Predicting potential lncRNA biomarkers for lung cancer and neuroblastoma based on an ensemble of a deep neural network and LightGBM - Su, Zhenguo; Lu, Huihui; Wu, Yan; Li, Zejun; Duan, Lian (Frontiers in Genetics, 2023)
Lung cancer is one of the most frequent neoplasms worldwide with approximately 2.2 million new cases and 1.8 million deaths each year. The expression levels of programmed death ligand-1 (PDL1) demonstrate dense association with lung cancer. Neuroblastoma is a high-risk malignant tumor and is mainly involved in childhood patients. Identification of new biomarkers for these two diseases can significantly promote their diagnosis and therapy. However, in vivo experiments to discover potential biomarkers for them are costly and laborious. Consequently, artificial intelligence technologies especially machine learning methods provide a powerful avenue to find new biomarkers for various diseases. Thus, we developed a machine learning-based method named LDAenDL to detect potential Long noncoding RNA (lncRNA) biomarkers for lung cancer and neuroblastoma using an ensemble of deep neural network and LightGBM. LDAenDL first computes Gaussian kernel similarity and functional similarity of lncRNAs and Gaussian kernel similarity and semantic similarity of diseases to obtain their similar networks. Next, LDAenDL combines graph convolutional network, graph attention network, and convolutional neural network to learn the biological features of lncRNAs and diseases based on their similarity networks. Third, these features are concatenated and fed to an ensemble model composed of deep neural network and LightGBM for finding new lncRNA-disease associations (LDAs). Finally, the proposed LDAenDL method is applied to identify possible lncRNA biomarkers associated with lung cancer and neuroblastoma. The experimental results show that LDAenDL computed the best AUCs of 0.8701, 0.8953, and 0.9110 under cross validations on lncRNAs, diseases, and lncRNA-disease pairs on Dataset 1, and 0.9490, 0.9157, and 0.9708 on Dataset 2, and AUPRs of 0.8903, 0.9061, and 0.9166 under three cross validations on Dataset 1, and 0.9582, 0.9122, and 0.9743 on Dataset 2. The results demonstrate that LDAenDL significantly outperformed the other four classical LDA prediction methods (i.e., SDLDA, LDNFSGB, IPCAF, and LDASR). Case studies demonstrate that CCDC26 and IFNG-AS1 may be new biomarkers of lung cancer, SNHG3 may associate with PDL1 for lung cancer, and HOTAIR and BDNF-AS may be a potential biomarker of neuroblastoma. We hope that the proposed LDAenDL method can help the development of targeted therapies in these two diseases.