Evidence map›Paper›PMID 39796131›Full record

ArticleInternational journal of molecular sciences2024

Elucidation of Factors Affecting the Age-Dependent Cancer Occurrence Rates.

Jun Xiao, Yangkun Cao, Xuan Li, Long Xu, Zhihang Wang, Zhenyu Huang, Xuechen Mu, Yinwei Qu, Ying Xu

Abstract read
In one paragraph

Article in International journal of molecular sciences, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed, 1 pooled it
–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

8 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Article
  6. Review
  7. Review
  8. Mechanistic study on the role of multi-pathway autophagy in ovarian aging: literature review.Apoptosis : an international journal on programmed cell death · 2025
    Review
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

9 authors.

Jun XiaoCollege of Computer Science and Technology, Jilin University, Changchun 130012, China.ORCID 0000-0002-4940-0174
Yangkun CaoSystems Biology Laboratory for Metabolic Reprogramming, School of Medicine, Southern University of Science and Technology, Shenzhen 518055, China.ORCID 0000-0001-7240-5486
Xuan LiCollege of Computer Science and Technology, Jilin University, Changchun 130012, China.ORCID 0009-0007-6853-5668
Long XuSystems Biology Laboratory for Metabolic Reprogramming, School of Medicine, Southern University of Science and Technology, Shenzhen 518055, China.
Zhihang WangCollege of Computer Science and Technology, Jilin University, Changchun 130012, China.
Zhenyu HuangCollege of Computer Science and Technology, Jilin University, Changchun 130012, China.ORCID 0009-0009-0029-1729
Xuechen MuSystems Biology Laboratory for Metabolic Reprogramming, School of Medicine, Southern University of Science and Technology, Shenzhen 518055, China.
Yinwei QuCollege of Computer Science and Technology, Jilin University, Changchun 130012, China.ORCID 0009-0008-5232-5284
Ying XuSystems Biology Laboratory for Metabolic Reprogramming, School of Medicine, Southern University of Science and Technology, Shenzhen 518055, China.

Funding

Key University Laboratory of Metabolism and Health of Guangdong, Southern University of Science and Technology 2022KSYS007National Natural Science Foundation of China T2350010National Natural Science Fund of China Research Fund for International Scientists W2431059
6 · The paper itself

Abstract

Cancer occurrence rates exhibit diverse age-related patterns, and understanding them may shed new and important light on the drivers of cancer evolution. This study systematically analyzes the age-dependent occurrence rates of 23 carcinoma types, focusing on their age-dependent distribution patterns, the determinants of peak occurrence ages, and the significant difference between the two genders. According to the SEER reports, these cancer types have two types of age-dependent occurrence rate (ADOR) distributions, with most having a unimodal distribution and a few having a bimodal distribution. Our modeling analyses have revealed that (1) the first type can be naturally and simply explained using two age-dependent parameters: the total number of stem cell divisions in an organ from birth to the current age and the availability levels of bloodborne growth factors specifically needed by the cancer (sub)type, and (2) for the second type, the first peak is due to viral infection, while the second peak can be explained as in (1) for each cancer type. Further analyses indicate that (i) the iron level in an organ makes the difference between the male and female cancer occurrence rates, and (ii) the levels of sex hormones are the key determinants in the onset age of multiple cancer types. This analysis deepens our understanding of the dynamics of cancer evolution shared by diverse cancer types and provides new insights that are useful for cancer prevention and therapeutic strategies, thereby addressing critical gaps in the current paradigm of oncological research.

Indexed as

NeoplasmsAdultAgedAge FactorsFemaleHumansIncidenceMaleMiddle AgedSEER Programcancer occurrencecell cycleFenton reactiongrowth signalviral infection

Identifiers

PMID39796131
PMCPMC11720044

What OpenQuestion 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 OpenQuestion graph.