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Comprehensive bioinformatic analysis of key genes and signaling pathways in glioma

  • 1.

    Department of Neurosurgery, the First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230001, China

  • 2.

    Anhui Key Laboratory of Brain Function and Diseases, Hefei 230001, China

Cite this:
https://doi.org/10.52396/JUSTC-2022-0010
More Information
  • Author Bio:

    Xiaoming Zhang is currently pursuing a doctoral degree in Clinical Medicine at the University of Science and Technology of China. His research focuses on the role of non-coding RNA in pathogenesis of glioblastoma

    Shanshan Hu received her Ph.D. degree in Cell Biology from the School of Life Sciences, University of Science and Technology of China. She is currently an associate research fellow at the First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China. Her research interest involves GSC derived exosome in glioma-associated environment, non-coding RNA in GBM progression and evolution

  • Corresponding author: E-mail: hss923@ustc.edu.cn
  • Received Date: 10 January 2022
  • Accepted Date: 16 March 2022
    Abstract

  • Abstract

    The identification of specific survival-related differentially expressed genes (DEGs) is a method for uncovering therapeutic approaches for various cancers, including glioma. However, the key target genes associated with the occurrence and development of gliomas remain unknown. In this study, we performed bioinformatics analysis on 17 GSE datasets and identified DEGs correlated with glioma. A total of 74 mutual-DEGs with downregulated expression in gliomas compared with that in normal brain tissues were found in 17 datasets. These DEGs were related to GABAergic synaptic transmission, chloride transmembrane transport, glutamate secretion, and gamma-aminobutyric acid signaling pathway. Gamma-aminobutyric acid type A receptor subunit gamma 2 (GABRG2) was identified as a hub gene in the protein-protein interaction network. GABRG2 exhibited lower expression in IDH wild-type astrocytoma than that in IDH mutant astrocytoma and indicated poor prognosis in glioma patients. GABRG2 may contribute to the progression of glioma by affecting GABA receptor-related pathways and is a potential biomarker for the diagnosis and treatment of glioma.

    Graphical abstract

    Bioinformatics analysis of large-scale samples reveals potential mechanism of GABRG2 in glioma progression.

    Abstract

    The identification of specific survival-related differentially expressed genes (DEGs) is a method for uncovering therapeutic approaches for various cancers, including glioma. However, the key target genes associated with the occurrence and development of gliomas remain unknown. In this study, we performed bioinformatics analysis on 17 GSE datasets and identified DEGs correlated with glioma. A total of 74 mutual-DEGs with downregulated expression in gliomas compared with that in normal brain tissues were found in 17 datasets. These DEGs were related to GABAergic synaptic transmission, chloride transmembrane transport, glutamate secretion, and gamma-aminobutyric acid signaling pathway. Gamma-aminobutyric acid type A receptor subunit gamma 2 (GABRG2) was identified as a hub gene in the protein-protein interaction network. GABRG2 exhibited lower expression in IDH wild-type astrocytoma than that in IDH mutant astrocytoma and indicated poor prognosis in glioma patients. GABRG2 may contribute to the progression of glioma by affecting GABA receptor-related pathways and is a potential biomarker for the diagnosis and treatment of glioma.

    • Public Summary

    • A total of 74 differentially expressed genes were downregulated in large-scale RNA sequencing in glioma.
    • GABRG2 was identified as a key gene in the glioma-related protein-protein interaction network and was correlated with poor prognosis of glioma patients.
    • GABRG2 may affect glioma biogenesis by regulating GABA receptor related pathways.

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    • References(50)
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    Figure  1.  Flowchart of data processing and DEG analysis.

    Figure  2.  Identification of 74 mutual differentially expressed genes (DEGs) in 17 GSE datasets. (a) Volcano plot of glioma-related DEGs in datasets. (b) Venn diagrams of glioma-related DEGs in four groups. (c) Venn diagram of mutual DEGs in 17 datasets.

    Figure  3.  Heat map of 74 mutual DEG expression in all samples.

    Figure  4.  Functional enrichment analyses of mutual DEGs in 17 datasets. (a) Gene ontology (GO) biological process analysis of 74 mutual DEGs. (b) GO cellular component analysis of 74 mutual DEGs. (c) GO molecular function of 74 mutual DEGs. (d) Bubble chart shows the result of KEGG pathway analysis of 74 mutual DEGs. P < 0.05 was considered as statistically significant.

    Figure  5.  Construction of protein-protein interaction (PPI) network and detection of key module. (a) PPI network of mutual DEGs in glioma. Circle size represents the significance of nodes in the network. (b) Core module of PPI network in glioma. (c) Results of functional enrichment analysis of core module in glioma performed using Metascape.

    Figure  6.  Identification and prognosis of hub genes in core module. (a) PPI network of ten hub DEGs in glioma. (b) Basic information of ten hub genes. The red-colored gene (GABRG2) indicates the highest degree of importance. (c) Prognosis of hub genes in glioma. P < 0.05 was considered as statistically significant.

    Figure  7.  Relative expression of GABRG2 and its potential molecular mechanism. (a) Relative expression of GABRG2 in 81 samples of normal brain tissue and 948 samples of glioma tissue (204 cases of WHO I, 134 cases of WHO II, 135 cases of WHO III, and 475 cases of WHO IV grade). (b) GABRG2 expression is downregulated in 20 glioma tissues compared with that in 15 normal brain tissues. (c) Relative expression of GABRG2 in glioma cell lines, primary cells, and HEB using qRT-PCR. (d) Potential molecular mechanisms of GABRG2 analyzed using GeneMANIA. ****P < 0.0001, ####P < 0.0001, &&&&P < 0.0001.

    [1]
    Louis D N, Perry A, Reifenberger G, et al. The 2016 World Health Organization Classification of Tumors of the Central Nervous System: a summary. Acta Neuropathol., 2016, 131 (6): 803–820. doi: 10.1007/s00401-016-1545-1
    [2]
    Wen P Y, Kesari S. Malignant gliomas in adults. N. Engl. J. Med., 2008, 359 (5): 492–507. doi: 10.1056/NEJMra0708126
    [3]
    Van Meir E G, Hadjipanayis C G, Norden A D, et al. Exciting new advances in neuro-oncology: the avenue to a cure for malignant glioma. CA Cancer J. Clin., 2010, 60 (3): 166–193. doi: 10.3322/caac.20069
    [4]
    Gong L Y, Zhang D, Dong Y P, et al. Integrated bioinformatics analysis for identificating the therapeutic targets of aspirin in small cell lung cancer. J. Biomed. Inform., 2018, 88: 20–28. doi: 10.1016/j.jbi.2018.11.001
    [5]
    Liu L, He C, Zhou Q, et al. Identification of key genes and pathways of thyroid cancer by integrated bioinformatics analysis. J. Cell. Physiol., 2019, 234 (12): 23647–23657. doi: 10.1002/jcp.28932
    [6]
    Liu L, Wang G Y, Wang L G, et al. Computational identification and characterization of glioma candidate biomarkers through multi-omics integrative profiling. Biol. Direct, 2020, 15 (1): 10. doi: 10.1186/s13062-020-00264-5
    [7]
    Barrett T, Wilhite S E, Ledoux P, et al. NCBI GEO: archive for functional genomics data sets-update. Nucleic Acids Res., 2013, 41: D991–D995. doi: 10.1093/nar/gks1193
    [8]
    Gusev Y, Bhuvaneshwar K, Song L, et al. The REMBRANDT study, a large collection of genomic data from brain cancer patients. Sci. Data, 2018, 5: 180158. doi: 10.1038/sdata.2018.158
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    Mascelli S, Barla A, Raso A, et al. Molecular fingerprinting reflects different histotypes and brain region in low grade gliomas. BMC Cancer, 2013, 13: 387. doi: 10.1186/1471-2407-13-387
    [10]
    Kawaguchi A, Yajima N, Tsuchiya N, et al. Gene expression signature-based prognostic risk score in patients with glioblastoma. Cancer Sci., 2013, 104 (9): 1205–1210. doi: 10.1111/cas.12214
    [11]
    Zakrzewski K, Jarząb M, Pfeifer A, et al. Transcriptional profiles of pilocytic astrocytoma are related to their three different locations, but not to radiological tumor features. BMC Cancer, 2015, 15: 778. doi: 10.1186/s12885-015-1810-z
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    [13]
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    [14]
    Griesinger A M, Birks D K, Donson A M, et al. Characterization of distinct immunophenotypes across pediatric brain tumor types. J. Immunol., 2013, 191 (9): 4880–4888. doi: 10.4049/jimmunol.1301966
    [15]
    Lambert S R, Witt H, Hovestadt V, et al. Differential expression and methylation of brain developmental genes define location-specific subsets of pilocytic astrocytoma. Acta Neuropathol., 2013, 126 (2): 291–301. doi: 10.1007/s00401-013-1124-7
    [16]
    Zhou J X, Xu T, Yan Y, et al. MicroRNA-326 functions as a tumor suppressor in glioma by targeting the Nin one binding protein (NOB1). PLoS One, 2013, 8 (7): e68469. doi: 10.1371/journal.pone.0068469
    [17]
    Sturm D, Witt H, Hovestadt V, et al. Hotspot mutations in H3F3A and IDH1 define distinct epigenetic and biological subgroups of glioblastoma. Cancer Cell, 2012, 22 (4): 425–437. doi: 10.1016/j.ccr.2012.08.024
    [18]
    Donson A M, Birks D K, Schittone S A, et al. Increased immune gene expression and immune cell infiltration in high-grade astrocytoma distinguish long-term from short-term survivors. J. Immunol., 2012, 189 (4): 1920–1927. doi: 10.4049/jimmunol.1103373
    [19]
    Paugh B S, Broniscer A, Qu C, et al. Genome-wide analyses identify recurrent amplifications of receptor tyrosine kinases and cell-cycle regulatory genes in diffuse intrinsic pontine glioma. J. Clin. Oncol., 2011, 29 (30): 3999–4006. doi: 10.1200/JCO.2011.35.5677
    [20]
    Grzmil M, Morin P Jr, Lino M M, et al. MAP kinase-interacting kinase 1 regulates SMAD2-dependent TGF-β signaling pathway in human glioblastoma. Cancer Res., 2011, 71 (6): 2392–2402. doi: 10.1158/0008-5472.CAN-10-3112
    [21]
    Liu Z Y, Yao Z Q, Li C, et al. Gene expression profiling in human high-grade astrocytomas. Comp. Funct. Genomics, 2011, 2011: 245137. doi: 10.1155/2011/245137
    [22]
    Wiedemeyer R, Brennan C, Heffernan T P, et al. Feedback circuit among INK4 tumor suppressors constrains human glioblastoma development. Cancer Cell, 2008, 13 (4): 355–364. doi: 10.1016/j.ccr.2008.02.010
    [23]
    Sharma M K, Mansur D B, Reifenberger G, et al. Distinct genetic signatures among pilocytic astrocytomas relate to their brain region origin. Cancer Res., 2007, 67 (3): 890–900. doi: 10.1158/0008-5472.CAN-06-0973
    [24]
    Sun L, Hui A M, Su Q, et al. Neuronal and glioma-derived stem cell factor induces angiogenesis within the brain. Cancer Cell, 2006, 9 (4): 287–300. doi: 10.1016/j.ccr.2006.03.003
    [25]
    Davis S, Meltzer P S. GEOquery: a bridge between the Gene Expression Omnibus (GEO) and BioConductor. Bioinformatics, 2007, 23: 1846–1847. doi: 10.1093/bioinformatics/btm254
    [26]
    Chen S L, Gao C, Wu Y Y, et al. Identification of prognostic miRNA signature and lymph node metastasis-related key genes in cervical cancer. Front. Pharmacol., 2020, 11: 544. doi: 10.3389/fphar.2020.00544
    [27]
    Gene Ontology Consortium. Gene Ontology Consortium: going forward. Nucleic Acids Res., 2015, 43: D1049–D1056. doi: 10.1093/nar/gku1179
    [28]
    Kanehisa M, Furumichi M, Tanabe M, et al. KEGG: new perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res., 2017, 45 (D1): D353–D361. doi: 10.1093/nar/gkw1092
    [29]
    Huang D W, Sherman B T, Lempicki R A. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat. Protoc., 2009, 4 (1): 44–57. doi: 10.1038/nprot.2008.211
    [30]
    Bu D C, Luo H T, Huo P P, et al. KOBAS-i: intelligent prioritization and exploratory visualization of biological functions for gene enrichment analysis. Nucleic Acids Res., 2021, 49 (W1): W317–W325. doi: 10.1093/nar/gkab447
    [31]
    Szklarczyk D, Gable A L, Lyon D, et al. STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res., 2019, 47 (D1): D607–D613. doi: 10.1093/nar/gky1131
    [32]
    Shannon P, Markiel A, Ozier O, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res., 2003, 13 (11): 2498–2504. doi: 10.1101/gr.1239303
    [33]
    Chin C H, Chen S H, Wu H H, et al. cytoHubba: identifying hub objects and sub-networks from complex interactome. BMC Syst. Biol., 2014, 8 ((Suppl 4)): S11. doi: 10.1186/1752-0509-8-S4-S11
    [34]
    Zhou Y Y, Zhou B, Pache L, et al. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat. Commun., 2019, 10 (1): 1523. doi: 10.1038/s41467-019-09234-6
    [35]
    Tang Z F, Kang B X, Li C W, et al. GEPIA2: an enhanced web server for large-scale expression profiling and interactive analysis. Nucleic Acids Res., 2019, 47 (W1): W556–W560. doi: 10.1093/nar/gkz430
    [36]
    Zhao Z, Zhang K N, Wang Q W, et al. Chinese Glioma Genome Atlas (CGGA): A comprehensive resource with functional genomic data from Chinese glioma patients. Genomics, Proteomics & Bioinformatics, 2021, 19 (1): 1–12. doi: 10.1016/j.gpb.2020.10.005
    [37]
    Warde-Farley D, Donaldson S L, Comes O, et al. The GeneMANIA prediction server: biological network integration for gene prioritization and predicting gene function. Nucleic Acids Res., 2010, 38: W214–W220. doi: 10.1093/nar/gkq537
    [38]
    Altman R. Current Progress in Bioinformatics 2016. Brief. Bioinform., 2016, 17 (1): 1. doi: 10.1093/bib/bbv105
    [39]
    Venkataramani V, Tanev D I, Strahle C, et al. Glutamatergic synaptic input to glioma cells drives brain tumour progression. Nature, 2019, 573 (7775): 532–538. doi: 10.1038/s41586-019-1564-x
    [40]
    Jung E, Alfonso J, Osswald M, et al. Emerging intersections between neuroscience and glioma biology. Nat. Neurosci., 2019, 22 (12): 1951–1960. doi: 10.1038/s41593-019-0540-y
    [41]
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