Profiles of immune infiltration in abdominal aortic aneurysm and their associated marker genes: a gene expression-based study

Identification of biomarkers for abdominal aortic aneurysm in Behçet's disease via mendelian randomization and integrated bioinformatics analyses

Behçet's disease (BD) is a complex autoimmune disorder impacting several organ systems. Although the involvement of abdominal aortic aneurysm (AAA) in BD is rare, it can be associated with severe consequences. In the present study, we identified diagnostic biomarkers in patients with BD having AAA. Mendelian randomization (MR) analysis was initially used to explore the potential causal association between BD and AAA. The Limma package, WGCNA, PPI and machine learning algorithms were employed to identify potential diagnostic genes. A receiver operating characteristic curve (ROC) for the nomogram was constructed to ascertain the diagnostic value of AAA in patients with BD. Finally, immune cell infiltration analyses and single-sample gene set enrichment analysis (ssGSEA) were conducted. The MR analysis indicated a suggestive association between BD and the risk of AAA (odds ratio [OR]: 1.0384, 95% confidence interval [CI]: 1.0081-1.0696, p = 0.0126). Three hub genes (CD247, CD2 and CCR7) were identified using the integrated bioinformatics analyses, which were subsequently utilised to construct a nomogram (area under the curve [AUC]: 0.982, 95% CI: 0.944-1.000). Finally, the immune cell infiltration assay revealed that dysregulation immune cells were positively correlated with the three hub genes. Our MR analyses revealed a higher susceptibility of patients with BD to AAA. We used a systematic approach to identify three potential hub genes (CD247, CD2 and CCR7) and developed a nomogram to assist in the diagnosis of AAA among patients with BD. In addition, immune cell infiltration analysis indicated the dysregulation in immune cell proportions.

Predicting feature genes correlated with immune infiltration in patients with abdominal aortic aneurysm based on machine learning algorithms

Abdominal aortic aneurysm (AAA) is a condition characterized by a pathological and progressive dilatation of the infrarenal abdominal aorta. The exploration of AAA feature genes is crucial for enhancing the prognosis of AAA patients. Microarray datasets of AAA were downloaded from the Gene Expression Omnibus database. A total of 43 upregulated differentially expressed genes (DEGs) and 32 downregulated DEGs were obtained. Function, pathway, disease, and gene set enrichment analyses were performed, in which enrichments were related to inflammation and immune response. AHR, APLNR, ITGA10 and NR2F6 were defined as feature genes via machine learning algorithms and a validation cohort, which indicated high diagnostic abilities by the receiver operating characteristic curves. The cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) method was used to quantify the proportions of immune infiltration in samples of AAA and normal tissues. We have predicted AHR, APLNR, ITGA10 and NR2F6 as feature genes of AAA. CD8 + T cells and M2 macrophages correlated with these genes may be involved in the development of AAA, which have the potential to be developed as risk predictors and immune interventions.

Decreased TLR7 expression was associated with airway eosinophilic inflammation and lung function in asthma: evidence from machine learning approaches and experimental validation

BACKGROUND

Asthma is a global public health concern. The underlying pathogenetic mechanisms of asthma were poorly understood. This study aims to explore potential biomarkers associated with asthma and analyze the pathological role of immune cell infiltration in the disease.

METHODS

The gene expression profiles of induced sputum were obtained from Gene Expression Omnibus datasets (GSE76262 and GSE137268) and were combined for analysis. Toll-like receptor 7 (TLR7) was identified as the core gene by the intersection of two different machine learning algorithms, namely, least absolute shrinkage and selector operation (LASSO) regression and support vector machine-recursive feature elimination (SVM-RFE), and the top 10 core networks based on Cytohubba. CIBERSORT algorithm was used to analyze the difference of immune cell infiltration between asthma and healthy control groups. Finally, the expression level of TLR7 was validated in induced sputum samples of patients with asthma.

RESULTS

A total of 320 differential expression genes between the asthma and healthy control groups were screened, including 184 upregulated genes and 136 downregulated genes. TLR7 was identified as the core gene after combining the results of LASSO regression, SVM-RFE algorithm, and top 10 hub genes. Significant differences were observed in the distribution of 13 out of 22 infiltrating immune cells in asthma. TLR7 was found to be closely related to the level of several infiltrating immune cells. TLR7 mRNA levels were downregulated in asthmatic patients compared with healthy controls (p = 0.0049). The area under the curve of TLR7 for the diagnosis of asthma was 0.7674 (95% CI 0.631-0.904, p = 0.006). Moreover, TLR7 mRNA levels were negatively correlated with exhaled nitric oxide fraction (r = - 0.3268, p = 0.0347) and the percentage of peripheral blood eosinophils (%) (r = - 0.3472, p = 0.041), and positively correlated with forced expiratory volume in the first second (FEV1) (% predicted) (r = 0.3960, p = 0.0071) and FEV1/forced vital capacity (r = 0.3213, p = 0.0314) in asthmatic patients.

CONCLUSIONS

Decreased TLR7 in the induced sputum of eosinophilic asthmatic patients was involved in immune cell infiltration and airway inflammation, which may serve as a new biomarker for the diagnosis of eosinophilic asthma.

Targeting Epidermal Growth Factor Receptor to Stimulate Elastic Matrix Regenerative Repair

Abdominal aortic aneurysms (AAAs) represent a multifactorial, proteolytic disorder involving disintegration of the matrix structure within the AAA wall. Intrinsic deficiency of adult vascular cells to regenerate and repair the wall elastic matrix, which contributes to vessel stretch and recoil, is a major clinical challenge to therapeutic reversal of AAA growth. In this study, we investigate the involvement of epidermal growth factor receptor-mitogen activated protein kinase (EGFR-MAPK) pathway in the activation of aneurysmal smooth muscle cells (SMCs) by neutrophil elastase, and how EGFR can be targeted for elastic matrix regeneration. We have demonstrated that neutrophil elastase activates EGFR and downregulates expression level of key elastin homeostasis genes (elastin, crosslinking enzyme-lysyl oxidase, and fibulin4) between a dose range of 1-10 μg/mL (p < 0.05). It also incites downstream proteolytic outcomes by upregulating p-extracellular signal-regulated kinase (ERK)1/2 (p < 0.0001) and matrix metalloprotease 2 (MMP2) at a protein level, which is significantly downregulated upon EGFR-specific inhibition by tyrosine kinase inhibitor AG1478 (p-ERK1/2 and MMP2 [p < 0.05]). Moreover, we have shown that EGFR inhibition suppresses collagen amounts in aneurysmal SMCs (p < 0.05) and promotes robust formation of elastic fibers by enhancing its deposition in the extracellular space. Hence, the EGFR-MAPK pathway in aneurysmal cells can be targeted to provide therapeutic effects toward stimulating vascular matrix regeneration. Impact statement Proteolytic disorders such as aortal expansions, called abdominal aortic aneurysms (AAAs), are characterized by naturally irreversible enzymatic breakdown and loss of elastic fibers, a problem that has not yet been surmounted by existing tissue engineering approaches. In this work, we show, for the first time, how epidermal growth factor receptor (EGFR) inhibition provides downstream benefits in elastic fiber assembly and deposition in aneurysmal smooth muscle cell cultures. This work can open future possibilities for development of EGFR-targeted drug-based therapies not only for vessel wall repair in AAAs but also other proteolytically compromised elastic tissues.

Synergism of cell adhesion regulatory genes and instant air pollutants on blood pressure elevation

Accumulating evidence suggests that an instant exposure to particulate matter (PM) may elevate blood pressure (BP), where cell-adhesion regulatory genes may be involved in the interplay. However, few studies to date critically examined their interaction, and it remained unclear whether these genes modified the association. To assess the association between instant PM exposure and BP, and to examine whether single-nucleotide polymorphisms (SNPs) mapped in four cell adhesion regulatory genes modify the relationship, a cross-sectional study was performed, based on the baseline of an ongoing family-based cohort in Beijing, China. A total of 4418 persons from 2089 families in Northern China were included in the analysis. Four tagged SNPs in cell adhesion regulatory genes were selected among ZFHX3, CXCL12, RASGRP1 and MIR146A. A generalized additive model (GAM) with a Gaussian link was adopted to estimate the change in blood pressure after instant PM_2.5 or PM₁₀ exposure. A cross-product term of PM_2.5/PM₁₀ and genotype was incorporated into the GAM model to test for interaction. The study observed that an instant exposure to either PM_2.5 or PM₁₀ was found to be associated with elevated systolic blood pressure (SBP). On average, a 10 μg/m³ increase in instant exposure to PM_2.5 and PM₁₀ concentration corresponded to 0.140% (95% CI: 0.014%-0.265%, P = 0.029) and 0.173% (95% CI: 0.080%-0.266%, P < 0.001) higher SBP. However, diastolic blood pressure (DBP) was not elevated as the PM_2.5 or PM₁₀ concentration increased (P > 0.05). A synergetic interaction on SBP was observed between SNPs in four cell adhesion regulatory genes (rs2910164 in MIR146A, rs2297630 in CXCL12, rs7403531 in RASGRP1, and rs7193343 in ZFHX3) and instant PM_2.5 exposure (P_{for interaction} <0.05). Briefly, as carriers of risk alleles in each of these four genes increased, an enhanced association was found between instant PM_2.5 exposure and SBP.

Genome-Wide Identification of Immune-Related Alternative Splicing and Splicing Regulators Involved in Abdominal Aortic Aneurysm

The molecular mechanism of AAA formation is still poorly understood and has not been fully elucidated. The study was designed to identify the immune-related genes, immune-RAS in AAA using bioinformatics methods. The GSE175683 datasets were downloaded from the GEO database. The DEseq2 software was used to identify differentially expressed genes (DEGs). SUVA pipeline was used to quantify AS events and RAS events. KOBAS 2.0 server was used to identify GO terms and KEGG pathways to sort out functional categories of DEGs. The CIBERSORT algorithm was used with the default parameter for estimating immune cell fractions. Nine samples from GSE175683 were used to construct the co-disturbed network between expression of SFs and splicing ratio of RAS events. PCA analysis was performed by R package factoextra to show the clustering of samples, and the pheatmap package in R was used to perform the clustering based on Euclidean distance. The results showed that there were 3,541 genes significantly differentially expressed, of which 177 immune-related genes were upregulated and 48 immune-related genes were downregulated between the WT and WTA group. Immune-RAS events were mainly alt5P and IR events, and about 60% of it was complex splicing events in AAA. The WT group and the WTA group can be clearly distinguished in the first principal component by using the splicing ratio of immune-RAS events. Two downregulated genes, Nr4a1 and Nr4a2, and eight upregulated genes, Adipor2, Akt2, Bcl3, Dhx58, Pparg, Ptgds, Sytl1, and Vegfa were identified among the immune-related genes with RAS and DEGs. Eighteen differentially expressed SFs were identified and displayed by heatmap. The proportion of different types of cells and ratio of the average ratio of different cells were quite different. Both M1 and M2 types of macrophages and plasma cells were upregulated, while M0 type was downregulated in AAA. The proportion of plasma cells in the WTA group had sharply increased. There is a correlation between SF expression and immune cells/immune-RAS. Sf3b1, a splicing factor with significantly different expression, was selected to bind on a mass of immune-related genes. In conclusion, our results showed that immune-related genes, immune-RAS, and SFs by genome-wide identification were involved in AAA.

Biomarkers in EndoVascular Aneurysm Repair (EVAR) and Abdominal Aortic Aneurysm: Pathophysiology and Clinical Implications

Circulating biomarkers have been recently investigated among patients undergoing endovascular aortic aneurysm repair (EVAR) for abdominal aortic aneurysm (AAA). Considering the plethora of small descriptive studies reporting potential associations between biomarkers and clinical outcomes, this review aims to summarize the current literature considering both the treated disease (post EVAR) and the untreated disease (AAA before EVAR). All studies describing outcomes of tissue biomarkers in patients undergoing EVAR and in patients with AAA were included, and references were checked for additional sources. In the EVAR scenario, circulating interleukin-6 (IL-6) is a marker of inflammatory reaction which might predict postoperative morbidity; cystatin C is a promising early marker of post-procedural acute kidney injury; plasma matrix metalloproteinase-9 (MMP-9) concentration after 3 months from EVAR might help in detecting post-procedural endoleak. This review also summarizes the current gaps in knowledge and future direction of this field of research. Among markers used in patients with AAA, galectin and granzyme appear to be promising and should be carefully investigated even in the EVAR setting. Larger prospective trials are required to establish and evaluate prognostic models with highest values with these markers.

POU class 2 homeobox associating factor 1 (POU2AF1) participates in abdominal aortic aneurysm enlargement based on integrated bioinformatics analysis

Abdominal aortic aneurysm (AAA) is life-threatening, its natural course is progressively sac expansion and rupture. Elegant studies have been conducted to investigate the molecular markers associated with AAA growth and expansion, this topic however, still needs to be further elucidated. This study aimed to identify potential genes for AAA growth and expansion based on comprehensive bioinformatics approaches. Firstly, 29 up-regulated genes were identified through DEGs analysis between large AAA and small AAA in GSE57691. Secondly, signed WGCNA analysis was conducted based on GSE57691 and the green module was found to exhibit the topmost correlation with large AAA as well as AAA, 133 WGCNA hub genes were further identified. Merged gene set including 29 up-regulated DEGs and 858 green module genes was subjected to constructing a PPI network where 195 PPI hub genes were identified. Subsequently, 4 crucial genes including POU2AF1, FCRLA, CD79B, HLA-DOB were recognized by Venn plot. In addition, by using GSE7084 and GSE98278 for verification, POU2AF1 showed potential diagnostic value between AAA and normal groups, and exhibited a significant higher expression level in large AAA samples compared with small AAA samples. Furthermore, immunohistochemistry results indicated up-regulation of POU2AF1 in large AAA samples than small AAA samples, which implies POU2AF1 may be a key regulator in AAA enlargement and growth. In summary, this study indicates that POU2AF1 has great predictive value for the expansion of AAA, and may contribute to the further exploration of pathogenesis and progression of AAA.