清晨好,您是今天最早来到科研通的研友!由于当前在线用户较少,发布求助请尽量完整的填写文献信息,科研通机器人24小时在线,伴您科研之路漫漫前行!

Characterization of ferroptosis-related genes in aplastic anaemia: an integrated analysis of bulk and single-cell RNA sequencing data

基因 生物 小桶 核糖核酸 间质细胞 免疫系统 单细胞分析 细胞 RNA序列 细胞生物学 基因表达 计算生物学 遗传学 癌症研究 转录组
作者
Changxian Shen,Fengming Wang
出处
期刊:Acta Haematologica [Karger Publishers]
卷期号:: 1-20
标识
DOI:10.1159/000543656
摘要

Introduction: Ferroptosis offers novel perspectives for treating multiple blood-related diseases, yet its role in aplastic anaemia (AA) is rare. This study aimed to explore key ferroptosis-related genes (FRGs) in AA using bulk and single-cell RNA sequencing (scRNA-seq) data. Methods: scRNA-seq and bulk RNA-seq data, along with FRG lists, were obtained from public databases. Differentially expressed FRGs (DEFRGs) between AA and control samples were identified, followed by functional enrichment and protein–protein interaction analyses. Single-cell analyses were conducted to reveal cell types in samples and DEFRGs activity in each cell was assessed. Moreover, DEGs between AA and control samples at the cellular level were explored, followed by integration with DEFRGs to determine common key genes. The KEGG pathway analysis of these genes was performed at the cellular level. Immune infiltration analysis evaluated the relationship between key genes and immune cells. Results: A total of 38 DEFRGs were identified, enriched in pathways such as the intrinsic apoptotic signalling pathway. scRNA-seq analysis identified seven cell types, with elevated DEFRGs activity in platelets and stromal cells. Key genes DDIT4 and NCF2, identified through integrated analysis, were involved in autophagy, mTOR signalling, and osteoclast differentiation pathways, with their expression positively correlated with activated dendritic cells, in AA samples. Conclusion: Our findings highlight the roles of DDIT4 and NCF2, in AA progression, providing potential insights for further mechanistic exploration of AA.

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
852应助健忘雁易采纳,获得10
15秒前
龙猫爱看书完成签到,获得积分10
16秒前
六一儿童节完成签到 ,获得积分10
16秒前
16秒前
25秒前
南风完成签到 ,获得积分10
27秒前
31秒前
HEROTREE完成签到 ,获得积分10
32秒前
creep2020完成签到,获得积分10
34秒前
37秒前
健忘雁易发布了新的文献求助10
42秒前
racill完成签到 ,获得积分10
44秒前
ljlbest1984完成签到,获得积分10
46秒前
李健应助ljlbest1984采纳,获得10
50秒前
xrose完成签到 ,获得积分10
51秒前
orixero应助健忘雁易采纳,获得10
1分钟前
1分钟前
AiQi完成签到 ,获得积分10
1分钟前
噗噗蝶pd发布了新的文献求助10
1分钟前
SciGPT应助maitiandehe采纳,获得10
1分钟前
情怀应助小路采纳,获得10
1分钟前
顾矜应助噗噗蝶pd采纳,获得10
1分钟前
1分钟前
小路完成签到,获得积分10
1分钟前
Wen完成签到 ,获得积分10
1分钟前
小路发布了新的文献求助10
1分钟前
asdwind完成签到,获得积分10
1分钟前
不良帅完成签到,获得积分10
1分钟前
1分钟前
小小康康发布了新的文献求助10
1分钟前
maitiandehe发布了新的文献求助10
1分钟前
小小康康完成签到,获得积分10
1分钟前
maitiandehe完成签到,获得积分10
2分钟前
呼呼呼完成签到,获得积分10
2分钟前
1461完成签到 ,获得积分10
2分钟前
poki完成签到 ,获得积分10
3分钟前
Akim应助兜兜齿皮皮采纳,获得10
3分钟前
Zhangfu完成签到,获得积分10
3分钟前
3分钟前
生信小菜鸟完成签到 ,获得积分10
4分钟前
高分求助中
Thinking Small and Large 500
Algorithmic Mathematics in Machine Learning 500
Getting Published in SSCI Journals: 200+ Questions and Answers for Absolute Beginners 300
The Monocyte-to-HDL ratio (MHR) as a prognostic and diagnostic biomarker in Acute Ischemic Stroke: A systematic review with meta-analysis (P9-14.010) 240
Interpretability and Explainability in AI Using Python 200
SPECIAL FEATURES OF THE EXCHANGE INTERACTIONS IN ORTHOFERRITE-ORTHOCHROMITES 200
Null Objects from a Cross-Linguistic and Developmental Perspective 200
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 物理 生物化学 纳米技术 计算机科学 化学工程 内科学 复合材料 物理化学 电极 遗传学 量子力学 基因 冶金 催化作用
热门帖子
关注 科研通微信公众号,转发送积分 3833864
求助须知:如何正确求助?哪些是违规求助? 3376318
关于积分的说明 10492595
捐赠科研通 3095843
什么是DOI,文献DOI怎么找? 1704723
邀请新用户注册赠送积分活动 820104
科研通“疑难数据库(出版商)”最低求助积分说明 771859