Evidence map›Paper›PMID 40316808›Full record

ArticleClinical and experimental medicine2025

Prognostic and therapeutic potential of copper-induced cell death-related lncRNAs in lung squamous cell carcinoma.

Zhe Tian, Lilan Cen, Haoming Hua, Feng Wei, Jue Dong, Yulan Huang, Zhibo Wang, Junhua Deng, Yujie Jiang

Abstract read
In one paragraph

Article in Clinical and experimental medicine, 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

9 authors.

Zhe Tian *Department of Pulmonary and Critical Care Medicine, Affiliated Hospital of Youjiang Medical University for Nationalities, No. 18, Zhongshan 2 Road, Youjiang District, Baise, 533000, Guangxi, China.
Lilan Cen *Guangxi Academy of Medical Sciences, Department of Infectious Disease, The People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, 530021, China.
Haoming Hua *Department of Pathology, The Second People's Hospital of Bengbu, Bengbu, 233400, China.
Feng WeiDepartment of Pulmonary and Critical Care Medicine, Affiliated Hospital of Youjiang Medical University for Nationalities, No. 18, Zhongshan 2 Road, Youjiang District, Baise, 533000, Guangxi, China.
Jue DongDepartment of Pulmonary and Critical Care Medicine, Affiliated Hospital of Youjiang Medical University for Nationalities, No. 18, Zhongshan 2 Road, Youjiang District, Baise, 533000, Guangxi, China.
Yulan HuangDepartment of Pulmonary and Critical Care Medicine, Affiliated Hospital of Youjiang Medical University for Nationalities, No. 18, Zhongshan 2 Road, Youjiang District, Baise, 533000, Guangxi, China.
Zhibo WangDepartment of Pulmonary and Critical Care Medicine, Affiliated Hospital of Youjiang Medical University for Nationalities, No. 18, Zhongshan 2 Road, Youjiang District, Baise, 533000, Guangxi, China.
Junhua DengDepartment of Pulmonary and Critical Care Medicine, Affiliated Hospital of Youjiang Medical University for Nationalities, No. 18, Zhongshan 2 Road, Youjiang District, Baise, 533000, Guangxi, China.
Yujie JiangDepartment of Pulmonary and Critical Care Medicine, Affiliated Hospital of Youjiang Medical University for Nationalities, No. 18, Zhongshan 2 Road, Youjiang District, Baise, 533000, Guangxi, China. jiangyujie@ymcn.edu.

Funding

Foundation for Innovative Research Groups of the National Natural Science Foundation of China 81860021Guangxi Medical and health key discipline construction project, the Baise City Science and Technology Development Program 20232086the Baise City Multi-Disease Joint Special Program Baike [2022] No. 41the Guangxi Natural Science Foundation 2021GXNSFAA325003the Guangxi University Basic Research Ability Improvement Program for Young and Middle-aged Teachers 2023KY0573the self-funded research fund of the Health and Family Planning Commission Z20180216
6 · The paper itself

Abstract

Lung squamous cell carcinoma (LUSC), a major subtype of non-small cell lung cancer, remains challenging to treat due to poor prognosis and limited therapeutic options. This study investigates the prognostic and therapeutic implications of copper-induced cell death-related long non-coding RNAs (lncRNAs) in LUSC using data from The Cancer Genome Atlas. Five lncRNAs (AC010328.1, LINC01740, AL358613.2, MIR3945HG, AC002467.1) were identified as independent prognostic markers and incorporated into a risk score model to stratify patients into high- and low-risk groups. Survival analyses revealed significant differences in overall survival, with the high-risk group exhibiting higher immune evasion potential and poorer response to immunotherapy. Functional enrichment analyses highlighted the involvement of these lncRNAs in drug metabolism and tumor biology. Furthermore, tumor mutation burden analysis and immune dysfunction evaluation confirmed the clinical relevance of the model, identifying high-risk patients as more sensitive to targeted drugs such as Quizartinib and Dasatinib. A Nomogram integrating lncRNA risk scores and clinical factors demonstrated robust predictive accuracy for 1-, 3-, and 5-year survival outcomes. This study provides novel biomarkers and actionable insights for improving prognostic assessment and personalizing immunotherapy strategies for LUSC patients.

Indexed as

Carcinoma, Non-Small-Cell LungCarcinoma, Squamous CellCell DeathCopperLung NeoplasmsRNA, Long NoncodingBiomarkers, TumorFemaleGene Expression Regulation, NeoplasticHumansMaleMiddle AgedNomogramsPrognosisSurvival AnalysisBiomarkers, TumorCopperRNA, Long NoncodingCopper protein apoptosisImmune suppressionLong non-coding RNALung squamous cell carcinomaPrognostic risk model

Identifiers

PMID40316808
PMCPMC12048434

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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.