Network medicine approaches for identification of novel prognostic systems biomarkers and drug candidates for papillary thyroid carcinoma

Abstract

Papillary thyroid carcinoma (PTC) is one of the most common endocrine carcinomas worldwide and the aetiology of this cancer is still not well understood. Therefore, it remains important to understand the disease mechanism and find prognostic biomarkers and/or drug candidates for PTC. Compared with approaches based on single‐gene assessment, network medicine analysis offers great promise to address this need. Accordingly, in the present study, we performed differential co‐expressed network analysis using five transcriptome datasets in patients with PTC and healthy controls. Following meta‐analysis of the transcriptome datasets, we uncovered common differentially expressed genes (DEGs) for PTC and, using these genes as proxies, found a highly clustered differentially expressed co‐expressed module: a ‘PTC‐module’. Using independent data, we demonstrated the high prognostic capacity of the PTC‐module and designated this module as a prognostic systems biomarker. In addition, using the nodes of the PTC‐module, we performed drug repurposing and text mining analyzes to identify novel drug candidates for the disease. We performed molecular docking simulations, and identified: 4‐demethoxydaunorubicin hydrochloride, AS605240, BRD‐A60245366, ER 27319 maleate, sinensetin, and TWS119 as novel drug candidates whose efficacy was also confirmed by in silico analyzes. Consequently, we have highlighted here the need for differential co‐expression analysis to gain a systems‐level understanding of a complex disease, and we provide candidate prognostic systems biomarker and novel drugs for PTC.