Hepatocellular carcinoma (HCC) is a common and fatal malignant tumor worldwide, which is difficult to diagnose early and has a low survival rate. In this paper, we investigated the temporal expression pattern of AKR1B10 gene in patients with hepatocellular carcinoma (HCC) and constructed a GL-TGRN model using LSTM neural network combined with gene regulatory network. By analyzing multiple sets of student data (including mRNA expression data, miRNA data, and DNA methylation data), we conducted an in-depth exploration of the temporal expression of the AKR1B10 gene and its relationship with hepatocellular carcinoma development. The results showed that the GL-TGRN model performed excellently in inferring AKR1B10 gene expression, and the AUROC and AUPR values were increased by 26.23% and 35.69%, which were significantly higher than the comparison methods (e.g., GC-SIN and JUMP). In addition, through differential expression analysis, we screened 786 differential genes and 14 miRNAs related to hepatocellular carcinoma, and these molecules are closely related to hepatocarcinogenesis. Ablation experiments demonstrated that the fusion of multi-omics features in the GL-TGRN model significantly improved the accuracy of gene regulatory inference. This paper provides new data support for early diagnosis and personalized treatment of hepatocellular carcinoma.
