Identification of key candidate genes and pathways in glioblastoma by integrated bioinformatical analysis
- Authors:
- Published online on: September 5, 2019 https://doi.org/10.3892/etm.2019.7975
- Pages: 3439-3449
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Copyright: © Li et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
Abstract
Introduction
Glioblastoma (GBM), also known as glioblastoma multiforme, comprising 30–40% of all brain tumors, is one of the most common primary brain tumors with high morbidity and mortality, and it is characterized by a high recurrence rate, invasiveness and a low cure rate (1,2). This is due to GBMs rapid growth rate, substantial molecular heterogeneity, high propensity to infiltrate vital brain structures and the regenerative capacity of treatment-resistant cancer stem cells. GBM is also the most lethal subtype of glioma, with a median survival of <2 years despite surgical resection, radiation, and chemotherapy (3–6). GBM has different histopathological features, mutations and clinical courses in an age-dependent manner (7).
All current high-grade gliomas (HGGs), such as GBM, are incurable as current standard treatments are insufficient, including maximal surgical tumor resection, radiotherapy and chemotherapy (4,8–10). Molecular approaches that target angiogenesis and single-compound-targeted therapies have also been unsuccessful (11).
Although the detailed mechanism of GBM formation and progression has been extensively studied, the exact etiology of GBM is poorly understood (12–14). The occurrence and malignant progression of GBM are associated with a variety of factors, such as gene aberrations (15). Due to the high morbidity and mortality of GBM, it is crucial to understand its etiology and potential molecular mechanisms; it is also necessary to find novel molecular biomarkers with potential diagnostic, individualized therapeutic and prognostic value. The screening of differentially expressed genes (DEGs) can be performed by using gene chips, which can easily detect all the genes within a sample and gather information at a specific time point (16). In the past few decades, the use of high-throughput microarrays to study the molecular mechanisms of GBM and to reconstruct the gene regulatory network in medical biology has made substantial progress (17). Microarrays are very valuable for screening genes associated with the occurrence, progression and targeted therapy of GBM (6). Bioinformatics analyses that use microarrays in screening have revealed that these genes are closely associated with cell signaling, cell metabolism, cytoskeleton, immunity, cell cycle and apoptosis (18,19). However, due to tissue heterogeneity in independent studies or the inherent shortcomings of the microarray technique, including small sample size, measurement error and information insufficiency, the results are always inconsistent (17,20). Therefore, unveiling the specific molecular mechanism underlying GBM remains a major challenge, although large numbers of DEGs have been identified between GBM and normal brain tissues by using microarray analysis (21). Integrated bioinformatics methods hypothesized to be capable of solving the aforementioned problems combined with expression profiling techniques have been carried out to identify the mechanisms underlying GBM (22,23). Kunkle et al (22) applied a comprehensive bioinformatics method using a genetic variation (small-scale variations and copy number variations) and environmental data integration that links with glioblastoma to distinguish genes that may be influenced by environmental exposures and associated with the development of GBM. Using this bioinformatics method, they identified 173 aberrantly expressed environmentally responsive genes that may be important to the pathogenesis of GBM. Additionally, through integrated bioinformatics methods, Li et al (23) discovered a series of gene pairs whose relationships were reversed in the progression from normal to GBM, i.e. from positive to negative correlation or vice versa, including cyclin-dependent kinase 2 and neuroblastoma RAS, fibroblast growth factor receptor and cyclin D1. DEGs were first identified from the GES4290 gene expression profile using Student t test. The relevant metabolic pathways including the cell cycle and mitogen-activated protein kinases were then extracted from the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway database (https://www.kegg.jp/). A total of 432 cancer genes were subsequently obtained from the database of Cancer Gene Census (http://www.sanger.ac.uk/genetics/CGP/Census/), before the gene pairs were analyzed between DEGs and cancer genes.
In the present study, three microarray datasets (GSE50161, GSE90598 and GSE104291) were downloaded. In total, there are 57 GBM and 22 normal brain tissue datasets available. A data processing standard was used to filter the DEGs on the Morpheus website, followed by Gene Ontology (GO) and pathway enrichment analyses using Database for Annotation, Visualization and Integrated Discovery (DAVID) software. The DEGs protein-protein interaction (PPI) network and modular analysis were integrated using Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) software to identify hub genes in GBMs.
Materials and methods
Identification of DEGs and microarray data information
GBM and non-cancerous brain tissue gene expression profiles from GSE50161, GSE90598 and GSE10429 were obtained from the NCBI-Gene Expression Omnibus (NCBI-GEO; http://www.ncbi.nlm.nih.gov/geo) database, which is a publicly accessible database of next-generation sequencing and microarray/gene profiles. The microarray data in GSE50161 were based on GPL570 microarray platforms (Affymetrix Human Genome U133 Plus 2.0 Array; Affymetrix; Thermo Fisher Scientific, Inc.) and included 37 American GBM and 13 normal brain tissues (submission date, 23 Aug 2013) (24). The GSE90598 data were based on GPL17692 microarray platforms (Affymetrix Human Gene 2.1 ST Array; Affymetrix; Thermo Fisher Scientific, Inc.) and included 16 Turkish GBM and 7 normal brain tissues (submission date, 28 Nov 2016) (25). The GSE104291 data was based on GPL570 microarray platforms and included 4 Swiss GBM and 2 normal brain tissues (submission date, 26 Sep 2017) (26,27). These three datasets were chosen due to their representation of three different racial populations. This project was performed with the permission of the Institutional Review Board of Tongji University (Shanghai, China).
High-throughput functional genomic expression data from the three GSE datasets were first integrated into GEO2R for further analysis (28). Files in .TXT format, which represents the analysis results from GEO2R, were subsequently generated by the GEO2R tool. The Venn diagrams were produced using Bioinformatics and Evolutionary Genomics software (http://bioinformatics.psb.ugent.be/webtools/Venn) and the Morpheus Website (https://software.broadinstitute.org/morpheus) to process the .TXT format data. DEGs were determined by comparing their expression levels in GBM and normal brain tissues. DEGs were identified using unpaired t-test and P<0.01 was considered to indicate a statistically significant DEGs and [log Fold Change]>1 was set as the cut-off criteria using the GEO2R tool (28). GEO2R is a dataset analysis tool based on R programming language. This tool can perform ANOVA or t-tests, both of which can be applied to compare two sets of data samples under the same experimental conditions to determine differentially expressed microRNAs or genes (28).
GO and pathway enrichment analysis
DEGs GO analysis, candidate DEGs functions and pathway enrichment were analyzed using the DAVID tool (Version 6.8; http://david.ncifcrf.gov/) with P<0.05 set as the cut-off criterion (17,29–33).
Integration of the PPI network
The online database STRING (http://string-db.org) (34) was used to construct a PPI network of the proteins encoded by DEGs. Then, Cytoscape software (Version 3.7.1, National Institute of General Medical Sciences) (35) was utilized to perform protein interaction association network analysis and analyze the interaction correlation of the candidate proteins encoded by the DEGs in GBM. Next, the CentiScaPe plugin (Version 2.2) for Cytoscape was applied to calculate node degree (the number of connections to the hub in the PPI network) (36). Finally, the MCODE module (Version 1.5.1) for Cytoscape was used to collect the significant modules in the PPI network complex (20).
Survival analysis of hub genes
The online database OncoLnc (http://www.oncolnc.org/), which can link The Cancer Genome Atlas (TCGA) survival data to mRNA, miRNA or lncRNA expression levels, was employed to explore the prognostic value of the hub genes (37). Kaplan-Meier survival curves were plotted using OncoLnc. Patients with GBM were sub-classified into low- and high-expression groups according to the median expression of each hub gene. Relapse-free survival (RFS) was used for the survival endpoints. For the log-rank test, P<0.05 was considered to indicated to be statistically significant difference.
Results
Identification of DEGs in GBMs
The GSE50161, GSE90598 and GSE104291 microarray data were obtained from NCBI-GEO. Using the aforementioned cut-off criteria, 4,482, 1,355 and 1,034 DEGs were extracted from GSE50161, GSE90598 and GSE104291, respectively. Following integrated bioinformatics analysis, a total of 378 DEGs were documented (Fig. 1), including 240 and 138 genes up- and downregulated in GBM tissues compared with normal brain tissues (Table I).
Table I.A total of 378 DEGs identified from three profile datasets, including 240 upregulated genes and 138 downregulated genes in the glioblastoma tissues compared with normal brain tissues. |
DEGs GO analysis in GBM
Following DEGs GO analysis using DAVID software, the DEGs were sub-classified into three functional groups including the cellular component group, the molecular function group and the biological process group (Fig. 2). The top 10 significantly enriched GO terms in each of the three groups were then determined (Fig. 2). In the biological process group, upregulated genes were involved in chemical synaptic transmission and nervous system development, and the downregulated genes were involved in cell adhesion, cell division, the positive regulation of gene expression and the innate immune response (Table II). In the molecular function group, the calcium ion binding GO term enriched both overexpressed and downregulated genes, which indicate that the molecular function of calcium binding may serve vital roles in the development of GBM. In addition, overexpressed genes were also involved in ATP binding, whilst downregulated genes were also involved in calcium and receptor binding (Table II). In the cellular component group, upregulated genes mainly included proteins integral to the plasma membrane and cell junction, while downregulated genes included those in the cytoplasm, extracellular exosomes and membrane (Table II). These results demonstrated that most of the DEGs were closely correlated with chemical synaptic transmission, protein binding, and plasma membrane. Significantly enriched GO terms containing the largest number of DEGs in GBM are listed in Table III. As shown in Fig. 3, in the biological process group, the most significant enriched GO term is chemical synaptic transmission. In the cellular component group, the most significant enriched GO term is cell junction. In the molecular function group, the most significant enriched GO term is syntaxin-1 binding.
Table II.The top 3 significantly enriched GO terms in Glioblastoma stratified by different functional groups of GO analysis. |
Table III.Significantly enriched GO terms that contain the largest number of differentially expressed genes in glioblastoma. |
Signaling pathway enrichment analysis
DAVID tools were used to perform DEGs functional and signaling pathway enrichment analyses. The DEGs including both upregulated and downregulated genes associated with glioblastoma were significantly enriched in signaling pathways involing retrograde endocannabinoid signaling, synaptic vesicle cycle, and dopaminergic synapse (Fig. 4). In particular, as shown in Table IV, the upregulated genes were mainly enriched in retrograde endocannabinoid signaling and calcium signaling pathways, whilst downregulated genes were mainly associated with pathways in cancer and the phosphatidylinositol 4,5-bisphosphate 3-kinase/RAC-α serine/threonine-protein kinase signaling pathway.
Identification of key candidate genes and pathways using DEGs PPI network and module analysis
Using Cytoscape software and the STRING database (34,35), 245 DEGs, including 92 downregulated genes and 153 upregulated genes out of 378, were filtered into the DEGs PPI network complex containing 245 nodes and 741 edges (Fig. 5A). Another 133 genes out of the 378 commonly altered DEGs failed to fall into the DEGs PPI network. The identification of 35 hub genes among 245 nodes was successfully achieved by filtering nodes with >11 degrees (also known as interactions or connections; Table I). Additionally, the 10 most vital nodes with >11 degrees were DLG4, SYT1, SNAP251, VAMP2, CACNA1B, SYN1, GNG3, GNG12, CD44 and GNAI3. Based on the degree of importance, two significant modules in the PPI network complex were collected for further analysis using the MCODE plugin. GO and pathway enrichment analyses revealed that 30 nodes and 152 edges existed in Module 1 (Fig. 5B and Table V), which were mainly associated with neurotransmitter secretion, plasma membrane, syntaxin-1 binding and the synaptic vesicle cycle. In addition, Module 2 included 27 nodes and 86 edges (Fig. 5C and Table V), mainly associated with extracellular matrix (ECM) organization, cytoplasm, protein binding and ECM-receptor interaction.
Survival analysis of hub genes
Based on the OncoLnc database, it was demonstrated that high calcium-binding protein 1 (CABP1) expression was negatively associated with the RFS of patients with GBM (Fig. 6).
Validation of the DEGs in the TCGA dataset
To confirm the reliability of the identified DEGs from the 3 datasets, the TCGA GBM dataset was downloaded and analyzed using the same strategy used in the current study. A total of 195 of the 240 upregulated genes identified in the present study were also overexpressed in the TCGA GBM dataset, whilst 123 of the 138 downregulated genes identified in the current study were also downregulated in the TCGA GBM dataset (data not shown). The total consistency of the up- and downregulated genes was 84.13%, suggesting that the results of the identified candidate genes are reliable.
Discussion
In the current study, a total of 378 genes were identified, including 240 upregulated genes and 138 downregulated genes. Almost all the DEGs obtained in the training set were verified by the validation set. The GO biological process analysis revealed that the upregulated DEGs were significantly associated with chemical synaptic transmission and nervous system development, while downregulated DEGs were significantly associated with cell adhesion, cell division, positive regulation of gene expression and the innate immune response. The onset and progression of GBM are associated with a complex network of cellular components, molecular functions and biological processes (38,39). For chemical synaptic transmission, available data demonstrated that neurotransmitters can influence the proliferation, quiescence and differentiation status of central nervous system-resident cells (40). Furthermore, the disruption of cell adhesion and the cell cycle were demonstrated to often be involved in the development of GBM (41). A study also revealed that the expression of pro-inflammatory and suppressive cytokines associated with the innate immune response changed significantly (42).
Among the DEGs, two co-expression modules comprising 30 and 27 genes, respectively, and 35 hub genes were identified using Cytoscape MCODE. Module 1 was associated with neurotransmitter secretion, plasma membrane, syntaxin-1 binding and the synaptic vesicle cycle, while Module 2 was associated with ECM organization, cytoplasm, protein binding and ECM-receptor interaction. Following identification of the hub genes, Kaplan-Meier analysis of hub genes revealed that increased expression of CABP1 was negatively associated with RFS. Therefore, CABP1 may be a key gene, and its associated biological processes may be a crucial mechanism of GBM progression. Additionally, all enriched GO terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways may participate in mechanisms underlying GBM occurrence and progression, thus further study is required.
CABP1 is a protein-coding gene located on chromosome 12q24.31 (43). The protein encoded by this gene can regulate the gating of L-type Ca2+ channels by inhibiting calcium-dependent inactivation via the voltage-dependent L-type calcium channel subunit-α1C to control neurotransmitter release, gene expression, muscle contraction, apoptosis, and disease processes (44). In addition, CABP1 can regulate the calcium-dependent activity of inositol 1,4,5-triphosphate receptors, P/Q-type voltage-gated calcium channels and the transient receptor potential channel, short transient receptor potential channel 5 (43). A study identified that CABP1 is involved in drug refractory epilepsy due to mesial temporal sclerosis, providing important insight into the understanding of the genomic basis of the condition (45). CABP1 was observed to be significantly upregulated during P. berghei infection (46). One study also demonstrated the importance of CABP1 in modulating stimulus-secretion coupling in excitable cells, and the ability of CABP1 to inhibit Ca2+ currents and exocytosis in bovine chromaffin cells (47). Thus, it is speculated that CABP1 works as a Ca2+-dependent regulator in GBM and may help to predict the prognosis of patients with GBM. However, CABP1 has rarely been studied in the field of oncology. Certain studies have demonstrated that aberrant CABP1 expression was associated with adenoma risk, particularly for multiple/advanced adenomas (44,48,49). In addition, Zhang et al (50) previously reported that CABP1 is upregulated in glioblastoma tissues compared with normal brain tissues. Using Cox regression analysis and log-rank test, CABP1 was identified to be one of the top six DEGs associated with GBM prognosis, where high CABP1 expression is negatively correlated with overall patient survival. However, the molecular underlying the role of CABP1 in the development and progression of GBM remains unknown. To the best of our knowledge, no previous study has investigated CABP1 in GBM.
In the present study, three datasets generated from Turkish, Swiss and American patients were used to represent three subsets of worldwide populations. Certain genes and small non-coding RNA molecules exhibit abnormal expression patterns in GBM (6). However, genes and biomarkers from different races vary widely (51), which may account for the low consistency of the DEGs from the three datasets.
To conclude, the current study identified a total of 378 DEGs. Among them, two co-expression modules and 35 hub genes were identified. The enriched GO terms and KEGG pathways may be closely associated with GBM occurrence. CABP1 may be a key gene associated with the prognosis of GBM. Altogether, these data introduced CABP1 as a good candidate for experimental studies into GBM. However, clinical experiments are urgently needed to evaluate the molecular role of CABP1 in GBM progression, as well as its specificity and sensitivity as a biomarker of GBM. Therefore, the molecular mechanisms and clinical applications of these genes and pathways require exploration in future studies.
Acknowledgements
The authors would like to thank Dr Sajan Pandey, Department of Neurosurgery, Shanghai Tenth People's Hospital, Tongji University School of Medicine (Shanghai, China) for his technical help and writing assistance.
Funding
The current study was supported by a grant given to Professor Liang Gao from the Health and Family Planning Commission (grant no. 03.02.16.001).
Availability of data and material
The datasets generated and/or analyzed during the current study are available in the NCBI-Gene Expression Omnibus (NCBI-GEO; http://www.ncbi.nlm.nih.gov/geo) database.
Authors' contributions
DC and LG conceived and designed the study. LL, XM, XD, TJ, PH, RW and HQ collected the data and performed data analysis. DC, XL and LL interpreted the result. LL and XL were major contributors in writing the manuscript. LG and XL revised the paper. All authors read and approved the final manuscript.
Ethics approval and consent to participate
The local ethics committee of Shanghai Tenth People's Hospital provided the ethical approval for the study.
Patient consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
Glossary
Abbreviations
Abbreviations:
GBM |
glioblastoma |
DEGs |
differentially expressed genes |
PPI |
protein-protein interaction network |
HGGs |
high-grade gliomas |
DIPG |
diffuse intrinsic pontine glioma |
GO |
Gene Ontology |
RFS |
relapse-free survival |
References
Wen WS, Hu SL, Ai Z, Mou L, Lu JM and Li S: Methylated of genes behaving as potential biomarkers in evaluating malignant degree of glioblastoma. J Cell Physiol. 232:3622–3630. 2017. View Article : Google Scholar : PubMed/NCBI | |
Yano S, Miwa S, Kishimoto H, Toneri M, Hiroshima Y, Yamamoto M, Bouvet M, Urata Y, Tazawa H, Kagawa S, et al: Experimental curative fluorescence-guided surgery of highly invasive glioblastoma multiforme selectively labeled with a killer-reporter adenovirus. Mol Ther. 23:1182–1188. 2015. View Article : Google Scholar : PubMed/NCBI | |
Stupp R, Taillibert S, Kanner A, Read W, Steinberg D, Lhermitte B, Toms S, Idbaih A, Ahluwalia MS, Fink K, et al: Effect of tumor-treating fields plus maintenance temozolomide vs maintenance temozolomide alone on survival in patients with glioblastoma: A randomized clinical trial. JAMA. 318:2306–2316. 2017. View Article : Google Scholar : PubMed/NCBI | |
Stupp R, Mason WP, van den Bent MJ, Weller M, Fisher B, Taphoorn MJ, Belanger K, Brandes AA, Marosi C, Bogdahn U, et al: Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma. N Engl J Med. 352:987–996. 2005. View Article : Google Scholar : PubMed/NCBI | |
Ostrom QT, Bauchet L, Davis FG, Deltour I, Fisher JL, Langer CE, Pekmezci M, Schwartzbaum JA, Turner MC, Walsh KM, et al: The epidemiology of glioma in adults: A ‘state of the science’ review. Neuro Oncol. 16:896–913. 2014. View Article : Google Scholar : PubMed/NCBI | |
Zeng T, Li L, Zhou Y and Gao L: Exploring long noncoding RNAs in glioblastoma: Regulatory mechanisms and clinical potentials. Int J Genomics. 2018:28959582018. View Article : Google Scholar : PubMed/NCBI | |
Filbin MG and Suvà ML: Gliomas genomics and epigenomics: Arriving at the start and knowing it for the first time. Annu Rev Pathol. 11:497–521. 2016. View Article : Google Scholar : PubMed/NCBI | |
Stupp R, Hegi ME, Gilbert MR and Chakravarti A: Chemoradiotherapy in malignant glioma: Standard of care and future directions. J Clin Oncol. 25:4127–4136. 2007. View Article : Google Scholar : PubMed/NCBI | |
Delgado-López PD and Corrales-García EM: Survival in glioblastoma: A review on the impact of treatment modalities. Clin Transl Oncol. 18:1062–1071. 2016. View Article : Google Scholar : PubMed/NCBI | |
Lim M, Xia Y, Bettegowda C and Weller M: Current state of immunotherapy for glioblastoma. Nat Rev Clin Oncol. 15:422–442. 2018. View Article : Google Scholar : PubMed/NCBI | |
Chinot OL, Wick W, Mason W, Henriksson R, Saran F, Nishikawa R, Carpentier AF, Hoang-Xuan K, Kavan P, Cernea D, et al: Bevacizumab plus radiotherapy-temozolomide for newly diagnosed glioblastoma. N Engl J Med. 370:709–722. 2014. View Article : Google Scholar : PubMed/NCBI | |
Furnari FB, Cloughesy TF, Cavenee WK and Mischel PS: Heterogeneity of epidermal growth factor receptor signalling networks in glioblastoma. Nat Rev Cancer. 15:302–310. 2015. View Article : Google Scholar : PubMed/NCBI | |
Ludwig K and Kornblum HI: Molecular markers in glioma. J Neurooncol. 134:505–512. 2017. View Article : Google Scholar : PubMed/NCBI | |
Yan Y, Xu Z, Li Z, Sun L and Gong Z: An insight into the increasing role of LncRNAs in the pathogenesis of gliomas. Front Mol Neurosci. 10:532017. View Article : Google Scholar : PubMed/NCBI | |
Sturm D, Bender S, Jones DT, Lichter P, Grill J, Becher O, Hawkins C, Majewski J, Jones C, Costello JF, et al: Paediatric and adult glioblastoma: Multiform (epi)genomic culprits emerge. Nat Rev Cancer. 14:92–107. 2014. View Article : Google Scholar : PubMed/NCBI | |
Vogelstein B, Papadopoulos N, Velculescu VE, Zhou S, Diaz LA Jr and Kinzler KW: Cancer genome landscapes. Science. 339:1546–1558. 2013. View Article : Google Scholar : PubMed/NCBI | |
Long H, Liang C, Zhang X, Fang L, Wang G, Qi S, Huo H and Song Y: Prediction and analysis of key genes in glioblastoma based on bioinformatics. Biomed Res Int. 2017:76531012017. View Article : Google Scholar : PubMed/NCBI | |
Huang SW, Ali ND, Zhong L and Shi J: MicroRNAs as biomarkers for human glioblastoma: Progress and potential. Acta Pharmacol Sin. 39:1405–1413. 2018. View Article : Google Scholar : PubMed/NCBI | |
Tan SY, Sandanaraj E, Tang C and Ang BT: Biobanking: An important resource for precision medicine in glioblastoma. Adv Exp Med Biol. 951:47–56. 2016. View Article : Google Scholar : PubMed/NCBI | |
Guo Y, Bao Y, Ma M and Yang W: Identification of key candidate genes and pathways in colorectal cancer by integrated bioinformatical analysis. Int J Mol Sci. 18:E7222017. View Article : Google Scholar : PubMed/NCBI | |
Tanwar MK, Gilbert MR and Holland EC: Gene expression microarray analysis reveals YKL-40 to be a potential serum marker for malignant character in human glioma. Cancer Res. 62:4364–4368. 2002.PubMed/NCBI | |
Kunkle B, Yoo C and Roy D: Discovering gene-environment interactions in glioblastoma through a comprehensive data integration bioinformatics method. Neurotoxicology. 35:1–14. 2013. View Article : Google Scholar : PubMed/NCBI | |
Li W, Li K, Zhao L and Zou H: Bioinformatics analysis reveals disturbance mechanism of MAPK signaling pathway and cell cycle in Glioblastoma multiforme. Gene. 547:346–350. 2014. View Article : Google Scholar : PubMed/NCBI | |
Griesinger AM, Birks DK, Donson AM, Amani V, Hoffman LM, Waziri A, Wang M, Handler MH and Foreman NK: Characterization of distinct immunophenotypes across pediatric brain tumor types. J Immunol. 191:4880–4888. 2013. View Article : Google Scholar : PubMed/NCBI | |
Gulluoglu S, Tuysuz EC, Sahin M, Kuskucu A, Kaan Yaltirik C, Ture U, Kucukkaraduman B, Akbar MW, Gure AO, Bayrak OF and Dalan AB: Simultaneous miRNA and mRNA transcriptome profiling of glioblastoma samples reveals a novel set of OncomiR candidates and their target genes. Brain Res. 1700:199–210. 2018. View Article : Google Scholar : PubMed/NCBI | |
Bady P, Diserens AC, Castella V, Kalt S, Heinimann K, Hamou MF, Delorenzi M and Hegi ME: DNA fingerprinting of glioma cell lines and considerations on similarity measurements. Neuro Oncol. 14:701–711. 2012. View Article : Google Scholar : PubMed/NCBI | |
Sciuscio D, Diserens AC, van Dommelen K, Martinet D, Jones G, Janzer RC, Pollo C, Hamou MF, Kaina B, Stupp R, et al: Extent and patterns of MGMT promoter methylation in glioblastoma- and respective glioblastoma-derived spheres. Clin Cancer Res. 17:255–266. 2011. View Article : Google Scholar : PubMed/NCBI | |
Barrett T, Wilhite SE, Ledoux P, Evangelista C, Kim IF, Tomashevsky M, Marshall KA, Phillippy KH, Sherman PM, Holko M, et al: NCBI GEO: Archive for functional genomics data sets-update. Nucleic Acids Res. 41:D991–D995. 2013. View Article : Google Scholar : PubMed/NCBI | |
He WQ, Gu JW, Li CY, Kuang YQ, Kong B, Cheng L, Zhang JH, Cheng JM and Ma Y: The PPI network and clusters analysis in glioblastoma. Eur Rev Med Pharmacol Sci. 19:4784–4790. 2015.PubMed/NCBI | |
Li Y, Min W, Li M, Han G, Dai D, Zhang L, Chen X, Wang X, Zhang Y, Yue Z and Liu J: Identification of hub genes and regulatory factors of glioblastoma multiforme subgroups by RNA-seq data analysis. Int J Mol Med. 38:1170–1178. 2016. View Article : Google Scholar : PubMed/NCBI | |
Wei B, Wang L, Du C, Hu G, Wang L, Jin Y and Kong D: Identification of differentially expressed genes regulated by transcription factors in glioblastomas by bioinformatics analysis. Mol Med Rep. 11:2548–2554. 2015. View Article : Google Scholar : PubMed/NCBI | |
Huang da W, Sherman BT and Lempicki RA: Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc. 4:44–57. 2009. View Article : Google Scholar : PubMed/NCBI | |
Huang da W, Sherman BT and Lempicki RA: Bioinformatics enrichment tools: Paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res. 37:1–13. 2009. View Article : Google Scholar : PubMed/NCBI | |
Franceschini A, Szklarczyk D, Frankild S, Kuhn M, Simonovic M, Roth A, Lin J, Minguez P, Bork P, von Mering C and Jensen LJ: STRING v9.1: Protein-protein interaction networks, with increased coverage and integration. Nucleic Acids Res. 41:D808–D815. 2013. View Article : Google Scholar : PubMed/NCBI | |
Chen WJ, Tang RX, He RQ, Li DY, Liang L, Zeng JH, Hu XH, Ma J, Li SK and Chen G: Clinical roles of the aberrantly expressed lncRNAs in lung squamous cell carcinoma: A study based on RNA-sequencing and microarray data mining. Oncotarget. 8:61282–61304. 2017.PubMed/NCBI | |
Scardoni G, Petterlini M and Laudanna C: Analyzing biological network parameters with CentiScaPe. Bioinformatics. 25:2857–2859. 2009. View Article : Google Scholar : PubMed/NCBI | |
Anaya J: OncoLnc: Linking TCGA survival data to mRNAs, miRNAs, and lncRNAs. Peer J Computer Sci. 2:e672016. View Article : Google Scholar | |
Iser IC, Pereira MB, Lenz G and Wink MR: The epithelial- to-mesenchymal transition-like process in glioblastoma: An updated systematic review and in silico investigation. Med Res Rev. 37:271–313. 2017. View Article : Google Scholar : PubMed/NCBI | |
Alshabi AM, Vastrad B, Shaikh IA and Vastrad C: Identification of crucial candidate genes and pathways in glioblastoma multiform by bioinformatics analysis. Biomolecules. 9:E2012019. View Article : Google Scholar : PubMed/NCBI | |
Caragher SP, Hall RR, Ahsan R and Ahmed AU: Monoamines in glioblastoma: Complex biology with therapeutic potential. Neuro Oncol. 20:1014–1025. 2018. View Article : Google Scholar : PubMed/NCBI | |
Cheng YC, Tsai WC, Sung YC, Chang HH and Chen Y: Interference with PSMB4 expression exerts an anti-tumor effect by decreasing the invasion and proliferation of human glioblastoma cells. Cell Physiol Biochem. 45:819–831. 2018. View Article : Google Scholar : PubMed/NCBI | |
Turkowski K, Brandenburg S, Mueller A, Kremenetskaia I, Bungert AD, Blank A, Felsenstein M and Vajkoczy P: VEGF as a modulator of the innate immune response in glioblastoma. Glia. 66:161–174. 2018. View Article : Google Scholar : PubMed/NCBI | |
Li C, Chan J, Haeseleer F, Mikoshiba K, Palczewski K, Ikura M and Ames JB: Structural insights into Ca2+-dependent regulation of inositol 1,4,5-trisphosphate receptors by CaBP1. J Biol Chem. 284:2472–2481. 2009. View Article : Google Scholar : PubMed/NCBI | |
Oz S, Tsemakhovich V, Christel CJ, Lee A and Dascal N: CaBP1 regulates voltage-dependent inactivation and activation of Ca(V)1.2 (L-type) calcium channels. J Biol Chem. 286:13945–13953. 2011. View Article : Google Scholar : PubMed/NCBI | |
Dixit AB, Banerjee J, Srivastava A, Tripathi M, Sarkar C, Kakkar A, Jain M and Chandra PS: RNA-seq analysis of hippocampal tissues reveals novel candidate genes for drug refractory epilepsy in patients with MTLE-HS. Genomics. 107:178–188. 2016. View Article : Google Scholar : PubMed/NCBI | |
Mubaraki MA, Hafiz TA, Al-Quraishy S and Dkhil MA: Oxidative stress and genes regulation of cerebral malaria upon Zizyphus spina-christi treatment in a murine model. Microb Pathog. 107:69–74. 2017. View Article : Google Scholar : PubMed/NCBI | |
Chen ML, Chen YC, Peng IW, Kang RL, Wu MP, Cheng PW, Shih PY, Lu LL, Yang CC and Pan CY: Ca2+ binding protein-1 inhibits Ca2+ currents and exocytosis in bovine chromaffin cells. J Biomed Sci. 15:169–181. 2008. View Article : Google Scholar : PubMed/NCBI | |
Zhao J, Zhu X, Shrubsole MJ, Ness RM, Hibler EA, Cai Q, Long J, Chen Z, Jiang M, Kabagambe EK, et al: Interactions between calcium intake and polymorphisms in genes essential for calcium reabsorption and risk of colorectal neoplasia in a two-phase study. Mol Carcinog. 56:2258–2266. 2017. View Article : Google Scholar : PubMed/NCBI | |
Sun P, Shrubsole MJ, Ness RM, Cai Q, Long J, Edwards T, Chen Z, Zhu X, Deng X, Luo J, et al: Calcium intake, CABP1 polymorphisms, and the risk of colorectal adenoma: Results from Tennessee Colorectal Polyp Study. Cancer Res. 71 (Suppl 8):S37632011. | |
Zhang Y, Xu J and Zhu X: A 63 signature genes prediction system is effective for glioblastoma prognosis. Int J Mol Med. 41:2070–2078. 2018.PubMed/NCBI | |
Delfino KR, Serão NV, Southey BR and Rodriguez-Zas SL: Therapy-, gender- and race-specific microRNA markers, target genes and networks related to glioblastoma recurrence and survival. Cancer Genomics Proteomics. 8:173–183. 2011.PubMed/NCBI |