Network-based protein-protein interaction prediction method maps perturbations of cancer interactome

Abstract

<jats:title>Abstract</jats:title><jats:p>The landscape of the gene relationship/network (such as activation, expression, phosphorylation, and binding) in cancer is found different from the general (non-disease) situation, and gene network perturbations are supposed to be the main cause of cancer. Thus, it makes no sense to use a regular gene relationship prediction method to map the cancer gene network. Here, we established a novel prediction method that we dubbed network-based cancer gene relationship (NECARE), which achieved a high performance with a Matthews correlation coefficient (MCC) = 0.71±0.01 and an F1 = 89±0.7%. Then, we investigated the cancer interactome atlas and revealed a large-scale perturbation in the gene network in cancer using NECARE. We found 2287 genes, which were named cancer hub genes, that were enriched with gene interaction perturbations, and over 56% of cancer treatment-related genes were hub genes. We further assessed the association of hub genes with the prognosis of 32 types of cancers and found that hub genes were significantly related to the cancer outcomes. Furthermore, the mutations occurring on residues that bind to macromolecules were overrepresented at cancer hub genes. By coimmunoprecipitation (co-IP), we confirmed that the NECARE prediction method was highly reliable and was 90% accurate. NECARE is available at: <jats:ext-link xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="uri" xlink:href="https://github.com/JiajunQiu/NECARE">https://github.com/JiajunQiu/NECARE</jats:ext-link>.</jats:p>

A systemic study on the vulnerability and fatality of prostate cancer patients towards COVID-19 through analysis of the TMPRSS2, CXCL10 and their co-expressed genes

“…PPI network analysis tools utilize state-of-the-art algorithms and data from peer-reviewed research articles to illustrate the connection between proteins for better interpretation of underlying biological mechanisms for different diseases. PPI network analysis has been widely used in multiple cancer studies as it eases the interpretation of protein matrices on a molecular level [65][66][67]. From the PPI network analysis results, it is evident that TMPRSS2 is a crucial factor in COVID-19 prognosis, as its nodes connect to ACE2 and as per previous studies, TM-PRSS2 is a notable ACE2 primer.…”