An integrative analysis of single-cell and bulk transcriptome and bidirectional mendelian randomization analysis identified C1Q as a novel stimulated risk gene for Atherosclerosis.

Cui HK, Tang CJ, Gao Y, Li ZA, Zhang J, Li YD
Front Immunol 2023
Open on PubMed

1. Front Immunol. 2023 Dec 21;14:1289223. doi: 10.3389/fimmu.2023.1289223. eCollection 2023. An integrative analysis of single-cell and bulk transcriptome and bidirectional mendelian randomization analysis identified C1Q as a novel stimulated risk gene for Atherosclerosis. Cui HK(#)(1), Tang CJ(#)(2), Gao Y(1), Li ZA(1), Zhang J(1), Li YD(1)(2). Author information: (1)Department of Neurological Intervention, The First Affiliated Hospital, Xinxiang Medical University, Xinxiang, Henan, China. (2)Institute of Diagnostic and Interventional Radiology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China. (#)Contributed equally BACKGROUND: The role of complement component 1q (C1Q) related genes on human atherosclerotic plaques (HAP) is less known. Our aim is to establish C1Q associated hub genes using single-cell RNA sequencing (scRNA-seq) and bulk RNA analysis to diagnose and predict HAP patients more effectively and investigate the association between C1Q and HAP (ischemic stroke) using bidirectional Mendelian randomization (MR) analysis. METHODS: HAP scRNA-seq and bulk-RNA data were download from the Gene Expression Omnibus (GEO) database. The C1Q-related hub genes was screened using the GBM, LASSO and XGBoost algorithms. We built machine learning models to diagnose and distinguish between types of atherosclerosis using generalized linear models and receiver operating characteristics (ROC) analyses. Further, we scored the HALLMARK_COMPLEMENT signaling pathway using ssGSEA and confirmed hub gene expression through qRT-PCR in RAW264.7 macrophages and apoE-/- mice. Furthermore, the risk association between C1Q and HAP was assessed through bidirectional MR analysis, with C1Q as exposure and ischemic stroke (IS, large artery atherosclerosis) as outcomes. Inverse variance weighting (IVW) was used as the main method. RESULTS: We utilized scRNA-seq dataset (GSE159677) to identify 24 cell clusters and 12 cell types, and revealed seven C1Q associated DEGs in both the scRNA-seq and GEO datasets. We then used GBM, LASSO and XGBoost to select C1QA and C1QC from the seven DEGs. Our findings indicated that both training and validation cohorts had satisfactory diagnostic accuracy for identifying patients with HPAs. Additionally, we confirmed SPI1 as a potential TF responsible for regulating the two hub genes in HAP. Our analysis further revealed that the HALLMARK_COMPLEMENT signaling pathway was correlated and activated with C1QA and C1QC. We confirmed high expression levels of C1QA, C1QC and SPI1 in ox-LDL-treated RAW264.7 macrophages and apoE-/- mice using qPCR. The results of MR indicated that there was a positive association between the genetic risk of C1Q and IS, as evidenced by an odds ratio (OR) of 1.118 (95%CI: 1.013-1.234, P = 0.027). CONCLUSION: The authors have effectively developed and validated a novel diagnostic signature comprising two genes for HAP, while MR analysis has provided evidence supporting a favorable association of C1Q on IS. Copyright © 2023 Cui, Tang, Gao, Li, Zhang and Li. DOI: 10.3389/fimmu.2023.1289223 PMCID: PMC10764496 PMID: 38179058 [Indexed for MEDLINE] Conflict of interest statement: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

13 Figures Extracted
Figure 1
Figure 1 PMC
Single-cell RNA-seq of human AP tissues. (A) The clustering tree of total scRNA-seq mate data was analyzed at different resolutions. (B) The top t...
Figure 2
Figure 2 PMC
C1Q hub genes selection from scRNA-seq and GEO dataset. (A) The top ten genes extract from C1Q cell cluster. (B) The 10 genes are detected in 781 ...
Figure 3
Figure 3 PMC
The expression and signaling pathways involved in characteristic genes in scRNA-seq. (A–C) The plots display the expression of C1QA, C1QC and SPI1 i...
Figure 4
Figure 4 PMC
Diagnostic prediction model for AP progression. (A) The actual and predicted samples with confusion matrices built from the diagnostic prediction mo...
Figure 5
Figure 5 PMC
Diagnostic prediction model for diagnosis and predicting HAP from normal controls. (A) The actual and predicted samples with confusion matrices buil...
Figure 6
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Immune microenvironment analysis based on C1Q hub genes. (A) Heatmap displaying enrichment of immune-infiltrating cells through 8 algorithms between...
Figure 7
Figure 7 PMC
Evaluation of immune signaling pathway and immunomodulators based on C1Q hub genes. (A–C) Comparison of 16 immune signaling pathway between high- an...
Figure 8
Figure 8 PMC
C1QA activated HALLMARK_COMPLEMENT signaling pathway in HAP. (A–F) GSEA analysis results of C1QA for three GEO datasets ( GSE43292 , GSE28829 and ...
Figure 9
Figure 9 PMC
C1QA correlated HALLMARK_COMPLEMENT signaling pathway in HAP. (A) Correlation between C1QA and HALLMARK_COMPLEMENT signaling pathway genes in the G...
Figure 10
Figure 10 PMC
SPI1 was identified as a potential key TF in HAP. (A–C) The potential TFs that may regulate the C1QA and C1QC gene was screened from three databases...
Figure 11
Figure 11 PMC
In vitro and in vivo validation of C1QA and C1QC. (A–C) Relative mRNA expression of C1QA and C1QC detected by real-time PCR in ox-LDL-treated RAW...
Figure 12
Figure 12 PMC
Visualization of the MR analysis of C1Q on ischemic stroke (IS). (A) Scatterplot of the MR analysis of the effect of C1Q on IS. (B) Forest plots o...
Figure 13
Figure 13 PMC
Visualization of the MR analysis of ischemic stroke (IS) on C1Q. (A) Scatterplot of the MR analysis of the effect of IS on C1Q. (B) Forest plots o...