Thyroid Cancer

Carr, Frances E.

doi:10.1002/9781119645214.ch23

Cited by 1 publication

(1 citation statement)

References 191 publications

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“…In recent years, great progress has been made in genomics research on PTC, [25][26][27] but the specific mechanism was still not clear. 28,29 Actually, future molecular tests are expected to be able to accurately predict the cancer risk of thyroid nodules and inform specific treatment decisions. 30,31 Bioinformatics is a new discipline emerging with the launch of the human genome project.…”

Section: Discussionmentioning

confidence: 99%

Exploration of the Potential Biomarkers of Papillary Thyroid Cancer (PTC) Based on RT2 Profiler PCR Arrays and Bioinformatics Analysis

Peng¹,

Zhang²,

Cao³

et al. 2020

CMAR

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Background: Papillary thyroid carcinoma (PTC) has increased rapidly over recent years, and radiation, hormone effects, gene mutations, and others were viewed as closely related. However, the molecular mechanisms of PTC have not been cleared. Therefore, we intended to screen more accurate key genes and pathways of PTC by combining RT 2 profiler PCR arrays and bioinformatics methods in this study. Materials and Methods: RT 2 profiler PCR arrays were firstly analyzed to identify differential expression genes (DEGs) in PTC. RT-qPCR were performed to verify the most significant differential expression genes. The TCGA database was used to further verify for expanded data. Enrichment analysis of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) was analyzed. To construct the protein-protein interaction (PPI) network, we used STRING and Cytoscape to make module analysis of these DEGs. Results: Sixteen differentially expressed genes were presented in RT 2 profiler PCR arrays, including 13 down-regulated DEGs (DEGs) and three up-regulated DEGs (DEGs), while 13 stable DEGs were eventually verified. A total of 155 DEGs were presented in the TCGA database, including 82 up-regulated DEGs (DEGs) and 73 down-regulated DEGs (dDEGs). A total of 29 important genes were extracted after integrating these two results, GO and KEGG analyses were used to observe the possible mechanisms of action of these DEGs. The PPI network was constructed to observe hub genes. Prognostic analysis further demonstrated the involvement of these genes in the biological processes of PTC. Conclusion: This study identified some potential molecular targets and signal pathways, which might help us raise our awareness of the mechanisms of PTC.

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