Background: Atrial fibrillation (AF) increases the risk of stroke and heart failure. Postoperative AF (POAF) increases the risk of mortality after cardiac surgery. This study aims to explore mechanisms underlying AF, analyze infiltration of immune cells in left atrium (LA) from patients with AF, and identify potential circular RNA (circRNA) biomarkers for POAF.Methods: Raw data of GSE797689, GSE115574, and GSE97455 were downloaded and processed. AF-related gene co-expression network was constructed using weighted gene correlation network analysis and enrichment analysis of genes in relevant module was conducted. Gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) were applied to investigate pathways significantly enriched in AF group. Infiltration of immune cells was analyzed using single-sample GSEA. Differentially expressed genes (DEGs) between patients with or without AF were identified and competing endogenous RNA (ceRNA) networks of DEGs were constructed. To screen biomarkers for POAF, differentially expressed circRNAs (DEcircRNAs) between patients with or without POAF were identified. Intersection between DEcircRNAs and circRNAs in ceRNA networks of DEGs were extracted and circRNAs in the intersection were further screened using support vector machine, random forest, and neural network to identify biomarkers for POAF.Results: Three modules were found to be relevant with AF and enrichment analysis indicated that genes in these modules were enriched in synthesis of extracellular matrix and inflammatory response. The results of GSEA and GSVA suggested that inflammatory response-related pathways were significantly enriched in AF group. Immune cells like macrophages, mast cells, and neutrophils were significantly infiltrated in LA tissues from patients with AF. The expression levels of immune genes such as CHGB, HLA-DRA, LYZ, IGKV1-17 and TYROBP were significantly upregulated in patients with AF, which were correlated with infiltration of immune cells. ceRNA networks of DEGs were constructed and has_circ_0006314 and hsa_circ_0055387 were found to have potential predictive values for POAF.Conclusion: Synthesis of extracellular matrix and inflammatory response were main processes involved in development and progression of AF. Infiltration of immune cells was significantly different between patients with or without AF. Has_circ_0006314 and hsa_circ_0055387 were found to have potential predictive values for POAF.
ObjectivesIdiopathic pulmonary artery hypertension (IPAH) is a rare but life-threaten disease. However, the mechanism underlying IPAH is unclear. In this study, underlying mechanism, infiltration of immune cells, and immune-related hub genes of IPAH were analyzed via bioinformatics.MethodsGSE15197, GSE48149, GSE113439, and GSE117261 were merged as lung dataset. Weighted gene correlation network analysis (WGCNA) was used to construct the co-expression gene networks of IPAH. Gene Ontology and pathway enrichment analysis were performed using DAVID, gene set enrichment analysis (GSEA), and gene set variation analysis (GSVA). Infiltration of immune cells in lung samples was analyzed using CIBERSORT. GSE22356 and GSE33463 were merged as peripheral blood mononuclear cells (PBMCs) dataset. Immune-related differentially expressed genes (IRDEGs) of lung and PBMCs dataset were analyzed. Based on the intersection between two sets of IRDEGs, hub genes were screened using machine learning algorithms and validated by RT-qPCR. Finally, competing endogenous RNA (ceRNA) networks of hub genes were constructed.ResultsThe gray module was the most relevant module and genes in the module enriched in terms like inflammatory and immune responses. The results of GSEA and GSVA indicated that increasement in cytosolic calcium ion, and metabolism dysregulation play important roles in IPAH. The proportions of T cells CD4 memory resting and macrophage M1 were significantly greater in IPAH group, while the proportions of monocytes and neutrophils were significantly lower in IPAH group. IRDEGs of two datasets were analyzed and the intersection between two set of IRDEGs were identified as candidate hub genes. Predictive models for IPAH were constructed using data from PBMCs dataset with candidate hub genes as potential features via LASSO regression and XGBoost algorithm, respectively. CXCL10 and VIPR1 were identified as hub genes and ceRNA networks of CXCL10 was constructed.ConclusionInflammatory response, increasement in cytosolic calcium ion, and metabolism dysregulation play important roles in IPAH. T cells CD4 memory resting and macrophage M1 were significantly infiltrated in lung samples from patients with IPAH. IRDEGs of lung dataset and PBMCs dataset were analyzed, and CXCL10 and VIPR1 were identified as hub genes.
ObjectiveThe mortality of type A aortic dissection (TAAD) is extremely high. The effect of postoperative hyperglycemia (PHG) on the prognosis of TAAD surgery is unclear. This study aims to investigate the prognosis of patients with PHG after TAAD surgery and construct prediction model for PHG.MethodsPatients underwent TAAD surgery from January 2016 to December 2020 in Xiangya Hospital were collected. A total of 203 patients were included and patients were divided into non PHG group and PHG group. The occurrence of postoperative delirium, cardiac complications, spinal cord complication, cerebral complications, acute kidney injury (AKI), hepatic dysfunction, hypoxemia, and in-hospital mortality were compared between two groups. Data from MIMIC-IV database were further applied to validate the relationship between PHG and clinical outcomes. The prediction model for PHG was then constructed using Extreme Gradient Boosting (XGBoost) analysis. The predictive value of selected features was further validated using patient data from MIMIC-IV database. Finally, the 28-days survival rate of patient with PHG was analyzed using data from MIMIC-IV database.ResultsThere were 86 patients developed PHG. The incidences of postoperative AKI, hepatic dysfunction, and in-hospital mortality were significant higher in PHG group. The ventilation time after surgery was significant longer in PHG group. Data from MIMIC-IV database validated these results. Neutrophil, platelet, lactic acid, weight, and lymphocyte were selected as features for prediction model. The values of AUC in training and testing set were 0.8697 and 0.8286 respectively. Then, five features were applied to construct another prediction model using data from MIMIC-IV database and the value of AUC in the new model was 0.8185. Finally, 28-days survival rate of patients with PHG was significantly lower and PHG was an independent risk factor for 28-days mortality after TAAD surgery.ConclusionPHG was significantly associated with the occurrence of AKI, hepatic dysfunction, increased ventilation time, and in-hospital mortality after TAAD surgery. The feature combination of neutrophil, platelet, lactic acid, weight, and lymphocyte could effectively predict PHG. The 28-days survival rate of patients with PHG was significantly lower. Moreover, PHG was an independent risk factor for 28-days mortality after TAAD surgery.
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