Background. Coronavirus disease 2019 (COVID-19) is highly contagious and continues to spread rapidly. However, there are no simple and timely laboratory techniques to determine the severity of COVID-19. In this meta-analysis, we assessed the potential of the neutrophil-lymphocyte ratio (NLR) as an indicator of severe versus nonsevere COVID-19 cases. Methods. A search for studies on the NLR in severe and nonsevere COVID-19 cases published from January 1, 2020, to July 1, 2021, was conducted on the PubMed, EMBASE, and Cochrane Library databases. The pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, diagnostic odds ratio (DOR), and area under the curve (AUC) analyses were done on Stata 14.0 and Meta-disc 1.4 to assess the performance of the NLR. Results. Thirty studies, including 5570 patients, were analyzed. Of these, 1603 and 3967 patients had severe and nonsevere COVID-19, respectively. The overall sensitivity and specificity were 0.82 (95 confidence interval (CI), 0.77-0.87) and 0.77 (95 CI, 0.70-0.83), respectively; positive and negative correlation ratios were 3.6 (95 CI, 2.7-4.7) and 0.23 (95 CI, 0.17-0.30), respectively; DOR was 16 (95 CI, 10-24), and the AUC was 0.87 (95 CI, 0.84-0.90). Conclusion. The NLR could accurately determine the severity of COVID-19 and can be used to identify patients with severe disease to guide clinical decision-making.

Background. Recent studies have shown that circulating microRNA-499 could be a powerful biomarker of acute myocardial infarction (AMI). Interest in circulating microRNA-499 for detecting AMI is increasing rapidly. To evaluate the diagnosis of circulating microRNA-499 for AMI, this study was performed. Methods. We searched PubMed, Embase, and the Cochrane Library for studies published up to December 31, 2018, as well as the reference lists of relevant studies. Studies were included if they assessed the accuracy of blood circulating microRNA-499 or cardiac troponin T (cTnT) for AMI and provided sufficient data to construct a 2×2 contingency table. Extracted data were analysed for sensitivity, specificity, diagnostic odds ratio (DOR), and summary receiver operator curve (SROC) analyses. Prespecified subgroup analysis and metaregression were also performed. Results. Fourteen studies including 3816 participants were included in this meta-analysis. The overall pooled sensitivity and specificity were 0.84 (95% CI: 0.64-0.94) and 0.97 (95% CI: 0.90-0.99), respectively. The area under the SROC curve (AUC) was 0.98 (95% CI: 0.96-0.99). The studies had substantial heterogeneity (I2=98.74%). Seven studies also used cTnT as a marker for the diagnosis of AMI. The overall pooled sensitivity and specificity of cTnT were 0.95 (95% CI: 0.87-0.98) and 0.96 (95% CI: 0.85-0.99), respectively. The area under the SROC curve (AUC) was 0.99 (95% CI: 0.97-0.99). The DOR of circulating miR-499 and cTnT were 188 (95% CI: 19-1815) and 420 (95% CI: 86-2038), respectively. Metaregression analysis suggested that specimen and healthy controls were the main sources of heterogeneity. No publication bias was suggested by Deeks’ regression test of asymmetrical funnel plot (t=0.85; p value = 0.41). Conclusion. The results showed that circulating microRNA-499 is a reliable biomarker for diagnosing AMI patients.

In this study, we evaluated the diagnostic value of key genes in myocardial infarction (MI) based on data from the Gene Expression Omnibus (GEO) database. We used data from GSE66360 to identify a set of significant differentially expressed genes (DEGs) between MI and healthy controls. Logistic regression, least absolute shrinkage and selection operator (LASSO) regression, support vector machine recursive feature elimination (SVM-RFE), and SignalP 3.0 server were used to identify the potential role of genes in predicting diagnosis in patients with MI. Principal component analysis (PCA), receiver operating characteristic (ROC) curve analyses, area under the curve (AUC) analyses, and C-index were used to estimate the diagnostic value of genes in patients with MI. The association was validated using six other independent data sets. Subsequently, bioinformatics analysis was conducted based on the aforementioned potential genes. A meta-analysis was performed to evaluate the diagnostic value of the genes in MI. Forty-four DEGs were selected from the GSE66360 dataset. A three-gene signature consisting of CCL20, IL1R2, and ITLN1 could effectively distinguish patients with MI. The three-gene signature was validated in seven independent cohorts. Functional enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed to reveal the involvement of the three-gene signature in inflammation-related biological processes and pathways. Moreover, diagnostic meta-analysis results of the three-gene signature showed that the pooled sensitivity, specificity, and AUC for MI were 0.80, 0.90, and 0.93, respectively. These results suggest that the three-gene signature is a novel candidate biomarker for distinguishing MI from healthy controls. Graphical abstract

Aim. Early diagnosis of paediatric sepsis is crucial for the proper treatment of children and reduction of hospitalization and mortality. Biomarkers are a convenient and effective method for diagnosing any disease. However, huge differences among the studies reporting biomarkers for diagnosing sepsis have limited their clinical application. Therefore, in this study, we aimed to evaluate the diagnostic value of key genes involved in paediatric sepsis based on the data of the Gene Expression Omnibus database. Methods. We used the GSE119217 dataset to identify differentially expressed genes (DEGs) between patients with and without paediatric sepsis. The most relevant gene modules of paediatric sepsis were screened through the weighted gene coexpression network analysis (WGCNA). Common genes (CGs) were found between DEGs and WGCNA. Genes with a potential diagnostic value in paediatric sepsis were selected from the CGs using least absolute shrinkage and selection operator regression and support vector machine recursive feature elimination. The principal component analysis, receiver operating characteristic curves, and C‐index were used to verify the diagnostic value of the identified genes in six other independent sepsis datasets. Subsequently, a meta‐analysis of the selected genes was performed to evaluate the value of these genes as biomarkers in paediatric sepsis. Results. A total of 41 CGs were selected from the GSE119217 dataset. A four‐gene signature composed of ANXA3, CD177, GRAMD1C, and TIGD3 effectively distinguished patients with paediatric sepsis from those in the control group. The signature was verified using six other independent datasets. In addition, the meta‐analysis results showed that the pooled sensitivity, specificity, and area under the curve values were 1.00, 0.98, and 1.00, respectively. Conclusion. The four‐gene signature can be used as new biomarkers to distinguish patients with paediatric sepsis from healthy individuals.

Aims. This study is aimed at investigating the pathogenesis of rheumatoid arthritis (RA) by identifying key biomarkers, associated immune infiltration, and small‐molecule compounds using bioinformatic analysis. Methods. Six datasets were obtained from the Gene Expression Omnibus database, and the batch effect was adjusted. Functional enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) were used to analyse differentially expressed genes (DEGs). Furthermore, candidate small‐molecule drugs associated with RA were selected from the Connectivity Map (CMap) database. The least absolute shrinkage and selection operator regression, support vector machine recursive feature elimination, and multivariate logistic regression analyses were performed on DEGs to screen for RA diagnostic markers. The receiver operating characteristic curve, concordance index, and GiViTi calibration band were the metrics used to assess the diagnostic markers of RA identified in this analysis. The single‐sample gene set enrichment analysis was performed to calculate the scores of infiltrating immune cells and evaluate the activities of immune‐related pathways. Finally, the correlation between screening markers and RA diagnosis was determined. Results. A total of 227 DEGs were identified. Functional enrichment analysis and KEGG revealed that DEGs were enriched by the immune response. CMap analysis identified 11 small‐molecule compounds with therapeutic potential for RA. In gene expression, the activities of 13 immune cells and 12 immune‐related pathways significantly differed between patients with RA and healthy controls. DPYSL3 and SPP1 had the potential to diagnose RA. SPP1 expression was positively correlated with DPYSL3 in 11 immune cells and 10 immune‐related pathways. Conclusion. This study comprehensively analysed DEGs and immune infiltration and screened for potential diagnostic markers and small‐molecule compounds of RA.