{c})}$$ (equation 1)

where 'a', indicates genes included in both Mi and N; 'd', included genes included in neither Mi nor N; 'b', included genes are only included in Mi and 'c', included genes only included in N. The threshold for the selection of eligible miRNAs, TFs or drugs was p<0.05.

Survival curve analysis

By recruiting the survival package in Bioconductor R, we assessed the median survival with the corresponding intervals from Kaplan-Meier (KM) curve (29), to detect the differences in survival time amongst the GBM subgroups.

Results

Pre-processing of the expression profile data and marker genes of GBM subgroups

A set of 11,078 genes was subjected to normalization in the GBM samples. As indicated in the box plot (Fig. 1), the gene expression levels among the different samples were relatively uniform. The top 1,500 genes with variable coefficient in the tumor samples were filtered out for the subgroup classification. The CCC was calculated with the 'k' value varying from 2 to 12, and achieved the highest level when 'k' was 4, which was defined as the final 'k' value for subgroup classification. In all 169 GBM samples, 13 samples were excluded from subsequent analyses for the lack of prognostic information. The remaining 156 samples were classified into 4 subgroups: 52 samples were divided into subgroup 1; 57 in subgroup 2; 26 in subgroup 3 and 21 in subgroup 4.

Figure 1.

Box plot of gene expression among different samples. The x-axis represents samples, while the y-axis represents the expression of genes.

DEGs between GBM and control samples in different subgroups and the disrupted functions and pathways

Based on the aforementioned criteria, a cohort of 19,515 DEGs between the GBM and control samples in the 4 subgroups was identified (subgroup 1: 4,430; subgroup 2: 3,271; subgroup 3: 4,046; subgroup 4: 7,768) (Fig. 2). The Venn diagram indicates substantially overlapping DEGs among the 4 subgroups. The overlapped DEGs in >2 subgroups were considered as the common DEGs, and we identified a set of 7,106 common DEGs, which were mainly enriched in the cell cycle and immunoreaction-related pathways (Table I). The heatmap of the clustering analysis of gene expression is presented in Fig. 3.

Figure 2.

Venn diagram of the differentially expressed genes in the 4 subgroups.

Table I.

Top 15 significantly enriched pathways of the common DEGs.

Source	Name	q-value Bonferroni
KEGG	Ribosome	5.21E-22
REACTOME	Gene expression	1.71E-14
REACTOME	Cell cycle, mitotic	5.82E-13
REACTOME	Metabolism of proteins	4.08E-10
REACTOME	M phase	6.87E-09
REACTOME	Interferon signaling	7.66E-08
REACTOME	Disease	8.23E-08
REACTOME	Cell cycle	1.01E-07
REACTOME	Cytokine signaling in immune system	1.57E-07
REACTOME	Class I MHC mediated antigen processing and presentation	1.88E-06
KEGG	Epstein-Barr virus infection	4.95E-06
REACTOME	Translation	1.04E-05
KEGG	Protein processing in endoplasmic reticulum	1.17E-05
KEGG	Ubiquitin mediated proteolysis	1.95E-05
REACTOME	Interferon γ signaling	3.50E-05

KEGG, Kyoto Encyclopedia of Genes and Genomes; DEGs, differentially expressed genes.

Figure 3.

Heat map of cluster analysis of differentially expressed genes. The x-axis represents samples, while the y-axis represents genes. Red represents subgroup 1; orange represents subgroup 2; yellow represents subgroup 3; green represents subgroup 4.

The PPI networks of the DEGs in the 4 subgroups

By integrating the information in the HPRD database, a set of 39,240 interactions was obtained, containing 11,390 interactions that involved 7,768 DEGs in the 4 GBM subgroups. Four PPI networks were then constructed based on the Pearson's correlation coefficient. The PPI network for DEGs in subgroup 1 consisted of 790 edges involving 821 nodes, and that for the DEGs in subgroup 2 was comprised of 864 edges and 830 nodes. There were 1,186 edges and 1,174 nodes in the network for subgroup 3, and 2,190 edges and 1,760 nodes in the network for subgroup 4 (data not shown). The top 10 hub nodes in the 4 subgroups are presented in Table II. Among these, CDK1 was the hub node in all 4 subgroups, while several nodes were only remarkable in a specific subgroup, such as lymphocyte cytosolic protein 2 (LCP2), breast cancer 1 (BRCA1), specificity protein 1 (Sp1) and chromodomain-helicase-DNA-binding protein 3 (CHD3) were the hub nodes in subgroups 1, 2, 3 and 4, respectively.

Table II.

Top 10 hub nodes in the 4 PPI networks from the 4 subgroups.

Subgroup 1		Subgroup 2		Subgroup 3		Subgroup 4
Name	Degree	Name	Degree	Name	Degree	Name	Degree
SYK	20	CDK1	26	CDK1	24	SRC	30
CDK1	18	SYK	22	SYK	21	YWHAG	26
LYN	16	LYN	16	PTPN6	20	CDK1	26
GRB2	16	PTPN6	14	CREBBP	18	CHD3	23
CREBBP	14	GRB2	14	LYN	17	ATN1	22
HCK	13	BRCA1	14	GRB2	17	CREBBP	22
PTPN6	12	HCK	14	PCNA	16	HTT	21
LCP2	11	PCNA	13	Sp1	16	SYK	20
VAV1	10	CREBBP	13	VAV1	13	EP300	20
WAS	10	MCM7	12	HCK	13	EWSR1	19

The GBM subgroup-related miRNAs, TFs and small molecular drugs

A total of 210,637 pair wise miRNA-target interactions was extracted, containing 207 TFs and 16,862 target genes. A total of 6,108 drug-target interactions was acquired, involving 1,348 small molecule drugs and 1,353 drug targets. According to the formula (equation 1, described above) and the predefined threshold, we screened 8, 17, 6 and 48 miRNAs separately in subgroups 1–4; 34, 50, 45 and 65 TFs respectively in subgroup 1–4; and 2 and 21 small molecule drugs in subgroups 1 and 2, but none in subgroups 3 and 4 (data not shown). The top 5 (ranked by the P-value) significant regulators (miRNAs, TFs and small molecular drugs) are listed in Table III. Among the significant TFs, paired box 5 (PAX5), adipocyte protein 2 (aP2), E2F transcription factor 1 (E2F1) and cAMP-response element-binding protein-1 (CREB1) were the specific regulators in subgroup 1, 2, 3 and 4, respectively. Several miRNAs, such as miR-147b, miR-770-5p, miR-220a and miR-1247 were the specific ones respectively in subgroups 1–4. Furthermore, a spectrum of small molecule drugs was identified in subgroup 2, including natalizumab, trastuzumab, efalizumab, bevacizumab and etanercept.

Table III.

Subgroup-specific miRNAs, TFs and small molecule drugs.

	miRNAs	TFs	Drugs
Group 1	miR-147b, miR-1269, miR-744, miR-483-5p, miR-1207-5p	Sp1, DAND5, PSG1, PAX5, Pax-5	Efalizumab, trastuzumab
Group 2	miR-770-5p, miR-1184, miR-133a, miR-516a-3p, miR-133b	DAND5, PSG1, Sp1, aP2-α, aP2-γ	Natalizumab, trastuzumab, efalizumab, bevacizumab, etanercept
Group 3	miR-220a, miR-492, miR-626, miR-24-1a, miR-489	Sp1, DAND5, PSG1, E2F1, E2F	NA
Group 4	miR-1247, miR-940,miR-198, miR-1289, miR-214	DAND5, PSG1, Sp1, Elk-1, CREB1	NA

NA, not applicable.

Prognostic analysis of GMB

The clinical survival time of the 156 samples was extracted from the TCGA database. With the KM method, the differences among the 4 subgroups were identified. There were 31, 37, 19 and 15 deaths in subgroups 1–4, respectively (data not shown). The KM analysis indicated no significant differences in survival time among the 4 subgroups (p=0.182). However, as shown in Fig. 4A, a similar trend in the survival rate was detected between subgroups 1 and 4, and between subgroups 2 and 3. Therefore, the data in subgroups 1 and 4 were merged into m-group 1, whereas the data in subgroups 2 and 3 were merged into m-group 2. m-group 1 exhibited a pronounced higher survival rate than m-group 2 (p=0.04; Fig. 4B).

Figure 4.

Survival rates of glioblastoma multiforme (GBM) patients by Kaplan-Meier (KM) estimation. (A) Survival rates in the 4 subgroups prior to merging. (B) Survival rates after merging.

Discussion

GBM is the most common malignant brain tumor and is characterized by invasiveness (30). Despite several effective therapeutic agents available, not all patients benefit from these due to individual differences (4). In this study, we utilized the RNA-seq data in the TCGA database to detect gene alterations during the progression of GBM and to further explore the possible mechanisms responsible for its pathogenesis. As a result, 4 subgroups of GBM were classified and a set of 19,515 DEGs between the GBM and control samples was identified; the majority of these genes were enriched in the cell cycle and immunoreaction-related pathways. In the PPI network, LCP2, BRCA1, Sp1 and CHD3 were 4 striking nodes in subgroups 1–4, respectively. PAX5, aP2, E2F1 and CREB1 were the specific TFs in subgroups 1–4, respectively. Several small molecule drugs, such as efalizumab and natalizumab were identified in subgroup 2.

Immunosuppression is the major hallmark of GBM patients, who have to receive radiation therapy in combination with with temozolomide for the restoration of the antitumor immune response (31). LCP2 is known to play significant roles in promoting T cell progression. It was recently reported that a splice variant of LCP2 resulted in severe immune dysregulation (32). The PAX5 encoded transcription factor is a PAX family member which plays important roles in B-cell development (33). Via the chromatin immunoprecipitation (ChIP) technique, PAX5 was found to regulate target genes by directly activating chromatin at the promoter or enhancer region, and PAX5-activated targets are crucial regulators involved in B-cell signaling, migration and immune function (34). Our findings revealed that LCP2 and PAX5 were (separately) the hub nodes in the PPI network and the prominent TFs for subgroup 1 of GBM, and were mainly enriched in the pathway of cytokine signaling in the immune system, suggesting that the LCP2- and PAX5-mediated immune pathway may play important roles in the progression of GBM in subgroup 1. Furthermore, miR-147b was identified as an oncomiR in prostate cancer (PC), but with no detectable targets (35). Our study indicated that miR-147b was the specific miRNA in subgroup 1, suggesting that it may be an oncomiR in this GBM subgroup.

BRCA1 is known to act as a tumor suppressor associated with DNA repair and genome integrity. The DNA repair pathway was established as a crucial pathway for identifying the glioblastoma cases-control status, and the SNP, rs799917 of BRCA1 was shown to be involved in this pathway (36). Additionally, the methylation PRKCDBP was implicated in GBM and identified as a BRCA1-interacting protein (37), suggesting that BRCA1 exerts its functions in GBM via its interactions with other proteins. EGFR abnormalities occur frequently in GBM patients. The aP2 protein is an essential component of growth factor receptor endocytosis (38), implying that aP2 may also play important roles in the progression of GBM. Notably, in a previous study, aP2-α was shown to be one of the putative TFs of BRCA1 through the combination of microarray and ChIP-chip analysis (39). Considering that BRCA1 and aP2 were both specific in subgroup 2 in our study, it can be speculated that aP2 may be the TF regulating the expression of BRCA1 in GBM patients in this subgroup. With regard to the miRNAs in subgroup 2, miR-770-5P was identified as the specific one, which was also predominant in the miRNA co-expression network of GBM (40). Moreover, miR-770-5P has been shown to be upregulated in neuronal stem cells which progress into GBM (41). All these data suggest the potential use of miR-770-5P as a target for GBM subgroup 2.

The disruption of cell cycle program is often involved in the development of GBM. For instance, JSI-124 functions as an inhibitor of GBM cell proliferation by inducing cell cycle arrest at G2/M phase (42). The protein Sp1 is a zinc finger TF that is involved in multiple cellular processes related to the cell cycle. p53 acts as a tumor suppressor in the development of various types of cancer, such as lung cancer (43) and GBM (44). The loss of p53 is often the initiation of oncogenesis. A previous study demonstrated that the p53-mediated repression of cyclin B1 (CCNB1), a cell cycle-related transcript, required the binding of Sp1 to the CCNB1 prompter (45). Emerging evidence has verified the interactions of p53 and Sp1 in the regulation of other cell cycle related genes, such as CDC25B (46). E2F1 is another vital TF that promotes cell progression. Δ9-tetrahydrocannabinol functions as an inhibitor of cell growth via the downregulation of E2F1 in GBM (47). Furthermore, E2F1 was suggested to be involved in the mediation of telomerase activity in malignant glioma cells and the overexpression of E2F1 and telomerase were proposed as markers for the prognosis of glioblastoma (48). Notably, the cyclin-dependent kinase inhibitor, p18INK4c, which plays significant roles in cell cycle progression and cellular differentiation, is regulated by the synergistic action of E2F1 and Sp1 (49). In the present study, Sp1 was the hub node and E2F1 was the crucial TF in subgroup 3, suggesting that they may play significant roles in the progression of GBM in this subgroup, both via the involvement of cell cycle regulation. miR-220 was predicted as a remarkable miRNA in subgroup 3. In Chinese patients with prostate cancer, miR-220 was also suggested to be a crucial therapeutic target (50). Additionally, the elevated level of miR-220 was also predicted based on an miRNA array analysis in colorectal cancer (51). Therefore, dysregulated miR-220 may also be a therapeutic marker of GBM in subgroup 3.

The CHD3 encoded protein is related to chromatin remodeling. The GO functional terms of this gene are helicase activity and ATP-dependent DNA helicase activity (52). It was discovered that CHD3 was one of the 60 genes significantly associated with the overall survival (OS) of GBM (53). The TF encoded by CREB1 was reported to be involved in a large number of cellular processes, such as survival and proliferation. CREB was implicated in the development of myeloid leukemia for it positively increased the expression of cell cycle-related genes and thus promoted the cellular transformation and survival of myeloid cells (54). Companied with other synergetic factors, such as forkhead box protein A1 (FOXA1) and MYB proto-oncogene like 2 (MYBL2), CREB1 induced histone H3 acetylation, which contributed to the transition of G1 to S phase cell cycle stage in castration-resistant prostate cancer (55). Our study revealed the dysregulated CHD3 and CREB1 in GBM subgroup 3, suggesting their roles as potential biomarkers of GBM in this subgroup.

miR-1247 has been shown to be markedly dysregulated in the tumor tissues in GBM (56,57), implying its role as a therapeutic target in GBM in subgroup 4. However, only two validated targets of this miRNA have been reported, such as the cartilage transcription factor, SRY-box 9 (SOX9) (58) and Myc-binding protein 2 (MYCBP2) (59).

As one of the approaches that target a specific integrin-ligand interaction, natalizumab has been applied for the treatment of GBM by targeting α4 integrin, which results in the blocking of α4β1 or α4β7 binding to vascular cell adhesion molecule 1 (VCAM-1) (60). The integrin, integrin subunit alpha 4 (ITGA4), was revealed as a target of natalizumab in GBM of subgroup 2 in the present study (data not shown), suggesting that ITGA4 may be a novel target of the small molecule of natalizumab in this subgroup.

The administration of the monoclonal antibodies of natalizumab has been shown to be responsible for the suppression of T-lymphocyte trafficking into the central nervous system (CNS); however it is also related to the risk of progressive multifocal leukoencephalopathy (61). Considering the adverse side-effects, caution should excised as regards the application of this agent for the management of GBM in subgroup 2.

The overall survival of GBM is often distinct among different subgroups, due to the factors of population, age, recursive partitioning analysis (RPA) class and other genetic factors (62). Our findings indicated that the survival rate of m-group 1 (subgroups 1 and 4) was significantly higher than that of m-group 2 (subgroups 2 and 3), suggesting that the same agent of GBM may be more beneficial to individuals in m-group 1 than that of m-group 2. This also supports the necessity of the use of specific agents in different subgroups. In conclusion, the molecular regulatory mechanisms responsible for the pathogenesis GBM were distinct in the different subgroups. Several crucial genes, TFs, miRNAs and small molecules in the different subgroups of GBM were identified, which may be used as potential markers in the 4 subgroups, respectively. Among these, the LCP2- and PAX5-mediated immune pathway, the transcriptional regulation of BRCA1 by aP2 in subgroup 2, the synergistic action of Sp1 and E2F1 and CREB1-modulated cell cycle process in subgroups 1–4, respectively, may play significant roles in the progression of GBM. Moreover, natalizumab may be used as a potent therapeutic agent for GBM in subgroup 2. However, all these predicted results need to be further verified by extensive experiments.