Predicting genes associated with ossification of the posterior longitudinal ligament using graph attention network

Abstract

Ossification of the posterior longitudinal ligament is a degenerative disease that severely impacts the spine, with a complex pathogenesis involving the interplay of multiple genes. This study utilizes a combination of graph neural networks and deep neural networks to systematically analyze genes associated with OPLL, leveraging genomics and bioinformatics techniques. By integrating gene data from the DisGeNET and HumanNetV2 databases, we constructed a GNN model to identify potential pathogenic genes for OPLL and validated the expression characteristics and mechanisms of these genes in different cell types. The findings indicate that the GNN model achieves remarkable accuracy and reliability in predicting genes associated with OPLL. Additionally, cellular trajectory analysis and immune cell infiltration studies uncovered distinct cellular environments and immune features in OPLL patients, emphasizing the significant roles of fibroblasts and mesenchymal stem cells in the disease's progression. Drug sensitivity analysis also sheds light on future personalized treatment options. This study not only enhances the understanding of OPLL's molecular mechanisms but also suggests new avenues for diagnostic and targeted therapy development.