■主动脉瘤(AA)在全球范围内普遍存在,明显缺乏药物治疗。因此,确定潜在的药物靶点至关重要.AA经常出现在老年人身上,再加上持续升高的血清炎症标志物。鉴于衰老和炎症是与AA进化相关的关键过程,我们通过各种生物信息学方法确定了参与AA发育炎症过程的关键基因,从而为进一步研究提供潜在的分子靶标。
■AA的转录组数据来自数据集GSE140947、GSE7084和GSE47472,来自NCBIGEO数据库,而衰老和炎症的基因数据是从GeneCards数据库获得的。为了确定关键基因,使用“Limma”软件包和WGCNA进行差异表达分析。蛋白质-蛋白质交叉(PPI)分析和机器学习(ML)算法用于筛选潜在的生物标志物。然后评估诊断价值.在获得枢纽炎症和AA相关差异表达基因(IADEGs)之后,建立了TFs-mRNA-miRNA调控网络。CIBERSORT算法用于研究AA中的免疫细胞浸润。还评估了枢纽IADEGs与浸润免疫细胞的相关性。最后,进行了湿实验室实验以确认集线器IADEGs的表达。
■从GSE140947和GSE7084数据集中识别的342和715个与AA相关的DEGs(ADEGs)是通过将“Limma”和WGCNA分析的结果相交而获得的。获得83个IADEGs后,PPI分析和ML算法分别确定了7个和5个集线器IADEGs候选对象,其中6例具有较高的诊断价值。免疫细胞浸润结果揭示了AA的免疫失调。在潮湿的实验室实验中,3个枢纽IADEGs,包括BLNK,HLA-DRA,和HLA-DQB1最终表现出与生物信息学分析结果一致的表达趋势。
■我们的研究确定了三个基因-BLNK,HLA-DRA,和HLA-DQB1-在通过炎症促进AA的发展中起重要作用,为未来对AA的理解和治疗干预提供新的见解。
Aortic aneurysms (AA) are prevalent worldwide with a notable absence of drug therapies. Thus, identifying potential drug targets is of utmost importance. AA often presents in the elderly, coupled with consistently raised serum inflammatory markers. Given that ageing and inflammation are pivotal processes linked to the evolution of AA, we have identified key genes involved in the inflammaging process of AA development through various bioinformatics methods, thereby providing potential molecular targets for further investigation.
The transcriptome data of AA was procured from the datasets GSE140947, GSE7084, and GSE47472, sourced from the NCBI GEO database, whilst gene data of ageing and inflammation were obtained from the GeneCards Database. To identify key genes, differentially expressed analysis using the \"Limma\" package and WGCNA were implemented. Protein-protein intersection (PPI) analysis and machine learning (ML) algorithms were employed for the screening of potential biomarkers, followed by an assessment of the diagnostic value. Following the acquisition of the hub inflammaging and AA-related differentially expressed genes (IADEGs), the TFs-mRNAs-miRNAs regulatory network was established. The CIBERSORT algorithm was utilized to investigate immune cell infiltration in AA. The correlation of hub IADEGs with infiltrating immunocytes was also evaluated. Lastly, wet laboratory experiments were carried out to confirm the expression of hub IADEGs.
342 and 715 AA-related DEGs (ADEGs) recognized from GSE140947 and GSE7084 datasets were procured by intersecting the results of \"Limma\" and WGCNA analyses. After 83 IADEGs were obtained, PPI analysis and ML algorithms pinpointed 7 and 5 hub IADEGs candidates respectively, and 6 of them demonstrated a high diagnostic value. Immune cell infiltration outcomes unveiled immune dysregulation in AA. In the wet laboratory experiments, 3 hub IADEGs, including BLNK, HLA-DRA, and HLA-DQB1, finally exhibited an expression trend in line with the bioinformatics analysis result.
Our research identified three genes - BLNK, HLA-DRA, and HLA-DQB1- that play a significant role in promoting the development of AA through inflammaging, providing novel insights into the future understanding and therapeutic intervention of AA.