Evidence map›Paper›PMID 38773187›Full record

ArticleScientific reports2024

The molecular evolution of cancer associated genes in mammals.

Nick MacDonald, Nynke Raven, Wendy Diep, Samantha Evans, Senuri Pannipitiya, Georgina Bramwell, Caitlin Vanbeek, Frédéric Thomas, Tracey Russell, Antoine M Dujon and 2 more

Abstract read
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Review
  2. Review
  3. Article
  4. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Nick MacDonaldSchool of Life and Environmental Sciences, Deakin University, Geelong, Waurn Ponds, Geelong, VIC, 3216, Australia.
Nynke Raven *School of Life and Environmental Sciences, Deakin University, Geelong, Waurn Ponds, Geelong, VIC, 3216, Australia.
Wendy DiepSchool of Life and Environmental Sciences, Deakin University, Geelong, Waurn Ponds, Geelong, VIC, 3216, Australia.
Samantha EvansSchool of Life and Environmental Sciences, Deakin University, Geelong, Waurn Ponds, Geelong, VIC, 3216, Australia.
Senuri PannipitiyaSchool of Life and Environmental Sciences, Deakin University, Geelong, Waurn Ponds, Geelong, VIC, 3216, Australia.
Georgina BramwellSchool of Life and Environmental Sciences, Deakin University, Geelong, Waurn Ponds, Geelong, VIC, 3216, Australia.
Caitlin VanbeekSchool of Life and Environmental Sciences, Deakin University, Geelong, Waurn Ponds, Geelong, VIC, 3216, Australia.
Frédéric ThomasCREEC, UMR IRD 224-CNRS 5290, Université de Montpellier, Montpellier, France.ORCID 0000-0003-2238-1978
Tracey RussellFaculty of Science, School of Life and Environmental Sciences, Sydney, NSW, Australia.
Antoine M DujonSchool of Life and Environmental Sciences, Deakin University, Geelong, Waurn Ponds, Geelong, VIC, 3216, Australia.
Marina Telonis-ScottSchool of Life and Environmental Sciences, Deakin University, Burwood, Burwood, VIC, 3125, Australia.
Beata Ujvari *School of Life and Environmental Sciences, Deakin University, Geelong, Waurn Ponds, Geelong, VIC, 3216, Australia. beata.ujvari@deakin.edu.au.ORCID 0000-0003-2391-2988

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cancer is a disease that many multicellular organisms have faced for millions of years, and species have evolved various tumour suppression mechanisms to control oncogenesis. Although cancer occurs across the tree of life, cancer related mortality risks vary across mammalian orders, with Carnivorans particularly affected. Evolutionary theory predicts different selection pressures on genes associated with cancer progression and suppression, including oncogenes, tumour suppressor genes and immune genes. Therefore, we investigated the evolutionary history of cancer associated gene sequences across 384 mammalian taxa, to detect signatures of selection across categories of oncogenes (GRB2, FGL2 and CDC42), tumour suppressors (LITAF, Casp8 and BRCA2) and immune genes (IL2, CD274 and B2M). This approach allowed us to conduct a fine scale analysis of gene wide and site-specific signatures of selection across mammalian lineages under the lens of cancer susceptibility. Phylogenetic analyses revealed that for most species the evolution of cancer associated genes follows the species' evolution. The gene wide selection analyses revealed oncogenes being the most conserved, tumour suppressor and immune genes having similar amounts of episodic diversifying selection. Despite BRCA2's status as a key caretaker gene, episodic diversifying selection was detected across mammals. The site-specific selection analyses revealed that the two apoptosis associated domains of the Casp8 gene of bats (Chiroptera) are under opposing forces of selection (positive and negative respectively), highlighting the importance of site-specific selection analyses to understand the evolution of highly complex gene families. Our results highlighted the need to critically assess different types of selection pressure on cancer associated genes when investigating evolutionary adaptations to cancer across the tree of life. This study provides an extensive assessment of cancer associated genes in mammals with highly representative, and substantially large sample size for a comparative genomic analysis in the field and identifies various avenues for future research into the mechanisms of cancer resistance and susceptibility in mammals.

Indexed as

Evolution, MolecularMammalsNeoplasmsPhylogenyAnimalsGenes, Tumor SuppressorGenetic Predisposition to DiseaseHumansOncogenesSelection, GeneticBioinformaticsCancer susceptibilityComparative genomicsEvolutionHyPhyMammals

Identifiers

PMID38773187
PMCPMC11109183

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.