Estimating the Prognosis of Low-Grade Glioma with Gene Attention Using Multi-Omics and Multi-Modal Schemes

Abstract

The prognosis estimation of low-grade glioma (LGG) patients with deep learning models using gene expression data has been extensively studied in recent years. However, the deep learning models used in these studies do not utilize the latest deep learning techniques, such as residual learning and ensemble learning. To address this limitation, in this study, a deep learning model using multi-omics and multi-modal schemes, namely the Multi-Prognosis Estimation Network (Multi-PEN), is proposed. When using Multi-PEN, gene attention layers are employed for each datatype, including mRNA and miRNA, thereby allowing us to identify prognostic genes. Additionally, recent developments in deep learning, such as residual learning and layer normalization, are utilized. As a result, Multi-PEN demonstrates competitive performance compared to conventional models for prognosis estimation. Furthermore, the most significant prognostic mRNA and miRNA were identified using the attention layers in Multi-PEN. For instance, MYBL1 was identified as the most significant prognostic mRNA. Such a result accords with the findings in existing studies that have demonstrated that MYBL1 regulates cell survival, proliferation, and differentiation. Additionally, hsa-mir-421 was identified as the most significant prognostic miRNA, and it has been extensively reported that hsa-mir-421 is highly associated with various cancers. These results indicate that the estimations of Multi-PEN are valid and reliable and showcase Multi-PEN’s capacity to present hypotheses regarding prognostic mRNAs and miRNAs.