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基于整合生物信息学分析鉴定和验证与颈动脉粥样硬化中晚期斑块和免疫细胞浸润相关的三个诊断自噬相关基因。

Identification and validation of three diagnostic autophagy-related genes associated with advanced plaques and immune cell infiltration in carotid atherosclerosis based on integrated bioinformatics analyses.

机构信息

Hengyang Medical School, University of South China, The First Affiliated Hospital, Department of Cardiology, Hengyang, Hunan, China.

University of South China, Hunan Provincial Key Laboratory of Multi-omics and Artificial Intelligence of Cardiovascular Diseases, Hengyang, Hunan, China.

出版信息

PeerJ. 2024 Nov 22;12:e18543. doi: 10.7717/peerj.18543. eCollection 2024.

Abstract

BACKGROUND

Autophagy plays a key role in the development of carotid atherosclerosis (CAS). This study aimed to identify key autophagy-related genes (ATGs) related with CAS using bioinformatics analysis, AS mouse model, and experiments.

METHODS

The GSE100927 and GSE28829 datasets were downloaded from the Gene Expression Omnibus (GEO) database. An integrated bioinformatics analyses of differentially expressed ATGs (DE-ATGs) was conducted. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed to identify the biological processes and pathways associated with DE-ATGs. Protein-protein interaction (PPI) network was constructed with the DE-ATGs to identify the key CAS-related DE-ATGs. Receiver operating characteristic (ROC) curve analysis was used to determine the diagnostic value of the key CAS-related DE-ATGs. CIBERSORT analysis was performed to determine the infiltration status of 22 immune cell types and their correlation with the expression levels of the key CAS-related DE-ATGs. Hematoxylin and eosin (HE) staining was used to estimate the plaque histology in the AS mouse model. Western blotting, quantitative real-time PCR (qRT-PCR), and immunohistochemistry (IHC) were performed to validate the protein and mRNA expression levels of the key CAS-related DE-ATGs in the and models.

RESULTS

We compared transcriptome profiles of 12 early CAS plaques and 29 advanced CAS plaques in the GSE100927 dataset and identified 41 DE-ATGs (33 up-regulated and eight down-regulated). Functional enrichment analysis showed that the DE-ATGs were closely related with apoptosis, autophagy, and immune activation. ROC curve analysis showed that the area under the curve (AUC) values for the three key CAS-related DE-ATGs (, , and ) were 0.707, 0.977, and 0.951, respectively. CIBERSORT analyses showed close association between the three key CAS-related DE-ATGs and the infiltration of immune cell types in the plaques. Finally, the western blot, qRT-PCR, and IHC staining confirmed that CCL2, LAMP2, and CTSB were highly expressed in the plaques of the AS model mice or ox-LDL-treated human umbilical vein endothelial cells (HUVECs) and human aorta vascular smooth muscle cells (HAoSMCs).

CONCLUSION

We identified and validated three key CAS-associated ATGs, namely, , , and with high diagnostic value. These three key CAS-associated ATGs are promising diagnostic markers and therapeutic targets for patients with CAS.

摘要

背景

自噬在颈动脉粥样硬化(CAS)的发展中起着关键作用。本研究旨在通过生物信息学分析、AS 小鼠模型和实验,鉴定与 CAS 相关的关键自噬相关基因(ATGs)。

方法

从基因表达综合数据库(GEO)下载 GSE100927 和 GSE28829 数据集。对差异表达的 ATGs(DE-ATGs)进行综合生物信息学分析。进行基因本体论(GO)和京都基因与基因组百科全书(KEGG)富集分析,以鉴定与 DE-ATGs 相关的生物学过程和途径。构建 DE-ATGs 的蛋白质-蛋白质相互作用(PPI)网络,以鉴定与关键 CAS 相关的 DE-ATGs。采用受试者工作特征(ROC)曲线分析确定关键 CAS 相关 DE-ATGs 的诊断价值。采用 CIBERSORT 分析确定 22 种免疫细胞类型的浸润状态及其与关键 CAS 相关 DE-ATGs 表达水平的相关性。苏木精和伊红(HE)染色评估 AS 小鼠模型中的斑块组织学。采用 Western blot、实时定量 PCR(qRT-PCR)和免疫组织化学(IHC)检测 AS 模型中关键 CAS 相关 DE-ATGs 的蛋白和 mRNA 表达水平。

结果

我们比较了 GSE100927 数据集中 12 个早期 CAS 斑块和 29 个晚期 CAS 斑块的转录组图谱,鉴定出 41 个 DE-ATGs(33 个上调和 8 个下调)。功能富集分析表明,DE-ATGs 与细胞凋亡、自噬和免疫激活密切相关。ROC 曲线分析显示,三个关键 CAS 相关 DE-ATGs(、和)的曲线下面积(AUC)值分别为 0.707、0.977 和 0.951。CIBERSORT 分析表明,三个关键 CAS 相关 DE-ATGs 与斑块中免疫细胞类型的浸润密切相关。最后,Western blot、qRT-PCR 和 IHC 染色证实,CCL2、LAMP2 和 CTSB 在 AS 模型小鼠斑块或 ox-LDL 处理的人脐静脉内皮细胞(HUVEC)和人主动脉血管平滑肌细胞(HAoSMC)中高表达。

结论

我们鉴定并验证了三个具有高诊断价值的关键 CAS 相关 ATGs,即、和。这三个关键的 CAS 相关 ATGs 是 CAS 患者有前途的诊断标志物和治疗靶点。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b59d/11587871/856ca9145f30/peerj-12-18543-g001.jpg

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