Lung adenocarcinoma-related target gene prediction and drug repositioning - Huang, Rui Xuan; Siriwanna, Damrongrat; Cho, William C.; Wan, Tsz Kin; Du, Yan Rong; Bennett, Adam N.; He, Qian Echo; Liu, Jun Dong; Huang, Xiao Tai; Chan, Kei Hang Katie (Frontiers in Pharmacology, 2022)
Lung cancer is one of the most common cancers globally, and lung adenocarcinoma (LUAD) is the most common type of lung cancer. Gene dysregulation plays an essential role in the development of LUAD. Drug repositioning based on associations between drug target genes and LUAD target genes is useful to discover potential new drugs for the treatment of LUAD while also reducing the monetary and time costs of new drug discovery and development. Here, we developed a pipeline based on machine learning to predict potential LUAD-related target genes through established graph attention networks (GATs). We then predicted potential drugs for the treatment of LUAD through gene coincidence-based and gene network distance-based methods. Using data from 535 LUAD tissue samples and 59 precancerous tissue samples from The Cancer Genome Atlas, 48,597 genes were identified and used for the prediction model building of the GAT. The GAT model achieved good predictive performance, with an area under the receiver operating characteristic curve of 0.90. 1,597 potential LUAD-related genes were identified from the GAT model. These LUAD-related genes were then used for drug repositioning. The gene overlap and network distance with the target genes were calculated for 3,070 drugs and 672 preclinical compounds approved by the US Food and Drug Administration. Bromoethylamine was predicted as a novel potential preclinical compound for the treatment of LUAD, and cimetidine and benzbromarone were predicted as potential therapeutic drugs for LUAD. The pipeline established in this study presents new possibilities for developing targeted therapies for LUAD.