Evidence map›Paper›PMID 39433978›Full record

ReviewNature reviews. Cancer2024

Emerging strategies to investigate the biology of early cancer.

Ran Zhou, Xiwen Tang, Yuan Wang

Abstract readReview
PubMed Publisher
In one paragraph

Review in Nature reviews. Cancer, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
34citing 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

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

  1. Pooled it
  2. Article
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  6. Article
  7. Article
  8. Article
  9. Article
  10. Review
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  12. Review
  13. Article
  14. Signaling pathways and targeted interventions for precancers.Signal transduction and targeted therapy · 2026
    Review
  15. Article
  16. Article
  17. Effects of Common Fig (International journal of molecular sciences · 2025
    Review
  18. Article
  19. Cell lineage tracing: Methods, applications, and challenges.Quantitative biology (Beijing, China) · 2025
    Review
  20. 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

3 authors.

Ran ZhouDepartment of Neurosurgery, State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, China.ORCID http://orcid.org/0000-0002-8175-2508
Xiwen TangDepartment of Neurosurgery, State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, China.ORCID http://orcid.org/0009-0009-7129-349X
Yuan WangDepartment of Neurosurgery, State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, China. wangyuan@scu.edu.cn.ORCID http://orcid.org/0000-0002-6324-6134

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Early detection and intervention of cancer or precancerous lesions hold great promise to improve patient survival. However, the processes of cancer initiation and the normal-precancer-cancer progression within a non-cancerous tissue context remain poorly understood. This is, in part, due to the scarcity of early-stage clinical samples or suitable models to study early cancer. In this Review, we introduce clinical samples and model systems, such as autochthonous mice and organoid-derived or stem cell-derived models that allow longitudinal analysis of early cancer development. We also present the emerging techniques and computational tools that enhance our understanding of cancer initiation and early progression, including direct imaging, lineage tracing, single-cell and spatial multi-omics, and artificial intelligence models. Together, these models and techniques facilitate a more comprehensive understanding of the poorly characterized early malignant transformation cascade, holding great potential to unveil key drivers and early biomarkers for cancer development. Finally, we discuss how these new insights can potentially be translated into mechanism-based strategies for early cancer detection and prevention.

Indexed as

NeoplasmsAnimalsArtificial IntelligenceBiomarkers, TumorCell Transformation, NeoplasticDisease ProgressionEarly Detection of CancerHumansMicePrecancerous ConditionsBiomarkers, Tumor

Identifiers

What OpenQuestion holds

Textmetadata
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.