A 阿瑾 Jin, Xiang 项炬 Ju, Meng 孟祥茂 Xiangmao, Sheng 盛岳 Yue, Peng 彭宏凌 Hongling, Li 李敏 Min
School of Computer Science and Engineering, Central South University, Changsha 410083, China.
School of Computer and Communication Engineering, Changsha University of Science & Technology, Changsha 410114, China.
Genomics Proteomics Bioinformatics. 2025 May 30;23(2). doi: 10.1093/gpbjnl/qzaf037.
Leukemia is a malignant disease characterized by progressive accumulation with high morbidity and mortality rates, and investigating its disease genes is crucial for understanding its etiology and pathogenesis. Network propagation methods have emerged and been widely employed in disease gene prediction, but most of them focus on static biological networks, which hinders their applicability and effectiveness in the study of progressive diseases. Moreover, there is currently a lack of special algorithms for the identification of leukemia disease genes. Here, we proposed a novel Dynamic Network-based model integrating Differentially expressed Genes (DyNDG) to identify leukemia-related genes. Initially, we constructed a time-series dynamic network to model the development trajectory of leukemia. Then, we built a background-temporal multilayer network by integrating both the dynamic network and the static background network, which was initialized with differentially expressed genes at each stage. To quantify the associations between genes and leukemia, we extended a random walk process to the background-temporal multilayer network. The results demonstrate that DyNDG achieves superior accuracy compared to several state-of-the-art methods. Moreover, after excluding housekeeping genes, DyNDG yields a set of promising candidate genes associated with leukemia progression or potential biomarkers, indicating the value of dynamic network information in identifying leukemia-related genes. The implementation of DyNDG is available at both https://ngdc.cncb.ac.cn/biocode/tool/BT7617 and https://github.com/CSUBioGroup/DyNDG.
白血病是一种恶性疾病，其特征是细胞进行性累积，发病率和死亡率都很高。研究白血病的致病基因对于了解其病因和发病机制至关重要。网络传播方法已出现并广泛应用于疾病基因预测，但大多数方法都集中在静态生物网络上，这限制了它们在进行性疾病研究中的适用性和有效性。此外，目前缺乏用于识别白血病致病基因的特殊算法。在此，我们提出了一种基于动态网络的新型模型——整合差异表达基因的动态网络模型（DyNDG），用于识别白血病相关基因。首先，我们构建了一个时间序列动态网络来模拟白血病的发展轨迹。然后，通过整合动态网络和静态背景网络构建了一个背景时间多层网络，该网络在每个阶段都由差异表达基因初始化。为了量化基因与白血病之间的关联，我们将随机游走过程扩展到背景时间多层网络。结果表明，与几种最先进的方法相比，DyNDG具有更高的准确性。此外，在排除管家基因后，DyNDG产生了一组与白血病进展相关的有前景的候选基因或潜在生物标志物，这表明动态网络信息在识别白血病相关基因方面的价值。DyNDG的实现可在https://ngdc.cncb.ac.cn/biocode/tool/BT7617和https://github.com/CSUBioGroup/DyNDG上获取。
