Purpose This study aimed to provide new biomarkers for predicting the disease course of COVID-19 by analyzing the dynamic changes of microRNA (miRNA) and its target gene expression in the serum of COVID-19 patients at different stages. Methods Serum samples were collected from all COVID-19 patients at three time points: the acute stage, the turn-negative stage, and the recovery stage. The expression level of miRNA and the target mRNA was measured by Quantitative Real-Time Polymerase Chain Reaction (RT-qPCR). The classification tree model was established to predict the disease course, and the prediction efficiency of independent variables in the model was analyzed using the receiver operating characteristic (ROC) curve. Results The expression of miR-125b-5p and miR-155-5p was significantly up-regulated in the acute stage and gradually decreased in the turn-negative and recovery stages. The expression of the target genes CDH5, STAT3, and TRIM32 gradually down-regulated in the acute, turn-negative, and recovery stages. MiR-125b-5p, miR-155-5p, STAT3, and TRIM32 constituted a classification tree model with 100% accuracy of prediction and AUC >0.7 for identification and prediction in all stages. Conclusion MiR-125b-5p, miR-155-5p, STAT3, and TRIM32 could be useful biomarkers to predict the time nodes of the acute, turn-negative, and recovery stages of COVID-19.

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Coronavirus disease 2019 is a serious threat to human life, and early diagnosis and screening can help control the COVID-19 pandemic. The high sensitivity of reverse transcriptase–polymerase chain reaction (RT-PCR) assay is the gold standard for the diagnosis of COVID-19, but there are still some false-negative results. Rapid antigen detection (RAD) is recommended by the World Health Organization (WHO) as a screening method for COVID-19. This review analyzed the characteristics of RDT and found that although the overall sensitivity of RAD was not as high as that of RT-PCR, but RAD was more sensitive in COVID-19 patients within 5 days of the onset of symptoms and in COVID-19 patients with Ct ≤ 25. Therefore, RAD can be used as an adjunct to RT-PCR for screening patients with early COVID-19. Finally, this review provides a combined diagnostic protocol for RAD and nucleic acid testing with the aim of providing a feasible approach for COVID-19 screening.

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Background: Iron metabolism-related genes have shown good predictive value for the prognosis of many solid tumours. However, iron metabolism-related genes have not been reported as prognostic biomarkers in bladder urothelial carcinoma.Methods: In this study, gene expression data and clinical data were downloaded from The Cancer Genome Atlas database. Differential gene expression analysis, univariate Cox regression analysis and the least absolute shrinkage and selection operator regression algorithm were used to screen prognostic iron metabolism-related genes and to construct a risk scoring model. Kaplan-Meier survival plots and receiver operating characteristic curve analysis were used to evaluate the prognostic performance of the risk scoring model in the TCGA-BLCA cohort. In addition, a nomogram model with the risk score was established, and its predictive performance was verified by receiver operating characteristic analysis and calibration plot analysis in the TCGA-BLCA cohort. Gene set enrichment analysis identified pathways and biological processes that may be enriched in the high-risk group. Finally, immune infiltration analysis was used to explore the characteristics of the tumour microenvironment related to the risk score. Results: We identified 14 iron metabolism-related genes with prognostic value and constructed a risk scoring model. Receiver operating characteristic analysis showed that the risk scoring model can accurately predict the 1-year, 3-year, and 5-year overall survival of BLCA patients in the TCGA-BLCA cohort. Kaplan-Meier analysis showed that the overall survival of the high-risk group was significantly lower than that of the low-risk group (P<0.001). The nomogram model effectively predicted the overall survival of BLCA patients in the TCGA-BLCA cohort. Gene set enrichment analysis indicated that iron metabolism-related genes may be involved in biological processes such as developmental processes, the cell cycle, mitosis, the RHO GTPase response, DNA repair, and extracellular matrix regulation. Immune infiltration analysis showed that the level of immune cell infiltration in the high-risk group was high, and the risk score was positively correlated with infiltrating immune cells. Conclusions: Our prognostic model based on iron metabolism-related genes in BLCA could help the prognostic assessment of BLCA patients and provide potential targets for BLCA inhibition.