Exploring a shared genetic signature and immune infiltration between spontaneous intracerebral hemorrhage and Helicobacter pylori infection.

BACKGROUND: Spontaneous intracerebral hemorrhage (ICH) is a devastating form of stroke with high morbidity, disability and mortality. Helicobacter pylori is a major pathogen responsible for chronic gastritis, leading to gastric ulcers and ultimately gastric cancer. Although it remains controversial whether H. pylori infection causes peptic ulcers under various traumatic stimuli, some related studies suggest that H. pylori infection may be an important factor in delaying peptic ulcer healing. However, the linking mechanism between ICH and H. pylori infection remain unclear. The purpose of this study was to examine the genetic features and pathways shared in ICH and H. pylori infection, and compare immune infiltration. METHODS: We used microarray data for ICH and H. pylori infection from the Gene Expression Omnibus (GEO) database. Differential gene expression analysis was performed on both datasets using the R software and the limma package to find the common differentially expressed genes (DEGs). In addition, we performed functional enrichment analysis on DEGs, determined protein-protein interactions (PPIs), identified Hub genes using the STRING database and Cytoscape software, and constructed microRNA-messenger RNA (miRNA-mRNA) interaction networks. Additionally, immune infiltration analysis was performed with the R software and related R packages. RESULTS: A total of 72 DEGs were identified between ICH and H. pylori infection, including 68 upregulated genes and 4 downregulated genes. Functional enrichment analysis revealed that multiple signaling pathways are closely linked to both diseases. In addition, the cytoHubba plugin identified 15 important hub genes, namely PLEK, NCF2, CXCR4, CXCL1, FGR, CXCL12, CXCL2, CD69, NOD2, RGS1, SLA, LCP1, HMOX1, EDN1, and ITGB3.Also, the correlation analysis of immune cell fractions revealed a limited link between their immune-related common genes and immune cells. CONCLUSION: Through bioinformatics methods, this study revealed that there are common pathways and hub genes between ICH and H. pylori infection. Thus, H. pylori infection may have common pathogenic mechanisms with the development of peptic ulcer after ICH. This study provided new ideas for early diagnosis and prevention of ICH and H. pylori infection.

Identification and Validation of a Novel Nomogram Predicting 7-day Death in Patients with Intracerebral Hemorrhage

Background: Intracerebral hemorrhage (ICH) is a serious brain condition with high mortality and disability rates. In recent decades, several risk factors related to death risk have been identified, with several models predicting mortality, but rarely used and accepted in daily clinical practice. Aims: To establish and validate a predictive nomogram of spontaneous ICH death that can be used to predict patient death within 7 days. Study Design: Cohort study. Methods: A cohort of 449 patients with ICH, diagnosed clinically from January 2015 to December 2017, were identified as the model training cohort. Univariate analysis and least absolute contraction and selection operator (Lasso) regression were used to determine the most powerful predictors of patients with ICH. Discrimination, calibration, and clinical applicability were used to assess the function of the new nomogram. In external validation, we also evaluated the nomogram in another 148 subjects (validation cohort) examined between January and December 2018. Results: We observed no significant differences in patient baseline characteristics in the training and validation cohorts, including sex, age, Glasgow coma scale (GCS) score, and one-week mortality rates. The model included three predictive variables from univariate and multivariate analysis, including GCS scores, hematoma volume, and brainstem hemorrhage (BSH). Internal validation revealed that the nomogram had a good discrimination, the area under the receiver operating characteristic curve (AUC) was 0.935, and calibration was good (U = -0.004, P = 0.801). Similarly, this nomogram also showed good differentiation ability (AUC = 0.925) and good accuracy (U = -0.007, P = 0.241) in the validation cohort data. Decision curve analysis indicated that the new prediction model was helpful. Conclusion: At the early stages of the condition, our prediction model accurately predicts the death of patients with ICH.

Development and Validation of a Nomogram for Predicting Death within 2 days After Intracerebral Hemorrhage.

OBJECTIVE: This study aimed to establish and verify a model for predicting death within 2 days after spontaneous cerebral hemorrhage based on the patient's characteristics at the time of admission. METHODS: During 2015-2017, the records of a cohort of 397 patients with clinically diagnosed cerebral hemorrhage were collected for model development. Minimum absolute contraction and the selection operator (lasso) regression model were used to determine factors that most consistently and correctly predicted death after cerebral hemorrhage. Discrimination and calibration were used to evaluate the performance of the resulting nomogram. After internal validation, the nomogram was further assessed during 2017-2018 using a different cohort of 200 consecutive subjects. RESULTS: The nomogram included four predictors from the lasso regression analysis: Glasgow Coma Scale, hematoma location, hematoma volume, and primary intraventricular hemorrhage. The nomogram showed good discrimination and good calibration for both training and verification cohorts. Decision curve analysis showed that the prediction nomogram was clinically useful. CONCLUSION: This prediction model can be used for early, simple, and accurate prediction of early death following cerebral hemorrhage.