Bioinformatics Institute (BII), Agency for Science, Technology and Research (A*STAR), Singapore, 138671, Republic of Singapore.
Department of Computer Science, Lakehead University, Thunder Bay, ON, P7B 5E1, Canada.
NPJ Syst Biol Appl. 2023 Jun 24;9(1):28. doi: 10.1038/s41540-023-00290-9.
Cancer is widely considered a genetic disease. Notably, recent works have highlighted that every human gene may possibly be associated with cancer. Thus, the distinction between genes that drive oncogenesis and those that are associated to the disease, but do not play a role, requires attention. Here we investigated single cells and bulk (cell-population) datasets of several cancer transcriptomes and proteomes in relation to their healthy counterparts. When analyzed by machine learning and statistical approaches in bulk datasets, both general and cancer-specific oncogenes, as defined by the Cancer Genes Census, show invariant behavior to randomly selected gene sets of the same size for all cancers. However, when protein-protein interaction analyses were performed, the oncogenes-derived networks show higher connectivity than those relative to random genes. Moreover, at single-cell scale, we observe variant behavior in a subset of oncogenes for each considered cancer type. Moving forward, we concur that the role of oncogenes needs to be further scrutinized by adopting protein causality and higher-resolution single-cell analyses.
癌症被广泛认为是一种遗传性疾病。值得注意的是，最近的研究工作强调，人类的每个基因都可能与癌症有关。因此，需要注意区分那些驱动致癌的基因和那些与疾病相关但不起作用的基因。在这里，我们研究了几种癌症转录组和蛋白质组的单细胞和批量（细胞群体）数据集，以及它们与健康对照组的关系。当在批量数据集中通过机器学习和统计方法进行分析时，癌症基因普查定义的一般和癌症特异性致癌基因，对于所有癌症的相同大小的随机选择基因集，表现出不变的行为。然而，当进行蛋白质-蛋白质相互作用分析时，致癌基因衍生的网络显示出比随机基因更高的连接性。此外，在单细胞水平上，我们观察到每个所考虑的癌症类型中，一部分致癌基因表现出不同的行为。向前推进，我们一致认为，需要通过采用蛋白质因果关系和更高分辨率的单细胞分析来进一步研究致癌基因的作用。
