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pmid:31322209

Identification and interaction analysis of key miRNAs in medullary thyroid carcinoma by bioinformatics analysis.

Lijie Zhang · Donghui Lu · Meiqin Liu · Mingjin Zhang · Quan Peng

Molecular medicine reports · 2019 doi:10.3892/mmr.2019.10463

Paper

complete

Experiment

complete

Data

partial

Analysis

complete

Execution

absent

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Record details sci_version · id · kind · revision · content_hash · schema · created_at · updated_at · generator
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pmid:31322209
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title
Identification and interaction analysis of key miRNAs in medullary thyroid carcinoma by bioinformatics analysis.
doi
10.3892/mmr.2019.10463
pmid
31322209
authors array · 5 items
[0]
Lijie Zhang
[1]
Donghui Lu
[2]
Meiqin Liu
[3]
Mingjin Zhang
[4]
Quan Peng
journal
Molecular medicine reports
year
2019
abstract
Medullary thyroid carcinoma (MTC) is an endocrine tumor and comprises 5‑10% of all primary thyroid malignancies. However, the biomechanical contribution to the development and progression of MTC remains unclear. In this study, To discover the key microRNAs (miRNAs or miRs) and their potential roles in the tumorigenesis of MTC, the microarray datasets GSE97070, GSE40807 and GSE27155 were analyzed. The datasets were downloaded from the Gene Expression Omnibus (GEO) database. The differentially expressed miRNAs (DEMs) and genes (DEGs) were accessed by R. Targets of DEMs and predicted using starBase, and functional and pathway enrichment analyses were performed using Metascape. A protein‑protein interaction (PPI) network and an analysis of modules were constructed using NetworkAnalyst. Finally, a network was constructed to show the regulatory association between transcription factors (TFs), DEMs and downstream genes. A total of 5 DEMs were found both in GSE97070 and GSE40807, including 3 upregulated DEMs and 2 downregulated DEMs. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses from Metascape revealed that the target genes of upregulated DEMs were significantly enriched in adherens junction, kinase and protein binding, while the target genes of downregulated DEMs were mainly involved in non‑canonical Wnt signaling pathway and RNA transport. From the PPI network, 13 nodes were screened as hub genes. Pathway enrichment analysis revealed that the top 5 modules were mostly enriched in the neurotrophin signaling pathway, mRNA surveillance pathway and MAPK signaling pathway. In addition, the TF‑DEMs‑target gene and DEGs regulatory network revealed that 17 TFs regulated 2 miRNAs, including upregulated or downregulated DEMs, CREB1 regulated all upregulated DEMs, and TGFB1 was an activator of hsa‑miR‑199a‑3p and a repressor of hsa‑miR‑429. Taken together, the present study identified several miRNAs and potential biological mechanisms involved in the tumorigenesis of MTC. This study identified the key DEMs and potential mechanisms underlying the development of MTC, and provided a series of biomarkers and targets for the management of MTC.
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ntial roles in the tumorigenesis of MTC, the microarray datasets GSE97070, GSE40807 and GSE27155 wer
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evidence array · 1 item
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The R software package was used to process the downloaded files and to convert and reject the unqualified data, and the limma R package was then used to identify DEMs and DEGs.
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Complete file
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In this study, To discover the key microRNAs (miRNAs or miRs) and their potential roles in the tumorigenesis of MTC, the microarray datasets GSE97070, GSE40807 and GSE27155 were analyzed. The datasets were downloaded from the Gene Expression Omnibus (GEO) database. The differentially expressed miRNAs (DEMs) and genes (DEGs) were accessed by R. Targets of DEMs and predicted using starBase, and functional and pathway enrichment analyses were performed using Metascape. A protein‑protein interaction (PPI) network and an analysis of modules were constructed using NetworkAnalyst. Finally, a network was constructed to show the regulatory association between transcription factors (TFs), DEMs and downstream genes. A total of 5 DEMs were found both in GSE97070 and GSE40807, including 3 upregulated DEMs and 2 downregulated DEMs. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses from Metascape revealed that the target genes of upregulated DEMs were significantly enriched in adherens junction, kinase and protein binding, while the target genes of downregulated DEMs were mainly involved in non‑canonical Wnt signaling pathway and RNA transport. From the PPI network, 13 nodes were screened as hub genes. Pathway enrichment analysis revealed that the top 5 modules were mostly enriched in the neurotrophin signaling pathway, mRNA surveillance pathway and MAPK signaling pathway. In addition, the TF‑DEMs‑target gene and DEGs regulatory network revealed that 17 TFs regulated 2 miRNAs, including upregulated or downregulated DEMs, CREB1 regulated all upregulated DEMs, and TGFB1 was an activator of hsa‑miR‑199a‑3p and a repressor of hsa‑miR‑429. Taken together, the present study identified several miRNAs and potential biological mechanisms involved in the tumorigenesis of MTC. This study identified the key DEMs and potential mechanisms underlying the development of MTC, and provided a series of biomarkers and targets for the management of MTC.", "claims": [{"id": "claim-method", "statement": "The R software package was used to process the downloaded files and to convert and reject the unqualified data, and the limma R package was then used to identify DEMs and DEGs.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:31322209", "locator": "sections[5] \"Data pre-processing and DEM analysis\" paragraph 2", "quote": "The R software package was used to process the downloaded files and to convert and reject the unqualified data, and the limma R package was then used to identify DEMs and DEGs."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:31322209", "locator": "sections[21] \"Availability of data and materials\" paragraph 2", "quote": "The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": null, "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:31322209"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. 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