+ Site Statistics
+ Search Articles
+ Subscribe to Site Feeds
Most Shared
PDF Full Text
+ PDF Full Text
Request PDF Full Text
+ Follow Us
Follow on Facebook
Follow on Twitter
Follow on LinkedIn
+ Translate
+ Recently Requested

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

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

International Journal of Molecular Medicine 38(4): 1170-1178

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 pre-defined 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.

(PDF emailed within 0-6 h: $19.90)

Accession: 058039571

Download citation: RISBibTeXText

PMID: 27572852

DOI: 10.3892/ijmm.2016.2717

Related references

Postoperative radiotherapy of glioblastoma multiforme - Analysis and critical assessment of different treatment strategies and predictive factors FT Postoperative Strahlentherapie des glioblastoma multiforme. Analyse pradiktiver faktoren. 2007

Genetic analysis of glioblastoma multiforme provides evidence for subgroups within the grade. Genes Chromosomes & Cancer. 21(3): 206, Ch, 1998

Identification of survival-related genes of the phosphatidylinositol 3'-kinase signaling pathway in glioblastoma multiforme. Cancer 112(7): 1575-1584, 2008

Molecular identification of regulatory DNA sequences for basal and gamma-interferon-induced expression of HLA DR alpha in human multiforme glioblastoma cell lines. Annals of the New York Academy of Sciences 540: 255-257, 1988

Identification of novel candidate target genes in amplicons of Glioblastoma multiforme tumors detected by expression and CGH microarray profiling. Molecular Cancer 5: 39, 2006

Clinically distinct subgroups of glioblastoma multiforme studied by comparative genomic hybridization. Laboratory Investigation; a Journal of Technical Methods and Pathology 74(1): 108-119, 1996

Data analysis and tissue type assignment for glioblastoma multiforme. Biomed Research International 2014: 762126, 2014

Statistical analysis of factors affecting survival after glioblastoma multiforme. Acta Neurochirurgica 37(1-2): 57-73, 1977

Survival analysis for valproic acid use in adult glioblastoma multiforme: a meta-analysis of individual patient data and a systematic review. Seizure 23(10): 830-835, 2015

Multivariate analysis of prognostic factors and survival in patients with "glioblastoma multiforme". La Clinica Terapeutica 159(4): 233-238, 2008

Why is there a lack of consensus on molecular subgroups of glioblastoma? Understanding the nature of biological and statistical variability in glioblastoma expression data. Plos One 6(7): E20826, 2011

Conventionally fractionated radiotherapy of glioblastoma multiforme. Results and analysis of possible influencing factors. Frontiers of Radiation Therapy and Oncology 33: 166-173, 1999

Identification of serum markers of glioblastoma multiforme patients using image-guided genomic and proteomic analysis. Journal of Clinical Oncology 24(18_suppl): 20008-20008, 2016

Molecular analysis of the PTEN, TP53 and CDKN2A tumor suppressor genes in long-term survivors of glioblastoma multiforme. Journal of Neuro-Oncology 48(2): 89-94, 2000

CT-Guided interstitial HDR Brachytherapy for recurrent glioblastoma Multiforme - Long-term results FT CT-gestutzte interstitielle HDR-Brachytherapie bei glioblastoma-multiforme-rezidiven. Langzeitergebnisse. 2007