Evidence map›Paper›PMID 40163162›Full record

ArticleDiscover oncology2025

Identification of a long non-coding RNA signature associated with cuproptosis for prognosis and immunotherapy response prediction in patients with lung adenocarcinoma.

Jie Zeng, Zhenyu Wu, Meijuan Luo, Zhibo Chen, Xie Xu, Guijing Xie, Quhai Chen, Wenjie Bai, Gang Xiao, Jianjiang Xie

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Discover oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

10 authors.

Jie Zeng *Department of Thoracic Surgery, Guangzhou First People's Hospital, South China University of Technology, Guangzhou, Guangdong, China.
Zhenyu Wu *Department of Urology, The First People's Hospital of Foshan, Foshan, Guangdong, China.
Meijuan Luo *State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, China.
Zhibo ChenDepartment of Thoracic Surgery, Guangzhou First People's Hospital, South China University of Technology, Guangzhou, Guangdong, China.
Xie XuDepartment of Thoracic Surgery, Guangzhou First People's Hospital, South China University of Technology, Guangzhou, Guangdong, China.
Guijing XieDepartment of Thoracic Surgery, Guangzhou First People's Hospital, South China University of Technology, Guangzhou, Guangdong, China.
Quhai ChenDepartment of Thoracic Surgery, Guangzhou First People's Hospital, South China University of Technology, Guangzhou, Guangdong, China.
Wenjie BaiDepartment of Thoracic Surgery, Guangzhou First People's Hospital, South China University of Technology, Guangzhou, Guangdong, China.
Gang XiaoDepartment of Thoracic Surgery, Guangzhou First People's Hospital, South China University of Technology, Guangzhou, Guangdong, China. eyxiaogang@scut.edu.cn.
Jianjiang XieDepartment of Thoracic Surgery, Guangzhou First People's Hospital, South China University of Technology, Guangzhou, Guangdong, China. eyxiejianjiang@scut.edu.cn.

Funding

Guangzhou Municipal Science and Technology Project No. 202201010053Guangzhou Municipal Science and Technology Project No. 2023A04J0622the Guangdong Province Natural Science Foundation No. 2020A1515011290
6 · The paper itself

Abstract

backgroundLung adenocarcinoma (LUAD), the most common histotype of lung cancer, exhibits high heterogeneity due to molecular variations. Cuproptosis is a newly discovered type of cell death that is linked to copper metabolism and long non-coding RNAs (lncRNAs) may play a significant role in this process. We conducted a comprehensive analysis of lncRNA related to cuproptosis and identified a CRLscore to predict the prognosis and immune landscape for LUAD patients.

methodsThe LUAD patient cohort obtained from TCGA database was divided into training and validation sets. A range of statistical methods were employed to identify lncRNAs associated with cuproptosis. Multivariate Cox regression was then utilized to develop the CRLscore, which was further used to construct and evaluate a nomogram. Additionally, we investigated the biological functions, gene mutations, and immune landscape.

resultsA CRLscore, comprising six cuproptosis-related lncRNAs, was developed to stratify patients into high- and low-risk groups. The CRLscore demonstrated its ability to independently predict prognosis in both the training set and the validation set. Utilizing the CRLscore, we constructed a nomogram that exhibited favorable predictive efficiency. Furthermore, the cuproptosis-related lncRNAs exhibited associations with important signaling pathways such as p53 signaling, MYC Targets V1, and G2M Checkpoint. Notably, the CRLscore displayed substantial differences in somatic mutations and immune landscape. Finally, qRT-PCR results showed the significant differential expression of five cuproptosis-related lncRNAs between LUAD and normal cells.

conclusionThe CRLscore could serve as a potential prognostic indicator and may predict the response to immunotherapy in LUAD patients.

Indexed as

CuproptosisImmunotherapylncRNALung adenocarcinomaPrognostic model

Identifiers

PMID40163162
PMCPMC11958909

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

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 Socratic graph.