Xu Jinling, Zhou Hui, Cheng Yangyang, Xiang Guangda
The First School of Clinical Medicine, Southern Medical University, Guangzhou, 510515 Guangdong China.
Department of Endocrinology, General Hospital of Central Theater Command, Wuhan, 430070 Hubei China.
EPMA J. 2022 Jul 20;13(3):433-449. doi: 10.1007/s13167-022-00289-y. eCollection 2022 Sep.
Atherosclerosis is a major contributor to morbidity and mortality worldwide. Although several molecular markers associated with atherosclerosis have been developed in recent years, the lack of robust evidence hinders their clinical applications. For these reasons, identification of novel and robust biomarkers will directly contribute to atherosclerosis management in the context of predictive, preventive, and personalized medicine (PPPM). This integrative analysis aimed to identify critical genetic markers of atherosclerosis and further explore the underlying molecular immune mechanism attributing to the altered biomarkers.
Gene Expression Omnibus (GEO) series datasets were downloaded from GEO. Firstly, differential expression analysis and functional analysis were conducted. Multiple machine-learning strategies were then employed to screen and determine key genetic markers, and receiver operating characteristic (ROC) analysis was used to assess diagnostic value. Subsequently, cell-type identification by estimating relative subsets of RNA transcript (CIBERSORT) and a single-cell RNA sequencing (scRNA-seq) data were performed to explore relationships between signatures and immune cells. Lastly, we validated the biomarkers' expression in human and mice experiments.
A total of 611 overlapping differentially expressed genes (DEGs) included 361 upregulated and 250 downregulated genes. Based on the enrichment analysis, DEGs were mapped in terms related to immune cell involvements, immune activating process, and inflaming signals. After using multiple machine-learning strategies, dehydrogenase/reductase 9 (DHRS9) and protein tyrosine phosphatase receptor type J (PTPRJ) were identified as critical biomarkers and presented their high diagnostic accuracy for atherosclerosis. From CIBERSORT analysis, both DHRS9 and PTPRJ were significantly related to diverse immune cells, such as macrophages and mast cells. Further scRNA-seq analysis indicated DHRS9 was specifically upregulated in macrophages of atherosclerotic lesions, which was confirmed in atherosclerotic patients and mice.
Our findings are the first to report the involvement of DHRS9 in the atherogenesis, and the proatherogenic effect of DHRS9 is mediated by immune mechanism. In addition, we confirm that DHRS9 is localized in macrophages within atherosclerotic plaques. Therefore, upregulated DHRS9 could be a novel potential target for the future predictive diagnostics, targeted prevention, patient stratification, and personalization of medical services in atherosclerosis.
The online version contains supplementary material available at 10.1007/s13167-022-00289-y.
动脉粥样硬化是全球发病和死亡的主要原因。尽管近年来已经开发了几种与动脉粥样硬化相关的分子标志物，但缺乏有力证据阻碍了它们的临床应用。因此，鉴定新的和可靠的生物标志物将直接有助于在预测、预防和个性化医学（PPPM）背景下的动脉粥样硬化管理。本综合分析旨在鉴定动脉粥样硬化的关键遗传标志物，并进一步探索导致生物标志物改变的潜在分子免疫机制。
从基因表达综合数据库（GEO）下载系列数据集。首先，进行差异表达分析和功能分析。然后采用多种机器学习策略筛选和确定关键遗传标志物，并使用受试者工作特征（ROC）分析评估诊断价值。随后，通过估计RNA转录本相对亚群（CIBERSORT）进行细胞类型鉴定和单细胞RNA测序（scRNA-seq）数据，以探索特征与免疫细胞之间的关系。最后，我们在人和小鼠实验中验证了生物标志物的表达。
共有611个重叠的差异表达基因（DEG），包括361个上调基因和250个下调基因。基于富集分析，DEG被映射到与免疫细胞参与、免疫激活过程和炎症信号相关的术语。使用多种机器学习策略后，脱氢酶/还原酶9（DHRS9）和蛋白酪氨酸磷酸酶受体J型（PTPRJ）被鉴定为关键生物标志物，并显示出对动脉粥样硬化的高诊断准确性。从CIBERSORT分析来看，DHRS9和PTPRJ均与多种免疫细胞显著相关，如巨噬细胞和肥大细胞。进一步的scRNA-seq分析表明，DHRS9在动脉粥样硬化病变的巨噬细胞中特异性上调，这在动脉粥样硬化患者和小鼠中得到了证实。
我们的研究结果首次报道了DHRS9参与动脉粥样硬化的发生，并且DHRS9的促动脉粥样硬化作用是由免疫机制介导的。此外，我们证实DHRS9定位于动脉粥样硬化斑块内的巨噬细胞中。因此，DHRS9上调可能是未来动脉粥样硬化预测诊断、靶向预防、患者分层和医疗服务个性化的新潜在靶点。
在线版本包含可在10.1007/s13167-022-00289-y获取的补充材料。
