Background Coronavirus disease 2019 (COVID-19) has killed over 2.5 million people worldwide, but effective care and therapy have yet to be discovered. We conducted this analysis to better understand tocilizumab treatment for COVID-19 patients. Main text We searched major databases for manuscripts reporting the effects of tocilizumab on COVID-19 patients. A total of 25 publications were analyzed with Revman 5.3 and R for the meta-analysis. Significant better clinical outcomes were found in the tocilizumab treatment group when compared to the standard care group [odds ratio (OR) = 0.70, 95% confidential interval (C): 0.54–0.90, P = 0.007]. Tocilizumab treatment showed a stronger correlation with good prognosis among COVID-19 patients that needed mechanical ventilation (OR = 0.59, 95% CI, 0.37–0.93, P = 0.02). Among stratified analyses, reduction of overall mortality correlates with tocilizumab treatment in patients less than 65 years old (OR = 0.68, 95% CI: 0.60–0.77, P < 0.00001), and with intensive care unit patients (OR = 0.62, 95% CI: 0.55–0.70, P < 0.00001). Pooled estimates of hazard ratio showed that tocilizumab treatment predicts better overall survival in COVID-19 patients (HR = 0.45, 95% CI: 0.24–0.84, P = 0.01), especially in severe cases (HR = 0.58, 95% CI 0.49–0.68, P < 0.00001). Conclusions Our study shows that tocilizumab treatment is associated with a lower risk of mortality and mechanical ventilation requirement among COVID-19 patients. Tocilizumab may have substantial effectiveness in reducing mortality among COVID-19 patients, especially among critical cases. This systematic review provides an up-to-date evidence of potential therapeutic role of tocilizumab in COVID-19 management. Graphical abstract
Objective The goal of this article was to identify potential biomarkers for early diagnosis of sepsis in order to improve their survival. Methods We analyzed differential gene expression between adult sepsis patients and controls in the GSE54514 dataset. Coexpression analysis was used to cluster coexpression modules, and enrichment analysis was performed on module genes. We also analyzed differential gene expression between neonatal sepsis patients and controls in the GSE25504 dataset, and we identified the subset of differentially expressed genes (DEGs) common to neonates and adults. All samples in the GSE54514 dataset were randomly divided into training and validation sets, and diagnostic signatures were constructed using least absolute shrink and selection operator (LASSO) regression. The key gene signature was screened for diagnostic value based on area under the receiver operating characteristic curve (AUC). STEM software identified dysregulated genes associated with sepsis-associated mortality. The ssGSEA method was used to quantify differences in immune cell infiltration between sepsis and control samples. Results A total of 6316 DEGs in GSE54514 were obtained spanning 10 modules. Module genes were mainly enriched in immune and metabolic responses. Screening 51 genes from among common genes based on AUC > 0.7 led to a LASSO model for the training set. We obtained a 25-gene signature, which we validated in the validation set and in the GSE25504 dataset. Among the signature genes, SLC2A6, C1ORF55, DUSP5 and RHOB were recognized as key genes (AUC > 0.75) in both the GSE54514 and GSE25504 datasets. SLC2A6 was identified by STEM as associated with sepsis-associated mortality and showed the strongest positive correlation with infiltration levels of Th1 cells. Conclusion In summary, our four key genes may have important implications for the early diagnosis of sepsis patients. In particular, SLC2A6 may be a critical biomarker for predicting survival in sepsis.
Coronary heart disease (CHD) is common in patients with diabetes mellitus (DM), however, the relevant mechanism remains elusive. The whole blood gene expression profiles of healthy control, patients with DM, patients with DM and CHD (DMCHD) were used to performed weight gene correlation network analysis (WGCNA) to identify the gene modules associated with DM-related atherogenesis. The candidate module was significantly involved in immune-and T cell activity-related biological process. GSEA results suggested that lysosome and apoptosis were enriched in DM and DMCHD samples. The protein-protein-KEGG pathway network may reveal the potential transcriptional regulatory network involving in DM-related atherosclerosis. Nineteen genes (RTKN,
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