Literature DB >> 33377150

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

Shuang Qiu1, Zheng An1, Renbo Tan1, Ping-An He2, Jingjing Jing3, Hongxia Li3, Shuang Wu4, Ying Xu5.   

Abstract

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.
© The Author(s) 2020. Published by Oxford University Press.

Entities:  

Keywords:  cancer occurrence rate; cancer risk; cervical cancer; testicular cancer; triple negative breast cancer

Year:  2021        PMID: 33377150      PMCID: PMC8294564          DOI: 10.1093/bib/bbaa349

Source DB:  PubMed          Journal:  Brief Bioinform        ISSN: 1467-5463            Impact factor:   11.622


  46 in total

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4.  ELK1-induced upregulation of long non-coding RNA MIR100HG predicts poor prognosis and promotes the progression of osteosarcoma by epigenetically silencing LATS1 and LATS2.

Authors:  Xiaochuan Su; Junyan Teng; Guoguo Jin; Jitian Li; Zhenjiang Zhao; Xiangyang Cao; Yanxing Guo; Malong Guo; Xiaoling Li; Jun Wu; Chuanzhen Wang; Zhiping Guo; Qing Guo
Journal:  Biomed Pharmacother       Date:  2018-11-05       Impact factor: 6.529

5.  Cancer etiology. Variation in cancer risk among tissues can be explained by the number of stem cell divisions.

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Journal:  Science       Date:  2015-01-02       Impact factor: 47.728

6.  Human housekeeping genes, revisited.

Authors:  Eli Eisenberg; Erez Y Levanon
Journal:  Trends Genet       Date:  2013-06-27       Impact factor: 11.639

Review 7.  Hallmarks of cancer: the next generation.

Authors:  Douglas Hanahan; Robert A Weinberg
Journal:  Cell       Date:  2011-03-04       Impact factor: 41.582

8.  NCBI GEO: archive for functional genomics data sets--update.

Authors:  Tanya Barrett; Stephen E Wilhite; Pierre Ledoux; Carlos Evangelista; Irene F Kim; Maxim Tomashevsky; Kimberly A Marshall; Katherine H Phillippy; Patti M Sherman; Michelle Holko; Andrey Yefanov; Hyeseung Lee; Naigong Zhang; Cynthia L Robertson; Nadezhda Serova; Sean Davis; Alexandra Soboleva
Journal:  Nucleic Acids Res       Date:  2012-11-27       Impact factor: 16.971

9.  Gene expression of human lung cancer cell line CL1-5 in response to a direct current electric field.

Authors:  Ching-Wen Huang; Huai-Yi Chen; Meng-Hua Yen; Jeremy J W Chen; Tai-Horng Young; Ji-Yen Cheng
Journal:  PLoS One       Date:  2011-10-05       Impact factor: 3.240

Review 10.  Roles of telomeres and telomerase in cancer, and advances in telomerase-targeted therapies.

Authors:  Mohammad A Jafri; Shakeel A Ansari; Mohammed H Alqahtani; Jerry W Shay
Journal:  Genome Med       Date:  2016-06-20       Impact factor: 11.117

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  2 in total

1.  i6mA-VC: A Multi-Classifier Voting Method for the Computational Identification of DNA N6-methyladenine Sites.

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Journal:  Interdiscip Sci       Date:  2021-04-08       Impact factor: 2.233

Review 2.  Protein nanoparticles in drug delivery: animal protein, plant proteins and protein cages, albumin nanoparticles.

Authors:  Ehsan Kianfar
Journal:  J Nanobiotechnology       Date:  2021-05-29       Impact factor: 10.435

  2 in total

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