Identification of Potential Biomarkers for Thyroid Cancer Using Bioinformatics Strategy: A Study Based on GEO Datasets.

Abstract

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:

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:

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:

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.

• Authors: Yujie Shen, Shikun Dong, Jinhui Liu, Liqing Zhang, Jiacheng Zhang, Han Zhou, Weida Dong
• Journal: BioMed research international (Biomed Res Int) Vol. 2020 Pg. 9710421 ( 2020) ISSN: 2314-6141 [Electronic] United States
• PMID: 32337286 (Publication Type: Journal Article)
• Copyright: Copyright © 2020 Yujie Shen et al.

Chemical References

• AVPR1A protein, human
• Androstenes
• Biomarkers, Tumor
• CHRDL1 protein, human
• Extracellular Matrix Proteins
• Eye Proteins
• FN1 protein, human
• Fibronectins
• Genetic Markers
• Lactams
• Nerve Tissue Proteins
• Receptors, Vasopressin
• Thyroid Hormones
• Transforming Growth Factor beta
• Tumor Suppressor Protein p53
• ikarugamycin
• Hexamethonium
• Tyrosine
• Heparin
• adrenosterone
• Clofazimine
• GNA14 protein, human
• GNAI1 protein, human
• GTP-Binding Protein alpha Subunits, Gi-Go
• GTP-Binding Protein alpha Subunits, Gq-G11

Topics

• Androstenes (metabolism)
• Biomarkers, Tumor (genetics)
• Clofazimine (metabolism)
• Computational Biology
• Databases, Genetic
• Exosomes (metabolism)
• Extracellular Matrix Proteins (genetics, metabolism)
• Eye Proteins (genetics, metabolism)
• Fibronectins (genetics, metabolism)
• GTP-Binding Protein alpha Subunits, Gi-Go (genetics, metabolism)
• GTP-Binding Protein alpha Subunits, Gq-G11 (genetics, metabolism)
• Gene Expression Regulation, Neoplastic
• Gene Ontology
• Gene Regulatory Networks
• Genetic Markers
• Heparin (metabolism)
• Hexamethonium (metabolism)
• Humans
• Lactams (metabolism)
• Nerve Tissue Proteins (genetics, metabolism)
• Prognosis
• Protein Interaction Maps (genetics)
• Receptors, Vasopressin (genetics, metabolism)
• Signal Transduction
• Thyroid Hormones (metabolism)
• Thyroid Neoplasms (diagnosis, genetics)
• Transcriptome
• Transforming Growth Factor beta (genetics, metabolism)
• Tumor Suppressor Protein p53 (genetics, metabolism)
• Tyrosine (metabolism)