Evidence map›Paper›PMID 33519933›Full record

ArticleJournal of oncology2021

Ferroptosis-Related Gene Model to Predict Overall Survival of Ovarian Carcinoma.

Liuqing Yang, Saisai Tian, Yun Chen, Chenyun Miao, Ying Zhao, Ruye Wang, Qin Zhang

Abstract read
In one paragraph

Article in Journal of oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.

0numbers the graph read from it
0cells of the map it votes in
29citing 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

29 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Article
  6. Role of SLC7A11/xCT in Ovarian Cancer.International journal of molecular sciences · 2024
    Review
  7. Review
  8. Review
  9. Article
  10. Article
  11. Article
  12. Review
  13. Translational cancer research · 2022
    Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Review
  19. Article
  20. Current progress of ferroptosis study in ovarian cancer.Frontiers in molecular biosciences · 2022
    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

7 authors.

Liuqing YangGuangxing Hospital Affiliated to Zhejiang Chinese Medical University, Hangzhou, Zhejiang 310007, China.
Saisai TianDepartment of Phytochemistry, School of Pharmacy, The Second Military Medical University, Shanghai 200433, China.
Yun ChenGuangxing Hospital Affiliated to Zhejiang Chinese Medical University, Hangzhou, Zhejiang 310007, China.
Chenyun MiaoZhejiang Chinese Medical University, Hangzhou, Zhejiang 310053, China.
Ying ZhaoZhejiang Chinese Medical University, Hangzhou, Zhejiang 310053, China.
Ruye WangZhejiang Chinese Medical University, Hangzhou, Zhejiang 310053, China.
Qin ZhangGuangxing Hospital Affiliated to Zhejiang Chinese Medical University, Hangzhou, Zhejiang 310007, China.ORCID https://orcid.org/0000-0002-9845-4099

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundOvarian cancer (OC) is the eighth most common cause of cancer death and the second cause of gynecologic cancer death in women around the world. Ferroptosis, an iron-dependent regulated cell death, plays a vital role in the development of many cancers. Applying expression of ferroptosis-related gene to forecast the cancer progression is helpful for cancer treatment. However, the relationship between ferroptosis-related genes and OC patient prognosis is still vastly unknown, making it still a challenge for developing ferroptosis therapy for OC.

methodsThe Cancer Genome Atlas (TCGA) data of OC were obtained and the datasets were randomly divided into training and test datasets. A novel ferroptosis-related gene signature associated with overall survival (OS) was constructed according to the training cohort. The test dataset and ICGC dataset were used to validate this signature.

resultsWe constructed a model containing nine ferroptosis-related genes, namely,

conclusionThe ferroptosis-related gene signature could predict the OS of OC patients and improve therapeutic decision-making.

Identifiers

PMID33519933
PMCPMC7817275

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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.