Identification of novel RNA-sequencing biomarkers for early detection of heart transplantation rejection utilizing machine learning techniques

医学 心脏移植 机器学习 人工智能 金标准(测试) 移植 心肌内膜活检 微阵列 鉴定(生物学) 基因表达谱 微阵列分析技术 生物信息学 基因表达 活检 特征(语言学) 移植物排斥 医学诊断 生物标志物 基因 深度学习 计算生物学 基因芯片分析 文本挖掘 微阵列数据库
作者
F A R A H Naghashzadeh,Masoume Avateffazeli,R E Z A Khayami,B A B A K Sharif Kashani,M A R Y A M Hajimoradi,A B D O L R Mohammadnia,E L H A M Nazari,M A H S H I Hajiali,S H A D I Shafaghi
出处
期刊:European Heart Journal [Oxford University Press]
卷期号:46 (Supplement_1)
标识
DOI:10.1093/eurheartj/ehaf784.1370
摘要

Abstract Introduction Heart transplantation (HTx) remains the primary therapeutic intervention for patients with end-stage heart failure; however, it is associated with various complications, notably the rejection of allograft tissue that occurs in about 30% of patients during the first year following HTx and subsequent mortality. The gold standard method for diagnosis of rejection is endomyocardial biopsy (EMB), which requires hospitalization and is an invasive method. Therefore, the exploration of non-invasive alternatives to EMB including molecular biomarkers is of paramount importance. Purpose In this work, we integrated bioinformatics and machine learning (ML), a subfield of artificial intelligence (AI), to investigate new and non-invasive biomarkers for the early detection of HTx rejection. Material and Methods Here, the gene expression omnibus (GEO) was used to download microarray data for analysis and validation. Subsequently, R programming was applied to determine the differentially expressed genes (DEGs) of samples of rejection vs no rejection. Finally, the biomarkers were separated into two training and testing groups and identified using deep learning, an ML technique. Results Following downloading data from 137 samples, the adjusted t-test and p-values were used to compare the candidate genes. Based on the feature importance score, the top ten genes (TYMS, WARS, AIM2, CXCL9, TRAT1, HLA-DRB3, TNFRSF9, GZMH, IL-32, and AIF1) were chosen as candidates for additional analysis, and expression of each gene had a notable rise in the rejection group. The top three genes for HTx rejection detection among these ten were CXCL9, TNFRSF9, and GZMH. The novel biomarkers for diagnosis are GZMH and TNFRSF9. However, CXCL9 was previously investigated as diagnostic gene for HTx rejection. Conclusion TNFRSF9 is a member of the tumor necrosis factor (TNF) receptor superfamily and is expressed by various cell types, including endotheliocytes, natural killer (NK) cells, and activated CD8+ and CD4+ T lymphocytes. This receptor plays a critical role in regulating apoptosis, cellular proliferation, and cell survival. The activity of CD8+ T cells is significantly regulated by TNFRSF9. Granzyme H (GZMH) is a protease that is also involved in phagosome formation, cytokine regulation, and the remodeling of the extracellular matrix. Upon the detection of target cells, perforins are secreted and integrate into the target cell membrane, enabling granzymes to enter and induce apoptosis. While Granzyme A has been previously noted for its strong expression during HTx rejection, our current findings indicate that GZMH is also highly expressed in this situation. CXCL9, which belongs to the CXC subfamily of chemokines, has emerged as a non-invasive biomarker for HTx rejection, corroborating the outcomes of our investigation.
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
刚刚
清爽的阑悦完成签到 ,获得积分10
2秒前
2秒前
3秒前
Ava应助海鲜采纳,获得10
3秒前
3秒前
ray发布了新的文献求助10
4秒前
小葛发布了新的文献求助10
4秒前
5秒前
5秒前
7秒前
7秒前
7秒前
万能图书馆应助Yueee采纳,获得10
8秒前
8秒前
ni完成签到 ,获得积分10
8秒前
勤劳黑猫完成签到,获得积分10
9秒前
孟一帆发布了新的文献求助10
9秒前
9秒前
林小心发布了新的文献求助10
10秒前
11秒前
11秒前
洪伟华发布了新的文献求助10
11秒前
12秒前
12秒前
13秒前
PJW完成签到,获得积分10
13秒前
彭于晏应助小白菜采纳,获得20
13秒前
等风的人完成签到,获得积分10
14秒前
14秒前
Yang发布了新的文献求助10
15秒前
sea发布了新的文献求助10
15秒前
豆豆发布了新的文献求助10
16秒前
老王完成签到,获得积分10
16秒前
唐少侠在江湖完成签到,获得积分10
16秒前
成就的沛菡完成签到,获得积分10
16秒前
wsc发布了新的文献求助10
17秒前
孟一帆完成签到,获得积分10
17秒前
llllllll完成签到,获得积分10
18秒前
森淼发布了新的文献求助10
18秒前
高分求助中
Ideology and Meaning-Making under the Putin Regime 750
Introduction to Industrial/Organizational Psychology 600
Prompt Engineering for Clinicians: Harnessing AI in Everyday Medical Practice 600
Handbook of Luminescence Dating 500
Safety Pharmacology 500
《KNN基无铅压电陶瓷电学性能优化与物理机理研究》 500
Isomerism In Coordination Compounds 400
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 计算机科学 化学工程 生物化学 物理 内科学 复合材料 催化作用 光电子学 物理化学 电极 细胞生物学 基因 遗传学
热门帖子
关注 科研通微信公众号,转发送积分 6935818
求助须知:如何正确求助?哪些是违规求助? 8622611
关于积分的说明 18288664
捐赠科研通 6363670
什么是DOI,文献DOI怎么找? 3075409
关于科研通互助平台的介绍 2113145
邀请新用户注册赠送积分活动 2052918