Identification of novel biomarkers and immune infiltration characteristics of ischemic stroke based on comprehensive bioinformatic analysis and machine learning.

Ischemic stroke (IS) is one of most common causes of disability in adults worldwide. However, there is still a lack of effective and reliable diagnostic markers and therapeutic targets in IS. Furthermore, immune cell dysfunction plays an important role in the pathogenesis of IS. Hence, in-depth research on immune-related targets in progressive IS is urgently needed. Expression profile data from patients with IS were downloaded from the Gene Expression Omnibus (GEO) database. Then, differential expression analysis and weighted gene coexpression network analysis (WGCNA) were performed to identify the significant modules and differentially expressed genes (DEGs). Key genes were obtained and used in functional enrichment analyses by overlapping module genes and DEGs. Next, hub candidate genes were identified by utilizing three machine learning algorithms: least absolute shrinkage and selection operator (LASSO), random forest, and support vector machine-recursive feature elimination (SVM-RFE). Subsequently, a diagnostic model was constructed based on the hub genes, and receiver operating characteristic (ROC) curves were constructed to validate the performances of the predictive models and candidate genes. Finally, the immune cell infiltration landscape of IS was explored with the CIBERSORT deconvolution algorithm. A total of 40 key DEGs were identified based on the intersection of the DEGs and module genes, and we found that these genes were mainly enriched in the regulation of lipolysis in adipocytes, neutrophil extracellular trap formation and complement and coagulation cascades. Based on the results from three advanced machine learning algorithms, we obtained 7 hub candidate genes (ABCA1, ARG1, C5AR1, CKAP4, HMFN0839, SDCBP and TLN1) as diagnostic biomarkers of IS and developed a reliable nomogram with high predictive performance (AUC = 0.987). In addition, immune cell infiltration dysregulation was implicated in IS, and compared with those in the normal group, IS patients had increased fractions of gamma delta T cells, monocytes, M0 macrophages, M2 macrophages and neutrophils and clearly lower percentages of naive B cells, CD8 T cells, CD4+ memory T cells, follicular helper T cells, regulatory T cells (Tregs) and resting dendritic cells. Furthermore, correlation analysis indicated a significant correlation between the hub genes and immune cells in progressive IS. In conclusion, our study identified 7 hub genes as diagnostic biomarkers and established a reliable model to predict the occurrence of IS. Meanwhile, we explored the immune cell infiltration pattern and investigated the relationship between candidate genes and immune cells in the pathogenesis of IS. Hence, our study provides new insights into the diagnosis and treatment of IS.

Hu S ，Cai J ，Chen S ，Wang Y ，Ren L ... -  《-》

Discovery and validation of molecular patterns and immune characteristics in the peripheral blood of ischemic stroke patients.

Stroke is a disease with high morbidity, disability, and mortality. Immune factors play a crucial role in the occurrence of ischemic stroke (IS), but their exact mechanism is not clear. This study aims to identify possible immunological mechanisms by recognizing immune-related biomarkers and evaluating the infiltration pattern of immune cells. We downloaded datasets of IS patients from GEO, applied R language to discover differentially expressed genes, and elucidated their biological functions using GO, KEGG analysis, and GSEA analysis. The hub genes were then obtained using two machine learning algorithms (least absolute shrinkage and selection operator (LASSO) and support vector machine-recursive feature elimination (SVM-RFE)) and the immune cell infiltration pattern was revealed by CIBERSORT. Gene-drug target networks and mRNA-miRNA-lncRNA regulatory networks were constructed using Cytoscape. Finally, we used RT-qPCR to validate the hub genes and applied logistic regression methods to build diagnostic models validated with ROC curves. We screened 188 differentially expressed genes whose functional analysis was enriched to multiple immune-related pathways. Six hub genes (ANTXR2, BAZ2B, C5AR1, PDK4, PPIH, and STK3) were identified using LASSO and SVM-RFE. ANTXR2, BAZ2B, C5AR1, PDK4, and STK3 were positively correlated with neutrophils and gamma delta T cells, and negatively correlated with T follicular helper cells and CD8, while PPIH showed the exact opposite trend. Immune infiltration indicated increased activity of monocytes, macrophages M0, neutrophils, and mast cells, and decreased infiltration of T follicular helper cells and CD8 in the IS group. The ceRNA network consisted of 306 miRNA-mRNA interacting pairs and 285 miRNA-lncRNA interacting pairs. RT-qPCR results indicated that the expression levels of BAZ2B, C5AR1, PDK4, and STK3 were significantly increased in patients with IS. Finally, we developed a diagnostic model based on these four genes. The AUC value of the model was verified to be 0.999 in the training set and 0.940 in the validation set. Our research explored the immune-related gene expression modules and provided a specific basis for further study of immunomodulatory therapy of IS.

Cong L ，He Y ，Wu Y ，Li Z ，Ding S ，Liang W ，Xiao X ，Zhang H ，Wang L ... -  《PeerJ》

Identification of immune-related genes in diagnosing retinopathy of prematurity with sepsis through bioinformatics analysis and machine learning.

Chen H ，Chen E ，Lu Y ，Xu Y ... -  《Frontiers in Genetics》

Identification of immune-related key genes in the peripheral blood of ischaemic stroke patients using a weighted gene coexpression network analysis and machine learning.

The immune system plays a vital role in the pathological process of ischaemic stroke. However, the exact immune-related mechanism remains unclear. The current research aimed to identify immune-related key genes associated with ischaemic stroke. CIBERSORT was utilized to reveal the immune cell infiltration pattern in ischaemic stroke patients. Meanwhile, a weighted gene coexpression network analysis (WGCNA) was utilized to identify meaningful modules significantly correlated with ischaemic stroke. The characteristic genes correlated with ischaemic stroke were identified by the following two machine learning methods: the support vector machine-recursive feature elimination (SVM-RFE) algorithm and least absolute shrinkage and selection operator (LASSO) logistic regression. The CIBERSORT results suggested that there was a decreased infiltration of naive CD4 T cells, CD8 T cells, resting mast cells and eosinophils and an increased infiltration of neutrophils, M0 macrophages and activated memory CD4 T cells in ischaemic stroke patients. Then, three significant modules (pink, brown and cyan) were identified to be significantly associated with ischaemic stroke. The gene enrichment analysis indicated that 519 genes in the above three modules were mainly involved in several inflammatory or immune-related signalling pathways and biological processes. Eight hub genes (ADM, ANXA3, CARD6, CPQ, SLC22A4, UBE2S, VIM and ZFP36) were revealed to be significantly correlated with ischaemic stroke by the LASSO logistic regression and SVM-RFE algorithm. The external validation combined with a RT‒qPCR analysis revealed that the expression levels of ADM, ANXA3, SLC22A4 and VIM were significantly increased in ischaemic stroke patients and that these key genes were positively associated with neutrophils and M0 macrophages and negatively correlated with CD8 T cells. The mean AUC value of ADM, ANXA3, SLC22A4 and VIM was 0.80, 0.87, 0.91 and 0.88 in the training set, 0.85, 0.77, 0.86 and 0.72 in the testing set and 0.87, 0.83, 0.88 and 0.91 in the validation samples, respectively. These results suggest that the ADM, ANXA3, SLC22A4 and VIM genes are reliable serum markers for the diagnosis of ischaemic stroke and that immune cell infiltration plays a crucial role in the occurrence and development of ischaemic stroke.

Zheng PF ，Chen LZ ，Liu P ，Pan HW ，Fan WJ ，Liu ZY ... -  《Journal of Translational Medicine》

Bioinformatics analysis of the immune cell infiltration characteristics and correlation with crucial diagnostic markers in pulmonary arterial hypertension.

Pulmonary arterial hypertension (PAH) is a pathophysiological syndrome, characterized by pulmonary vascular remodeling. Immunity and inflammation are progressively recognized properties of PAH, which are crucial for the initiation and maintenance of pulmonary vascular remodeling. This study explored immune cell infiltration characteristics and potential biomarkers of PAH using comprehensive bioinformatics analysis. Microarray data of GSE117261, GSE113439 and GSE53408 datasets were downloaded from Gene Expression Omnibus database. The differentially expressed genes (DEGs) were identified in GSE117261 dataset. The proportions of infiltrated immune cells were evaluated by CIBERSORT algorithm. Feature genes of PAH were selected by least absolute shrinkage and selection operator (LASSO) regression analysis and validated by fivefold cross-validation, random forest and logistic regression. The GSE113439 and GSE53408 datasets were used as validation sets and logistic regression and receiver operating characteristic (ROC) curve analysis were performed to evaluate the prediction value of PAH. The PAH-associated module was identified by weighted gene association network analysis (WGCNA). The intersection of genes in the modules screened and DEGs was used to construct protein-protein interaction (PPI) network and the core genes were selected. After the intersection of feature genes and core genes, the hub genes were identified. The correlation between hub genes and immune cell infiltration was analyzed by Pearson correlation analysis. The expression level of LTBP1 in the lungs of monocrotaline-induced PAH rats was determined by Western blotting. The localization of LTBP1 and CD4 in lungs of PAH was assayed by immunofluorescence. A total of 419 DEGs were identified, including 223 upregulated genes and 196 downregulated genes. Functional enrichment analysis revealed that a significant enrichment in inflammation, immune response, and transforming growth factor β (TGFβ) signaling pathway. CIBERSORT analysis showed that ten significantly different types of immune cells were identified between PAH and control. Resting memory CD4+ T cells, CD8+ T cells, γδ T cells, M1 macrophages, and resting mast cells in the lungs of PAH patients were significantly higher than control. Seventeen feature genes were identified by LASSO regression for PAH prediction. WGCNA identified 15 co-expression modules. PPI network was constructed and 100 core genes were obtained. Complement C3b/C4b receptor 1 (CR1), thioredoxin reductase 1 (TXNRD1), latent TGFβ binding protein 1 (LTBP1), and toll-like receptor 1 (TLR1) were identified as hub genes and LTBP1 has the highest diagnostic efficacy for PAH (AUC = 0.968). Pearson correlation analysis showed that LTBP1 was positively correlated with resting memory CD4+ T cells, but negatively correlated with monocytes and neutrophils. Western blotting showed that the protein level of LTBP1 was increased in the lungs of monocrotaline-induced PAH rats. Immunofluorescence of lung tissues from rats with PAH showed increased expression of LTBP1 in pulmonary arteries as compared to control and LTBP1 was partly colocalized with CD4+ cells in the lungs. LTBP1 was correlated with immune cell infiltration and identified as the critical diagnostic maker for PAH.

Lian G ，You J ，Lin W ，Gao G ，Xu C ，Wang H ，Luo L ... -  《BMC Pulmonary Medicine》