Zhang Yanhui, Dong Kunjie, Sun Wenli, Gao Zhenbo, Zhang Jianjun, Lin Xiaohui
School of Computer Science and Technology, Dalian University of Technology, Dalian 116024, China.
Department of Gastric Surgery, Cancer Hospital of Dalian University of Technology (Liaoning Cancer Hospital & Institute), Shenyang 110042, China.
Genes (Basel). 2025 Jul 28;16(8):902. doi: 10.3390/genes16080902.
The identification of microRNA (miRNA) biomarkers is crucial in advancing disease research and improving diagnostic precision. Network-based analysis methods are powerful for identifying disease-related biomarkers. However, it is a challenge to generate a robust molecular network that can accurately reflect miRNA interactions and define reliable miRNA biomarkers. To tackle this issue, we propose a disease-related miRNA biomarker identification method based on the knowledge-enhanced bio-network (BIM-Ken) by combining the miRNA expression data and prior knowledge. BIM-Ken constructs the miRNA cooperation network by examining the miRNA interactions based on the miRNA expression data, which contains characteristics about the specific disease, and the information of the network nodes (miRNAs) is enriched by miRNA knowledge (i.e., miRNA-disease associations) from databases. Further, BIM-Ken optimizes the miRNA cooperation network using the well-designed GAE (graph auto-encoder). We improve the loss function by introducing the functional consistency and the difference prompt, so as to facilitate the optimized network to keep the intrinsically important characteristics of the miRNA data about the specific disease and the prior knowledge. The experimental results on the public datasets showed the superiority of BIM-Ken in classification. Subsequently, BIM-Ken was applied to analyze renal cell carcinoma data, and the defined key modules demonstrated involvement in the cancer-related pathways with good discrimination ability.
微小RNA（miRNA）生物标志物的识别对于推进疾病研究和提高诊断精度至关重要。基于网络的分析方法在识别疾病相关生物标志物方面很强大。然而，生成一个能够准确反映miRNA相互作用并定义可靠miRNA生物标志物的稳健分子网络是一项挑战。为了解决这个问题，我们通过结合miRNA表达数据和先验知识，提出了一种基于知识增强生物网络（BIM-Ken）的疾病相关miRNA生物标志物识别方法。BIM-Ken通过基于包含特定疾病特征的miRNA表达数据检查miRNA相互作用来构建miRNA合作网络，并且网络节点（miRNAs）的信息通过来自数据库的miRNA知识（即miRNA-疾病关联）得到丰富。此外，BIM-Ken使用精心设计的图自动编码器（GAE）优化miRNA合作网络。我们通过引入功能一致性和差异提示来改进损失函数，以便促进优化后的网络保留关于特定疾病的miRNA数据和先验知识的内在重要特征。在公共数据集上的实验结果表明BIM-Ken在分类方面具有优越性。随后，BIM-Ken被应用于分析肾细胞癌数据，所定义的关键模块显示出参与癌症相关途径且具有良好的区分能力。
