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
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
刚刚
xmj完成签到,获得积分10
1秒前
galioo3000完成签到,获得积分10
1秒前
Gina完成签到 ,获得积分10
2秒前
2秒前
传奇3应助LOTUS采纳,获得10
2秒前
陈明阳完成签到,获得积分10
2秒前
活泼的狗完成签到,获得积分10
2秒前
KevinT应助科研通管家采纳,获得30
3秒前
Sylar发布了新的文献求助10
3秒前
66666666666666完成签到,获得积分10
3秒前
zyj完成签到,获得积分10
3秒前
隐形曼青应助科研通管家采纳,获得10
3秒前
积极向上的银杏完成签到,获得积分10
3秒前
ShawnJohn应助科研通管家采纳,获得10
3秒前
科研通AI6应助科研通管家采纳,获得10
3秒前
土木研学僧完成签到,获得积分10
4秒前
Vanilla应助科研通管家采纳,获得10
4秒前
Greg应助科研通管家采纳,获得10
4秒前
养头猪饿了吃完成签到,获得积分10
4秒前
zgrmws应助科研通管家采纳,获得10
4秒前
zgrmws应助科研通管家采纳,获得10
4秒前
雨姐科研应助科研通管家采纳,获得10
4秒前
科研通AI6应助科研通管家采纳,获得10
4秒前
雨姐科研应助科研通管家采纳,获得10
4秒前
科研通AI6应助科研通管家采纳,获得10
4秒前
Greg应助科研通管家采纳,获得10
5秒前
雨姐科研应助科研通管家采纳,获得10
5秒前
5秒前
任性的沅完成签到,获得积分10
5秒前
研友_VZG7GZ应助科研通管家采纳,获得30
5秒前
格兰德法泽尔完成签到,获得积分10
5秒前
雨姐科研应助科研通管家采纳,获得10
5秒前
kvvcp完成签到,获得积分10
5秒前
小二郎应助科研通管家采纳,获得10
5秒前
阔达萤完成签到 ,获得积分10
5秒前
satuo完成签到,获得积分10
5秒前
5秒前
LL完成签到,获得积分10
5秒前
RockRedfoo完成签到 ,获得积分10
6秒前
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
Clinical Microbiology Procedures Handbook, Multi-Volume, 5th Edition 2000
The Cambridge History of China: Volume 4, Sui and T'ang China, 589–906 AD, Part Two 1000
The Composition and Relative Chronology of Dynasties 16 and 17 in Egypt 1000
Russian Foreign Policy: Change and Continuity 800
Real World Research, 5th Edition 800
Qualitative Data Analysis with NVivo By Jenine Beekhuyzen, Pat Bazeley · 2024 800
热门求助领域 (近24小时)
化学 材料科学 生物 医学 工程类 计算机科学 有机化学 物理 生物化学 纳米技术 复合材料 内科学 化学工程 人工智能 催化作用 遗传学 数学 基因 量子力学 物理化学
热门帖子
关注 科研通微信公众号,转发送积分 5715880
求助须知:如何正确求助?哪些是违规求助? 5237687
关于积分的说明 15275397
捐赠科研通 4866497
什么是DOI,文献DOI怎么找? 2613022
邀请新用户注册赠送积分活动 1563137
关于科研通互助平台的介绍 1520689