AbstractRecently, cellular senescence-induced unstable carotid plaques have gained increasing attention. In this study, we utilized bioinformatics and machine learning methods to investigate the correlation between cellular senescence and the pathological mechanisms of unstable carotid plaques. Our aim was to elucidate the causes of unstable carotid plaque progression and identify new therapeutic strategies.

摘要最近，细胞衰老引起的不稳定颈动脉斑块越来越受到关注。在这项研究中，我们利用生物信息学和机器学习方法来研究细胞衰老与不稳定颈动脉斑块病理机制之间的相关性。我们的目的是阐明不稳定颈动脉斑块进展的原因，并确定新的治疗策略。

First, differential expression analysis was performed on the test set GSE43292 to identify differentially expressed genes (DEGs) between the unstable plaque group and the control group. These DEGs were intersected with cellular senescence-associated genes to obtain 40 cellular senescence-associated DEGs.

首先，对测试集GSE43292进行差异表达分析，以鉴定不稳定斑块组和对照组之间的差异表达基因（DEG）。这些DEG与细胞衰老相关基因相交，获得40个细胞衰老相关DEG。

Subsequently, key genes were then identified through weighted gene co-expression network analysis, random forest, Recursive Feature Elimination for Support Vector Machines algorithm and cytoHubba plugin. The intersection yielded 3 CSA-signature genes, which were validated in the external validation set GSE163154.

随后，通过加权基因共表达网络分析，随机森林，支持向量机算法的递归特征消除和cytoHubba插件识别关键基因。该交叉点产生了3个CSA签名基因，这些基因已在外部验证集GSE163154中进行了验证。

Additionally, we assessed the relationship between these CSA-signature genes and the immune landscape of the unstable plaque group. This study suggests that cellular senescence may play an important role in the progression mechanism of unstable plaques and is closely related to the influence of the immune microenvironment.

此外，我们评估了这些CSA特征基因与不稳定斑块组的免疫景观之间的关系。这项研究表明，细胞衰老可能在不稳定斑块的进展机制中起重要作用，并且与免疫微环境的影响密切相关。

Our research lays the foundation for studying the progression mechanism of unstable carotid plaques and provides some reference for targeted therapy..

我们的研究为研究不稳定颈动脉斑块的进展机制奠定了基础，并为靶向治疗提供了一些参考。。

IntroductionAtherosclerosis is a chronic inflammatory, progressive, and age-related disease characterized by plaque formation in the walls of large and medium-sized arteries1. The carotid artery is a common site for AS, typically located after the carotid bifurcation, with a prevalence of approximately 18-26.6%2,3.

引言动脉粥样硬化是一种慢性炎症，进行性和年龄相关的疾病，其特征在于大中型动脉壁中的斑块形成1。颈动脉是AS的常见部位，通常位于颈动脉分叉后，患病率约为18-26.6%2,3。

It is a high-risk factor for stroke, which is the leading cause of morbidity and mortality worldwide4. With the aging population structure, the prevalence of carotid plaques continues to rise, seriously affecting population health.Traditionally, the size of carotid plaques and associated luminal stenosis have been the focus of research5.

它是中风的高危因素，是全球发病率和死亡率的主要原因4。随着人口结构的老龄化，颈动脉斑块的患病率持续上升，严重影响人群健康。传统上，颈动脉斑块的大小和相关的管腔狭窄一直是研究的重点5。

Clinically, surgical indications are usually based on the degree of carotid stenosis or neurological complications. However, recent studies have shown that plaque stability is closely related to the risk of ischemic stroke and depends more on their composition2,3. As a result, unstable carotid plaques have gradually become a research hotspot.Unstable plaques typically have characteristics such as thinner fibrous caps, enlarged lipid cores, intraplaque hemorrhage, and increased inflammation6.

临床上，手术适应症通常基于颈动脉狭窄程度或神经系统并发症。然而，最近的研究表明，斑块稳定性与缺血性卒中的风险密切相关，并且更多地取决于它们的组成2,3。因此，不稳定颈动脉斑块逐渐成为研究热点。不稳定斑块通常具有纤维帽变薄，脂质核心增大，斑块内出血和炎症增加等特征6。

These features make plaques prone to rupture or ulceration, potentially leading to intracerebral thrombosis or embolism. In contrast, stable plaques usually have a simple structure, consisting of lipid accumulation and foam cells7. Currently, the early clinical identification of unstable plaques relies on imaging techniques: for example, carotid ultrasound to detect plaque size and morphology and observe fibrous cap thickness; CTA to assess calcification and intraplaque hemorrhage; and high-resolution MRI to evaluate plaque composition, such as lipid core, fibrous cap thickness, and intraplaque hemorrhage7,8.

这些特征使斑块易于破裂或溃疡，可能导致脑内血栓形成或栓塞。相反，稳定的斑块通常具有简单的结构，由脂质积聚和泡沫细胞组成7。目前，不稳定斑块的早期临床鉴定依赖于成像技术：例如，颈动脉超声检测斑块大小和形态并观察纤维帽厚度；CTA评估钙化和斑块内出血；和高分辨率MRI评估斑块组成，如脂质核心，纤维帽厚度和斑块内出血7,8。

However, pathological staging.

然而，病理分期。

Data availability

数据可用性

The Bulk-RNA datasets used in this study are available at the following URLs: GSE43292: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE43292; GSE163154: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE163154. The gene sets related to cell senescence used in this study are available from the Cell Senescence Database: http://csgene.bioinfo-minzhao.org..

本研究中使用的大量RNA数据集可在以下URL获得：GSE43292：https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE43292；GSE163154：https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE163154.本研究中使用的与细胞衰老相关的基因组可从细胞衰老数据库中获得：http://csgene.bioinfo-minzhao.org..

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Download referencesAuthor informationAuthors and AffiliationsDepartment of Neurosurgery, Fujian Medical University Union Hospital, No. 29 Xinquan Road, Fuzhou, 350001, Fujian, ChinaGang-Feng Cai & Cai-Hou LinDepartment of Neurosurgery, Quanzhou Orthopedic-Traumatological Hospital, Quanzhou, Fujian, ChinaShao-Wei Chen, Jin-Kai Huang, Shi-Rong Lin & Guo-He HuangAuthorsGang-Feng CaiView author publicationsYou can also search for this author in.

下载参考文献作者信息作者和附属机构福建医科大学附属协和医院神经外科，福建省福州市新泉路29号，350001，中国福建省泉州市泉州创伤骨科医院神经外科，福建省泉州市邵伟陈，黄金凯，林世荣和黄国和作者冈丰才观点作者出版物您也可以在中搜索这位作者。

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PubMed Google ScholarContributionsCai-Hou Lin analyzed the data and wrote this manuscript. Gang-Feng Cai, Guo-He Huang, Shao-Wei Chen, Shi-Rong Lin and Jin-Kai Huang assisted in analyzing the data and revising the manuscript. Gang-Feng Cai critically read and edited the manuscript.Corresponding authorCorrespondence to.

PubMed谷歌学术贡献Cai Hou Lin分析了数据并撰写了这份手稿。蔡刚，黄国和，陈绍伟，林世荣和黄金凯协助分析数据和修改手稿。蔡刚峰批判性地阅读并编辑了手稿。对应作者对应。

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Reprints and permissionsAbout this articleCite this articleCai, GF., Chen, SW., Huang, JK. et al. Decoding marker genes and immune landscape of unstable carotid plaques from cellular senescence.

转载和许可本文引用本文Cai，GF。，Chen，SW。，黄，JK。从细胞衰老中解码不稳定颈动脉斑块的标记基因和免疫景观。

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KeywordsUnstable plaquesCellular agingGeneticsImmune infiltrationMachine learning

关键词稳定斑块细胞衰老遗传学免疫浸润机器学习