Evidence map›Paper›PMID 39875356›Full record

ReviewCell death discovery2025

Targeting ferroptosis: a promising approach for treating lung carcinoma.

Ziyang Wu, Yan Zhang, Wendi Zhong, Kunjian Wu, Tian Zhong, Tao Jiang

Abstract readReview
In one paragraph

Review in Cell death discovery, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing 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

13 citing papers in PubMed.

  1. Review
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  5. Frontiers in oncology · 2026
    Article
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  7. Review
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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

6 authors.

Ziyang WuSchool of Life Sciences, Zhuhai College of Science and Technology, Zhuhai, Guangdong, China.
Yan ZhangDepartment of Thoracic Surgery, The First Hospital of Jilin University, Changchun, Jilin, China.
Wendi ZhongSchool of Life Sciences, Zhuhai College of Science and Technology, Zhuhai, Guangdong, China.
Kunjian WuSchool of Life Sciences, Zhuhai College of Science and Technology, Zhuhai, Guangdong, China.
Tian ZhongFaculty of Medicine, Macau University of Science and Technology, Taipa, Macao.
Tao JiangSchool of Life Sciences, Zhuhai College of Science and Technology, Zhuhai, Guangdong, China. jiangtao@zcst.edu.cn.ORCID http://orcid.org/0000-0003-0002-8890

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Lung carcinoma incidence and fatality rates remain among the highest on a global scale. The efficacy of targeted therapies and immunotherapies is commonly compromised by the emergence of drug resistance and other factors, resulting in a lack of durable therapeutic benefits. Ferroptosis, a distinct pattern of cell death marked by the buildup of iron-dependent lipid peroxides, has been shown to be a novel and potentially more effective treatment for lung carcinoma. However, the mechanism and regulatory network of ferroptosis are exceptionally complex, and many unanswered questions remain. In addition, research on ferroptosis in the diagnosis and treatment of lung cancer has been growing exponentially. Therefore, it is necessary to provide a thorough summary of the latest advancements in the field of ferroptosis. Here, we comprehensively analyze the mechanisms underlying the preconditions of ferroptosis, the defense system, and the associated molecular networks. The potential strategies of ferroptosis in the treatment of lung carcinoma are also highlighted. Targeting ferroptosis improves tumor cell drug resistance and enhances the effectiveness of targeted drugs and immunotherapies. These findings may shed fresh light on the diagnosis and management of lung carcinoma, as well as the development of drugs related to ferroptosis.

Identifiers

PMID39875356
PMCPMC11775225

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Registered trials

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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.