Genome-wide discovery of hidden genes mediating known drug-disease association using KDDANet

Hua Yu,Jin Zhang,Chen Li,Ming Chen,Lu Lu

doi:10.1038/s41525-021-00216-6

Abstract

Many of genes mediating Known Drug-Disease Association (KDDA) are escaped from experimental detection. Identifying of these genes (hidden genes) is of great significance for understanding disease pathogenesis and guiding drug repurposing. Here, we presented a novel computational tool, called KDDANet, for systematic and accurate uncovering the hidden genes mediating KDDA from the perspective of genome-wide functional gene interaction network. KDDANet demonstrated the competitive performances in both sensitivity and specificity of identifying genes in mediating KDDA in comparison to the existing state-of-the-art methods. Case studies on Alzheimer’s disease (AD) and obesity uncovered the mechanistic relevance of KDDANet predictions. Furthermore, when applied with multiple types of cancer-omics datasets, KDDANet not only recapitulated known genes mediating KDDAs related to cancer, but also revealed novel candidates that offer new biological insights. Importantly, KDDANet can be used to discover the shared genes mediating multiple KDDAs. KDDANet can be accessed at http://www.kddanet.cn and the code can be freely downloaded at https://github.com/huayu1111/KDDANet.

Highlights

The conventional development of novel promising drugs for treating specific diseases is a time-consuming and effort-costing process, including discovery of new chemical entities, target detection and verification, preclinical and clinical trials and so on[1]
For a given Known Drug-Disease Association (KDDA), we proposed a hypothesis that the Known Drug Target Genes (KDTGs) and Known Disease-Related Genes (KDRGs) should be highly overlapped if the genes mediating this KDDA have been fully identified
Our KDDANet pipeline depends on three between KDTGs and KDRGs of 53124 KDDAs obtained from Comparative Toxicogenomics Database (CTD)[12]

Summary

Introduction

The conventional development of novel promising drugs for treating specific diseases is a time-consuming and effort-costing process, including discovery of new chemical entities, target detection and verification, preclinical and clinical trials and so on[1]. There are multiple examples of repositioned drugs that are on the market including Minoxidil, a drug designed to treat hypertension but is used to treat hair loss[3], and Sildenafil, a drug originally developed for patients with heart disease but is commonly used to treat erectile dysfunction[4]. These examples of repositioned drugs were primarily based on clinical observations of the side effects of the drug[5]. Thanks to the advance in nextgeneration omics sequencing and qualification technologies, a large volume of biomedical data, for example, the pharmacogenomics datasets produced by Connective Map project, The Cancer

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