Evidence map›Paper›PMID 37384293›Full record

ArticleFrontiers in oncology2023

Characterization of a cuproptosis-related signature to evaluate immune features and predict prognosis in colorectal cancer.

Lei Li, Fengyuan Sun, Fanyang Kong, Yongpu Feng, Yingxiao Song, Yiqi Du, Feng Liu, Xiangyu Kong

Abstract read
In one paragraph

Article in Frontiers in oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

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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

5 citing papers in PubMed.

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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 LiDigestive Endoscopy Center, Shanghai Tenth People's Hospital, Shanghai, China.
Fengyuan SunDepartment of Gastroenterology, Changhai Hospital, Naval Medical University, Shanghai, China.
Fanyang KongDepartment of Gastroenterology, Changhai Hospital, Naval Medical University, Shanghai, China.
Yongpu FengDepartment of Gastroenterology, Changhai Hospital, Naval Medical University, Shanghai, China.
Yingxiao SongDepartment of Gastroenterology, Changhai Hospital, Naval Medical University, Shanghai, China.
Yiqi DuDepartment of Gastroenterology, Changhai Hospital, Naval Medical University, Shanghai, China.
Feng LiuDigestive Endoscopy Center, Shanghai Tenth People's Hospital, Shanghai, China.
Xiangyu KongDepartment of Gastroenterology, Changhai Hospital, Naval Medical University, Shanghai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: Cuproptosis is a newly discovered type of cell death. Little is known about the roles that cuproptosis related genes (CRGs) play in colorectal cancer (CRC). The aim of this study is to evaluate the prognostic value of CRGs and their relationship with tumor immune microenvironment. Methods: TCGA-COAD dataset was used as the training cohort. Pearson correlation was employed to identify CRGs and paired tumor-normal samples were used to identify those CRGs with differential expression pattern. A risk score signature was constructed using LASSO regression and multivariate Cox stepwise regression methods. Two GEO datasets were used as validation cohorts for confirming predictive power and clinical significance of this model. Expression patterns of seven CRGs were evaluated in COAD tissues. Results: A total of 771 differentially expressed CRGs were identified in the training cohort. A predictive model termed riskScore was constructed consisting of 7 CRGs and two clinical parameters (age and stage). Survival analysis suggested that patients with higher riskScore showed shorter OS than those with lower ( Conclusions: The cuproptosis-related gene signature could serve as a potential prognostic predictor for colorectal cancer patients and may offer novel insights into clinical cancer therapeutics.

Indexed as

colorectal cancercuproptosiselesclomolimmune infiltrationprognosis

Identifiers

PMID37384293
PMCPMC10299831

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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.