Using computational strategies to predict potential drugs for nasopharyngeal carcinoma.

Abstract

Nasopharyngeal carcinoma (NPC) is a unique cancer. Refinement of current therapy by discovering potential drugs may be approached by several computational strategies. We collected NPC genes from published microarray data and the literature. The NPC disease network was constructed via a protein-protein interaction (PPI) network. The Connectivity Map (CMap) was used to predict potential chemicals, and support vector machines (SVMs) were further utilized to classify the effectiveness of tested drugs against NPC using their gene expression from CMap. A highly interconnected network was obtained. Several chemically sensitive genes were identified and 87 drugs were predicted with the potential for treating NPC by SVM, in which nearly half of them have anticancer effects according to the literature. The 2 top-ranked drugs, thioridazine and vorinostat, were demonstrated to be effective in inhibiting NPC cells. This in silico approach provides a promising strategy for screening potential therapeutic drugs for NPC treatment.