Molecular Network-Based Drug Prediction in Thyroid Cancer
AbstractAs a common malignant tumor disease, thyroid cancer lacks effective preventive and therapeutic drugs. Thus, it is crucial to provide an effective drug selection method for thyroid cancer patients. The connectivity map (CMAP) project provides an experimental validated strategy to repurpose and optimize cancer drugs, the rationale behind which is to select drugs to reverse the gene expression variations induced by cancer. However, it has a few limitations. Firstly, CMAP was performed on cell lines, which are usually different from human tissues. Secondly, only gene expression information was considered, while the information about gene regulations and modules/pathways was more or less ignored. In this study, we first measured comprehensively the perturbations of thyroid cancer on a patient including variations at gene expression level, gene co-expression level and gene module level. After that, we provided a drug selection pipeline to reverse the perturbations based on drug signatures derived from tissue studies. We applied the analyses pipeline to the cancer genome atlas (TCGA) thyroid cancer data consisting of 56 normal and 500 cancer samples. As a result, we obtained 812 up-regulated and 213 down-regulated genes, whose functions are significantly enriched in extracellular matrix and receptor localization to synapses. In addition, a total of 33,778 significant differentiated co-expressed gene pairs were found, which form a larger module associated with impaired immune function and low immunity. Finally, we predicted drugs and gene perturbations that could reverse the gene expression and co-expression changes incurred by the development of thyroid cancer through the Fisher’s exact test. Top predicted drugs included validated drugs like baclofen, nevirapine, glucocorticoid, formaldehyde and so on. Combining our analyses with literature mining, we inferred that the regulation of thyroid hormone secretion might be closely related to the inhibition of the proliferation of thyroid cancer cells. View Full-Text
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Xu, X.; Long, H.; Xi, B.; Ji, B.; Li, Z.; Dang, Y.; Jiang, C.; Yao, Y.; Yang, J. Molecular Network-Based Drug Prediction in Thyroid Cancer. Int. J. Mol. Sci. 2019, 20, 263.
Xu X, Long H, Xi B, Ji B, Li Z, Dang Y, Jiang C, Yao Y, Yang J. Molecular Network-Based Drug Prediction in Thyroid Cancer. International Journal of Molecular Sciences. 2019; 20(2):263.Chicago/Turabian Style
Xu, Xingyu; Long, Haixia; Xi, Baohang; Ji, Binbin; Li, Zejun; Dang, Yunyue; Jiang, Caiying; Yao, Yuhua; Yang, Jialiang. 2019. "Molecular Network-Based Drug Prediction in Thyroid Cancer." Int. J. Mol. Sci. 20, no. 2: 263.
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