IMI-driver：整合多层基因网络与多组学数据用于癌症驱动基因识别

IMI-driver: Integrating multi-level gene networks and multi-omics for cancer driver gene identification

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PLoS Computational Biology

影响因子:3.6

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

DOI: 10.1371/journal.pcbi.1012389

摘要

癌症驱动基因的识别对早期检测、有效治疗和精准医疗至关重要。癌症由多层调控异常引起，但现有技术仅捕获有限的调控信息，可能影响其效果。本研究提出IMI-driver，一种将多组学数据整合入八个生物网络，通过多视角协作网络嵌入（Multi-view Collaborative Network Embedding）将基因调控信息嵌入低维向量空间以识别癌症驱动基因的方法。我们将IMI-driver应用于来自癌症基因组图谱（TCGA）的29种癌症类型，并与九种其他方法在九个基准数据集上进行性能对比。结果显示，IMI-driver优于其他方法，验证了多层网络整合可提高预测准确性。我们还对全癌种进行了候选驱动基因的泛癌分析，几乎所有筛选出的候选基因都已知或潜在为驱动基因。个案研究中的新阳性基因提示其在癌症发展和进展中的潜在作用。

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.