We identified 14 immune-related differentially Expressed Genes (DEGs) between COVID-19 patients and normal controls and the receiver operator characteristic curve results showed that they could be used to discriminate COVID-19 patients from healthy controls. Single-sample gene set enrichment analysis and CIBERSORT analysis displayed immune landscape of COVID-19 patients that the fraction of immune cells (like B cell subtypes and T cell subtypes) decreased distinctly in the first SARS-CoV-2 infection which may further weaken immunity of cancer patients and increasing inflammatory cells (Neutrophils and Macrophages) may further promote inflammatory response of cancer patients. Based on expression levels of 14 DEGs we found that first SARS-CoV-2 infection may accelerate progression of cancer patients by Kaplan-Meier survival, immune subtypes and tumor microenvironment analyses, and may weaken anti-PD-1 monoclonal antibody treatment effect of cancer patients by weighted gene co-expression network, tumor mutation burden and microsatellite instability analysis. The second SARS-CoV-2 infection was beneficial to control development of tumor seemingly, but it may be difficult for cancer patients to experience destroy successfully from first SARS-CoV-2 infection, let alone benefits from second SARS-CoV-2 infection. In addition, this study also emphasized significance of multi-factor analysis when analyzing impacts of SARS-CoV-2 infection on cancer patients.
Background At present, there are still no specific therapeutic drugs and appropriate vaccines for Dengue. Therefore, it is important to explore distinct clinical diagnostic indicators. Methods In this study, we combined differentially expressed genes (DEGs) analysis, weighted co-expression network analysis (WGCNA) and Receiver Operator Characteristic Curve (ROC) to screen a stable and robust biomarker with diagnosis value for Dengue patients. CIBERSORT was used to evaluate immune landscape of Dengue patients. Gene Ontology (GO) enrichment, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis and Gene set enrichment analysis (GSEA) were applied to explore potential functions of hub genes. Results CD38 and Plasma cells have excellent Area Under the Curve (AUC) in distinguishing clinical stages for Dengue patients, and activated memory CD4+ T cells and Monocytes have good AUC for this function. ZNF595 has acceptable AUC in discriminating dengue hemorrhagic fever (DHF) from dengue fever (DF) in whole acute stages. Analyzing any serotype, we can obtain consistent results. Negative inhibition of viral replication based on GO, KEGG and GSEA analysis results, up-regulated autophagy genes and the impairing immune system are potential reasons resulting in DHF. Conclusions CD38, Plasma cells, activated memory CD4+ T cells and Monocytes can be used to distinguish clinical stages for dengue patients, and ZNF595 can be used to discriminate DHF from DF, regardless of serotypes. Graphical abstract
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