杯突相关 lncRNA 预测 LUAD 的预后和免疫反应

IF 1.8 4区 医学 Q3 PHARMACOLOGY & PHARMACY
Pharmacogenomics & Personalized Medicine Pub Date : 2024-06-25 eCollection Date: 2024-01-01 DOI:10.2147/PGPM.S452625
Qianhui Zhou, Yi Liu, Yan Gao, Lingli Quan, Lin Wang, Hao Wang
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引用次数: 0

摘要

背景:肺癌是全球癌症死亡的主要原因,主要是肺腺癌(LUAD)。然而,程序性细胞死亡的异质性导致了不同的预后和预测结果。本研究旨在开发一种基于杯突相关lncRNAs的LUAD评估标志物:首先,从癌症基因组图谱(TCGA)中下载与LUAD相关的转录组数据和临床数据,分析杯突相关基因,以确定杯突相关的lncRNAs。通过单变量、LASSO和多变量Cox回归分析,构建杯突相关lncRNA模型。利用预后风险值将LUAD患者分为高风险组和低风险组。采用Kaplan-Meier分析、PCA、GSEA和提名图来评估和验证结果:结果:发现了7个与杯突症相关的lncRNA,并建立了一个风险模型。高危肿瘤中有95.54%的病例出现杯突相关基因改变,而低危肿瘤中有85.65%的病例出现改变,主要涉及TP53。在预测1年、3年和5年总生存率方面,风险值优于其他临床变量和肿瘤突变负荷。基于杯突相关lncRNA的风险模型在LUAD评估中表现出高度有效性,可能会影响个体化治疗方法。四种候选杯突相关lncRNA(AL606834.1、AL161431.1、AC007613.1和LINC02835)在LUAD组织和邻近正常组织中的表达分析表明,AL606834.1和AL161431.1在LUAD组织中的表达水平显著较高,与肿瘤分期、淋巴结转移和组织病理学分级呈正相关。相反,AC007613.1 和 LINC02835 的表达水平较低,与这些因素呈负相关。AL606834.1和AL161431.1的高表达表明预后不良,而AC007613.1和LINC02835的低表达则与不良预后相关。单变量和多变量分析证实,这些lncRNA是影响LUAD预后的独立危险因素:4个杯突相关的lncRNAsAL606834.1、AL161431.1、AC007613.1和LINC02835可以准确预测LUAD患者的预后,并可能为临床应用和免疫治疗提供新的见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Cuproptosis-Related lncRNA Predict Prognosis and Immune Response of LUAD.

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.

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来源期刊
Pharmacogenomics & Personalized Medicine
Pharmacogenomics & Personalized Medicine Biochemistry, Genetics and Molecular Biology-Molecular Medicine
CiteScore
3.30
自引率
5.30%
发文量
110
审稿时长
16 weeks
期刊介绍: Pharmacogenomics and Personalized Medicine is an international, peer-reviewed, open-access journal characterizing the influence of genotype on pharmacology leading to the development of personalized treatment programs and individualized drug selection for improved safety, efficacy and sustainability. In particular, emphasis will be given to: Genomic and proteomic profiling Genetics and drug metabolism Targeted drug identification and discovery Optimizing drug selection & dosage based on patient''s genetic profile Drug related morbidity & mortality intervention Advanced disease screening and targeted therapeutic intervention Genetic based vaccine development Patient satisfaction and preference Health economic evaluations Practical and organizational issues in the development and implementation of personalized medicine programs.
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