School of Computer Engineering and Science, Shanghai University, NanChen Road 333, Shanghai 200444, China.
Laboratory of Molecular Neural Biology, School of Life Sciences, Shanghai University, Nanchen Road 333, Shanghai 200444, China.
Biomolecules. 2020 Feb 17;10(2):318. doi: 10.3390/biom10020318.
Glioblastoma (GBM) is a fast-growing type of malignant primary brain tumor. To explore the mechanisms in GBM, complex biological networks are used to reveal crucial changes among different biological states, which reflect on the development of living organisms. It is critical to discover the kernel differential subgraph (KDS) that leads to drastic changes. However, identifying the KDS is similar to the Steiner Tree problem that is an NP-hard problem. In this paper, we developed a criterion to explore the KDS (CKDS), which considered the connectivity and scale of KDS, the topological difference of nodes and function relevance between genes in the KDS. The CKDS algorithm was applied to simulated datasets and three single-cell RNA sequencing (scRNA-seq) datasets including GBM, fetal human cortical neurons (FHCN) and neural differentiation. Then we performed the network topology and functional enrichment analyses on the extracted KDSs. Compared with the state-of-art methods, the CKDS algorithm outperformed on simulated datasets to discover the KDSs. In the GBM and FHCN, seventeen genes (one biomarker, nine regulatory genes, one driver genes, six therapeutic targets) and KEGG pathways in KDSs were strongly supported by literature mining that they were highly interrelated with GBM. Moreover, focused on GBM, there were fifteen genes (including ten regulatory genes, three driver genes, one biomarkers, one therapeutic target) and KEGG pathways found in the KDS of neural differentiation process from activated neural stem cells (aNSC) to neural progenitor cells (NPC), while few genes and no pathway were found in the period from NPC to astrocytes (Ast). These experiments indicated that the process from aNSC to NPC is a key differentiation period affecting the development of GBM. Therefore, the CKDS algorithm provides a unique perspective in identifying cell-type-specific genes and KDSs.
胶质母细胞瘤（GBM）是一种快速生长的恶性原发性脑肿瘤。为了探索 GBM 中的机制，复杂的生物网络被用来揭示不同生物状态之间的关键变化，这些变化反映了生物体的发展。发现导致剧烈变化的核心差异子图（KDS）至关重要。然而，识别 KDS 类似于 Steiner 树问题，这是一个 NP 难问题。在本文中，我们开发了一种探索 KDS 的标准（CKDS），该标准考虑了 KDS 的连通性和规模、节点的拓扑差异以及 KDS 中基因之间的功能相关性。CKDS 算法应用于模拟数据集和三个单细胞 RNA 测序（scRNA-seq）数据集，包括 GBM、胎儿人类皮质神经元（FHCN）和神经分化。然后，我们对提取的 KDS 进行了网络拓扑和功能富集分析。与最先进的方法相比，CKDS 算法在模拟数据集中表现更好，能够发现 KDS。在 GBM 和 FHCN 中，有 17 个基因（一个生物标志物、9 个调控基因、一个驱动基因、6 个治疗靶点）和 KDS 中的 KEGG 途径在文献挖掘中得到了强有力的支持，它们与 GBM 高度相关。此外，在 GBM 中，在从激活的神经干细胞（aNSC）到神经祖细胞（NPC）的神经分化过程的 KDS 中发现了 15 个基因（包括 10 个调控基因、3 个驱动基因、一个生物标志物和一个治疗靶点）和 KEGG 途径，而在 NPC 到星形胶质细胞（Ast）的过程中则没有发现基因和途径。这些实验表明，从 aNSC 到 NPC 的过程是一个关键的分化时期，影响 GBM 的发展。因此，CKDS 算法为识别细胞类型特异性基因和 KDS 提供了独特的视角。
