Yu Chenxi, Zhang Yifeng, Chen Hui, Chen Zhongli, Yang Ke
Department of Cardiovascular Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, People's Republic of China.
Department of Cardiology, Shanghai Ninth People's Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, 200011, People's Republic of China.
J Inflamm Res. 2024 May 28;17:3459-3473. doi: 10.2147/JIR.S453100. eCollection 2024.
Aortic valve sclerosis (AVS) is a pathological state that can progress to aortic stenosis (AS), which is a high-mortality valvular disease. However, effective medical therapies are not available to prevent this progression. This study aimed to explore potential biomarkers of AVS-AS advancement.
A microarray dataset and an RNA-sequencing dataset were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were screened from AS and AVS samples. Functional enrichment analysis, protein-protein interaction (PPI) network construction, and machine learning model construction were conducted to identify diagnostic genes. A receiver operating characteristic (ROC) curve was generated to evaluate diagnostic value. Immune cell infiltration was then used to analyze differences in immune cell proportion between tissues. Finally, immunohistochemistry was applied to further verify protein concentration of diagnostic factors.
A total of 330 DEGs were identified, including 92 downregulated and 238 upregulated genes. The top 5% of DEGs (n = 17) were screened following construction of a PPI network. IL-7 and VCAM-1 were identified as the most significant candidate genes via least absolute shrinkage and selection operator (LASSO) regression. The diagnostic value of the model and each gene were above 0.75. Proportion of anti-inflammatory M2 macrophages was lower, but the fraction of pro-inflammatory gamma-delta T cells was elevated in AS samples. Finally, levels of IL-7 and VCAM-1 were validated to be higher in AS tissue than in AVS tissue using immunohistochemistry.
IL-7 and VCAM-1 were identified as biomarkers during the disease progression. This is the first study to analyze gene expression differences between AVS and AS and could open novel sights for future studies on alleviating or preventing the disease progression.
主动脉瓣硬化（AVS）是一种可进展为主动脉瓣狭窄（AS）的病理状态，AS是一种高死亡率的瓣膜疾病。然而，目前尚无有效的药物疗法来预防这种进展。本研究旨在探索AVS-AS进展的潜在生物标志物。
从基因表达综合数据库（GEO）获得一个微阵列数据集和一个RNA测序数据集。从AS和AVS样本中筛选差异表达基因（DEG）。进行功能富集分析、蛋白质-蛋白质相互作用（PPI）网络构建和机器学习模型构建以鉴定诊断基因。生成受试者工作特征（ROC）曲线以评估诊断价值。然后利用免疫细胞浸润分析组织间免疫细胞比例的差异。最后，应用免疫组织化学进一步验证诊断因子的蛋白质浓度。
共鉴定出330个DEG，包括92个下调基因和238个上调基因。构建PPI网络后筛选出前5%的DEG（n = 17）。通过最小绝对收缩和选择算子（LASSO）回归将IL-7和VCAM-1鉴定为最显著的候选基因。模型和每个基因的诊断价值均高于0.75。AS样本中抗炎性M2巨噬细胞的比例较低，但促炎性γδT细胞的比例升高。最后，通过免疫组织化学验证AS组织中IL-7和VCAM-1的水平高于AVS组织。
IL-7和VCAM-1被鉴定为疾病进展过程中的生物标志物。这是第一项分析AVS和AS之间基因表达差异的研究，可为未来缓解或预防疾病进展的研究开辟新视野。
