Thyroid Cancer

Thyroid cancer is a disease in which malignant (cancer) cells form in the tissues of the thyroid gland. Thyroid nodules are common but usually are not cancer. There are different types of thyroid cancer. Age, gender, and being exposed to radiation can affect the risk of thyroid cancer. Papillary thyroid cancer (PTC) is the most common type of thyroid cancer, accounting for about 80% of all thyroid cancers. A point mutation in the BRAF gene, rearrangements of RET and NTRK1, activation of the mitogenactivated protein kinase (MAPK) pathways, are associated with PTC. It is essential to clarify the tumorigenesis mechanisms of PTC and identify unknown biomarkers.

Data Sources

• Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/)
• ArrayExpress Datasets (https://www.ebi.ac.uk/arrayexpress/)
• Kyoto Encyclopedia of Genes and Genomes (KEGG, http://www.kegg.jp)
• The Database for Annotation, Visualization and Integrated Discovery (DAVID, https://david.ncifcrf.gov/)
• Search Tool for the Retrieval of Interacting Genes (STRING, https://string-db.org/)

Computational Approaches

With the wide application of gene chips, large amount of the highthroughput functional genomics data are deposited in public databases. The development of computational tools serve as an alternative method to identify novel genes.

• Differentially Expressed Genes (DEGs) Identification and Integration
• Process raw data in the R statistical software.
• Preprocess data with RMA algorithm in Affy package.
• Carry out DEGs analysis by limma package.
• Perform classical t test to identify DEGs.
• Integrate commonly changed DEGs from the datasets using Venn analysis.

• Gene Ontology and Pathway Enrichment Analyses
• Use Gene ontology analysis (GO)  to identify characteristic biological attributes for DEGs.
• Kyoto Encyclopedia of
• Perform Genes and Genomes pathway (KEGG) enrichment analysis to identify functional attributes for DEGs.

• PPI Network Construction
• Use the Search Tool for the Retrieval of Interacting Gene (STRING) database to construct protein–protein interaction (PPI) network.
• Utilize Cytoscape software to construct protein interaction relationship network.

• Central Gene Identification
• Perform the CentiScape to scale degree, closeness and betweenness of the PPI network.
• Perform KEGG analysis for central genes.

• Survival Analysis of Central Genes
• Use survival data for Kaplan–Meier survival analysis and to generate overall survival plots.

CD ComputaBio utilizes microarray, deep sequencing platforms, advanced biostatistical and computational analyses methods to detect biological signals in highly dimensional and often noisy genomic data. We are also interested in how the machine learning-based integration of multi-omic datasets can aid in the discovery of new cancer subgroups and biomarkers.

Moreover, CD ComputaBio has multiple resources including academic research and preclinical works in the identification of a suitable disease target and its corresponding hit. Contact us for more service details.

Reference:

1. Liang W, Sun F. Identification of key genes of papillary thyroid cancer using integrated bioinformatics analysis. J Endocrinol Invest. 2018 Oct;41(10):1237-1245.