Identifying potential signatures for atherosclerosis in the context of predictive, preventive, and personalized medicine using integrative bioinformatics approaches and machine-learning strategies

Jinling Xu; Hui Zhou; Yangyang Cheng; Guangda Xiang

doi:10.1007/s13167-022-00289-y

Identifying potential signatures for atherosclerosis in the context of predictive, preventive, and personalized medicine using integrative bioinformatics approaches and machine-learning strategies

EPMA J. 2022 Jul 20;13(3):433-449. doi: 10.1007/s13167-022-00289-y. eCollection 2022 Sep.

Authors

Jinling Xu^#^{1

2}, Hui Zhou^#³, Yangyang Cheng^{1

2}, Guangda Xiang^{1

2}

Affiliations

¹ The First School of Clinical Medicine, Southern Medical University, Guangzhou, 510515 Guangdong China.
² Department of Endocrinology, General Hospital of Central Theater Command, Wuhan, 430070 Hubei China.
³ Department of General Surgery, Central South University, The Third Xiangya Hospital, Changsha, 410013 Hunan China.

^# Contributed equally.

Abstract

Background: 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.

Methods: 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.

Results: 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.

Conclusions: 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.

Supplementary information: The online version contains supplementary material available at 10.1007/s13167-022-00289-y.

Keywords: Atherosclerosis; Biomarkers; DHRS9; Integrative analysis; Machine-learning strategy; Macrophage; Predictive preventive personalized medicine (PPPM).