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Genetic expression profile-based screening of genes and pathways associated with papillary thyroid carcinoma.

Shubin Li | Yihang Yin | Hong Yu
Oncology letters | 2018

Papillary thyroid carcinoma (PTC) is the most common subtype of thyroid cancer; however, the specific genes and signaling pathways involved in this cancer remain largely unclear. The present study analyzed three profile datasets, GSE6004, GSE29265 and GSE60542, which were comprised of 47 PTC and 41 normal thyroid tissue samples, to identify key genes and pathways associated with PTC. Initially, differentially-expressed genes (DEGs) between PTC and normal thyroid tissue were screened using R 3.4.0 (2017-04-21, R Foundation, Vienna, Austria, http://www.R-project.org/). These DEGs were then clustered by gene ontology functional terms and representative signaling pathways. Additionally, specific key gene nodes were filtered out from a constructed protein-protein interaction (PPI) network. The results identified a total of 423 shared DEGs associated with PTC, including 211 upregulated and 212 downregulated genes. These 423 genes were primarily enriched in glycosaminoglycan binding, sulfur compound binding, heparin binding, enzyme activator activity, peptidase activator activity and hsa04512: Extracellular matrix (ECM)-receptor interaction. A total of 21 central node genes were identified as key genes in the PTC disease process including complement factor D (CFD), Collagen Type I α 1 Chain (COL1A1), Extracellular Matrix Protein 1 (ECM1) and Fibronectin 1 (FN1). These genes are involved in protease binding, G-protein coupled receptor binding, extracellular matrix structural constituent and peptidase regulator activity. To conclude, using bioinformatics analysis, the present study identified candidate DEGs and critical pathways in PTC that may improve the current understanding regarding the underlying mechanisms of PTC. These genes and pathways may be used as potential therapeutic targets of PTC in the future.

Pubmed ID: 30344727

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This is a list of tools and resources that we have found mentioned in this publication.


R Project for Statistical Computing (tool)

RRID:SCR_001905

Software environment and programming language for statistical computing and graphics. R is integrated suite of software facilities for data manipulation, calculation and graphical display. Can be extended via packages. Some packages are supplied with the R distribution and more are available through CRAN family.It compiles and runs on wide variety of UNIX platforms, Windows and MacOS.

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Cytoscape (tool)

RRID:SCR_003032

Software platform for complex network analysis and visualization. Used for visualization of molecular interaction networks and biological pathways and integrating these networks with annotations, gene expression profiles and other state data.

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PANTHER (tool)

RRID:SCR_004869

System that classifies genes by their functions, using published scientific experimental evidence and evolutionary relationships to predict function even in absence of direct experimental evidence. Orthologs view is curated orthology relationships between genes for human, mouse, rat, fish, worm, and fly.

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STRING (tool)

RRID:SCR_005223

Database of known and predicted protein interactions. The interactions include direct (physical) and indirect (functional) associations and are derived from four sources: Genomic Context, High-throughput experiments, (Conserved) Coexpression, and previous knowledge. STRING quantitatively integrates interaction data from these sources for a large number of organisms, and transfers information between these organisms where applicable. The database currently covers 5''214''234 proteins from 1133 organisms. (2013)

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KEGG (tool)

RRID:SCR_012773

Integrated database resource consisting of 16 main databases, broadly categorized into systems information, genomic information, and chemical information. In particular, gene catalogs in completely sequenced genomes are linked to higher-level systemic functions of cell, organism, and ecosystem. Analysis tools are also available. KEGG may be used as reference knowledge base for biological interpretation of large-scale datasets generated by sequencing and other high-throughput experimental technologies.

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