Lung adenocarcinoma-related target gene prediction and drug repositioning

Abstract

Lung cancer is the leading cause of cancer deaths 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 are 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. At which, 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 approach for developing targeted therapies for LUAD.

Predicting active compounds for lung cancer based on quantitative structure-activity relationships

“…The model achieved an F1 score of 0.98 with a final mean squared error of 0.02. This score is higher compared to that reached by the study in [35], where an F1 score of 0.76 was obtained. Moreover, we compared the ability of our model to predict drugs that were revealed as highly active in the study [35].…”

“…This score is higher compared to that reached by the study in [35], where an F1 score of 0.76 was obtained. Moreover, we compared the ability of our model to predict drugs that were revealed as highly active in the study [35]. Then, we ranked top-10 highly active molecules in lung cancer, and Table 5 shows a list of these drugs.…”

“…Specific KRAS mutations are responsible for lung cancer, and patients with these mutations are often resistant to targeted drugs such as those ranked 3 and 7 [39]. Moreover, drugs predicted to be active in NSCLC by the study in [35] were also present in our top-10 list (drugs ranked 4, 6, 8, 9, and 10), and have been validated by many studies [40]- [45].…”