Open Access

Identification of microRNAs associated with the aggressiveness of prolactin pituitary tumors using bioinformatic analysis

  • Authors:
    • Zihao Wang
    • Lu Gao
    • Xiaopeng Guo
    • Chenzhe Feng
    • Kan Deng
    • Wei Lian
    • Bing Xing
  • View Affiliations

  • Published online on: May 28, 2019     https://doi.org/10.3892/or.2019.7173
  • Pages: 533-548
  • Copyright: © Wang et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

Aggressive prolactin pituitary tumors, which exhibit aggressive behaviors and resistance to conventional treatments, are a huge challenge for neurosurgeons. Many studies have investigated the roles of microRNAs (miRNAs) in pituitary tumorigenesis, invasion and metastasis, but few have explored aggressiveness‑associated miRNAs in aggressive pituitary tumors. Differentially expressed miRNAs (DEMs) between aggressive and nonaggressive prolactin pituitary tumors were screened using the GSE46294 miRNA expression profile downloaded from the GEO database. The potential target genes of the top three most highly upregulated and downregulated DEMs were predicted by miRTarBase, and potential functional annotation and pathway enrichment analysis were performed using the DAVID database. Protein‑protein interaction (PPI) and miRNA‑hub gene interaction networks were constructed by Cytoscape software. A total of 43 DEMs were identified, including 19 upregulated and 24 downregulated miRNAs, between aggressive and nonaggressive prolactin pituitary tumors. One hundred and seventy and 680 target genes were predicted for the top three most highly upregulated and downregulated miRNAs, respectively, and these genes were involved in functional enrichment pathways, such as regulation of transcription from RNA polymerase II promoter, DNA‑templated transcription, Wnt signaling pathway, protein binding, and transcription factor activity (sequence‑specific DNA binding). In the PPI network, the top 10 genes with the highest degree of connectivity of the upregulated and downregulated DEMs were selected as hub genes. By constructing an miRNA‑hub gene network, it was found that most hub genes were potentially modulated by hsa‑miR‑489 and hsa‑miR‑520b. Targeting hsa‑miR‑489 and hsa‑miR‑520b may provide new clues for the diagnosis and treatment of aggressive prolactin pituitary tumors.

Introduction

Pituitary tumors represent approximately 10–15% of intracranial tumors, of which prolactin-secreting pituitary adenomas (prolactinoma) are the most common subtypes, accounting for 30–40% of pituitary tumors (1,2). Most of these tumors are noninvasive, show slow growth and are easily treated by surgery or medical treatment, including cabergoline and dopamine agonists. However, a small subset, accounting for 2.5–10% of pituitary adenomas, are defined as aggressive pituitary tumors and can exhibit aggressive behaviors, resistance to conventional treatments and/or temozolomide (TMZ), and multiple recurrences despite standard therapies combining surgical, medical and radiotherapy treatment approaches (3,4). Early identification of aggressive pituitary tumors is challenging but is of major clinical importance as these tumors are associated with increased morbidity and mortality (5). Numerous studies have been performed to explore potential predictive and prognostic biomarkers for the molecular pathogenesis underlying the aggressive behavior and malignant transformation of pituitary tumors, yet research results remain fairly unreliable and controversial (4,6,7).

MicroRNAs (miRNAs/miRs) are a large family of short endogenous noncoding RNAs, approximately 21–25 nucleotides in length, that can directly bind to the 3′-untranslated region of messenger RNA (mRNA), thereby leading to suppression of protein translation or mRNA degradation (8,9). Subsequently, miRNAs can negatively regulate the expression of target genes involved in proliferation, apoptosis, cell cycle differentiation, invasion and metabolism (9). Aberrant expression of miRNAs contributes to tumorigenesis, invasion and metastasis by derepressing or silencing key regulatory proteins in various types of tumors, including pituitary adenomas (1012). Many studies have investigated the roles of miRNAs in pituitary tumorigenesis, dysfunction, neurodegeneration and metastasis by comparing tumoral to normal pituitary tissues (1316). However, currently, there are few studies that have explored aggressiveness-associated miRNAs in ‘aggressive’ pituitary tumors, especially aggressive prolactinoma, one of the most common subtypes of pituitary adenomas, based on large-scale human tissue datasets.

In recent years, microarray technology and bioinformatic analysis have been widely used to help us discover novel clues to identify reliable and functional miRNAs. In the present study, differentially expressed miRNAs (DEMs, DE-miRNAs) between aggressive and nonaggressive prolactin pituitary tumors were screened using the GSE46294 miRNA expression profile (17). The potential target genes of the top three most highly upregulated and downregulated DE-miRNAs were predicted by miRTarBase. Subsequently, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment and protein-protein interaction (PPI) network analyses were performed to help us understand the molecular mechanisms underlying the aggressiveness of pituitary tumors. Finally, 20 hub genes were identified, and an miRNA-hub gene network was constructed by Cytoscape software. In conclusion, our study aimed to explore the aggressiveness-associated miRNAs in aggressive prolactin pituitary tumors and their potential molecular mechanisms based on bioinformatic analysis and to provide candidate biomarkers for early diagnosis and individualized treatment of aggressive prolactin pituitary tumors.

Materials and methods

Microarray data

The Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih.gov/geo/) is a public functional genomics data repository of high-throughput gene expression data, chips and microarrays (18). After extensive data screening in the GEO database, only the GSE46294 dataset was selected as it compared the miRNA expression of aggressive and nonaggressive prolactin pituitary tumors (17). GSE46294, based on the GPL13264 platform (Agilent-021827 Human miRNA Microarray), contained four aggressive prolactin pituitary tumor samples and eight nonaggressive prolactin pituitary tumor samples.

Data processing

GEO2R (http://www.ncbi.nlm.nih.gov/geo/geo2r/) is an interactive web tool that can compare different groups of samples from the GEO series to identify DEMs across experimental conditions (19). The DEMs between aggressive and nonaggressive prolactin pituitary tumor samples were screened using GEO2R. Adjusted P-values (adj. P) were applied to correct the false-positive results by using the default Benjamini-Hochberg false discovery rate method. Adj. P<0.01 and |fold change (FC)| >2 were considered the cut-off values for identifying DEMs. A DEM hierarchical clustering heat map was constructed using MeV (Multiple Experiment Viewer, http://mev.tm4.org/), which is a cloud-based application supporting the analysis, visualization, and stratification of large genomic data, particularly RNASeq and microarray data. The potential target genes of the top three most highly upregulated and downregulated DE-miRNAs were predicted by miRTarBase (http://mirtarbase.mbc.nctu.edu.tw/php/index.php/), which is a database for experimentally validated miRNA-target interactions (20).

Functional and pathway enrichment analyses

The Database for Annotation, Visualization and Integrated Discovery (DAVID, http://david.ncifcrf.gov/) is an online tool for gene functional classification, which is an essential foundation for high-throughput gene analysis to understand the biological significance of genes (21). DAVID was introduced to perform functional annotation and pathway enrichment analysis, including GO (Gene Ontology) enrichment and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway analysis, for the predicted target genes of 6 selected DEMs (22,23). A P-value <0.05 was considered statistically significant.

PPI network construction and module analysis

The target genes obtained from the upregulated and downregulated DEMs were first mapped to the STRING database (http://string-db.org) to assess functional associations among these target genes, with a combined score >0.4 defined as significant (24). Then, PPI networks were constructed using Cytoscape, which is a biological graph visualization software for integrated models of biologic molecular interaction networks (25). The Molecular Complex Detection (MCODE) plugin of Cytoscape was used to identify the most significant module in the PPI networks (26). The criteria for selection were as follows: Degree cut-off=2, node score cut-off=0.2, maximum depth=100 and k-core=2. Moreover, GO and KEGG enrichment analyses were performed using DAVID for genes in the modules.

Hub gene analysis and miRNA-hub gene network construction

Hub genes were selected by considering the high degree of connectivity in the PPI networks analyzed by the cytohubba plugin of Cytoscape. The top 10 genes with the highest degree of connectivity were selected as the hub genes of the upregulated and downregulated DEMs, respectively. Subsequently, GO and KEGG enrichment analyses were performed for the selected 20 hub genes. The biological process analysis of hub genes was performed and visualized using the Biological Networks Gene Oncology tool (BiNGO) plugin of Cytoscape (27). The latest information of functional roles of hub genes was downloaded from GeneCards in Nov. 2018 (https://www.genecards.org/). Subsequently, an miRNA-hub gene network was constructed by Cytoscape.

Results

Identification of DEMs and their target genes

Following analysis of the GSE46294 dataset using GEO2R, a total of 43 DEMs were identified, including 19 upregulated and 24 downregulated miRNAs between aggressive and nonaggressive prolactin pituitary tumors. For better visualization, the top 10 most highly upregulated miRNAs and the top 10 most highly downregulated miRNAs are presented in Table I, and the hierarchical clustering heat map of the DEMs is presented in Fig. S1. According to their FC values, hsa-miR-489, hsa-let-7d* and hsa-miR-138-1* were the top 3 most highly upregulated miRNAs, and hsa-miR-520b, hsa-miR-875-5p and hsa-miR-671-3p were the top 3 most highly downregulated miRNAs (Table I). One hundred seventy potential target genes were predicted for the top 3 most highly upregulated miRNAs and 680 potential target genes were predicted for the top 3 most highly downregulated miRNAs by miRTarBase.

Table I.

Top 10 upregulated and downregulated DEMs between aggressive and nonaggressive prolactin pituitary tumors.

Table I.

Top 10 upregulated and downregulated DEMs between aggressive and nonaggressive prolactin pituitary tumors.

miRNAs (DEMs)P-valuetBlogFC
Upregulated
  hsa-miR-4890.006773.25−4.587.07
  hsa-let-7d*0.025912.53−4.586.09
  hsa-miR-138-1*0.025692.54−4.585.26
  hsa-miR-886-3p0.001913.94−4.584.36
  hsa-miR-576-5p0.047732.2−4.593.83
  hsa-miR-135b0.016712.77−4.583.72
  hsa-miR-1370.038772.32−4.593.29
  hsa-miR-886-3p0.002353.82−4.583.2
  hsa-miR-551b0.020742.66−4.583.04
  hsa-miR-296-3p0.045242.23−4.593.02
Downregulated
  hsa-miR-520b0.00732−3.21−4.58−6.36
  hsa-miR-875-5p0.04037−2.29−4.59−5.66
  hsa-miR-671-3p0.01453−2.85−4.58−5.49
  hsa-miR-3720.00348−3.61−4.58−5.49
  hsa-miR-5860.02631−2.53−4.58−5.44
  hsa-miR-367*0.02421−2.57−4.58−4.84
  hsa-miR-302b0.01052−3.02−4.58−4.49
  hsa-miR-1870.0322−2.42−4.59−4.35
  hsa-miR-193b*0.02207−2.62−4.58−4.31
  hsa-miR-452*0.00322−3.65−4.58−4.17

[i] miRNA names with ‘*’ are also mature miRNAs as annotated in miRBase (http://www.mirbase.org). For example, hsa-let-7d* is hsa-let-7d-3p; hsa-miR-138-1* is hsa-miR-138-1-3p; hsa-miR-367* is hsa-miR-367-5p; hsa-miR-193b* is hsa-miR-193b-5p; hsa-miR-452* is hsa-miR-452-3p. DEMs, differentially expressed miRNAs; hsa, Homo sapiens.

Functional and pathway enrichment analyses

GO analysis, including biological process (BP), cellular component (CC) and molecular function (MF), was performed on the potential target genes of top 3 most highly upregulated miRNAs (Table II) and the top 3 most highly downregulated miRNAs (Table III). GO functional annotation analysis showed that in the BP category, the target genes of the top 3 most highly upregulated miRNAs were significantly enriched in DNA-templated transcription, signal transduction, and positive regulation of transcription from RNA polymerase II promoter (Fig. 1A), while the target genes of the top 3 most highly downregulated miRNAs were enriched in DNA-templated transcription, DNA-templated regulation of transcription, and regulation of transcription from RNA polymerase II promoter (Fig. 1B). In the CC category, the target genes of the top three most highly upregulated miRNAs were significantly enriched in cytoplasm, nucleus and cytosol (Fig. 2A), while the target genes of the top three most highly downregulated miRNAs were enriched in nucleus, nucleoplasm and cytosol (Fig. 2B). In the MF category, the target genes of the top 3 most highly upregulated miRNAs were significantly enriched in protein binding, transcription factor activity, sequence-specific DNA binding, transcriptional activator activity, and RNA polymerase II core promoter proximal region sequence-specific binding (Fig. 3A), while the target genes of the top 3 most highly downregulated miRNAs were enriched in protein binding, DNA binding and transcription factor activity, and sequence-specific DNA binding (Fig. 3B). In addition, KEGG pathway analysis revealed that the target genes of the top 3 most highly upregulated miRNAs were mainly enriched in the Wnt signaling pathway, cGMP-PKG signaling pathway and renal cell carcinoma (Fig. 4A), while the target genes of the top three most highly downregulated miRNAs were mainly enriched in pathways in cancer, proteoglycans in cancer, measles and influenza A (Fig. 4B) (Tables II and III).

Table II.

Functional and pathway enrichment analysis for target genes of the top 3 upregulated miRNAs.

Table II.

Functional and pathway enrichment analysis for target genes of the top 3 upregulated miRNAs.

CategoryTermPathway descriptionCountP-value
Upregulated miRNAs
GO BPGO:0060412Ventricular septum morphogenesis30.020464503
GO BPGO:0007286Spermatid development40.021020749
GO BPGO:0000122Negative regulation of transcription from RNA polymerase II promoter120.021742388
GO BPGO:0006351Transcription, DNA-templated240.022393279
GO BPGO:0030154Cell differentiation90.025194909
GO BPGO:0097411Hypoxia-inducible factor-1α signaling pathway20.030146509
GO BPGO:0030177Positive regulation of Wnt signaling pathway30.030678983
GO BPGO:0007165Signal transduction160.030948235
GO BPGO:0030336Negative regulation of cell migration40.036066871
GO BPGO:0045944Positive regulation of transcription from RNA polymerase II promoter140.03646379
GO CCGO:0005737Cytoplasm520.0134897
GO CCGO:0031519PcG protein complex30.016939042
GO CCGO:0005634Nucleus520.026624876
GO CCGO:0005794Golgi apparatus130.026655792
GO CCGO:0005654Nucleoplasm290.053523267
GO CCGO:0031526Brush border membrane30.054869988
GO CCGO:0000139Golgi membrane90.072820488
GO CCGO:0044798Nuclear transcription factor complex20.078554642
GO CCGO:0005829Cytosol320.094144731
GO MFGO:0005515Protein binding830.007060503
GO MFGO:0050693LBD domain binding20.030452531
GO MFGO:0003700Transcription factor activity, sequence-specific DNA binding140.034027538
GO MFGO:0001077Transcriptional activator activity, RNA polymerase II core sequence-specific binding60.035934263
GO MFGO:0030620U2 snRNA binding20.045331887
GO MFGO:0008517Folic acid transporter activity20.052686367
GO MFGO:0001078Transcriptional repressor activity, RNA polymerase II core promoter proximal region sequence-specific binding40.054345955
GO MFGO:0004726Non-membrane spanning protein tyrosine phosphatase activity20.059984623
GO MFGO:0003714Transcription corepressor activity50.071931973
GO MFGO:0004871Signal transducer activity50.07295342
KEGGhsa04310Wnt signaling pathway50.006641183
KEGGhsa04022cGMP-PKG signaling pathway50.01255563
KEGGhsa05211Renal cell carcinoma30.049309583

[i] In the event there were more than five terms enriched in this category, the top 5 terms were selected per P-value. GO, Gene Ontology; BP, biological process; CC, cellular component; MF, molecular function; KEGG, Kyoto Encyclopedia of Genes and Genomes; Count, numbers of enriched genes in each term; hsa, Homo sapiens.

Table III.

Functional and pathway enrichment analysis for target genes of the top 3 downregulated miRNAs.

Table III.

Functional and pathway enrichment analysis for target genes of the top 3 downregulated miRNAs.

CategoryTermDescriptionCountP-value
Downregulated miRNAs
  GO BPGO:0046777Protein autophosphorylation150.000170888
  GO BPGO:0006355Regulation of transcription, DNA-templated590.001639464
  GO BPGO:0006357Regulation of transcription from RNA polymerase II promoter230.002882721
  GO BPGO:0016567Protein ubiquitination200.002898481
  GO BPGO:0006351Transcription, DNA-templated710.003602574
  GO BPGO:0006123Mitochondrial electron transport, cytochrome c to oxygen40.014446477
  GO BPGO:0042119Neutrophil activation30.017126856
  GO BPGO:0007223Wnt signaling pathway, calcium modulating pathway50.018278676
  GO BPGO:0008654Phospholipid biosynthetic process50.019902891
  GO BPGO:0048468Cell development50.019902891
  GO CCGO:0005654Nucleoplasm1126.68468E-07
  GO CCGO:0005634Nucleus1700.001202665
  GO CCGO:0017053Transcriptional repressor complex70.002719148
  GO CCGO:0005758Mitochondrial intermembrane space80.002811554
  GO CCGO:0005813Centrosome220.003323275
  GO CCGO:0005739Mitochondrion500.006368195
  GO CCGO:0031463Cul3-RING ubiquitin ligase complex70.007248149
  GO CCGO:0005829Cytosol1060.009417134
  GO CCGO:0015629Actin cytoskeleton130.010602362
  GO CCGO:0005741Mitochondrial outer membrane100.014535489
  GO MFGO:0005515Protein binding2699.14069E-05
  GO MFGO:0003677DNA binding620.004456722
  GO MFGO:0004842Ubiquitin-protein transferase activity180.005935513
  GO MFGO:0003700Transcription factor activity, sequence-specific DNA binding390.006954496
  GO MFGO:0004672Protein kinase activity180.013435998
  GO MFGO:0004879RNA polymerase II transcription factor activity, ligand-activated sequence-specific DNA binding50.013877869
  GO MFGO:0003707Steroid hormone receptor activity60.015112205
  GO MFGO:0043565Sequence-specific DNA binding230.017535243
  GO MFGO:0031625Ubiquitin protein ligase binding150.018757483
  GO MFGO:0004674Protein serine/threonine kinase activity180.020173241
KEGGhsa05162Measles100.005987506
KEGGhsa05215Prostate cancer80.006312095
KEGGhsa05200Pathways in cancer190.009215433
KEGGhsa05205Proteoglycans in cancer120.011467731
KEGGhsa05219Bladder cancer50.018691786
KEGGhsa04962 Vasopressin-regulated water reabsorption50.023655555
KEGGhsa04919Thyroid hormone signaling pathway80.023895596
KEGGhsa05164Iinfluenza A100.030429321
KEGGhsa05218Melanoma60.032052212
KEGGhsa04390Hippo signaling pathway90.035379929

[i] If there were more than five terms enriched in this category, the top 5 terms were selected per the P-value. GO, Gene Ontology; BP, biological process; CC, cellular component; MF, molecular function; KEGG, Kyoto Encyclopedia of Genes and Genomes; Count, numbers of enriched genes in each term; hsa, Homo sapiens.

PPI network construction and module analysis

The PPI networks of the target genes of the top 3 most highly upregulated and downregulated DEMs were constructed (Fig. 5), and the most significant module was obtained using the MCODE plugin of Cytoscape. The genes in the most significant module of the upregulated DEMs were SF1, SNRPD3 and SNRPA1, while the genes in the most significant module of the downregulated DEMs were RNF34, RNF19B, ASB16, FBXL7, UBE2V2, RBBP6, KBTBD6, WSB1, KLHL21, CUL3, TCEB1, UBOX5 and RNF115. Functional analyses of the genes involved in the module of the downregulated DEMs were performed using DAVID, showing that genes in this module were mainly enriched in protein K48-linked ubiquitination (BP), polar microtubule (CC), ubiquitin-protein transferase activity (MF), and ubiquitin-mediated proteolysis(KEGG).

Hub gene analysis and miRNA-hub gene network construction

For the upregulated miRNAs, the hub genes included RHOB, PTPN11, SNAI2, UBE2D1, SF1, PDPN, NUP43, YY1, HIF1A and SNRPD3. For the downregulated miRNAs, the hub genes were EGFR, CTNNB1, ESR1, CDKN1A, CCND1, CYCS, DNAJC10, IL8, CUL3 and IGF1R. The abbreviations, full names and functions of these 20 hub genes are shown in Table IV. Among these genes, EGFR (epidermal growth factor receptor) demonstrated the highest node degrees, which suggested that EGFR may be a key target associated with prolactin pituitary tumor aggressiveness. Biological process analysis of the hub genes is shown in Fig. 6A. Functional and pathway enrichment analyses for the hub genes of the top 3 upregulated and downregulated miRNAs are presented in Tables V and VI. As shown in Fig. 6, KEGG analysis showed that the hub genes of the upregulated miRNAs were mainly enriched in renal cell carcinoma and proteoglycans in cancer (Fig. 6B, Table V), while the hub genes of the downregulated miRNAs were mainly enriched in proteoglycans in cancer, prostate cancer and pathways in cancer (Fig. 6C, Table VI).

Table IV.

Functional roles of the hub genes of the top 3 upregulated/downregulated miRNAs identified in the PPI interaction.

Table IV.

Functional roles of the hub genes of the top 3 upregulated/downregulated miRNAs identified in the PPI interaction.

Gene symbolDegreeFull nameFunction
Upregulated miRNAs
  RHOB16Ras homolog family member BProtein coding gene. Among its related pathways are ERK signaling and focal adhesion. GO annotations related to this gene include GTP binding and GDP binding.
  PTPN1115Protein tyrosine phosphatase, non-receptor type 11Protein coding gene. Among its related pathways are immune response Fcε RI pathway and EGF/EGFR signaling pathway. GO annotations related to this gene include protein domain-specific binding and protein tyrosine phosphatase activity.
  SNAI215Snail family transcriptional repressor 2Protein coding gene. Among its related pathways are ERK signaling and adherens junction. GO annotations related to this gene include sequence-specific DNA binding and tran scriptional repressor activity, RNA polymerase II proximal promoter sequence-specific DNA binding.
  UBE2D114Ubiquitin conjugating enzyme E2 D1Protein coding gene. Among its related pathways are gene expression and cell cycle, mitotic. GO annotations related to this gene include ligase activity and acid-amino acid ligase activity.
  SF114Splicing factor 1Protein Coding gene. Among its related pathways are Oct4 in mammalian ESC pluripotency and mRNA splicing-major pathway. GO annotations related to this gene include nucleic acid binding and RNA binding.
  PDPN14PodoplaninProtein coding gene. Among its related pathways are cytoskel etal signaling and response to elevated platelet cytosolic Ca2+. GO annotations related to this gene include amino acid trans membrane transporter activity and folic acid transmembrane transporter activity.
  NUP4313Nucleoporin 43Protein coding gene. Among its related pathways are cell cycle, mitotic and transport of the SLBP independent mature mRNA.
  YY113YY1 transcription factorProtein coding gene. Among its related pathways are gene expression and translational control. GO annotations related to this gene include DNA binding transcription factor activity and transcription coactivator activity.
  HIF1A11Hypoxia inducible factor 1 subunit αProtein coding gene. Among its related pathways are ERK signaling and central carbon metabolism in cancer. GO anno tations related to this gene include DNA binding transcription factor activity and protein heterodimerization activity.
SNRPD311Small nuclear ribonu cleoprotein D3 polypeptideProtein coding gene. Among its related pathways are mRNA splicing-major pathway and processing of capped intronless pre-mRNA. GO annotations related to this gene include histone pre-mRNA DCP binding.
Downregulated miRNAs
  EGFR33Epidermal growth factor receptorProtein coding gene. Among its related pathways are ERK signaling and gene expression. GO annotations related to this gene include identical protein binding and protein kinase activity.
  CTNNB131Catenin β1Protein coding gene. Among its related pathways are ERK signaling and focal adhesion. GO annotations related to this gene include DNA binding transcription factor activity and binding.
  ESR125Estrogen receptor 1Estrogen resistance and myocardial infarction. Among its related pathways are gene expression and integrated breast cancer pathway. GO annotations related to this gene include DNA binding transcription factor activity and identical protein binding.
  CDKN1A25Cyclin dependent kinase inhibitor 1AProtein coding gene. Among its related pathways are gene expression and Akt signaling. GO annotations related to this gene include ubiquitin protein ligase binding and cyclin binding.
  CCND124Cyclin D1Protein coding gene. Diseases associated with CCND1 include myeloma, multiple and Von Hippel-Lindau syndrome. Among its related pathways are ERK signaling and focal adhesion. GO annotations related to this gene include protein kinase activity and enzyme binding.
  CYCS23Cytochrome c, somaticProtein coding gene. Diseases associated with CYCS include thrombocytopenia 4 and autosomal thrombocytopenia with normal platelets. Among its related pathways are gene expression and activation of caspases through apoptosome-mediated cleavage. GO annotations related to this gene include iron ion binding and electron transfer activity.
  DNAJC1021DNAJ heat shock protein family (Hsp40) member C10Protein coding gene. Among its related pathways are protein processing in endoplasmic reticulum. GO annotations related to this gene include chaperone binding and protein disulfide oxidoreductase activity.
  IL821C-X-C motif chemokine ligand 8Protein coding gene. Among its related pathways are Akt signaling and rheumatoid arthritis. GO annotations related to this gene include chemokine activity and interleukin-8 receptor binding.
  CUL320Cullin 3Protein Coding gene. Among its related pathways are RET signaling and Class I MHC mediated antigen processing and presentation. GO annotations related to this gene include protein homodimerization activity and ubiquitin-protein trans ferase activity.
  IGF1R19Insulin like growth factor 1 receptorProtein coding gene. Among its related pathways are ERK signaling and mTOR pathway. GO annotations related to this gene include identical protein binding and protein kinase activity.

[i] PPI, protein-protein interaction; GO, Gene Ontology. Online database GeneCards (https://www.genecards.org).

Table V.

Functional and pathway enrichment analysis for the hub genes of the top 3 upregulated miRNAs.

Table V.

Functional and pathway enrichment analysis for the hub genes of the top 3 upregulated miRNAs.

CategoryTermPathway descriptionGenes
Upregulated miRNAs
  GO BPGO:0032364Oxygen homeostasisHIF1A
  GO BPGO:0032909Regulation of transforming growth factor β2 productionHIF1A
  GO BPGO:0033483Gas homeostasisHIF1A
  GO BPGO:0032642Regulation of chemokine productionSNAI2, HIF1A
  GO BPGO:0046885Regulation of hormone biosynthetic processHIF1A
  GO BPGO:0043619Regulation of transcription from RNA polymerase II promoter in response to oxidative stressHIF1A
  GO BPGO:0070099Regulation of chemokine-mediated signaling pathwayHIF1A
  GO BPGO:0032352Positive regulation of hormone metabolic processHIF1A
  GO BPGO:0010839Negative regulation of keratinocyte proliferationSNAI2
  GO BPGO:0071364Cellular response to epidermal growth factor stimulusSNAI2, PTPN11
  GO CCGO:0031528Microvillus membranePDPN
  GO CCGO:0000243Commitment complexSNRPD3
  GO CCGO:0005683U7 snRNPSNRPD3
  GO CCGO:0005687U4 snRNPSNRPD3
  GO CCGO:0034709MethylosomeSNRPD3
  GO CCGO:0031527Filopodium membranePDPN
  GO CCGO:0071437InvadopodiumPDPN
  GO CCGO:0031011Ino80 complexYY1
  GO CCGO:0005685U1 snRNPSNRPD3
  GO CCGO:0031258Lamellipodium membranePDPN
  GO MFGO:0000400Four-way junction DNA bindingYY1
  GO MFGO:0001227Transcriptional repressor activity, RNA polymerase II transcription regulatory region sequence-specific bindingYY1, SNAI2
  GO MFGO:0019956Chemokine bindingPDPN
  GO MFGO:0043565Sequence-specific DNA bindingYY1, SNAI2, HIF1A
  GO MFGO:0061631Ubiquitin conjugating enzyme activityUBE2D1
  GO MFGO:0000217DNA secondary structure bindingYY1
  GO MFGO:0061650Ubiquitin-like protein conjugating enzyme activityUBE2D1
  GO MFGO:0005158Insulin receptor bindingPTPN11
  GO MFGO:0035326Enhancer bindingYY1
  GO MFGO:0001078Transcriptional repressor activity, RNA polymerase II core promoter proximal region sequence-specific bindingYY1, SNAI2
KEGGhsa05211Renal cell carcinomaPTPN11, HIF1A
KEGGhsa05205Proteoglycans in cancerPTPN11, HIF1A
KEGGhsa04150mTOR signaling pathwayHIF1A
KEGGhsa05120Epithelial cell signaling in Helicobacter pylori infectionPTPN11
KEGGhsa05230Central carbon metabolism in cancerHIF1A
KEGGhsa05220Chronic myeloid leukemiaPTPN11
KEGGhsa04920Adipocytokine signaling pathwayPTPN11
KEGGhsa04520Adherens junctionSNAI2
KEGGhsa05231Choline metabolism in cancerHIF1A
KEGGhsa04066HIF-1 signaling pathwayHIF1A

[i] GO, Gene Ontology; BP, biological process; CC, cellular component; MF, molecular function; KEGG, Kyoto Encyclopedia of Genes and Genomes; hsa, Homo sapiens.

Table VI.

Functional and pathway enrichment analysis for the hub genes of top 3 downregulated miRNAs.

Table VI.

Functional and pathway enrichment analysis for the hub genes of top 3 downregulated miRNAs.

CategoryTermPathway descriptionGenes
Downregulated miRNAs
  GO BPGO:0070141Response to UV-ACCND1, EGFR
  GO BPGO:0097193Intrinsic apoptotic signaling pathwayCDKN1A, CUL3, DNAJC10, CYCS
  GO BPGO:0032355Response to estradiolCTNNB1, ESR1, EGFR
  GO BPGO:1903798Regulation of production of miRNAs involved in gene silencing by miRNAESR1, EGFR
  GO BPGO:0033674Positive regulation of kinase activityCDKN1A, EGFR, IGF1R
  GO BPGO:0001934Positive regulation of protein phosphorylationCDKN1A, CCND1, EGFR, IGF1R
  GO BPGO:0045737Positive regulation of cyclin-dependent protein serine/threonine kinase activityCCND1, EGFR
  GO BPGO:0045740Positive regulation of DNA replicationEGFR, IGF1R
  GO BPGO:0006367Transcription initiation from RNA polymerase II promoterCDKN1A, CCND1, ESR1
  GO BPGO:0034333Adherens junction assemblyCTNNB1
  GO CCGO:0030128Clathrin coat of endocytic vesicleEGFR
  GO CCGO:0030122AP-2 adaptor complexEGFR
  GO CCGO:0030131Clathrin adaptor complexEGFR
  GO CCGO:1990907β-catenin-TCF complexCTNNB1
  GO CCGO:0005719Nuclear euchromatinCTNNB1
  GO CCGO:0000791EuchromatinCTNNB1
  GO CCGO:0035327Transcriptionally active chromatinESR1
  GO CCGO:0000790Nuclear chromatinCTNNB1, ESR1
  GO CCGO:0005758Mitochondrial intermembrane spaceCYCS
  GO CCGO:0016342Catenin complexCTNNB1
  GO MFGO:0097472Cyclin-dependent protein kinase activityCDKN1A, CCND1
  GO MFGO:0019900Kinase bindingCDKN1A, CCND1, CTNNB1, ESR1
  GO MFGO:0004693Cyclin-dependent protein serine/threonine kinase activityCDKN1A, CCND1
  GO MFGO:0004709MAP kinase kinase kinase activityEGFR, IGF1R
  GO MFGO:0001223Transcription coactivator bindingESR1
  GO MFGO:0044389Ubiquitin-like protein ligase bindingCDKN1A, CUL3, EGFR
  GO MFGO:0019901Protein kinase bindingCDKN1A, CCND1, ESR1, EGFR, IGF1R
  GO MFGO:0030331Estrogen receptor bindingCTNNB1, ESR1
  GO MFGO:0016671Oxidoreductase activity, acting on a sulfur group of donors, disulfide as acceptorDNAJC10
  GO MFGO:0046934 Phosphatidylinositol-4,5-bisphosphate 3-kinase activityESR1, EGFR
  KEGGhsa05205Proteoglycans in cancerCDKN1A, CCND1, ESR1, CTNNB1, EGFR, IGF1R
  KEGGhsa05215Prostate cancerCDKN1A, CCND1, CTNNB1, EGFR, IGF1R
  KEGGhsa05200Pathways in cancerCDKN1A, CCND1, CTNNB1, CYCS, EGFR, IGF1R
  KEGGhsa05214GliomaCDKN1A, CCND1, EGFR, IGF1R
  KEGGhsa05218MelanomaCDKN1A, CCND1, EGFR, IGF1R
  KEGGhsa04068FoxO signaling pathwayCDKN1A, CCND1, EGFR, IGF1R
  KEGGhsa04510Focal adhesionCCND1, CTNNB1, EGFR, IGF1R
  KEGGhsa05213Endometrial cancerCCND1, CTNNB1, EGFR
  KEGGhsa05219Bladder cancerCDKN1A, CCND1, EGFR
  KEGGhsa05210Colorectal cancerCCND1, CYCS, CTNNB1

[i] GO, Gene Ontology; BP, biological process; CC, cellular component; MF, molecular function; KEGG, Kyoto Encyclopedia of Genes and Genomes; hsa, Homo sapiens.

Subsequently, miRNA-hub gene networks were constructed by Cytoscape (Fig. 7). As shown in Fig. 7A, hsa-miR-489, the most highly upregulated DEM, potentially could target 9 (RHOB, PTPN11, SNAI2, UBE2D1, SF1, PDPN, NUP43, YY1 and HIF1A) of 10 hub genes. Five hub genes and 2 hub genes potentially were regulated by upregulated hsa-miR-138-1-3p and hsa-let-7d*, respectively. Additionally, according to Fig. 7B, hsa-miR-520b, the most highly downregulated DEM, potentially could also target 9 (EGFR, ESR1, CDKN1A, CCND1, CYCS, DNAJC10, IL8, CUL3 and IGF1R) of 10 hub genes. Three hub genes and 1 hub gene potentially were regulated by downregulated hsa-miR-875-5p and hsa-miR-671-3p, respectively. The results suggested that hsa-miR-489 and hsa-miR-520b may be the most important regulators of prolactin pituitary tumor aggressiveness.

Discussion

Prolactin-secreting pituitary adenoma is the most common (30–40%) subtype of pituitary tumors and commonly presents with headache, visual disturbances, amenorrhea, galactorrhea, infertility and hyposexuality (1,2). Most prolactinomas are noninvasive and easily treated by surgery, radiotherapy or medical treatment, including cabergoline and dopamine agonists, which are highly effective drugs for prolactinoma. However, aggressive prolactin pituitary tumors, with unknown incidence, are entities whose pathological behaviors lie between those of benign pituitary adenomas and malignant pituitary carcinomas. They display a rather distinct aggressive behavior with marked invasion of nearby anatomical structures, a tendency for resistance to conventional treatments and/or TMZ, and early postoperative recurrences (3,4). Extensive research has been performed to explore potential biomarkers for early diagnosis and treatment of aggressive pituitary tumors. The Raf/MEK/ERK, PI3K/Akt/mTOR, and VEGFR pathways were found to be upregulated in pituitary tumors, suggesting that these pathways may be utilized to control pituitary tumor growth and progression (2832). However, most targeted therapies based on the above pathways have been administered to patients with aggressive pituitary tumors without success (3234). Therefore, further research is needed to discover aggressiveness-associated biomarkers with diagnostic and therapeutic value for aggressive prolactin pituitary tumors.

miRNAs are a group of small, endogenous noncoding RNAs that can repress protein expression by cleaving mRNA or inhibiting translation (8,9). Mostly, miRNAs are recognized as having a significant role in the negative regulation of target gene expression, which contributes to tumorigenesis, invasion and metastasis in various types of tumors (1012). Recent studies have shown that aberrant miRNA expression is involved in tumorigenesis and tumor development of pituitary adenomas, especially prolactin pituitary tumors (1316). D'Angelo et al (35) found that miR-603, miR-34b, miR-548c-3p, miR-326, miR-570 and miR-432 were downregulated in prolactinomas, which can affect the G1-S transition process. Mussnich et al (36) found that miR-15, miR-26a, miR-196a-2, miR-16, Let-7a and miR-410 were downregulated in prolactinomas, which can negatively regulate pituitary cell proliferation. Roche et al (17) demonstrated that miR-183 was downregulated in aggressive prolactin tumors and inhibited tumor cell proliferation by directly targeting KIAA0101, which is involved in cell cycle activation and the inhibition of p53-p21-mediated cell cycle arrest. However, few studies, except for one reported by Roche et al (17) in 2015, have been performed to explore aggressiveness-associated miRNAs in aggressive prolactin pituitary tumors based on large-scale human tissue datasets. Additionally, based on the GSE46294 dataset, our study obtained different DEMs compared with those reported by Roche et al. The reasons may be due to different softwares or different algorithms when analyzing differentially expressed genes or RNAs, and due to the small sample size of the GSE46294 dataset (37).

In the present study, some aggressiveness-associated miRNAs were screened by performing a differential expression analysis based on an miRNA expression profile downloaded from the GEO database. The potential target genes of the top 3 most highly upregulated and most highly downregulated DEMs were collectively enriched for regulation of transcription from RNA polymerase II promoter, DNA-templated transcription, Wnt signaling pathway, protein binding, and transcription factor activity (sequence-specific DNA binding). Moreover, by constructing PPI networks, we identified the top 10 hub genes with the highest degree of connectivity with the top 3 most highly upregulated and downregulated DEMs. Hub genes of the upregulated DEMs were mainly enriched for proteoglycans in cancer, while hub genes of the downregulated DEMs were mainly enriched for proteoglycans in cancer, pathways in cancer, FoxO signaling pathway, and focal adhesion. Those pathways were all reported by previous studies to be associated with tumor growth, progression invasion and metastasis of various tumors (3840). In our study, proteoglycan in cancer is the enriched pathway shared by both upregulated and downregulated DEMs. However, there is little research on proteoglycan in tumorigenesis, invasiveness and progression of pituitary tumors. Matano et al reported that endocan, a novel soluble dermatan sulfate proteoglycan, can function as a new invasion and angiogenesis marker of pituitary adenomas (40). More studies are needed to further research the functions of proteoglycan in pituitary adenomas, especially aggressive tumors.

Among the 20 hub genes, EGFR demonstrated the highest node degrees, suggesting that EGFR was a key target associated with the aggressiveness of prolactin pituitary tumors, which is consistent with previous studies (4,41). EGFR encodes a transmembrane glycoprotein that is located on the cell surface and binds to epidermal growth factor (EGF). Binding of the protein to a ligand induces receptor dimerization and tyrosine autophosphorylation, leading to cell proliferation. EGFR involvement in the tumorigenesis and invasion of pituitary tumors, especially aggressive prolactinomas, has been reported by previous studies, and mutations in this gene can be utilized as potential targets in the treatment of aggressive prolactinomas. As reported in the literature, tyrosine kinase inhibitors (TKIs), such as lapatanib, sunitinib and erlotinib, have been trialed as first- or second-line treatments based on the VEGFR pathway, but most of them have failed (4,2932,34). In addition, in the present study, we found that EGFR may be negatively modulated by hsa-miR-520b using the miRTarBase database; furthermore, hsa-miR-520b can be regulated by EGFR due to its association with the biological process regulation of production of miRNAs involved in gene silencing by miRNA (3032). This interesting finding may allow the use of this potential pathway for the diagnosis or treatment of aggressive prolactinomas in the future.

Subsequently, by constructing an miRNA-hub gene network, we found that most hub genes were potentially modulated by hsa-miR-489 and hsa-miR-520b, suggesting that these miRNAs may be the most important regulators of prolactin pituitary tumor aggressiveness. Recent studies demonstrated that hsa-miR-489 acts as a tumor suppressor in hepatocellular carcinoma (42), gastric cancer (43), breast cancer (44), glioma (45), hypopharyngeal squamous cell carcinoma (46), bladder cancer (47) and colorectal cancer (48). Downregulation of miR-489 was reported to be associated with the tumorigenesis, invasion, and metastasis of various tumors, suggesting an important role for hsa-miR-489 in predicting prognosis and acting as a drug target. However, the roles of hsa-miR-489 in pituitary tumors, especially aggressive prolactinomas, have not been previously studied. Additionally, hsa-miR-520b was reported to have a suppressive effect on tumor cell proliferation, migration, invasion and epithelial-to-mesenchymal transition (EMT) in colorectal cancer (49), glioblastoma (50), hepatoma (51), head-neck cancer (52), breast cancer (53), lung cancer (54) and gastric cancer (55). Expression of hsa-miR-520b is lower in tumor tissues than in normal tissues, significantly promoting the proliferation, migration, and invasion of tumor cells. Unlike other tumors, Liang et al (56) reported that hsa-miR-520b was upregulated in nonfunctioning and gonadotropin-secreting pituitary adenomas relative to normal pituitaries, which indicated that miR-520b functions as a tumor inducer in benign pituitary adenoma (56). However, whether hsa-miR-520b acts as a promoter or suppressor in aggressive prolactin pituitary tumors has not been previously studied. According to our study, we speculate that upregulation of hsa-miR-489 suppresses aggressiveness and progression, while downregulation of hsa-miR-520b promotes the aggressiveness and progression of aggressive prolactinomas. Such ambivalent miRNA expression might be one of the reasons that aggressive prolactin pituitary tumors lie on the spectrum between ‘benign’ pituitary adenomas and ‘malignant’ pituitary carcinomas. It will be extremely meaningful to authenticate the functions of hsa-miR-489 and hsa-miR-520b and elucidate the mechanisms by which they regulate aggressive behaviors, resistance to treatments and early recurrence in aggressive prolactin pituitary tumors.

There are some limitations of the present study. First, the sample size of GSE46294 is rather small (only 12 samples), which may cause some bias when identifying the differentially expressed miRNAs. Second, the expression of the differentially expressed miRNAs was not validated by RT-qPCR analysis with our clinical pituitary samples. Further studies are needed to experimentally verify the results of this study.

In conclusion, we successfully identified one key target gene, EGFR, and two crucial miRNAs, hsa-miR-489 and hsa-miR-520b, associated with aggressiveness based on bioinformatic analysis. These findings may provide potential candidate biomarkers for the early diagnosis and individualized treatment of aggressive prolactin pituitary tumors. However, further research is needed to experimentally verify the results of this study.

Supplementary Material

Supporting Data

Acknowledgements

Not applicable.

Funding

Not applicable.

Availability of data and materials

The GSE46294 datasets analyzed during the present study are available in the GEO repository (http://www.ncbi.nlm.nih.gov/geo/). The potential target genes of DEMs were predicted by miRTarBase (http://mirtarbase.mbc.nctu.edu.tw/). The DAVID database (http://david.ncifcrf.gov/) was used to perform functional annotation and pathway enrichment analysis for genes. The STRING database (http://string-db.org) was used to assess functional associations among genes.

Authors' contributions

All authors conceived and designed the study. LG, XG and CF performed data curation and analysis. KD and WL analyzed and interpreted the results. ZW and BX drafted and reviewed the manuscript. All authors read and approved the final manuscript.

Ethics approval and consent to participate

Not applicable.

Patient consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Glossary

Abbreviations

Abbreviations:

miRNAs

microRNAs

DEMs

differentially expressed miRNAs

PPI

protein-protein interaction

TMZ

temozolomide

mRNA

messenger RNA

DE-miRNAs

differentially expressed miRNAs

GO

Gene Ontology

KEGG

Kyoto Encyclopedia of Genes and Genomes

GEO

Gene Expression Omnibus

DAVID

Database for Annotation, Visualization and Integrated Discovery

MCODE

Molecular Complex Detection

BiNGO

Biological Networks Gene Oncology tool

BP

biological process

CC

cellular component

MF

molecular function

EGFR

epidermal growth factor receptor

EGF

epidermal growth factor

TKI

tyrosine kinase inhibitor

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August 2019
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APA
Wang, Z., Gao, L., Guo, X., Feng, C., Deng, K., Lian, W., & Xing, B. (2019). Identification of microRNAs associated with the aggressiveness of prolactin pituitary tumors using bioinformatic analysis. Oncology Reports, 42, 533-548. https://doi.org/10.3892/or.2019.7173
MLA
Wang, Z., Gao, L., Guo, X., Feng, C., Deng, K., Lian, W., Xing, B."Identification of microRNAs associated with the aggressiveness of prolactin pituitary tumors using bioinformatic analysis". Oncology Reports 42.2 (2019): 533-548.
Chicago
Wang, Z., Gao, L., Guo, X., Feng, C., Deng, K., Lian, W., Xing, B."Identification of microRNAs associated with the aggressiveness of prolactin pituitary tumors using bioinformatic analysis". Oncology Reports 42, no. 2 (2019): 533-548. https://doi.org/10.3892/or.2019.7173