Evidence map›Paper›PMID 36249422›Full record

ArticleJournal of immunology research2022

Construction of a Cuprotosis-Related Gene-Based Model to Improve the Prognostic Evaluation of Patients with Gastric Cancer.

Chunyan Han, Kai Zhang, XinKai Mo

Abstract read
In one paragraph

Article in Journal of immunology research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. The Role of Cuproptosis Key Factor FDX1 in Gastric Cancer.Current pharmaceutical biotechnology · 2024
    Article
  5. The role of cuproptosis in gastric cancer.Frontiers in immunology · 2024
    Review
  6. Article
  7. 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.

Chunyan HanDepartments of Radiotherapy, The Third Affiliated Hospital of Shandong First Medical University, Jinan, 250031 Shandong, China.
Kai ZhangDepartments of Medicine-Oncology, The Third Affiliated Hospital of Shandong First Medical University, Jinan, 250031 Shandong, China.ORCID https://orcid.org/0000-0001-7438-1545
XinKai MoDepartment of Clinical Laboratory, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, 250031 Shandong, China.ORCID https://orcid.org/0000-0002-4803-6024

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Gastric cancer (GC) is one of the most serious gastrointestinal malignancies with bad prognosis. The association between GC and cuprotosis-related genes has not been reported. Methods: The clinical and RNA expression of patients with GC were downloaded from TCGA database. The CIBERSORT package was used to quantify the abundance of specific cell types. Using the Cox regression analysis, we conducted a prognostic nomogram model based on cuprotosis-related differential genes in GC. We evaluated the prognostic power of this model using the Kaplan-Meier (K-M) survival curve analysis, decision curve analysis (DCA), and receiver operating characteristic (ROC) curve analysis. Results: The plasma cells, monocytes, and mast cells in GC tissue were significantly less than those in adjacent tissue ( Conclusion: A novel cuprotosis-related gene signature impacts on the prognosis of GC. Our research provides new insights and potential targets for studying the link between GC and cuprotosis point, thereby providing new insights into understanding the molecular mechanism of GC.

Indexed as

Stomach NeoplasmsBiomarkers, TumorHumansKaplan-Meier EstimatePrognosisRNABiomarkers, TumorRNA

Identifiers

PMID36249422
PMCPMC9553444

What OpenQuestion holds

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LicenceCC BY
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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.