Single-Cell Sequencing Analysis and Multiple Machine Learning Methods Identified G0S2 and HPSE as Novel Biomarkers for Abdominal Aortic Aneurysm

Abstract

Identifying biomarkers for abdominal aortic aneurysms (AAA) is key to understanding their pathogenesis, developing novel targeted therapeutics, and possibly improving patients outcomes and risk of rupture. Here, we identified AAA biomarkers from public databases using single-cell RNA-sequencing, weighted co-expression network (WGCNA), and differential expression analyses. Additionally, we used the multiple machine learning methods to identify biomarkers that differentiated large AAA from small AAA. Biomarkers were validated using GEO datasets. CIBERSORT was used to assess immune cell infiltration into AAA tissues and investigate the relationship between biomarkers and infiltrating immune cells. Therefore, 288 differentially expressed genes (DEGs) were screened for AAA and normal samples. The identified DEGs were mostly related to inflammatory responses, lipids, and atherosclerosis. For the large and small AAA samples, 17 DEGs, mostly related to necroptosis, were screened. As biomarkers for AAA, G0/G1 switch 2 (G0S2) (Area under the curve [AUC] = 0.861, 0.875, and 0.911, in GSE57691, GSE47472, and GSE7284, respectively) and for large AAA, heparinase (HPSE) (AUC = 0.669 and 0.754, in GSE57691 and GSE98278, respectively) were identified and further verified by qRT-PCR. Immune cell infiltration analysis revealed that the AAA process may be mediated by T follicular helper (Tfh) cells and the large AAA process may also be mediated by Tfh cells, M1, and M2 macrophages. Additionally, G0S2 expression was associated with neutrophils, activated and resting mast cells, M0 and M1 macrophages, regulatory T cells (Tregs), resting dendritic cells, and resting CD4 memory T cells. Moreover, HPSE expression was associated with M0 and M1 macrophages, activated and resting mast cells, Tregs, and resting CD4 memory T cells. Additional, G0S2 may be an effective diagnostic biomarker for AAA, whereas HPSE may be used to confer risk of rupture in large AAAs. Immune cells play a role in the onset and progression of AAA, which may improve its diagnosis and treatment.

Differential expression analyses on aortic tissue reveal novel genes and pathways associated with abdominal aortic aneurysm onset and progression

“…However, a discrepancy was noted in the levels of activated dendritic cells, which were more present in controls in our study but slightly higher in AAA samples in the previous work. Additional single-cell data demonstrated a higher proportion of T follicular helper cells and lower proportions of M1 and M2 macrophages in AAA samples compared to controls[40].…”

“…To better understand the effects of the inflammatory infiltrate in AAA development, we compared the proportions of inflammatory infiltrates between AAA and control samples. Some previous studies have analyzed the inflammatory infiltrates in AAA tissue [15,19,40]. In one study [15], immune cell proportions were estimated in AAA tissue layers (media and adventitia), without comparing with controls.…”

Signaling pathways in vascular function and hypertension: molecular mechanisms and therapeutic interventions

“…Utilization of high-throughput sequencing and machine learning are cuttingedge methods to further mine the role of biomarkers. 656,657 It is worth mentioning that hypertension has been defined as a disease for over a century. However, modern medicine tends to view hypertension as a risk factor for vascular disease rather than a disease in itself.…”

WGCNA and multiple machine learning methods identified SV2C and DENR as novel biomarkers for Parkinson's disease

“…Among these methodologies are LASSO analysis, random forest algorithms, and SVM-REF models, to name a few. To exemplify, through a synergistic integration of Weighted Gene Co-expression Network Analysis (WGCNA), Random Forest and SVM-REF modeling, coupled with LASSO analysis, G0S2, and HPSE in peripheral serum were de ned as underlying diagnostic biomarkers for abdominal aortic aneurysm (Xiong et al, 2022). However, it is worth noting that the integration of WGCNA and machine learning in bioinformatics studies of PD is seldom explored.…”