A ten-genes-based diagnostic signature for atherosclerosis

Identification of co-expressed genes and immune infiltration features related to the progression of atherosclerosis

Atherosclerosis is a chronic inflammatory disease that affects arterial walls and is a leading cause of cardiovascular disease. Gene co-expression modules can provide insight into the molecular mechanisms underlying atherosclerosis progression. In this study, gene co-expression network analysis (WGCNA) was done to identify gene co-expression modules associated with atherosclerosis progression. Before conducting WGCNA, preprocessing and soft power selection were performed on the GSE28829, GSE100927, GSE43292, GSE10334, and GSE16134 datasets ( https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi ). Co-expression modules were identified using dynamic tree cuts, and their correlations and trait associations were visualized. Enrichment analysis was performed on the blue and magenta modules to identify biological processes (BP) and pathways related to atherosclerosis. The CIBERSORT algorithm was used to predict immune cell infiltration in early and advanced atherosclerotic plaques. We identified 12 co-expression modules, in which blue and magenta were most highly correlated with atherosclerosis progression. The blue module was enriched for inflammation- and immune-related BP and pathways, including phagosome, lysosome, osteoclast differentiation, chemokine signaling pathway, platelet activation, NF-kappa B signaling pathway, Fc gamma R-mediated phagocytosis, lipid and atherosclerosis, autophagy, and apoptosis. The magenta module was significantly enriched for vascular permeability regulation, positive and negative regulation of epithelial to mesenchymal transition, and lamellipodium. Additionally, the CIBERSORT algorithm predicted less abundance of T regulatory cells and monocytes in advanced compared to early atherosclerotic plaques. The enrichment analysis of BP, cellular components, molecular functions, and atherosclerosis-related pathways in the blue and magenta modules showed that inflammation and immune response played a key role in the progression of atherosclerosis. Our study provides insights into the molecular mechanisms underlying atherosclerosis progression and identifies potential therapeutic targets for the treatment of atherosclerosis. The identification of immune cell subtypes associated with atherosclerosis could lead to the development of immunomodulatory therapies to prevent or treat atherosclerosis.

Exploring the pathogenesis of chronic atrophic gastritis with atherosclerosis via microarray data analysis

Although several studies have reported a link between chronic atrophic gastritis (CAG) and atherosclerosis, the underlying mechanisms have not been elucidated. The present study aimed to investigate the molecular mechanisms common to both diseases from a bioinformatics perspective. Gene expression profiles were obtained from the Gene Expression Omnibus database. Data on atherosclerosis and CAG were downloaded from the GSE28829 and GSE60662 datasets, respectively. We identified the differentially expressed genes co-expressed in CAG and atherosclerosis before subsequent analyses. We constructed and identified the hub genes and performed functional annotation. Finally, the transcription factor (TF)-target genes regulatory network was constructed. In addition, we validated core genes and certain TFs. We identified 116 common differentially expressed genes after analyzing the 2 datasets (GSE60662 and GSE28829). Functional analysis highlighted the significant contribution of immune responses and the positive regulation of tumor necrosis factor production and T cells. In addition, phagosomes, leukocyte transendothelial migration, and cell adhesion molecules strongly correlated with both diseases. Furthermore, 16 essential hub genes were selected with cytoHubba, including PTPRC, TYROBP, ITGB2, LCP2, ITGAM, FCGR3A, CSF1R, IRF8, C1QB, TLR2, IL10RA, ITGAX, CYBB, LAPTM5, CD53, CCL4, and LY86. Finally, we searched for key gene-related TFs, especially SPI1. Our findings reveal a shared pathogenesis between CAG and atherosclerosis. Such joint pathways and hub genes provide new insights for further studies.

Transcriptional profiles associated with coronary artery disease in type 2 diabetes mellitus

Background

Coronary artery disease (CAD) is a common complication of Type 2 diabetes mellitus (T2DM). Understanding the pathogenesis of this complication is essential in both diagnosis and management. Thus, this study aimed to characterize the presence of CAD in T2DM using molecular markers and pathway analyses.

Methods

The study is a sex- and age-frequency matched case-control design comparing 23 unrelated adult Filipinos with T2DM-CAD to 23 controls (DM with CAD). Healthy controls served as a reference. Total RNA from peripheral blood mononuclear cells (PBMCs) underwent whole transcriptomic profiling using the Illumina HumanHT-12 v4.0 expression beadchip. Differential gene expression with gene ontogeny analyses was performed, with supporting correlational analyses using weighted correlation network analysis (WGCNA).

Results

The study observed that 458 genes were differentially expressed between T2DM with and without CAD (FDR<0.05). The 5 top genes the transcription factor 3 (TCF3), allograft inflammatory factor 1 (AIF1), nuclear factor, interleukin 3 regulated (NFIL3), paired immunoglobulin-like type 2 receptor alpha (PILRA), and cytoskeleton-associated protein 4 (CKAP4) with AUCs >89%. Pathway analyses show differences in innate immunity activity, which centers on the myelocytic (neutrophilic/monocytic) theme. SNP-module analyses point to a possible causal dysfunction in innate immunity that triggers the CAD injury in T2DM.

Conclusion

The study findings indicate the involvement of innate immunity in the development of T2DM-CAD, and potential immunity markers can reflect the occurrence of this injury. Further studies can verify the mechanistic hypothesis and use of the markers.

Online application for the diagnosis of atherosclerosis by six genes

BACKGROUND

Atherosclerosis (AS) is a primary contributor to cardiovascular disease, leading to significant global mortality rates. Developing effective diagnostic indicators and models for AS holds the potential to substantially reduce the fatalities and disabilities associated with cardiovascular disease. Blood sample analysis has emerged as a promising avenue for facilitating diagnosis and assessing disease prognosis. Nonetheless, it lacks an accurate model or tool for AS diagnosis. Hence, the principal objective of this study is to develop a convenient, simple, and accurate model for the early detection of AS.

METHODS

We downloaded the expression data of blood samples from GEO databases. By dividing the mean values of housekeeping genes (meanHGs) and applying the comBat function, we aimed to reduce the batch effect. After separating the datasets into training, evaluation, and testing sets, we applied differential expression analyses (DEA) between AS and control samples from the training dataset. Then, a gradient-boosting model was used to evaluate the importance of genes and identify the hub genes. Using different machine learning algorithms, we constructed a prediction model with the highest accuracy in the testing dataset. Finally, we make the machine learning models publicly accessible by shiny app construction.

RESULTS

Seven datasets (GSE9874, GSE12288, GSE20129, GSE23746, GSE27034, GSE90074, and GSE202625), including 403 samples with AS and 325 healthy subjects, were obtained by comprehensive searching and filtering by specific requirements. The batch effect was successfully removed by dividing the meanHGs and applying the comBat function. 331 genes were found to be related to atherosclerosis by the DEA analysis between AS and health samples. The top 6 genes with the highest importance values from the gradient boosting model were identified. Out of the seven machine learning algorithms tested, the random forest model exhibited the most impressive performance in the testing datasets, achieving an accuracy exceeding 0.8. While the batch effect reduction analysis in our study could have contributed to the increased accuracy values, our comparison results further highlight the superiority of our model over the genes provided in published studies. This underscores the effectiveness of our approach in delivering superior predictive performance. The machine-learning models were then uploaded to the Shiny app's server, making it easy for users to distinguish AS samples from normal samples.

CONCLUSIONS

A prognostic Shiny application, built upon six potential atherosclerosis-associated genes, has been developed, offering an accurate diagnosis of atherosclerosis.

Pathogenetic mechanisms of repeated adverse cardiovascular events development in patients with coronary heart disease: the role of chronic inflammation

Stent restenosis is the most unfavorable complication of interventional treatment for coronary heart disease. We already know from various literature sources that the causes for stent restenosis in patients are both mechanical damage (partial opening, stent breakage, extended stented area, calcification, incomplete stent coverage of atherosclerotic plaque, weak radial stiffness of the stent metal frame, lack of stent drug coating), and the neointimal hyperplasia formation which is closely related to the de novo atherosclerosis development, being a predictor of the recurrent cardiovascular event.

[Validation and comparison of diabetic retinopathy-based diagnostic models for diabetic nephropathy]

OBJECTIVE

To validate and compare the efficacy of two noninvasive diagnostic models for diabetic nephropathy (DN) based on diabetic retinopathy (DR).

METHODS

A total of 565 patients with type 2 diabetes undergoing kidney biopsy in the Department of Nephrology, PLA General Hospital from January, 1993 to December, 2014 were studied. The patients were divided into DN group and non-diabetic nephropathy (NDRD) group according to renal pathological diagnosis. The data from the 22-year period were divided into 3 stages based on chronological order: early stage (from 1993 to 2003), middle stage (from 2004 to April, 2012), and late stage (from May, 2012 to December, 2014). The changes in clinical features and pathological diagnosis of the patients with renal biopsy over the 22 years were analyzed. The published DNT model and JDB model, both based on DR, were validated and compared for diagnostic effectiveness of DN, and the characteristics of the misdiagnosed cases were analyzed.

RESULTS

The incidences of hypertension and DR and levels of glycosylated hemoglobin (HbA1c), creatinine and 24-h urinary protein were all significantly higher, while hemoglobin and triglyceride levels were lower in DN group than in NDRD group (P<0.05). The proportion of NDRD cases increased gradually over time, with IgA nephropathy and membranous nephropathy as the main pathological types. The AUC of JDB model was 0.946, similar to that of NDT model (0.925; P=0.198). The disease course of diabetes, hematuria and incidence of DR were important clinical features affecting the diagnostic accuracy of the models.

CONCLUSION

The clinical features and pathological diagnosis of DR change over time. The non-invasive diagnostic models based on DR have good diagnostic efficacy for DN.

PseU-ST: A new stacked ensemble-learning method for identifying RNA pseudouridine sites

Background: Pseudouridine (Ψ) is one of the most abundant RNA modifications found in a variety of RNA types, and it plays a significant role in many biological processes. The key to studying the various biochemical functions and mechanisms of Ψ is to identify the Ψ sites. However, identifying Ψ sites using experimental methods is time-consuming and expensive. Therefore, it is necessary to develop computational methods that can accurately predict Ψ sites based on RNA sequence information. Methods: In this study, we proposed a new model called PseU-ST to identify Ψ sites in Homo sapiens (H. sapiens), Saccharomyces cerevisiae (S. cerevisiae), and Mus musculus (M. musculus). We selected the best six encoding schemes and four machine learning algorithms based on a comprehensive test of almost all of the RNA sequence encoding schemes available in the iLearnPlus software package, and selected the optimal features for each encoding scheme using chi-square and incremental feature selection algorithms. Then, we selected the optimal feature combination and the best base-classifier combination for each species through an extensive performance comparison and employed a stacking strategy to build the predictive model. Results: The results demonstrated that PseU-ST achieved better prediction performance compared with other existing models. The PseU-ST accuracy scores were 93.64%, 87.74%, and 89.64% on H_990, S_628, and M_944, respectively, representing increments of 13.94%, 6.05%, and 0.26%, respectively, higher than the best existing methods on the same benchmark training datasets. Conclusion: The data indicate that PseU-ST is a very competitive prediction model for identifying RNA Ψ sites in H. sapiens, M. musculus, and S. cerevisiae. In addition, we found that the Position-specific trinucleotide propensity based on single strand (PSTNPss) and Position-specific of three nucleotides (PS3) features play an important role in Ψ site identification. The source code for PseU-ST and the data are obtainable in our GitHub repository (https://github.com/jluzhangxinrubio/PseU-ST).

Analyzing the pathogenesis of systemic lupus erythematosus complicated by atherosclerosis using transcriptome data

Background

Accumulating evidence supports the predisposition of systemic lupus erythematosus (SLE) to atherosclerosis (AS). However, the common pathogenesis of these two diseases remains unclear. This study aimed to explore the mechanisms of SLE complicated by AS.

Methods

Gene expression profiles of SLE (GSE50772) and AS (GSE100927) were downloaded from the Gene Expression Omnibus. We analyzed differentially expressed genes (DEGs) of SLE and AS and performed enrichment analyses separately. After analyzing the common DEGs (CDEGs), we performed functional enrichment analysis, protein-protein interaction (PPI) network analysis, and hub genes (HGs) identification of CDEGs. Then, we performed a co-expression analysis of HGs and verified their expression and diagnostic value. We further explored immune cell infiltration and analyzed the correlation between HGs and infiltrating immune cells (IICs). Finally, we verified the reliability of the screening pathway.

Results

We obtained 530 DEGs from the GSE50772 dataset and 448 DEGs from the GSE100927 dataset. The results of the enrichment analysis showed that there were many similar immune- and inflammation-related processes between the two diseases. We analyzed 26 CDEGs (two downregulated genes and 24 upregulated genes) and enrichment analysis highlighted the important role of the IL-17 signaling pathway. We identified five HGs (CCR1, CD163, IL1RN, MMP9, and SIGLEC1) using the CytoHubba plugin and HGs validation showed that the five HGs screened were reliable. Co-expression networks showed that five HGs can affect mononuclear cell migration. Immune cell infiltration analysis indicated monocytes in SLE and M0 macrophages in AS accounted for a high proportion of all IICs, and the difference in infiltration was obvious. We also found a significant positive correlation between CCR1, CD163, IL1RN, and MMP9 and monocytes in SLE, and a significant positive correlation between CCR1, IL1RN, MMP9, and SIGLEC1 and M0 macrophages in AS. Pathway validation also demonstrated that the IL-17 signaling pathway was a key pathway for the differentiation of monocytes into macrophages.

Conclusions

The five HGs may promote the differentiation of monocytes into macrophages by influencing the IL-17 signaling pathway, leading to SLE complicated by AS. Our study provides insights into the mechanisms of SLE complicated by AS.

The Integrated Analysis Identifies Three Critical Genes as Novel Diagnostic Biomarkers Involved in Immune Infiltration in Atherosclerosis

Atherosclerosis (AS), a chronic inflammatory disease of the blood vessels, is the primary cause of cardiovascular disease, the leading cause of death worldwide. This study aimed to identify possible diagnostic markers for AS and determine their correlation with the infiltration of immune cells in AS. In total, 10 serum samples from AS patients and 10 samples from healthy subjects were collected. The original gene expression profiles of GSE43292 and GSE57691 were downloaded from the Gene Expression Omnibus database. Least absolute shrinkage and selection operator regression model and support vector machine recursive feature elimination analyses were carried out to identify candidate markers. The diagnostic values of the identified biomarkers were determined using receiver operating characteristic assays. The compositional patterns of the 22 types of immune cell fraction in AS were estimated using CIBERSORT. RT-PCR was performed to further determine the expression of the critical genes. This study identified 17 differentially expressed genes (DEGs) in AS samples. The identified DEGs were mainly involved in non-small cell lung carcinoma, pulmonary fibrosis, polycystic ovary syndrome, glucose intolerance, and T-cell leukemia. FHL5, IBSP, and SCRG1 have been identified as the diagnostic genes in AS. The expression of SCRG1 and FHL5 was distinctly downregulated in AS samples, and the expression of IBSP was distinctly upregulated in AS samples, which was further confirmed using our cohort by RT-PCR. Moreover, immune assays revealed that FHL5, IBSP, and SCRG1 were associated with several immune cells, such as CD8 T cells, naïve B cells, macrophage M0, activated memory CD4 T cells, and activated NK cells. Overall, future investigations into the occurrence and molecular mechanisms of AS may benefit from using the genes FHL5, IBSP, and SCRG1 as diagnostic markers for the condition.

Diagnostic and prognostic significance of lncRNA SOX2-OT in patients with carotid atherosclerosis

Background

This paper aimed to analyze IncRNA SOX2-OT expression in patients with carotid atherosclerosis and to elucidate the predictive significance of SOX2-OT on carotid atherosclerosis.

Methods

The levels of SOX2-OT from 185 participants were tested. The relationship between CIMT levels and SOX2-OT expression was examined by Pearson analysis. The clinical value of SOX2-OT was investigated by the ROC curve, K–M curve, and COX regression analysis. The comparison of SOX2-OT expression between patients with good prognosis and poor prognosis was also performed.

Results

The expression of SOX2-OT was augmented in the patients with carotid atherosclerosis and was correlated with the level of CIMT. The high level of SOX2-OT might be a risk factor for carotid atherosclerosis. An enhancement of SOX2-OT expression was found in patients with poor prognosis. SOX2-OT might be an independent prognostic biomarker.

Conclusions

SOX2-OT was upregulated in patients with carotid atherosclerosis and might be a predictive indicator in the progression of carotid atherosclerosis.