Abstract
Background: Thyroid cancer is a common endocrine malignancy; however, its treatment is still surgical. With the development and application of targeted therapy in cancer treatment, there are great development prospects in researching targeted drugs for thyroid cancer. Methods: Differentially expressed mRNAs between thyroid cancerous tissue and normal thyroid tissues were screened from The Cancer Genome Atlas (TCGA) database. Using weighted gene coexpression network analysis (WGCNA) to build co-expression modules and combined with differentially methylated gene (DMG) analysis. The druggability was analyzed by PockDrug-Server. Due to drug repositioning to seek targeted drugs to treat thyroid cancer we constructed a protein-protein interaction (PPI) network, and screened out a drug target of thyroid cancer. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) were used to analysis the protein enrichment of PPI network. Results: In the present study, the red module was significantly correlated with thyroid cancer. With DMG analysis, we screened out three genes: HEY2 , TNIK and LRP4 . These three genes were hypomethylation in tumors. The druggability based on PockDrug-Server predicted that only TNIK had protein pocket druggability. With PPI model for TNIK, there were ten genes interacted with TNIK. These genes were enriched in the MAPK and Wnt pathways, which are correlated with tumor proliferation, differentiation, and development. Upon searching for drugs against these 10 genes in Drugbank, it was determined that the targeted drug Binimetinib which is MEK1/2 inhibition. Therefore, we hypothesized that Binimetinib can be used as a targeted drug and TNIK can be regard as drug target for thyroid cancer therapy. Conclusion: Our research provides a bioinformatics method for screening drugs target and provides a theoretical basis for targeted therapy for thyroid cancer.
Prediction of a potential drug target based on protein druggability for thyroid cancer
