Evidence map›Paper›PMID 37554538›Full record

ArticleTranslational andrology and urology2023

Construction of a two-gene prognostic model related to ferroptosis in renal cell carcinoma.

Lei Li, Yawei Xu, Wuping Yang, Kenan Zhang, Zedan Zhang, Jingcheng Zhou, Yanqing Gong, Kan Gong

Abstract read
In one paragraph

Article in Translational andrology and urology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
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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

8 authors.

Lei Li *Department of Urology, Peking University First Hospital, Beijing, China.
Yawei Xu *Department of Urology, Peking University First Hospital, Beijing, China.
Wuping YangDepartment of Urology, Peking University First Hospital, Beijing, China.
Kenan ZhangDepartment of Urology, Peking University First Hospital, Beijing, China.
Zedan ZhangDepartment of Urology, Peking University First Hospital, Beijing, China.
Jingcheng ZhouDepartment of Urology, Peking University First Hospital, Beijing, China.
Yanqing GongDepartment of Urology, Peking University First Hospital, Beijing, China.
Kan GongDepartment of Urology, Peking University First Hospital, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Renal cell carcinoma (RCC) is a common and aggressive tumor. A newly discovered form of programmed cell death, ferroptosis, plays an important role in tumor development and progression. However, a clear prognostic correlation between Ferroptosis-related genes (FRGs) and RCC has not yet been established. In this study, prognostic markers associated with FRGs were investigated to improve the therapeutic, diagnostic, and preventive strategies available to patients with renal cancer. Methods: The present study analyzed the predictive value of 23 FRGs in RCC through bioinformatics techniques, including Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) tools, Kaplan-Meier survival analysis, Cox regression modeling, tumor mutational burden (TMB), CIBERSORT, and half maximal inhibitory concentration (IC Results: We screened FRGs by differentially expressed genes (DEGs) and overall survival (OS). Four candidate genes were obtained by hybridization. Then, we constructed a two-gene prognostic signature ( Conclusions: We constructed a prognostic model associated with ferroptosis, which may provide clinicians with a reliable predictive assessment tool and offer new perspectives for the future clinical management of RCC.

Indexed as

drug sensitivityferroptosis-related genes (FRGs)prognosisRenal cell carcinoma (RCC)risk score

Identifiers

PMID37554538
PMCPMC10406542

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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.