Evidence map›Paper›PMID 36524120›Full record

ArticleFrontiers in immunology2022

Development of a copper metabolism-related gene signature in lung adenocarcinoma.

Wuguang Chang, Hongmu Li, Leqi Zhong, Tengfei Zhu, Zenghao Chang, Wei Ou, Siyu Wang

Open access · goldAbstract read
In one paragraph

Article in Frontiers in immunology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.

0numbers the graph read from it
0cells of the map it votes in
27citing papers in PubMed
6.8field-weighted citation impact, top 2% of its field
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

27 citing papers in PubMed, 42 citations in OpenAlex.

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  16. Identification of a novel ADCC-related gene signature for predicting the prognosis and therapy response in lung adenocarcinoma.Inflammation research : official journal of the European Histamine Research Society ... [et al.] · 2024
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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

7 authors at 2 institutions in 1 country.

Wuguang ChangDepartment of Thoracic Surgery, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Guangzhou, China.
Hongmu LiDepartment of Thoracic Surgery, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Guangzhou, China.
Leqi ZhongDepartment of Thoracic Surgery, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Guangzhou, China.
Tengfei ZhuDepartment of Thoracic Surgery, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Guangzhou, China.
Zenghao ChangDepartment of Thoracic Surgery, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Guangzhou, China.
Wei OuDepartment of Thoracic Surgery, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Guangzhou, China.
Siyu WangDepartment of Thoracic Surgery, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Guangzhou, China.
Sun Yat-sen University · CNSun Yat-sen University Cancer Center · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: The dysregulation of copper metabolism is closely related to the occurrence and progression of cancer. This study aims to investigate the prognostic value of copper metabolism-related genes (CMRGs) in lung adenocarcinoma (LUAD) and its characterization in the tumor microenvironment (TME). Methods: The differentially expressed CMRGs were identified in The Cancer Genome Atlas (TCGA) of LUAD. The least absolute shrinkage and selection operator regression (LASSO) and multivariate Cox regression analysis were used to establish the copper metabolism-related gene signature (CMRGs), which was also validated in Gene Expression Omnibus (GEO) database (GSE72094). The expression of key genes was verified by quantitative real-time PCR (qRT-PCR). Then, the CMRGS was used to develop a nomogram to predict the 1-year, 3-year, and 5-year overall survival (OS). In addition, differences in tumor mutation burden (TMB), biological characteristics and immune cell infiltration between high-risk and low-risk groups were systematically analyzed. Immunophenoscore (IPS) and an anti-PD-L1 immunotherapy cohort (IMvigor210) were used to verify whether CMRGS can predict the response to immunotherapy in LUAD. Results: 34 differentially expressed CMRGs were identified in the TCGA dataset, 11 of which were associated with OS. The CMRGS composed of 3 key genes (LOXL2, SLC31A2 and SOD3) had showed good clinical value and stratification ability in the prognostic assessment of LUAD patients. The results of qRT-PCR confirmed the expression of key CMRGs in LUAD and normal tissues. Then, all LUAD patients were divided into low-risk and high-risk groups based on median risk score. Those in the low-risk group had a significantly longer OS than those in the high-risk group (P<0.0001). The area under curve (AUC) values of the nomogram at 1, 3, and 5 years were 0.734, 0.735, and 0.720, respectively. Calibration curves comparing predicted and actual OS were close to ideal model, indicating a good consistency between prediction and actual observation. Functional enrichment analysis showed that the low-risk group was enriched in a large number of immune pathways. The results of immune infiltration analysis also confirmed that there were a variety of immune cell infiltration in the low-risk group. In addition, multiple immune checkpoints were highly expressed in the low-risk group and may benefit better from immunotherapy. Conclusion: CMRGS is a promising biomarker to assess the prognosis of LUAD patients and may be serve as a guidance on immunotherapy.

Indexed as

Adenocarcinoma of LungLung NeoplasmsCopperHumansImmunotherapyRisk FactorsTumor MicroenvironmentCopperbiomarkercoppercopper metabolism-related geneimmunotherapyprognosistumor microenvironment

Identifiers

PMID36524120
PMCPMC9744782
OpenAlexW4310166336

What OpenQuestion holds

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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.