Lu Chun-Yu, Liu Zi, Arif Muhammad, Alam Tanvir, Qiu Wang-Ren
School of information engineering, Jingdezhen Ceramic University, Jingdezhen, China.
College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar.
Front Genet. 2024 Dec 23;15:1513938. doi: 10.3389/fgene.2024.1513938. eCollection 2024.
The early symptoms of hepatocellular carcinoma patients are often subtle and easily overlooked. By the time patients exhibit noticeable symptoms, the disease has typically progressed to middle or late stages, missing optimal treatment opportunities. Therefore, discovering biomarkers is essential for elucidating their functions for the early diagnosis and prevention. In practical research, challenges such as high-dimensional features, low sample size, and the complexity of gene interactions impact the reliability of biomarker discovery and disease diagnosis when using single-omics approaches. To address these challenges, we thus propose, Multi-level attention graph neural network (MLA-GNN) model for analyzing integrated multi-omics data related to liver cancer. The proposed protocol are using feature selection strategy by removing the noise and redundant information from gene expression and DNA methylation data. Additionally, it employs the Cartesian product method to integrate multi-omics datasets. The study also analyzes gene interactions using WGCNA and identifies potential genes through the MLA-GNN model, offering innovative approaches to resolve these issues. Furthermore, this paper identifies FOXL2 as a promising liver cancer marker through gene ontology and survival analysis. Validation using box plots showed that the expression of the gene FOXL2 was higher in patients with hepatocellular carcinoma than in normal individuals. The drug sensitivity correlation and molecular docking results of FOXL2 with the liver cancer-targeting agent lenvatinib emphasized its potential role in hepatocellular carcinoma treatment and highlighted the importance of FOXL2 in hepatocellular carcinoma treatment.
肝细胞癌患者的早期症状往往不明显，容易被忽视。当患者出现明显症状时，疾病通常已发展到中晚期，错失了最佳治疗时机。因此，发现生物标志物对于阐明其在早期诊断和预防中的作用至关重要。在实际研究中，诸如高维特征、小样本量以及基因相互作用的复杂性等挑战，影响了使用单组学方法进行生物标志物发现和疾病诊断的可靠性。为应对这些挑战，我们提出了用于分析与肝癌相关的综合多组学数据的多级注意力图神经网络（MLA-GNN）模型。所提出的方案通过从基因表达和DNA甲基化数据中去除噪声和冗余信息来使用特征选择策略。此外，它采用笛卡尔积方法来整合多组学数据集。该研究还使用WGCNA分析基因相互作用，并通过MLA-GNN模型识别潜在基因，为解决这些问题提供了创新方法。此外，本文通过基因本体论和生存分析将FOXL2鉴定为一种有前景的肝癌标志物。使用箱线图进行的验证表明，FOXL2基因在肝细胞癌患者中的表达高于正常个体。FOXL2与肝癌靶向药物乐伐替尼的药物敏感性相关性和分子对接结果强调了其在肝细胞癌治疗中的潜在作用，并突出了FOXL2在肝细胞癌治疗中的重要性。
