Identification of Potential Biomarkers for Thyroid Cancer Using Bioinformatics Strategy: A Study Based on GEO Datasets.
Androstenes
/ metabolism
Biomarkers, Tumor
/ genetics
Clofazimine
/ metabolism
Computational Biology
Databases, Genetic
Exosomes
/ metabolism
Extracellular Matrix Proteins
/ genetics
Eye Proteins
/ genetics
Fibronectins
/ genetics
GTP-Binding Protein alpha Subunits, Gi-Go
/ genetics
GTP-Binding Protein alpha Subunits, Gq-G11
/ genetics
Gene Expression Regulation, Neoplastic
Gene Ontology
Gene Regulatory Networks
Genetic Markers
Heparin
/ metabolism
Hexamethonium
/ metabolism
Humans
Lactams
/ metabolism
Nerve Tissue Proteins
/ genetics
Prognosis
Protein Interaction Maps
/ genetics
Receptors, Vasopressin
/ genetics
Signal Transduction
Thyroid Hormones
/ metabolism
Thyroid Neoplasms
/ diagnosis
Transcriptome
Transforming Growth Factor beta
/ genetics
Tumor Suppressor Protein p53
/ genetics
Tyrosine
/ metabolism
Journal
BioMed research international
ISSN: 2314-6141
Titre abrégé: Biomed Res Int
Pays: United States
ID NLM: 101600173
Informations de publication
Date de publication:
2020
2020
Historique:
received:
06
08
2019
revised:
29
01
2020
accepted:
05
03
2020
entrez:
28
4
2020
pubmed:
28
4
2020
medline:
20
1
2021
Statut:
epublish
Résumé
The molecular mechanisms and genetic markers of thyroid cancer are unclear. In this study, we used bioinformatics to screen for key genes and pathways associated with thyroid cancer development and to reveal its potential molecular mechanisms. The GSE3467, GSE3678, GSE33630, and GSE53157 expression profiles downloaded from the Gene Expression Omnibus database (GEO) contained a total of 164 tissue samples (64 normal thyroid tissue samples and 100 thyroid cancer samples). The four datasets were integrated and analyzed by the RobustRankAggreg (RRA) method to obtain differentially expressed genes (DEGs). Using these DEGs, we performed gene ontology (GO) functional annotation, pathway analysis, protein-protein interaction (PPI) analysis and survival analysis. Then, CMap was used to identify the candidate small molecules that might reverse thyroid cancer gene expression. By integrating the four datasets, 330 DEGs, including 154 upregulated and 176 downregulated genes, were identified. GO analysis showed that the upregulated genes were mainly involved in extracellular region, extracellular exosome, and heparin binding. The downregulated genes were mainly concentrated in thyroid hormone generation and proteinaceous extracellular matrix. Pathway analysis showed that the upregulated DEGs were mainly attached to ECM-receptor interaction, p53 signaling pathway, and TGF-beta signaling pathway. Downregulation of DEGs was mainly involved in tyrosine metabolism, mineral absorption, and thyroxine biosynthesis. Among the top 30 hub genes obtained in PPI network, the expression levels of FN1, NMU, CHRDL1, GNAI1, ITGA2, GNA14 and AVPR1A were associated with the prognosis of thyroid cancer. Finally, four small molecules that could reverse the gene expression induced by thyroid cancer, namely ikarugamycin, adrenosterone, hexamethonium bromide and clofazimine, were obtained in the CMap database. The identification of the key genes and pathways enhances the understanding of the molecular mechanisms for thyroid cancer. In addition, these key genes may be potential therapeutic targets and biomarkers for the treatment of thyroid cancer.
Sections du résumé
BACKGROUND
BACKGROUND
The molecular mechanisms and genetic markers of thyroid cancer are unclear. In this study, we used bioinformatics to screen for key genes and pathways associated with thyroid cancer development and to reveal its potential molecular mechanisms.
METHODS
METHODS
The GSE3467, GSE3678, GSE33630, and GSE53157 expression profiles downloaded from the Gene Expression Omnibus database (GEO) contained a total of 164 tissue samples (64 normal thyroid tissue samples and 100 thyroid cancer samples). The four datasets were integrated and analyzed by the RobustRankAggreg (RRA) method to obtain differentially expressed genes (DEGs). Using these DEGs, we performed gene ontology (GO) functional annotation, pathway analysis, protein-protein interaction (PPI) analysis and survival analysis. Then, CMap was used to identify the candidate small molecules that might reverse thyroid cancer gene expression.
RESULTS
RESULTS
By integrating the four datasets, 330 DEGs, including 154 upregulated and 176 downregulated genes, were identified. GO analysis showed that the upregulated genes were mainly involved in extracellular region, extracellular exosome, and heparin binding. The downregulated genes were mainly concentrated in thyroid hormone generation and proteinaceous extracellular matrix. Pathway analysis showed that the upregulated DEGs were mainly attached to ECM-receptor interaction, p53 signaling pathway, and TGF-beta signaling pathway. Downregulation of DEGs was mainly involved in tyrosine metabolism, mineral absorption, and thyroxine biosynthesis. Among the top 30 hub genes obtained in PPI network, the expression levels of FN1, NMU, CHRDL1, GNAI1, ITGA2, GNA14 and AVPR1A were associated with the prognosis of thyroid cancer. Finally, four small molecules that could reverse the gene expression induced by thyroid cancer, namely ikarugamycin, adrenosterone, hexamethonium bromide and clofazimine, were obtained in the CMap database.
CONCLUSION
CONCLUSIONS
The identification of the key genes and pathways enhances the understanding of the molecular mechanisms for thyroid cancer. In addition, these key genes may be potential therapeutic targets and biomarkers for the treatment of thyroid cancer.
Identifiants
pubmed: 32337286
doi: 10.1155/2020/9710421
pmc: PMC7152968
doi:
Substances chimiques
AVPR1A protein, human
0
Androstenes
0
Biomarkers, Tumor
0
CHRDL1 protein, human
0
Extracellular Matrix Proteins
0
Eye Proteins
0
FN1 protein, human
0
Fibronectins
0
Genetic Markers
0
Lactams
0
Nerve Tissue Proteins
0
Receptors, Vasopressin
0
Thyroid Hormones
0
Transforming Growth Factor beta
0
Tumor Suppressor Protein p53
0
ikarugamycin
36531-78-9
Hexamethonium
3C9PSP36Z2
Tyrosine
42HK56048U
Heparin
9005-49-6
adrenosterone
AE4E9102GY
Clofazimine
D959AE5USF
GNA14 protein, human
EC 3.6.5.1
GNAI1 protein, human
EC 3.6.5.1
GTP-Binding Protein alpha Subunits, Gi-Go
EC 3.6.5.1
GTP-Binding Protein alpha Subunits, Gq-G11
EC 3.6.5.1
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
9710421Informations de copyright
Copyright © 2020 Yujie Shen et al.
Déclaration de conflit d'intérêts
The author(s) declare(s) that they have no conflicts of interest.
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