TY - JOUR
T1 - Functionally Enigmatic Genes in Cancer
T2 - Using TCGA Data to Map the Limitations of Annotations
AU - Maertens, Alexandra
AU - Tran, Vy H.
AU - Maertens, Mikhail
AU - Kleensang, Andre
AU - Luechtefeld, Thomas H.
AU - Hartung, Thomas
AU - Paller, Channing J.
N1 - Funding Information:
V.H.T. is supported by NIH training grant number 2T32ES007141. C.P. is supported by NIH grant numbers P30CA006973 and K23 CA197526.
Publisher Copyright:
© 2020, The Author(s).
PY - 2020/12/1
Y1 - 2020/12/1
N2 - 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.
AB - 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.
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U2 - 10.1038/s41598-020-60456-x
DO - 10.1038/s41598-020-60456-x
M3 - Article
C2 - 32139709
AN - SCOPUS:85081366199
SN - 2045-2322
VL - 10
JO - Scientific Reports
JF - Scientific Reports
IS - 1
M1 - 4106
ER -