Heterogeneous graph framework for predicting the association between lncRNA and disease and case on uterine fibroid.

Autor: Sheng QJ; Department of Gynecology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tong Ji University, Shanghai, China; Shanghai Key Laboratory of Maternal and Fetal Medicine, Shanghai First Maternity and Infant Hospital, Shanghai, China., Tan Y; Department of Integrated Traditional Chinese Medicine (TCM) & Western Medicine, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, China; Shanghai Key Laboratory of Maternal and Fetal Medicine, Shanghai First Maternity and Infant Hospital, Shanghai, China., Zhang L; School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China., Wu ZP; Department of Gynecology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tong Ji University, Shanghai, China; Shanghai Key Laboratory of Maternal and Fetal Medicine, Shanghai First Maternity and Infant Hospital, Shanghai, China., Wang B; Department of Gynecology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tong Ji University, Shanghai, China; Shanghai Key Laboratory of Maternal and Fetal Medicine, Shanghai First Maternity and Infant Hospital, Shanghai, China., He XY; Department of Gynecology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tong Ji University, Shanghai, China; Shanghai Key Laboratory of Maternal and Fetal Medicine, Shanghai First Maternity and Infant Hospital, Shanghai, China. Electronic address: hexiaoying@51mch.com.
Jazyk: angličtina
Zdroj: Computers in biology and medicine [Comput Biol Med] 2023 Oct; Vol. 165, pp. 107331. Date of Electronic Publication: 2023 Aug 09.
DOI: 10.1016/j.compbiomed.2023.107331
Abstrakt: Long non-coding RNAs (lncRNAs) play crucial regulatory roles in various cellular processes, including gene expression, chromatin remodeling, and protein localization. Dysregulation of lncRNAs has been linked to several diseases, making it essential to understand their functions in disease mechanisms and therapeutic strategies. However, traditional experimental methods for studying lncRNA function are time-consuming, expensive, and offer limited insights. In recent years, computational methods have emerged as valuable tools for predicting lncRNA functions and their associations with diseases. However, many existing methods focus on constructing separate networks for lncRNA and disease similarity, resulting in information loss and insufficient processing capacity for isolated nodes. To address this, we developed 'RGLD' by combining Random Walk with restarting (RWR), Graph Neural Network (GNN), and Graph Attention Networks (GAT) to predict lncRNA-disease associations in a heterogeneous network. RGLD achieved an impressive AUC of 0.88, outperforming other methods. It can also predict novel associations between lncRNAs and diseases. RGLD identified HOTAIR, MEG3, and PVT1 as lncRNAs associated with uterine fibroids. Biological experiments directly or indirectly verified the involvement of these three lncRNAs in uterine fibroids, validating the accuracy of RGLD's predictions. Furthermore, we extensively discussed the functions of the target genes regulated by these lncRNAs in uterine fibroids, providing evidence for their role in the development and progression of the disease.
Competing Interests: Declaration of competing interest The authors declare that they have no conflict of interest.
(Copyright © 2023 Elsevier Ltd. All rights reserved.)
Databáze: MEDLINE
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