Identification of diagnostic biomarkers and therapeutic targets in peripheral immune landscape from coronary artery disease

Abstract

Background Peripheral biomarkers are increasingly vital non-invasive methods for monitoring coronary artery disease (CAD) progression. Their superiority in early detection, prognosis evaluation and classified diagnosis is becoming irreplaceable. Nevertheless, they are still less explored. This study aimed to determine and validate the diagnostic and therapeutic values of differentially expressed immune-related genes (DE-IRGs) in CAD. Methods We downloaded clinical information and RNA sequence data from the GEO database. We used R software, GO, KEGG and Cytoscape to analyze and visualize the data. A LASSO method was conducted to identify key genes for diagnostic model construction. The ssGSEA analysis was used to investigate the differential immune cell infiltration. Besides, we constructed CAD mouse model (low-density lipoprotein receptor deficient mice with high fat diet) to discover the correlation between the screened genes and severe CAD progress. We further uncovered the role of IL13RA1 might play in atherosclerosis. Results A total of 762 differential genes were identified between the peripheral blood of 218 controls and 199 CAD patients, which were significantly associated with infection, immune response and neural activity. 58 DE-IRGs were obtained by overlapping the differentially expressed genes(DEGs) and immune-related genes downloaded from ImmpDb database. Through LASSO regression, CCR9, CER1, CSF2, IL13RA1, INSL5, MBL2, MMP9, MSR1, NTS, TNFRSF19, CXCL2, HTR3C, IL1A, and NR4A2 were distinguished as peripheral biomarkers of CAD with eligible diagnostic capabilities in the training set (AUC = 0.968) and test set (AUC = 0.859). The ssGSEA analysis showed that the peripheral immune cells had characteristic distribution in CAD and also close relationship with specific DE-IRGs. RT-qPCR test showed that CCR9, CSF2, IL13RA1, and NTS had a significant correlation with LDLR−/− mice. IL13RA1 knocked down in RAW264.7 cell lines decreased SCARB1 and ox-LDL-stimulated CD36 mRNA expression, TGF-β, VEGF-C and α-SMA protein levels and increased the production of IL-6, with downregulation of JAK1/STAT3 signal pathway. Conclusions We constructed a diagnostic model of advanced-stage CAD based on the screened 14 DE-IRGs. We verified 4 genes of them to have a strong correlation with CAD, and IL13RA1 might participate in the inflammation, fibrosis, and cholesterol efflux process of atherosclerosis by regulating JAK1/STAT3 pathway.

Bioinformatic analysis of underlying mechanisms of Kawasaki disease via Weighted Gene Correlation Network Analysis (WGCNA) and the Least Absolute Shrinkage and Selection Operator method (LASSO) regression model

“…And it was applied to the expression matrix of candidate genes to construct the prognostic multi-gene signature. Multiple RNA risk signatures were identified through LASSO penalized Cox regression analysis [43,44]. Besides, the combination of these two methods has become a tendency recently, characteristic genes were determined in endometriosis [45] and sepsis [46,47] with the application of the combination of WGCNA and LASSO.…”

“…MiRNAs can induce gene degradation by binding to the 3'UTR of target mRNAs, and lncRNAs can competitively bind miRNAs, which are known as competing endogenous RNAs (ceRNAs). Both miRNAs and lncRNAs have been claimed to be involved in KD [44,46,47]; hence, we analyzed the target lncRNAs of the miRNAs interacting with node genes through online databases. The mRNA-miRNA pairs were presented in Fig.…”

Identification of Potential Biomarkers for Coronary Artery Disease Based on Cuproptosis

“…The biological functions of most marker genes are consistent with that reported previously, which also reflects the reliability of our results. CAD is caused by coronary atherosclerosis, a progressive inflammatory disease [34]. Researchers pointed out that the immune microenvironment plays an important role in the initiation and progression of CAD [35].…”

Single-cell RNA sequencing reveals in vivo osteoimmunology interactions between the immune and skeletal systems

“…Investigating the correlations of gene interactions with BMD may contribute to a better understanding of the osteoimmunology process of monocyte. So, we applied multiple approaches such as cell-specific network (CSN) (19) and Least absolute shrinkage and selection operator (LASSO) (20) analysis to assess gene interactions in expression profiles of circulating monocyte from male and female samples in the current study. CSN analysis allows construction of separate gene regulatory networks for individual RNA-seq samples, thereby enabling identification of multiple genes and their expression correlations based on different sample status such as age and BMD level (19).…”

“…CSN analysis allows construction of separate gene regulatory networks for individual RNA-seq samples, thereby enabling identification of multiple genes and their expression correlations based on different sample status such as age and BMD level (19). LASSO is a machine learning method that performs both variable selection and regularization to enhance the interpretability and accuracy of the prediction model (20). LASSO has been used to select prognosis/clinical character-associated genes and avoid overfitting in expression profiles of different diseases (21)(22)(23).…”