A functional variant of the long noncoding RNA AL110200 is associated with the risk of ischaemic stroke recurrence

Construction and comparison of short-term prognosis prediction model based on machine learning in acute ischemic stroke

Objective

To construct and compared the short-term prognosis prediction models of acute ischemic stroke (AIS) by machine learning (ML).

Methods

Retrospectively study. The group W (mRS≤3) was clustered, and combined with group P (mRS>3) to form the post-clustering dataset for modeling. The "glmnet", "rpart", "xgboost", "randomForest", "neuralnet" packages were used to construct ML models. The accuracy, sensitivity, specificity, positive predict value (PPV), negative predict value (NPV) among the models were compared. Four external clinical datasets were used for external clinical validation. The optimal prediction model was determined by variable screening ability, model visualization, and external clinical validation performance.

Results

The post-clustering dataset contains 139 patients (group W) and 122 patients (group P). The neutrophil multiplied by D-dimer (NDM) has predictive value in all ML prediction models in this study. In the decision tree model, NDMQ occupies the first tree node, When NDM≤5.62 and the age<74.5, the probability of poor prognosis of AIS is less than 20 %. When NDM>5.62 and accompanied by pneumonia, the incidence of poor prognosis of AIS is about 90 %. In the Random Forest (RF) model, NDMQ had the highest Gini index. The variable combination screened by the RF model had the best performance in the neural network, and the accuracy, sensitivity, specificity, PPV, and NPV of the external validation were 0.800, 0.774, 0.833, 0.857, and 0.741, respectively. The RF model had the best performance in the external clinical validation datasets, with accuracies of 0.646, 0.697, 0.695, and 0.713, respectively.

Conclusions

NDM shows predictive value for AIS short-term prognosis in all ML models in this study. The optimal model in screening characteristic variables and the performance of in external clinical datasets was RF model. In the analysis of medical data with small sample size and outcome as categorical variables, RF could be used as the main algorithm to build a model.

Neutrophil count multiplied by D-dimer combined with pneumonia may better predict short-term outcomes in patients with acute ischemic stroke

Objective

To investigate the predictive value of neutrophil, D-dimer and diseases associated with stroke for short-term outcomes of acute ischemic stroke (AIS).

Methods

By collecting the subitems of laboratory data especially routine blood and coagulation test in AIS patients, and recording their clinical status, the correlation, regression and predictive value of each subitem with the short-term outcomes of AIS were analyzed. The predict model was constructed.

Results

The neutrophil count multiplied by D-dimer (NDM) had the best predictive value among the subitems, and the area under the receiver operating characteristic (ROC) curve reached 0.804. When clinical information was not considered, the Youden index of NDM was calculated to be 0.48, corresponding to an NDM value of 7.78, a diagnostic sensitivity of 0.79, specificity of 0.69, negative predictive value of 96%. NDM were divided into 5 quintiles, the five grade of NDM (quintile) were < = 1.82, 1.83–2.41, 2.42–3.27, 3.28–4.49, 4.95+, respectively. The multivariate regression analysis was conducted between NDM (quintile), Babinski+, pneumonia, cardiac disease and poor outcomes of AIS. Compared with the first grade of NDM (quintile), the second grade of NDM (quintile) was not significant, but the third grade of NDM (quintile) showed 7.061 times, the fourth grade of NDM (quintile) showed 11.776 times, the fifth grade of NDM (quintile) showed 23.394 times in short-term poor outcomes occurrence. Babinski sign + showed 1.512 times, pneumonia showed 2.995 times, cardiac disease showed 1.936 times in short-term poor outcomes occurrence compared with those negative patients.

Conclusions

NDM combined with pneumonia may better predict short-term outcomes in patients with AIS. Early prevention, regular examination and timely intervention should be emphasized for patients, which may reduce the risk of short-term poor outcomes.

SNHG15 is a negative regulator of inflammation by mediating TRAF2 ubiquitination in stroke-induced immunosuppression

BACKGROUND

Abnormal expression of long noncoding RNAs (lncRNAs) has been reported in the acute stage of acute ischemic stroke (AIS). This study aimed to explore differential lncRNA expression in the subpopulations of peripheral blood mononuclear cells (PBMCs) from AIS patients and further evaluate its underlying mechanisms in stroke-induced immunosuppression.

METHODS

We reanalyzed lncRNA microarray data and investigated abnormally expressed lncRNAs in the subpopulations of PBMCs by magnetic cell sorting and real-time quantitative PCR. The potential mechanism of small nucleolar RNA host gene 15 (SNHG15) was explored through in vitro and in vivo approaches.

RESULTS

The stroke-induced SNHG15 acted as a checkpoint to inhibit peripheral inflammatory responses. Functional studies showed that SNHG15 promoted M2 macrophage polarization. Mechanistically, SNHG15 expression was dysregulated through the Janus kinase (JAK)-signal transducer and activator of transcription 6 (STAT6) signaling pathway. SNHG15, localized in the cytoplasm, interfered with K63-linked ubiquitination of tumor necrosis factor receptor-associated factor 2 and thereby repressed the activation of mitogen-activated protein kinase and nuclear factor kappa-B signaling pathways and prevented the production of proinflammatory cytokines. Administration of an adenovirus targeting SNHG15 improved stroke-induced immunosuppression in mice.

CONCLUSIONS

This study identified SNHG15 as a negative regulator of inflammation in stroke-induced immunosuppression, suggesting it as a novel biomarker and therapeutic target in stroke-associated infection. Trial registration ClinicalTrials.gov NCT04175691. Registered November 25, 2019, https://www.clinicaltrials.gov/ct2/show/NCT04175691 .

New Drug Targets to Prevent Death Due to Stroke: A Review Based on Results of Protein-Protein Interaction Network, Enrichment, and Annotation Analyses

This study used established biomarkers of death from ischemic stroke (IS) versus stroke survival to perform network, enrichment, and annotation analyses. Protein-protein interaction (PPI) network analysis revealed that the backbone of the highly connective network of IS death consisted of IL6, ALB, TNF, SERPINE1, VWF, VCAM1, TGFB1, and SELE. Cluster analysis revealed immune and hemostasis subnetworks, which were strongly interconnected through the major switches ALB and VWF. Enrichment analysis revealed that the PPI immune subnetwork of death due to IS was highly associated with TLR2/4, TNF, JAK-STAT, NOD, IL10, IL13, IL4, and TGF-β1/SMAD pathways. The top biological and molecular functions and pathways enriched in the hemostasis network of death due to IS were platelet degranulation and activation, the intrinsic pathway of fibrin clot formation, the urokinase-type plasminogen activator pathway, post-translational protein phosphorylation, integrin cell-surface interactions, and the proteoglycan-integrin extracellular matrix complex (ECM). Regulation Explorer analysis of transcriptional factors shows: (a) that NFKB1, RELA and SP1 were the major regulating actors of the PPI network; and (b) hsa-mir-26-5p and hsa-16-5p were the major regulating microRNA actors. In conclusion, prevention of death due to IS should consider that current IS treatments may be improved by targeting VWF, the proteoglycan-integrin-ECM complex, TGF-β1/SMAD, NF-κB/RELA and SP1.