Evidence map›Paper›PMID 39688960›Full record

ArticleeLife2024

The theory of massively repeated evolution and full identifications of cancer-driving nucleotides (CDNs).

Lingjie Zhang, Tong Deng, Zhongqi Liufu, Xueyu Liu, Bingjie Chen, Zheng Hu, Chenli Liu, Miles E Tracy, Xuemei Lu, Hai-Jun Wen and 1 more

Abstract read
In one paragraph

Article in eLife, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

11 authors.

Lingjie ZhangState Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, China.ORCID https://orcid.org/0000-0002-6506-4457
Tong DengState Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, China.
Zhongqi LiufuState Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, China.
Xueyu LiuState Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, China.
Bingjie ChenState Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, China.
Zheng HuCAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.ORCID https://orcid.org/0000-0003-1552-0060
Chenli LiuCAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
Miles E TracyState Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, China.
Xuemei LuState Key Laboratory of Genetic Resources and Evolution/Yunnan Key Laboratory of Biodiversity Information, Kunming Institute of Zoology, The Chinese Academy of Sciences, Kunming, China.ORCID https://orcid.org/0000-0001-6044-6002
Hai-Jun WenState Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, China.ORCID https://orcid.org/0000-0001-8676-1254
Chung-I WuState Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, China.ORCID https://orcid.org/0000-0001-7263-4238

Funding

Guangdong Basic and Applied Basic Research Foundation 2023A1515010016Guangdong Key R&D Project of China 2022B1111030001National Key Research and Development Program of China 2021YFC0863400National Key Research and Development Program of China 2021YFC2301300National Natural Science Foundation of China 32150006National Natural Science Foundation of China 32200493National Natural Science Foundation of China 32293190National Natural Science Foundation of China 32293193National Natural Science Foundation of China 32370659National Natural Science Foundation of China 82341092Yunnan Revitalization Talent Support Program Top Team 202405AS350022
6 · The paper itself

Abstract

Tumorigenesis, like most complex genetic traits, is driven by the joint actions of many mutations. At the nucleotide level, such mutations are cancer-driving nucleotides (CDNs). The full sets of CDNs are necessary, and perhaps even sufficient, for the understanding and treatment of each cancer patient. Currently, only a small fraction of CDNs is known as most mutations accrued in tumors are not drivers. We now develop the theory of CDNs on the basis that cancer evolution is massively repeated in millions of individuals. Hence, any advantageous mutation should recur frequently and, conversely, any mutation that does not is either a passenger or deleterious mutation. In the TCGA cancer database (sample size

Indexed as

Evolution, MolecularMutationNeoplasmsNucleotidesCarcinogenesisHumansModels, GeneticNucleotidescancer biologycancer driverscancer evolutionevolutionary biologyhumanpoint mutations

Identifiers

PMID39688960
PMCPMC11651657

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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 Socratic graph.