Research article

Identification of potential and novel target genes in pituitary prolactinoma by bioinformatics analysis

  • Received: 05 November 2020 Accepted: 29 January 2021 Published: 07 February 2021
  • Pituitary prolactinoma is one of the most complicated and fatally pathogenic pituitary adenomas. Therefore, there is an urgent need to improve our understanding of the underlying molecular mechanism that drives the initiation, progression, and metastasis of pituitary prolactinoma. The aim of the present study was to identify the key genes and signaling pathways associated with pituitary prolactinoma using bioinformatics analysis. Transcriptome microarray dataset GSE119063 was downloaded from Gene Expression Omnibus (GEO) database. Limma package in R software was used to screen DEGs. Pathway and Gene ontology (GO) enrichment analysis were conducted to identify the biological role of DEGs. A protein-protein interaction (PPI) network was constructed and analyzed by using HIPPIE database and Cytoscape software. Module analyses was performed. In addition, a target gene-miRNA regulatory network and target gene-TF regulatory network were constructed by using NetworkAnalyst and Cytoscape software. Finally, validation of hub genes by receiver operating characteristic (ROC) curve analysis. A total of 989 DEGs were identified, including 461 up regulated genes and 528 down regulated genes. Pathway enrichment analysis showed that the DEGs were significantly enriched in the retinoate biosynthesis II, signaling pathways regulating pluripotency of stem cells, ALK2 signaling events, vitamin D3 biosynthesis, cell cycle and aurora B signaling. Gene Ontology (GO) enrichment analysis showed that the DEGs were significantly enriched in the sensory organ morphogenesis, extracellular matrix, hormone activity, nuclear division, condensed chromosome and microtubule binding. In the PPI network and modules, SOX2, PRSS45, CLTC, PLK1, B4GALT6, RUNX1 and GTSE1 were considered as hub genes. In the target gene-miRNA regulatory network and target gene-TF regulatory network, LINC00598, SOX4, IRX1 and UNC13A were considered as hub genes. Using integrated bioinformatics analysis, we identified candidate genes in pituitary prolactinoma, which might improve our understanding of the molecular mechanisms of pituitary prolactinoma.

    Citation: Vikrant Ghatnatti, Basavaraj Vastrad, Swetha Patil, Chanabasayya Vastrad, Iranna Kotturshetti. Identification of potential and novel target genes in pituitary prolactinoma by bioinformatics analysis[J]. AIMS Neuroscience, 2021, 8(2): 254-283. doi: 10.3934/Neuroscience.2021014

    Related Papers:

  • Pituitary prolactinoma is one of the most complicated and fatally pathogenic pituitary adenomas. Therefore, there is an urgent need to improve our understanding of the underlying molecular mechanism that drives the initiation, progression, and metastasis of pituitary prolactinoma. The aim of the present study was to identify the key genes and signaling pathways associated with pituitary prolactinoma using bioinformatics analysis. Transcriptome microarray dataset GSE119063 was downloaded from Gene Expression Omnibus (GEO) database. Limma package in R software was used to screen DEGs. Pathway and Gene ontology (GO) enrichment analysis were conducted to identify the biological role of DEGs. A protein-protein interaction (PPI) network was constructed and analyzed by using HIPPIE database and Cytoscape software. Module analyses was performed. In addition, a target gene-miRNA regulatory network and target gene-TF regulatory network were constructed by using NetworkAnalyst and Cytoscape software. Finally, validation of hub genes by receiver operating characteristic (ROC) curve analysis. A total of 989 DEGs were identified, including 461 up regulated genes and 528 down regulated genes. Pathway enrichment analysis showed that the DEGs were significantly enriched in the retinoate biosynthesis II, signaling pathways regulating pluripotency of stem cells, ALK2 signaling events, vitamin D3 biosynthesis, cell cycle and aurora B signaling. Gene Ontology (GO) enrichment analysis showed that the DEGs were significantly enriched in the sensory organ morphogenesis, extracellular matrix, hormone activity, nuclear division, condensed chromosome and microtubule binding. In the PPI network and modules, SOX2, PRSS45, CLTC, PLK1, B4GALT6, RUNX1 and GTSE1 were considered as hub genes. In the target gene-miRNA regulatory network and target gene-TF regulatory network, LINC00598, SOX4, IRX1 and UNC13A were considered as hub genes. Using integrated bioinformatics analysis, we identified candidate genes in pituitary prolactinoma, which might improve our understanding of the molecular mechanisms of pituitary prolactinoma.


    Abbreviations

    BioGRID

    Biological General Repository for Interaction Datasets

    BP

    biological process

    CC

    cellular component

    DEGs

    Differentially Expressed Genes

    GEO

    Gene Expression Omnibus

    GO

    Gene ontology

    HIPPIE

    Human Integrated Protein-Protein Interaction rEference

    KEGG

    Kyoto Encyclopedia of Genes and Genomes

    MF

    molecular function

    SMPDB

    Small Molecule Pathway Database

    加载中

    Acknowledgments



    I thank Ni Li, Institute of Health Sciences, Shanghai Institute for Biological Sciences, Chinese Academy of Sciences, China, very much, the author who deposited their microarray dataset, GSE119063, into the public GEO database.

    Ethical approval



    This article does not contain any studies with human participants or animals performed by any of the authors.

    Author contributions



    Vikrant Ghatnatti: Methodology and validation; Basavaraj Vastrad: Writing original draft, investigation, and review and editing; Swetha Patil: Formal analysis and validation; Chanabasayya Vastrad: Software and investigation; Iranna Kotturshetti: Supervision and resources.

    Availability of data and materials



    The datasets supporting the conclusions of this article are available in the GEO (Gene Expression Omnibus) (https://www.ncbi.nlm.nih.gov/geo/) repository. [(GSE119063) (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE119063)].

    Conflict of interest



    The authors declare that they have no conflict of interest.

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