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
hhhhhhw完成签到,获得积分20
1秒前
周小台完成签到 ,获得积分10
1秒前
释然zc完成签到,获得积分10
1秒前
2秒前
qq完成签到 ,获得积分10
2秒前
yilin完成签到,获得积分10
2秒前
2秒前
3秒前
花花不花完成签到 ,获得积分10
3秒前
3秒前
4秒前
deng完成签到 ,获得积分10
4秒前
阿正嗖啪完成签到,获得积分10
4秒前
Lucas应助weinaonao采纳,获得10
4秒前
tlrelax完成签到,获得积分10
4秒前
4秒前
迫切完成签到,获得积分10
5秒前
6秒前
马伊完成签到,获得积分10
6秒前
6秒前
李健应助恶毒的婆婆采纳,获得10
7秒前
7秒前
7秒前
7秒前
7秒前
Just森完成签到,获得积分10
7秒前
胡云晗发布了新的文献求助10
7秒前
量子星尘发布了新的文献求助10
8秒前
小星发布了新的文献求助10
8秒前
8秒前
一一一完成签到 ,获得积分10
8秒前
龍越发布了新的文献求助10
9秒前
9秒前
充电宝应助阿吟采纳,获得10
9秒前
被门夹到鸟完成签到,获得积分10
9秒前
爱喝点啤酒完成签到,获得积分20
9秒前
lily336699完成签到,获得积分10
9秒前
9秒前
Orange应助DYZ采纳,获得10
9秒前
JJ完成签到 ,获得积分10
10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
Binary Alloy Phase Diagrams, 2nd Edition 8000
Building Quantum Computers 800
Translanguaging in Action in English-Medium Classrooms: A Resource Book for Teachers 700
Natural Product Extraction: Principles and Applications 500
Exosomes Pipeline Insight, 2025 500
Qualitative Data Analysis with NVivo By Jenine Beekhuyzen, Pat Bazeley · 2024 500
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 5665352
求助须知:如何正确求助?哪些是违规求助? 4876309
关于积分的说明 15113352
捐赠科研通 4824419
什么是DOI,文献DOI怎么找? 2582766
邀请新用户注册赠送积分活动 1536717
关于科研通互助平台的介绍 1495328