Predicting potential lncRNA biomarkers for lung cancer and neuroblastoma based on an ensemble of a deep neural network and LightGBM

Frontiers in Genetics

Genetics

Zhenguo Su¹  Huihui Lu²  Lian Duan³  Yan Wu⁴  Zejun Li⁵ 

[1] Clinical Lab, Yantai Affiliated Hospital of Binzhou Medical University, Yantai, China;Department of Thoracic Cardiovascular Surgery, Hunan Province Directly Affiliated TCM Hospital, Zhuzhou, China;Faculty of Pediatrics, The Chinese PLA General Hospital, Beijing, China;Department of Pediatric Surgery, The Seventh Medical Center of PLA General Hospital, Beijing, China;National Engineering Laboratory for Birth Defects Prevention and Control of Key Technology, Beijing, China;Beijing Key Laboratory of Pediatric Organ Failure, Beijing, China;Geneis (Beijing) Co., Ltd., Beijing, China;School of Computer Science, Hunan Institute of Technology, Hengyang, China;

关键词: lncRNA;    biomarker;    lung cancer;    neuroblastoma;    deep neural network;    LightGBM;   

DOI  :  10.3389/fgene.2023.1238095

 received in 2023-06-10, accepted in 2023-07-19,  发布年份 2023

来源: Frontiers

【 摘 要 】

Introduction: 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 a complex 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 are costly and laborious. Consequently, artificial intelligence technologies, especially machine learning methods, provide a powerful avenue to find new biomarkers for various diseases.Methods: 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 a deep neural network and LightGBM. LDAenDL first computes the Gaussian kernel similarity and functional similarity of lncRNAs and the Gaussian kernel similarity and semantic similarity of diseases to obtain their similar networks. Next, LDAenDL combines a graph convolutional network, graph attention network, and convolutional neural network to learn the biological features of the lncRNAs and diseases based on their similarity networks. Third, these features are concatenated and fed to an ensemble model composed of a deep neural network and LightGBM to find new lncRNA–disease associations (LDAs). Finally, the proposed LDAenDL method is applied to identify possible lncRNA biomarkers associated with lung cancer and neuroblastoma.Results: The experimental results show that LDAenDL computed the best AUCs of 0.8701, 107 0.8953, and 0.9110 under cross-validation on lncRNAs, diseases, and lncRNA‐disease pairs on Dataset 1, respectively, and 0.9490, 0.9157, and 0.9708 on Dataset 2, respectively. Furthermore, AUPRs of 0.8903, 0.9061, and 0.9166 under three cross‐validations were obtained 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 potential biomarkers of neuroblastoma.Conclusion: We hope that the proposed LDAenDL method can help the development of targeted therapies for these two diseases.

【 授权许可】

Unknown   
Copyright © 2023 Su, Lu, Wu, Li and Duan.