A Novel Cuprotosis-Related lncRNA Signature Effectively Predicts Prognosis in Glioma Patients

胶质瘤 比例危险模型 癌变 肿瘤科 长非编码RNA 生物 内科学 计算生物学 癌症 生物信息学 医学 基因 癌症研究 核糖核酸 遗传学
作者
Shuaishuai Wu,Augustine K. Ballah,Wenqiang Che,Xiangyu Wang
出处
期刊:Journal of Molecular Neuroscience [Springer Nature]
卷期号:73 (2-3): 185-204 被引量:2
标识
DOI:10.1007/s12031-023-02102-5
摘要

Cuprotosis is a novel and different cell death mechanism from the existing known ones that can be used to explore new approaches to treating cancer. Just like ferroptosis and pyroptosis, cuprotosis-related genes regulate various types of tumorigenesis, invasion, and metastasis. However, the relationship between cuprotosis-related long non-coding RNA (cuprotosis-related lncRNA) in glioma development and prognosis has not been investigated. We obtained relevant data from the Genotype-Tissue Expression (GTEx), Cancer Genome Atlas (TCGA), Chinese Glioma Genome Atlas (CGGA), and published articles. First, we identified 365 cuprotosis-related lncRNAs based on 10 cuprotosis-related differential genes (|R2|> 0.4, p < 0.001). Then using Lasso and Cox regression analysis methods, 12 prognostic cuprotosis-related lncRNAs were obtained and constructed the CuLncSigi risk score formula. Our next step was to divide the tumor gliomas into two groups (high risk and low risk) based on the median risk score, and we found that patients in the high-risk group had a significantly worse prognosis. We used internal and external validation methods to simultaneously analyze and validate that the risk score model has good predictive power for patients with glioma. Next, we also performed enrichment analyses such as GSEA and aaGSEA and evaluated the relationship between immune-related drugs and tumor treatment. In conclusion, we successfully constructed a formula of cuprotosis-related lncRNAs with a powerful predictive function. More importantly, our study paves the way for exploring cuprotosis mechanisms in glioma occurrence and development and helps to find new relevant biomarkers for glioma early identification and diagnosis and to investigate new therapeutic approaches.

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
hipig完成签到 ,获得积分10
2秒前
11秒前
唠叨的凌雪完成签到,获得积分10
12秒前
申燕婷完成签到 ,获得积分10
12秒前
TianFuAI完成签到,获得积分10
12秒前
白江虎发布了新的文献求助10
17秒前
科钱钱完成签到 ,获得积分10
18秒前
风清扬应助科研通管家采纳,获得10
20秒前
qingmoheng应助科研通管家采纳,获得10
20秒前
风清扬应助科研通管家采纳,获得10
20秒前
情怀应助科研通管家采纳,获得10
20秒前
科研王应助科研通管家采纳,获得10
21秒前
风清扬应助科研通管家采纳,获得10
21秒前
shhoing应助科研通管家采纳,获得10
21秒前
风清扬应助科研通管家采纳,获得10
21秒前
科研王应助科研通管家采纳,获得10
21秒前
风清扬应助科研通管家采纳,获得10
21秒前
风清扬应助科研通管家采纳,获得10
21秒前
shhoing应助科研通管家采纳,获得10
21秒前
量子星尘发布了新的文献求助10
22秒前
yeahCZY应助一个小胖子采纳,获得10
24秒前
慕青应助白江虎采纳,获得10
25秒前
柯友卉完成签到,获得积分10
27秒前
一个小胖子完成签到,获得积分10
39秒前
漂亮的秋天完成签到 ,获得积分10
41秒前
tu完成签到 ,获得积分10
42秒前
量子星尘发布了新的文献求助10
48秒前
俭朴的芝麻完成签到,获得积分10
50秒前
王昭完成签到 ,获得积分10
52秒前
2316690509完成签到 ,获得积分10
52秒前
嘻嘻不嘻嘻完成签到 ,获得积分10
53秒前
cccc完成签到 ,获得积分10
57秒前
慕何完成签到 ,获得积分10
1分钟前
南浔完成签到 ,获得积分10
1分钟前
高高的以山完成签到 ,获得积分10
1分钟前
qqqdewq完成签到,获得积分10
1分钟前
高大厉完成签到 ,获得积分10
1分钟前
1分钟前
自信的高山完成签到,获得积分10
1分钟前
1分钟前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
List of 1,091 Public Pension Profiles by Region 1581
以液相層析串聯質譜法分析糖漿產品中活性雙羰基化合物 / 吳瑋元[撰] = Analysis of reactive dicarbonyl species in syrup products by LC-MS/MS / Wei-Yuan Wu 1000
Biology of the Reptilia. Volume 21. Morphology I. The Skull and Appendicular Locomotor Apparatus of Lepidosauria 600
The Scope of Slavic Aspect 600
Foregrounding Marking Shift in Sundanese Written Narrative Segments 600
Rousseau, le chemin de ronde 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 纳米技术 计算机科学 内科学 化学工程 复合材料 物理化学 基因 遗传学 催化作用 冶金 量子力学 光电子学
热门帖子
关注 科研通微信公众号,转发送积分 5539156
求助须知:如何正确求助?哪些是违规求助? 4625957
关于积分的说明 14597178
捐赠科研通 4566766
什么是DOI,文献DOI怎么找? 2503614
邀请新用户注册赠送积分活动 1481546
关于科研通互助平台的介绍 1453063