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Understanding the unimodal distributions of cancer occurrence rates: it takes two factors for a cancer to occur

Data from the SEER reports reveal that the occurrence rate of a cancer type generally follows a unimodal distribution over age, peaking at an age that is cancer-type specific and ranges from 30+ through 70+. Previous studies attribute such bell-shaped distributions to the reduced proliferative poten...

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Autores principales: Qiu, Shuang, An, Zheng, Tan, Renbo, He, Ping-an, Jing, Jingjing, Li, Hongxia, Wu, Shuang, Xu, Ying
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8294564/
https://www.ncbi.nlm.nih.gov/pubmed/33377150
http://dx.doi.org/10.1093/bib/bbaa349
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author Qiu, Shuang
An, Zheng
Tan, Renbo
He, Ping-an
Jing, Jingjing
Li, Hongxia
Wu, Shuang
Xu, Ying
author_facet Qiu, Shuang
An, Zheng
Tan, Renbo
He, Ping-an
Jing, Jingjing
Li, Hongxia
Wu, Shuang
Xu, Ying
author_sort Qiu, Shuang
collection PubMed
description Data from the SEER reports reveal that the occurrence rate of a cancer type generally follows a unimodal distribution over age, peaking at an age that is cancer-type specific and ranges from 30+ through 70+. Previous studies attribute such bell-shaped distributions to the reduced proliferative potential in senior years but fail to explain why some cancers have their occurrence peak at 30+ or 40+. We present a computational model to offer a new explanation to such distributions. The model uses two factors to explain the observed age-dependent cancer occurrence rates: cancer risk of an organ and the availability level of the growth signals in circulation needed by a cancer type, with the former increasing and the latter decreasing with age. Regression analyses were conducted of known occurrence rates against such factors for triple negative breast cancer, testicular cancer and cervical cancer; and all achieved highly tight fitting results, which were also consistent with clinical, gene-expression and cancer-drug data. These reveal a fundamentally important relationship: while cancer is driven by endogenous stressors, it requires sufficient levels of exogenous growth signals to happen, hence suggesting the realistic possibility for treating cancer via cleaning out the growth signals in circulation needed by a cancer.
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spelling pubmed-82945642021-07-22 Understanding the unimodal distributions of cancer occurrence rates: it takes two factors for a cancer to occur Qiu, Shuang An, Zheng Tan, Renbo He, Ping-an Jing, Jingjing Li, Hongxia Wu, Shuang Xu, Ying Brief Bioinform Case Study Data from the SEER reports reveal that the occurrence rate of a cancer type generally follows a unimodal distribution over age, peaking at an age that is cancer-type specific and ranges from 30+ through 70+. Previous studies attribute such bell-shaped distributions to the reduced proliferative potential in senior years but fail to explain why some cancers have their occurrence peak at 30+ or 40+. We present a computational model to offer a new explanation to such distributions. The model uses two factors to explain the observed age-dependent cancer occurrence rates: cancer risk of an organ and the availability level of the growth signals in circulation needed by a cancer type, with the former increasing and the latter decreasing with age. Regression analyses were conducted of known occurrence rates against such factors for triple negative breast cancer, testicular cancer and cervical cancer; and all achieved highly tight fitting results, which were also consistent with clinical, gene-expression and cancer-drug data. These reveal a fundamentally important relationship: while cancer is driven by endogenous stressors, it requires sufficient levels of exogenous growth signals to happen, hence suggesting the realistic possibility for treating cancer via cleaning out the growth signals in circulation needed by a cancer. Oxford University Press 2020-12-30 /pmc/articles/PMC8294564/ /pubmed/33377150 http://dx.doi.org/10.1093/bib/bbaa349 Text en © The Author(s) 2020. Published by Oxford University Press. https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) ), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Case Study
Qiu, Shuang
An, Zheng
Tan, Renbo
He, Ping-an
Jing, Jingjing
Li, Hongxia
Wu, Shuang
Xu, Ying
Understanding the unimodal distributions of cancer occurrence rates: it takes two factors for a cancer to occur
title Understanding the unimodal distributions of cancer occurrence rates: it takes two factors for a cancer to occur
title_full Understanding the unimodal distributions of cancer occurrence rates: it takes two factors for a cancer to occur
title_fullStr Understanding the unimodal distributions of cancer occurrence rates: it takes two factors for a cancer to occur
title_full_unstemmed Understanding the unimodal distributions of cancer occurrence rates: it takes two factors for a cancer to occur
title_short Understanding the unimodal distributions of cancer occurrence rates: it takes two factors for a cancer to occur
title_sort understanding the unimodal distributions of cancer occurrence rates: it takes two factors for a cancer to occur
topic Case Study
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8294564/
https://www.ncbi.nlm.nih.gov/pubmed/33377150
http://dx.doi.org/10.1093/bib/bbaa349
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