Evidence map›Paper›PMID 38952778›Full record

ArticlePharmacogenomics and personalized medicine2024

Cuproptosis-Related lncRNA Predict Prognosis and Immune Response of LUAD.

Qianhui Zhou, Yi Liu, Yan Gao, Lingli Quan, Lin Wang, Hao Wang

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Article in Pharmacogenomics and personalized medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

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3citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

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3 citing papers in PubMed.

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4 · The record

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5 · Who and what money

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6 authors.

Qianhui ZhouDepartment of Respiratory and Critical Care Medicine, Zhuzhou Central Hospital, Zhuzhou, 412000, People's Republic of China.
Yi LiuDepartment of Respiratory and Critical Care Medicine, Zhuzhou Central Hospital, Zhuzhou, 412000, People's Republic of China.
Yan GaoDepartment of Respiratory and Critical Care Medicine, Zhuzhou Central Hospital, Zhuzhou, 412000, People's Republic of China.
Lingli QuanDepartment of Respiratory and Critical Care Medicine, Zhuzhou Central Hospital, Zhuzhou, 412000, People's Republic of China.
Lin WangDepartment of Respiratory and Critical Care Medicine, Zhuzhou Central Hospital, Zhuzhou, 412000, People's Republic of China.
Hao WangDepartment of Urology, The First Affiliated Hospital, Hengyang Medical School, University of South China, HengYang, Hunan, 421005, People's Republic of China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Lung cancer is the leading cause of cancer deaths worldwide, primarily due to lung adenocarcinoma (LUAD). However, the heterogeneity of programmed cell death results in varied prognostic and predictive outcomes. This study aimed to develop an LUAD evaluation marker based on cuproptosis-related lncRNAs. Methods: First, transcriptome data and clinical data related to LUAD were downloaded from the Cancer Genome Atlas (TCGA), and cuproptosis-related genes were analyzed to identify cuproptosis-related lncRNAs. Univariate, LASSO, and multivariate Cox regression analyses were conducted to construct cuproptosis-associated lncRNA models. LUAD patients were categorized into high-risk and low-risk groups using prognostic risk values. Kaplan-Meier analysis, PCA, GSEA, and nomograms were employed to evaluate and validate the results. Results: 7 cuproptosis-related lncRNAs were identified, and a risk model was created. High-risk tumors exhibited cuproptosis-related gene alterations in 95.54% of cases, while low-risk tumors showed alterations in 85.65% of cases, mainly involving TP53. The risk value outperformed other clinical variables and tumor mutation burden as a predictor of 1-, 3-, and 5-year overall survival. The cuproptosis-related lncRNA-based risk model demonstrated high validity for LUAD evaluation, potentially influencing individualized treatment approaches. Expression analysis of four candidate cuproptosis-related lncRNAs (AL606834.1, AL161431.1, AC007613.1, and LINC02835) in LUAD tissues and adjacent normal tissues revealed significantly higher expression levels of AL606834.1 and AL161431.1 in LUAD tissues, positively correlating with tumor stage, lymph node metastasis, and histopathological grade. Conversely, AC007613.1 and LINC02835 exhibited lower expression levels, negatively correlating with these factors. High expression of AL606834.1 and AL161431.1 indicated poor prognosis, while low expression of AC007613.1 and LINC02835 was associated with unfavorable outcomes. Univariate and multivariate analyses confirmed these lncRNAs as independent risk factors for LUAD prognosis. Conclusion: The 4 cuproptosis-related (lncRNAsAL606834.1, AL161431.1, AC007613.1, and LINC02835) can accurately predict the prognosis of patients with LUAD and may provide new insights into clinical applications and immunotherapy.

Indexed as

cuproptosisimmunotherapeutic responselong noncoding RNAlung adenocarcinomatumor mutation load

Identifiers

PMID38952778
PMCPMC11215279

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