基于生物信息学分析鉴定胸主动脉夹层的关键生物标志物和免疫浸润。

Identification of key biomarkers and immune infiltration in the thoracic acute aortic dissection by bioinformatics analysis.

机构信息

Department of Cardiothoracic Surgery, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing, China.

Chongqing Medical University, Chongqing, China.

出版信息

BMC Cardiovasc Disord. 2023 Feb 8;23(1):75. doi: 10.1186/s12872-023-03110-4.

BACKGROUND

Thoracic acute aortic dissection (TAAD), one of the most fatal cardiovascular diseases, leads to sudden death, however, its mechanism remains unclear.

METHODS

Three Gene Expression Omnibus datasets were employed to detect differentially expressed genes (DEGs). A similar function and co-expression network was identified by weighted gene co-expression network analysis. The least absolute shrinkage and selection operator, random forest, and support vector machines-recursive feature elimination were utilized to filter diagnostic TAAD markers, and then screened markers were validated by quantitative real-time PCR and another independent dataset. CIBERSORT was deployed to analyze and evaluate immune cell infiltration in TAAD tissues.

RESULTS

Twenty-five DEGs were identified and narrowed down to three after screening. Finally, two genes, SLC11A1 and FGL2, were verified by another dataset and qRT-PCR. Function analysis revealed that SLC11A1 and FGL2 play significant roles in immune-inflammatory responses.

CONCLUSION

SLC11A1 and FGL2 are differently expressed in aortic dissection and may be involved in immune-inflammatory responses.

背景

胸主动脉夹层（TAAD）是最致命的心血管疾病之一，可导致猝死，但其发病机制尚不清楚。

方法

本研究使用了三个基因表达综合数据集来检测差异表达基因（DEGs）。通过加权基因共表达网络分析鉴定了具有相似功能和共表达网络的基因。使用最小绝对收缩和选择算子（LASSO）、随机森林和支持向量机-递归特征消除来筛选诊断 TAAD 标志物，然后通过定量实时 PCR 和另一个独立数据集对筛选出的标志物进行验证。CIBERSORT 用于分析和评估 TAAD 组织中的免疫细胞浸润。