Ferroptosis-related lncRNAs guiding osteosarcoma prognosis and immune microenvironment

比例危险模型 医学 单变量 背景(考古学) 接收机工作特性 肿瘤科 生存分析 转录组 免疫系统 Lasso(编程语言) 骨肉瘤 计算生物学 肿瘤微环境 单变量分析 多元分析 生物信息学 内科学 免疫学 基因 生物 癌症研究 多元统计 基因表达 计算机科学 机器学习 遗传学 古生物学 万维网
作者
Mingyi Yang,Yani Su,Ke Xu,Haishi Zheng,Qiling Yuan,Yongsong Cai,Yirixiati Aihaiti,Peng Xu
出处
期刊:Journal of Orthopaedic Surgery and Research [Springer Nature]
卷期号:18 (1) 被引量:3
标识
DOI:10.1186/s13018-023-04286-3
摘要

To investigate the ferroptosis-related long non-coding RNAs (FRLncs) implicated in influencing the prognostic and immune microenvironment in osteosarcoma (OS), and to establish a foundational framework for informing clinical decision making pertaining to OS management.Transcriptome data and clinical data pertaining to 86 cases of OS, the GSE19276, GSE16088 and GSE33382 datasets, and a list of ferroptosis-related genes (FRGs) were used to establish a risk prognostic model through comprehensive analysis. The identification of OS-related differentially expressed FRGs was achieved through an integrated analysis encompassing the aforementioned 86 OS transcriptome data and the GSE19276, GSE16088 and GSE33382 datasets. Concurrently, OS-related FRLncs were ascertained via co-expression analysis. To establish a risk prognostic model for OS, Univariate Cox regression analysis and Lasso Cox regression analysis were employed. Subsequently, a comprehensive evaluation was conducted, comprising risk curve analysis, survival analysis, receiver operating characteristic curve analysis and independent prognosis analysis. Model validation with distinct clinical subgroups was performed to assess the applicability of the risk prognostic model to diverse patient categories. Moreover, single sample gene set enrichment analysis (ssGSEA) was conducted to investigate variations in immune cell populations and immune functions within the context of the risk prognostic model. Furthermore, an analysis of immune checkpoint differentials yielded insights into immune checkpoint-related genes linked to OS prognosis. Finally, the risk prognosis model was verified by dividing the samples into train group and test group.We identified a set of seven FRLncs that exhibit potential as prognostic markers and influence factors of the immune microenvironment in the context of OS. This ensemble encompasses three high-risk FRLncs, denoted as APTR, AC105914.2 and AL139246.5, alongside four low-risk FRLncs, designated as DSCR8, LOH12CR2, AC027307.2 and AC025048.2. Furthermore, our analysis revealed notable down-regulation in the high-risk group across four distinct immune cell types, namely neutrophils, natural killer cells, plasmacytoid dendritic cells and tumor-infiltrating lymphocytes. This down-regulation was also reflected in four key immune functions, antigen-presenting cell (APC)-co-stimulation, checkpoint, cytolytic activity and T cell co-inhibition. Additionally, we identified seven immune checkpoint-associated genes with significant implications for OS prognosis, including CD200R1, HAVCR2, LGALS9, CD27, LAIR1, LAG3 and TNFSF4.The findings of this study have identified FRLncs capable of influencing OS prognosis and immune microenvironment, as well as immune checkpoint-related genes that are linked to OS prognosis. These discoveries establish a substantive foundation for further investigations into OS survival and offer valuable insights for informing clinical decision making in this context.
最长约 10秒,即可获得该文献文件

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
月是故乡明完成签到,获得积分10
刚刚
MarcoPolo发布了新的文献求助10
1秒前
1秒前
英俊的铭应助tolman采纳,获得10
3秒前
5秒前
5秒前
善学以致用应助杨萌采纳,获得50
5秒前
6秒前
小雯完成签到 ,获得积分10
6秒前
Su_1124完成签到,获得积分10
7秒前
温茶发布了新的文献求助10
7秒前
深情安青应助无聊的幻露采纳,获得10
10秒前
10秒前
安渝发布了新的文献求助10
10秒前
Akim应助1111采纳,获得10
12秒前
12秒前
12秒前
13秒前
王老吉马克完成签到,获得积分10
13秒前
miky完成签到 ,获得积分10
14秒前
一碗晚月完成签到,获得积分10
15秒前
小立发布了新的文献求助10
15秒前
寒冷白亦发布了新的文献求助10
15秒前
16秒前
学者完成签到,获得积分10
16秒前
Wei完成签到,获得积分10
16秒前
CipherSage应助iknj采纳,获得10
17秒前
17秒前
17秒前
18秒前
18秒前
20秒前
Zxc关闭了Zxc文献求助
20秒前
今夜无人入眠完成签到,获得积分20
21秒前
22秒前
杨萌完成签到,获得积分10
23秒前
23秒前
科研通AI6.3应助于冬雪采纳,获得10
23秒前
蓝天应助Tree_QD采纳,获得10
25秒前
从容完成签到 ,获得积分10
25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
Modern Epidemiology, Fourth Edition 5000
Kinesiophobia : a new view of chronic pain behavior 5000
Molecular Biology of Cancer: Mechanisms, Targets, and Therapeutics 3000
Digital Twins of Advanced Materials Processing 2000
Weaponeering, Fourth Edition – Two Volume SET 2000
Signals, Systems, and Signal Processing 610
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 纳米技术 化学工程 生物化学 物理 计算机科学 内科学 复合材料 催化作用 物理化学 光电子学 电极 冶金 细胞生物学 基因
热门帖子
关注 科研通微信公众号,转发送积分 6018687
求助须知:如何正确求助?哪些是违规求助? 7608739
关于积分的说明 16159862
捐赠科研通 5166400
什么是DOI,文献DOI怎么找? 2765269
邀请新用户注册赠送积分活动 1746904
关于科研通互助平台的介绍 1635397