IMI-Driver：整合多级基因网络和多兆以用于癌症驱动器基因鉴定
IMI-driver: Integrating multi-level gene networks and multi-omics for cancer driver gene identification
影响因子:3.60000
分区:生物学2区 / 生化研究方法2区 数学与计算生物学2区
发表日期:2024 Aug
作者:
Peiting Shi, Junmin Han, Yinghao Zhang, Guanpu Li, Xionghui Zhou
摘要
癌症驱动基因的鉴定对于早期发现，有效的治疗和癌症的精确药物至关重要。癌症是由各种调节水平的几个基因失调引起的。但是，当前技术仅捕获有限的监管信息，这可能会阻碍其功效。在这项研究中，我们提出了IMI-Driver，该模型将多摩学数据集成到八个生物网络中，并应用多视图协作网络嵌入嵌入从生物网络中的基因调节信息嵌入到低维矢量空间中以识别癌症驱动因素。我们将IMi-Driver应用于癌症基因组图集（TCGA）的29种癌症类型，并将其与9个基准数据集中的其他9种方法相比。 IMI-Driver优于其他方法，表明多级网络集成提高了预测准确性。我们还使用IMI-Driver鉴定的基因进行泛伴奏分析，该基因几乎证实了我们所选候选基因已知或潜在驱动因素。新阳性基因的案例研究表明它们在癌症发展和进展中的作用。
Abstract
The identification of cancer driver genes is crucial for early detection, effective therapy, and precision medicine of cancer. Cancer is caused by the dysregulation of several genes at various levels of regulation. However, current techniques only capture a limited amount of regulatory information, which may hinder their efficacy. In this study, we present IMI-driver, a model that integrates multi-omics data into eight biological networks and applies Multi-view Collaborative Network Embedding to embed the gene regulation information from the biological networks into a low-dimensional vector space to identify cancer drivers. We apply IMI-driver to 29 cancer types from The Cancer Genome Atlas (TCGA) and compare its performance with nine other methods on nine benchmark datasets. IMI-driver outperforms the other methods, demonstrating that multi-level network integration enhances prediction accuracy. We also perform a pan-cancer analysis using the genes identified by IMI-driver, which confirms almost all our selected candidate genes as known or potential drivers. Case studies of the new positive genes suggest their roles in cancer development and progression.