Prognostic value of chemokines in patients with newly diagnosed atrial fibrillation

Identication and validation of cell senescence biomarkers in idiopathic pulmonary hypertension via integrated transcriptome analyses and machine learning

BACKGROUND

Idiopathic pulmonary hypertension (IPAH) is a rare and severe disease that affects the pulmonary vasculature. As the diagnosis of IPAH requires invasive right heart catheterization surgery, early detection of this condition is notoriously challenging. Therefore, it is of utmost importance to investigate biomarkers present in peripheral blood that could aid physicians in the early identification and management of IPAH.

METHOD

We speculate that cellular senescence may be involved in the occurrence and development of IPAH through various pathways. In this study, we utilized integrated transcriptome analyses and machine learning-based approach to develop a diagnostic model for IPAH cell senescence. To select genetic features, we employed two machine learning algorithms: the Least Absolute Shrinkage and Selection Operator (LASSO) and Random Forest (RF). Additionally, we validated our findings through both external data sets and qRT-PCR experiments.

RESULTS

The resulting diagnostic nomogram was able to identify five important biomarkers that can aid in the diagnosis of IPAH, including TNFRSF1B, CCL16, GCLM, IL15, and SOD1. These genes are primarily associated with the immune system, as well as with cell senescence and apoptosis.

CONCLUSION

Our study demonstrates the utility of machine learning algorithms in making accurate diagnoses of IPAH, providing clinicians with a more directed approach to the diagnosis and treatment of this disease.

Pilot study of peripheral blood chemokines as biomarkers for atrial fibrillation-related thromboembolism and bleeding in elderly patients

Background

The scoring systems currently used to identify the potential for thrombosis and bleeding events in high-risk atrial fibrillation patients have certain limitations. The aim of this pilot study was to identify inflammatory chemokines with potential utility as sensitive biomarkers for the risk of thrombosis and bleeding in elderly patients with non-valvular atrial fibrillation.

Methods

From January 1, 2014, to December 31, 2017, 200 consecutive elderly patients with atrial fibrillation (average age: 87.6 ± 7.7 years) were enrolled and followed up for 2 years to observe thromboembolic (arterial and venous) and bleeding events. Serum was collected upon enrollment, and the baseline levels of 27 chemokines were analyzed. During the 2-year follow-up, 12 patients were lost to follow-up. Among the 188 patients, there were 32 cases (17.0%) of AF-related thrombosis, 36 cases (19.1%) of arterial thrombosis, and 35 cases (18.6%) of major bleeding events.

Results

Among 188 patients, 30 patients without clinical events (control group), 23 with arterial thrombosis, 15 with atrial fibrillation-related venous thromboembolism, and 12 with major bleeding were selected and randomly matched to compare chemokine levels. The baseline levels of interleukin-6, interleukin-10, vascular cell adhesion molecule-1, chemokine C-C-motif ligand, B-lymphocyte chemoattractant 1, interleukin-4, E-selectin, fractalkine, C-X-C motif chemokine 12, and granulocyte chemotactic protein 2 were found to differ statistically among the four groups (p < 0.05). Compared with that in the control group, the level of interleukin-4 in patients with atrial fibrillation-related thrombosis, arterial thrombosis, or major bleeding increased by 53-fold (0.53 vs. 0.01 pg/ml), 17-fold (0.17 vs. 0.01 pg/ml), and 19-fold (0.19 vs. 0.01 pg/ml), respectively. Compared with that in the control group, the level of interleukin-6 in patients with arterial thrombosis increased by six-fold (39.78 vs. 4.98 pg/ml).

Conclusions

Among elderly patients with atrial fibrillation at high risk of thromboembolism and bleeding, the baseline levels of interleukin-6, interleukin-4, and E-selectin were significantly increased in those that experienced thrombosis and bleeding events during the 2-year follow-up, indicating that these chemokines may serve as potential biomarkers for an increased risk of thrombosis and bleeding in this population.

Clinical trial registration number

ChiCTR-OCH-13003479.

Hub Genes Identification, Small Molecule Compounds Prediction for Atrial Fibrillation and Diagnostic Model Construction Based on XGBoost Algorithm

Background

Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia and engenders significant global health care burden. The underlying mechanisms of AF is remained to be revealed and current treatment options for AF have limitations. Besides, a detection system can help identify those at risk of developing AF and will enable personalized management.

Materials and Methods

In this study, we utilized the robust rank aggregation method to integrate six AF microarray datasets from the Gene Expression Omnibus database, and identified a set of differentially expressed genes between patients with AF and controls. Potential compounds were identified by mining the Connectivity Map database. Functional modules and closely-interacted clusters were identified using weighted gene co-expression network analysis and protein–protein interaction network, respectively. The overlapped hub genes were further filtered. Subsequent analyses were performed to analyze the function, biological features, and regulatory networks. Moreover, a reliable Machine Learning-based diagnostic model was constructed and visualized to clarify the diagnostic features of these genes.

Results

A total of 156 upregulated and 34 downregulated genes were identified, some of which had not been previously investigated. We showed that mitogen-activated protein kinase and epidermal growth factor receptor inhibitors were likely to mitigate AF based on Connectivity Map analysis. Four genes, including CXCL12, LTBP1, LOXL1, and IGFBP3, were identified as hub genes. CXCL12 was shown to play an important role in regulation of local inflammatory response and immune cell infiltration. Regulation of CXCL12 expression in AF was analyzed by constructing a transcription factor-miRNA-mRNA network. The Machine Learning-based diagnostic model generated in this study showed good efficacy and reliability.

Conclusion

Key genes involving in the pathogenesis of AF and potential therapeutic compounds for AF were identified. The biological features of CXCL12 in AF were investigated using integrative bioinformatics tools. The results suggested that CXCL12 might be a biomarker that could be used for distinguishing subsets of AF, and indicated that CXCL12 might be an important intermediate in the development of AF. A reliable Machine Learning-based diagnostic model was constructed. Our work improved understanding of the mechanisms of AF predisposition and progression, and identified potential therapeutic avenues for treatment of AF.

The Increase in Circulating Levels of Pro-Inflammatory Chemokines, Cytokines, and Complement C5 in Canines with Impaired Kidney Function

Chronic low-grade inflammation is a key contributor to the progression of kidney disease. The release of cytokines and other pro-inflammatory proteins may further contribute to detrimental kidney health by increasing interstitial edema and renal fibrosis. The aim of the present study was to investigate the inflammatory markers in canines who developed renal disease naturally and were diagnosed with renal disease either during life or following necropsy, as assessed by a veterinarian. RNA was isolated from canine blood obtained at necropsy and stored as bioarchived samples from ten canines with renal disease (9.6−14.7 yr) and ten controls (10.1−14.8 yr). At the time of death, the mean blood creatinine concentration and BUN were elevated in dogs with renal disease compared to control (both p < 0.01). Samples were assessed for changes in gene expression using the Canine cytokine RT2 Profiler PCR Array for inflammation. There was a significant increase in C-C Motif Chemokine Ligand 16 (CCL16), C-X-C Motif Chemokine Ligand 5 (CXCL5), Interleukin 16 (IL-16), and Complement Component 5 (C5) (all p < 0.05 vs. con). In addition, there was also a statistically non-significant increase in 49 genes and a down-regulation in 35 genes from a panel of total 84 genes. Pro-inflammatory genes including CCL16, CXCL5, IL-16, and C5 can all contribute to renal inflammation and fibrosis through different signaling pathways and may lead to a progressive impairment of kidney function. Blockade of their activation may be important in ameliorating the initiation and/or the progression of renal disease.

Recent Developments in Clinical Plasma Proteomics—Applied to Cardiovascular Research

The human plasma proteome mirrors the physiological state of the cardiovascular system, a fact that has been used to analyze plasma biomarkers in routine analysis for the diagnosis and monitoring of cardiovascular diseases for decades. These biomarkers address, however, only a very limited subset of cardiovascular diseases, such as acute myocardial infarct or acute deep vein thrombosis, and clinical plasma biomarkers for the diagnosis and stratification cardiovascular diseases that are growing in incidence, such as heart failure and abdominal aortic aneurysm, do not exist and are urgently needed. The discovery of novel biomarkers in plasma has been hindered by the complexity of the human plasma proteome that again transforms into an extreme analytical complexity when it comes to the discovery of novel plasma biomarkers. This complexity is, however, addressed by recent achievements in technologies for analyzing the human plasma proteome, thereby facilitating the possibility for novel biomarker discoveries. The aims of this article is to provide an overview of the recent achievements in technologies for proteomic analysis of the human plasma proteome and their applications in cardiovascular medicine.

Clinical impact of perioperative atrial fibrillation on long-term recurrence of malignancy

Perioperative complications have been reported to be associated with a lower incidence of cancer-free survival. Perioperative atrial fibrillation (POAF) is one of occasionally observed complications in patients with malignancies who undergo noncardiac surgeries. However, the long-term clinical impact of POAF on those with malignancies have remained unknown. This was a prospective, single-center, observational study. Patients who underwent noncardiac surgeries for definitive malignancies between 2014 and 2017 were included. The primary and secondary endpoints were 3-year recurrence of malignancies and cancer death, respectively. The present study included consecutive 752 patients (mean age, 68 ± 11 years; males, 62%), and POAF was observed in 77 patients. The follow-up duration was 1037 (interquartile range, 699-1408) days. The 3-year recurrence of malignancies was observed in 239 (32%) patients (POAF, 32 [42%]; non-POAF, 207 [31%]) and 3-year mortality was 130 patients (17%). Cardiac, noncardiac, and cancer deaths were observed in 4 (0.5%), 126 (17%), and 111 (15%) patients, respectively. Multivariate Cox regression analysis demonstrated that POAF was associated with 3-year recurrence of malignancies (hazard ratio [HR], 1.70; 95% confidence interval [CI], 1.15-2.52). Landmark analysis demonstrated that POAF tended to be correlated with the incidence of 3-year cancer death (HR, 1.79; 95% CI, 0.96-3.31). In conclusion, POAF is associated with the subsequent recurrence of malignancies. The association of arrhythmia with cancer death may be revealed under longer follow-up durations.Clinical Trial Registration: https://upload.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000018270 . UMIN ID: UMIN000016146.

CXCL12/CXCR4 axis as a key mediator in atrial fibrillation via bioinformatics analysis and functional identification

Atrial fibrillation (AF) is an increasingly prevalent arrhythmia with significant health and socioeconomic impact. The underlying mechanism of AF is still not well understood. In this study, we sought to identify hub genes involved in AF, and explored their functions and underlying mechanisms based on bioinformatics analysis. Five microarray datasets in GEO were used to identify the differentially expressed genes (DEGs) by Robust Rank Aggregation (RRA), and hub genes were screened out using protein-protein interaction (PPI) network. AF model was established using a mixture of acetylcholine and calcium chloride (Ach-CaCl2) by tail vein injection. We totally got 35 robust DEGs that mainly involve in extracellular matrix formation, leukocyte transendothelial migration, and chemokine signaling pathway. Among these DEGs, we identified three hub genes involved in AF, of which CXCL12/CXCR4 axis significantly upregulated in AF patients stands out as one of the most potent targets for AF prevention, and its effect on AF pathogenesis and underlying mechanisms were investigated in vivo subsequently with the specific CXCR4 antagonist AMD3100 (6 mg/kg). Our results demonstrated an elevated transcription and translation of CXCL12/CXCR4 axis in AF patients and mice, accompanied with the anabatic atrial inflammation and fibrosis, thereby providing the substrate for AF maintenance. Blocking its signaling via AMD3100 administration in AF model mice reduced AF inducibility and duration, partly ascribed to decreased atrial inflammation and structural remodeling. Mechanistically, these effects were achieved by reducing the recruitment of CD3+ T lymphocytes and F4/80+ macrophages, and suppressing the hyperactivation of ERK1/2 and AKT/mTOR signaling in atria of AF model mice. In conclusion, this study provides new evidence that antagonizing CXCR4 prevents the development of AF, and suggests that CXCL12/CXCR4 axis may be a potential therapeutic target for AF.

Potential Target Genes in the Development of Atrial Fibrillation: A Comprehensive Bioinformatics Analysis

Background

Atrial fibrillation (AF) is the most prevalent arrhythmia worldwide. Although it is not life-threatening, the accompanying rapid and irregular ventricular rate can lead to hemodynamic deterioration and obvious symptoms, especially the risk of cerebrovascular embolism. Our study aimed to identify novel and promising genes that could explain the underlying mechanism of AF development.

Material/Methods

Expression profiles GSE41177, GSE79768, and GSE14975 were acquired from the Gene Expression Omnibus Database. R software was used for identifying differentially expressed genes (DEGs), and Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were subsequently performed. A protein–protein interaction network was constructed in Cytoscape software. Next, a least absolute shrinkage and selection operator (LASSO) model was constructed and receiver-operating characteristic curve analysis was conducted to assess the specificity and sensitivity of the key genes.

Results

We obtained 204 DEGs from the datasets. The DEGs were mostly involved in immune response and cell communication. The primary pathways of the DEGs were related to the course or maintenance of autoimmune and chronic inflammatory diseases. The top 20 hub genes (high scores in cytoHubba) were selected in the PPI network. Finally, we identified 6 key genes (FCGR3B, CLEC10A, FPR2, IGSF6, S100A9, and S100A12) via the LASSO model.

Conclusions

We present 6 target genes that are potentially involved in the molecular mechanisms of AF development. In addition, these genes are likely to serve as potential therapeutic targets.