Globally invariant behavior of oncogenes and random genes at population but not at single cell level

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.

Automated summary

Cancer is considered a genetic disease, and recent studies have shown that every human gene may be associated with cancer. The distinction between oncogenes that drive cancer and those that are associated but do not play a role is important. Through analysis of single-cell and bulk datasets of cancer transcriptomes and proteomes, it was found that oncogenes have higher connectivity in protein-protein interaction networks compared to random genes. Additionally, at the single-cell level, there is variant behavior in a subset of oncogenes for each cancer type. Further study using protein causality and higher-resolution single-cell analysis is needed to understand the role of oncogenes.