Identification of CCR7 and CBX6 as key biomarkers in abdominal aortic aneurysm: Insights from multi-omics data and machine learning analysis.

IF 1.9 4区 生物学 Q4 CELL BIOLOGY
Xi Yong, Xuerui Hu, Tengyao Kang, Yanpiao Deng, Sixuan Li, Shuihan Yu, Yani Hou, Jin You, Xiaohe Dai, Jialin Zhang, Junjia Zhang, Junlin Zhou, Siyu Zhang, Jianghua Zheng, Qin Yang, Jingdong Li
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引用次数: 0

Abstract

Abdominal aortic aneurysm (AAA) is a severe vascular condition, marked by the progressive dilation of the abdominal aorta, leading to rupture if untreated. The objective of this study was to identify key biomarkers and decipher the immune mechanisms underlying AAA utilising multi-omics data analysis and machine learning techniques. Single-cell RNA sequencing disclosed a heightened presence of macrophages and CD8-positive alpha-beta T cells in AAA, highlighting their critical role in disease pathogenesis. Analysis of cell-cell communication highlighted augmented interactions between macrophages and dendritic cells derived from monocytes. Enrichment analysis of differential expression gene indicated substantial involvement of immune and metabolic pathways in AAA pathogenesis. Machine learning techniques identified CCR7 and CBX6 as key candidate biomarkers. In AAA, CCR7 expression is upregulated, whereas CBX6 expression is downregulated, both showing significant correlations with immune cell infiltration. These findings provide valuable insights into the molecular mechanisms underlying AAA and suggest potential biomarkers for diagnosis and therapeutic intervention.

鉴定作为腹主动脉瘤关键生物标记物的 CCR7 和 CBX6:多组学数据和机器学习分析的启示
腹主动脉瘤(AAA)是一种严重的血管疾病,其特征是腹主动脉逐渐扩张,如不及时治疗会导致破裂。本研究的目的是利用多组学数据分析和机器学习技术确定关键生物标志物,并破译AAA的免疫机制。单细胞RNA测序显示,AAA中的巨噬细胞和CD8阳性α-βT细胞增多,突出了它们在疾病发病机制中的关键作用。对细胞-细胞通讯的分析突出显示了巨噬细胞与源自单核细胞的树突状细胞之间增强的相互作用。差异表达基因的富集分析表明,免疫和新陈代谢途径在 AAA 发病机制中的重要作用。机器学习技术发现 CCR7 和 CBX6 是关键的候选生物标记物。在 AAA 中,CCR7 表达上调,而 CBX6 表达下调,两者均与免疫细胞浸润有显著相关性。这些发现为了解 AAA 的分子机制提供了宝贵的视角,并为诊断和治疗干预提供了潜在的生物标志物。
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来源期刊
IET Systems Biology
IET Systems Biology 生物-数学与计算生物学
CiteScore
4.20
自引率
4.30%
发文量
17
审稿时长
>12 weeks
期刊介绍: IET Systems Biology covers intra- and inter-cellular dynamics, using systems- and signal-oriented approaches. Papers that analyse genomic data in order to identify variables and basic relationships between them are considered if the results provide a basis for mathematical modelling and simulation of cellular dynamics. Manuscripts on molecular and cell biological studies are encouraged if the aim is a systems approach to dynamic interactions within and between cells. The scope includes the following topics: Genomics, transcriptomics, proteomics, metabolomics, cells, tissue and the physiome; molecular and cellular interaction, gene, cell and protein function; networks and pathways; metabolism and cell signalling; dynamics, regulation and control; systems, signals, and information; experimental data analysis; mathematical modelling, simulation and theoretical analysis; biological modelling, simulation, prediction and control; methodologies, databases, tools and algorithms for modelling and simulation; modelling, analysis and control of biological networks; synthetic biology and bioengineering based on systems biology.
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