Identification of Potential Diagnostic Biomarkers From Circulating Cells During the Course of Sleep Deprivation-Related Myocardial Infarction Based on Bioinformatics Analyses

Interference between immune cells and insomnia: a bibliometric analysis from 2000 to 2023

Background

Insomnia is a widespread sleep disorder that significantly affects the quality of life and contributes to immune dysfunction, which in turn leads to chronic diseases. Despite extensive research on sleep disturbances and immune modulation, the relationship between insomnia and immune responses remains underexplored.

Objectives

The primary objective of this study was to conduct a bibliometric analysis to explore the interaction between immune cells and insomnia, identifying key immune responses involved and their potential roles in the development of insomnia and associated comorbidities.

Methods

A bibliometric analysis was conducted using data from the Web of Science Core Collection (WoSCC), focusing on research articles published between 2000 and 2023. The analysis aimed to identify trends, key research areas, and the role of immune system cells (T cells, B cells, NK cells, etc.) in insomnia.

Results

The analysis revealed that various immune cells, including T cells, B cells, NK cells, neutrophils, and monocytes, play crucial roles in insomnia pathogenesis. These immune cells contribute to immune modulation and inflammatory responses, which are linked to sleep disturbances. The study also identified that insomnia is closely associated with comorbidities such as cardiovascular diseases, obesity, depression, and cancer, all of which involve immune dysfunction. The regulation of the immune system was found to be a key factor in improving sleep quality.

Conclusion

This study provides valuable insights into the complex interaction between the immune system and insomnia. The findings underscore the importance of immune regulation in the treatment of insomnia, suggesting that future research should focus on integrating immune modulation into therapeutic strategies for insomnia. Further studies are needed to explore targeted therapies for immune-related insomnia and its comorbidities, emphasizing interdisciplinary research in this area.

Using Bioinformatics and Machine Learning to Predict the Genetic Characteristics of Ferroptosis-Cuproptosis-Related Genes Associated with Sleep Deprivation

Purpose

Sleep deprivation (SD), a common sleep disease in clinic, has certain risks, and its pathogenesis is still unclear. This study aimed to identify ferroptosis-cuproptosis-related genes (FCRGs) associated with SD through bioinformatics and machine learning, thus elucidating their biological significance and clinical value.

Methods

SD-DEGs were obtained from GEO. We intersected key WGCNA module genes of DE-FCRGs with SD-DEGs to obtain SD-DE-FCRGs. GO and KEGG analyses were performed. Machine learning was used to screen SD-DE-FCRGs, and filtered genes were intersected to obtain SD characteristic genes. ROC curves were used to evaluate the accuracy of SD characteristic genes. CIBERSORT was used to analyze the correlation between SD-DE-FCRGs and immune cells. We constructed a ceRNA network of SD-DE-FCRGs and used DGIbd to predict gene drug targets.

Results

The 156 DEGs were identified from GSE98566. Five SD-DE-FCRGs from DE- FCRGs and SD-DEGs were analyzed via WGCNA, and enrichment analysis involved mainly ribosome regulation, mitochondrial pathways, and neurodegenerative diseases. Machine learning was used to obtain Four SD-DE-FCRGs (IKZF1, JCHAIN, MGST3, and UQCR11), and these gene analyses accurately evaluated the distribution model (AUC=0.793). Immune infiltration revealed that SD hub genes were correlated with most immune cells. Unsupervised cluster analysis revealed significant differential expression of immune-related genes between two subtypes. GSVA and GSEA revealed that enriched biological functions included oxidative phosphorylation, ribonucleic acid, metabolic diseases, activation of oxidative phosphorylation, and other pathways. Four SD-DE-FCRGs associated with 29 miRNAs were identified via the construction of a ceRNA network. The important target lenalidomide of IKZF1 was predicted.

Conclusion

We first used bioinformatics and machine learning to screen four SD-DE-FCRGs. These genes may affect the involvement of infiltrating immune cells in pathogenesis of SD by regulating FCRGs. We predicted that lenalidomide may target IKZF1 from SD-DE-FCRGs.

ERG mediates the inhibition of NK cell cytotoxicity through the HLX/STAT4/Perforin signaling pathway, thereby promoting the progression of myocardial infarction

This study aimed to investigate the role of ERG in the HLX/STAT4/Perforin signaling axis, impacting natural killer (NK) cell cytotoxicity and myocardial infarction (MI) progression. NK cell cytotoxicity was assessed via co-culture and 51Cr release assays. Datasets GSE34198 and GSE97320 identified common differentially expressed genes in MI. NK cell gene expression was analyzed in MI patients and healthy individuals using qRT-PCR and Western blotting. ERG's regulation of HLX and STAT4's regulation of perforin were studied through computational tools (MEM) and ChIP experiments. HLX's influence on STAT4 was explored with the MG132 proteasome inhibitor. Findings were validated in a mouse MI model.ERG, a commonly upregulated gene, was identified in NK cells from MI patients and mice. ERG upregulated HLX, leading to STAT4 proteasomal degradation and reduced Perforin expression. Consequently, NK cell cytotoxicity decreased, promoting MI progression. ERG mediates the HLX/STAT4/Perforin axis to inhibit NK cell cytotoxicity, fostering MI progression. These results provide vital insights into MI's molecular mechanisms.

Screening of Potential Circulating Diagnostic Biomarkers and Molecular Mechanisms of Systemic Lupus Erythematosus-Related Myocardial Infarction by Integrative Analysis

Background

The risk of acute myocardial infarction (AMI) is elevated in patients with systemic lupus erythematosus (SLE), and it is of great clinical value to identify potential molecular mechanisms and diagnostic markers of AMI associated with SLE by analyzing public database data and transcriptome sequencing data.

Methods

AMI and SLE-related sequencing datasets GSE62646, GSE60993, GSE50772 and GSE81622 were downloaded from the Gene Expression Omnibus (GEO) database and divided into prediction and validation cohorts. To identify the key genes associated with AMI related to SLE, WGCNA and DEGs analysis were performed for the prediction and validation cohorts, respectively. The related signaling pathways were identified by GO/KEGG enrichment analysis. Peripheral blood mononuclear cells (PBMCs) from patients with AMI were collected for transcriptome sequencing to validate the expression of key genes in patients with AMI. Least absolute shrinkage and selection operator (LASSO) regression analysis was applied to screen diagnostic biomarkers. The diagnostic efficacy of biomarkers was validated by ROC analysis, and the CIBERSORTx platform was used to analyze the composition of immune cells in AMI and SLE.

Results

A total of 108 genes closely related to AMI and SLE were identified in the prediction cohort, and GO/KEGG analysis showed significantly enriched signaling pathways. The results of differential analysis in validation cohort were consistent with them. By transcriptional sequencing of PBMCs from peripheral blood of AMI patients, combined with the results of prediction and validation cohort analysis, seven genes were finally screened out. LASSO analysis finally identifies DYSF, LRG1 and CSF3R as diagnostic biomarkers of SLE-related-AMI. CIBERSORTx analysis revealed that the biomarkers were highly correlated with neutrophils.

Conclusion

Neutrophil degranulation and NETs formation play important roles in SLE-related AMI, and DYSF, LRG1 and CSF3R were identified as important diagnostic markers for the development and progression of SLE-related AMI.

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

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.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-022-03614-1.

Unexpected Actors in Inflammatory Bowel Disease Revealed by Machine Learning from Whole-Blood Transcriptomic Data

Although big data from transcriptomic analyses have helped transform our understanding of inflammatory bowel disease (IBD), they remain underexploited. We hypothesized that the application of machine learning using lasso regression to transcriptomic data from IBD patients and controls can help identify previously overlooked genes. Transcriptomic data provided by Ostrowski et al. (ENA PRJEB28822) were subjected to a two-stage process of feature selection to discriminate between IBD and controls. First, a principal component analysis was used for dimensionality reduction. Second, the least absolute shrinkage and selection operator (lasso) regression was employed to identify genes potentially involved in the pathobiology of IBD. The study included data from 294 participants: 100 with ulcerative colitis (48 adults and 52 children), 99 with Crohn's disease (45 adults and 54 children), and 95 controls (46 adults and 49 children). IBD patients presented a wide range of disease severity. Lasso regression preceded by principal component analysis successfully selected interesting features in the IBD transcriptomic data and yielded 12 models. The models achieved high discriminatory value (range of the area under the receiver operating characteristic curve 0.61-0.95) and identified over 100 genes as potentially associated with IBD. PURA, GALNT14, and FCGR1A were the most consistently selected, highlighting the role of the cell cycle, glycosylation, and immunoglobulin binding. Several known IBD-related genes were among the results. The results included genes involved in the TGF-beta pathway, expressed in NK cells, and they were enriched in ontology terms related to immunity. Future IBD research should emphasize the TGF-beta pathway, immunoglobulins, NK cells, and the role of glycosylation.