Evidence map›Paper›PMID 39816567›Full record

ArticleTranslational cancer research2024

Construction of a cuproptosis-tricarboxylic acid cycle-associated lncRNA model to predict the prognosis of non-small cell lung cancer.

Xiang Li, Yunlong Zhao, Shengjie Wei, Yuqing Dai, Chun Yi

Abstract read
In one paragraph

Article in Translational cancer research, 2024. 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

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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

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

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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

5 authors.

Xiang LiFaculty of Medicine, Hunan University of Chinese Medicine, Changsha, China.
Yunlong ZhaoFaculty of Medicine, Hunan University of Chinese Medicine, Changsha, China.
Shengjie WeiFaculty of Medicine, Hunan University of Chinese Medicine, Changsha, China.
Yuqing DaiFaculty of Medicine, Hunan University of Chinese Medicine, Changsha, China.
Chun YiDepartment of Pathology, Faculty of Medicine, Hunan University of Chinese Medicine, Changsha, China.ORCID https://orcid.org/0000-0003-1740-0329

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: In cuproptosis, excess copper ions induce cell death via fatty acylation in the tricarboxylic acid (TCA) cycle. However, the effects of cuproptosis-TCA-related long non-coding RNAs (lncRNAs) on the clinical prognosis of non-small cell lung cancer (NSCLC) and the associated tumor microenvironment remain unclear. The purpose of this study is to use cuproptosis-TCA related lncRNAs to predict the prognosis of NSCLC. Methods: Molecular signature databases and cuproptosis-related publications were made use of identifying cuproptosis-TCA-related genes. They were identified based on Pearson correlation analysis. The prognostic features associated with these lncRNAs were evaluated using the absolute contraction and selection operator and a receiver operating characteristic curve analysis. Additionally, downstream functional enrichment and immunoinfiltration were analyzed to examine the immunotherapeutic responses of patients with NSCLC. Results: Eleven cuproptosis-TCA-associated lncRNAs were identified. A high-risk group was compared with a low-risk group based on risk scores, and the high-risk group had a significantly lower overall survival (OS). We established a prognostic risk profile, and based on these characteristics and clinical staging, a nomogram was constructed. An analysis of functional enrichment revealed the involvement of pathways associated with cellular and humoral immunity and fatty acylation. Risk scores differed significantly based on immune cells and pathways (antigen-presenting cell co-stimulation). Moreover, TP53, TTN, and MUC16 mutation status were strongly associated with risk scores, with patients identified as having a higher risk of NSCLC being more responsive to immunotherapy. Conclusions: Eleven cuproptosis-TCA-associated lncRNAs can be used to predict the prognosis of NSCLC patients, thereby providing a new theoretical basis for immunotherapy.

Indexed as

Cuproptosis long non-coding RNAs (cuproptosis lncRNAs)non-small cell lung cancer (NSCLC)prognostic risk scoretricarboxylic acid cycle (TCA cycle)

Identifiers

PMID39816567
PMCPMC11729758

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