Title: Functionally Enigmatic Genes in Cancer: Using TCGA Data to Map the Limitations of Annotations.

Authors: Maertens, Alexandra; Tran, Vy P; Maertens, Mikhail; Kleensang, Andre; Luechtefeld, Thomas H; Hartung, Thomas; Paller, Channing J

Published In Sci Rep, (2020 03 05)

Abstract: Cancer is a comparatively well-studied disease, yet despite decades of intense focus, we demonstrate here using data from The Cancer Genome Atlas that a substantial number of genes implicated in cancer are relatively poorly studied. Those genes will likely be missed by any data analysis pipeline, such as enrichment analysis, that depends exclusively on annotations for understanding biological function. There is no indication that the amount of research - indicated by number of publications - is correlated with any objective metric of gene significance. Moreover, these genes are not missing at random but reflect that our information about genes is gathered in a biased manner: poorly studied genes are more likely to be primate-specific and less likely to have a Mendelian inheritance pattern, and they tend to cluster in some biological processes and not others. While this likely reflects both technological limitations as well as the fact that well-known genes tend to gather more interest from the research community, in the absence of a concerted effort to study genes in an unbiased way, many genes (and biological processes) will remain opaque.

PubMed ID: 32139709

MeSH Terms: No MeSH terms associated with this publication