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Therefore, it is important to explore distinct clinical diagnostic indicators.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Methods<\/jats:title>\n                <jats:p>In this study, we combined differentially expressed genes\u00a0(DEGs) analysis, weighted co-expression network analysis\u00a0(WGCNA) and Receiver Operator Characteristic Curve\u00a0(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.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p><jats:italic>CD38<\/jats:italic> and Plasma cells have excellent Area Under the Curve (AUC) in distinguishing clinical stages for Dengue patients, and activated memory CD4+\u2009T cells and Monocytes have good AUC for this function. <jats:italic>ZNF595<\/jats:italic> 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.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusions<\/jats:title>\n                <jats:p><jats:italic>CD38<\/jats:italic>, Plasma cells, activated memory CD4+\u2009T cells and Monocytes can be used to distinguish clinical stages for dengue patients, and <jats:italic>ZNF595<\/jats:italic> can be used to discriminate DHF from DF, regardless of serotypes.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Graphical abstract<\/jats:title>\n                \n              <\/jats:sec>","DOI":"10.1186\/s12985-022-01853-8","type":"journal-article","created":{"date-parts":[[2022,8,2]],"date-time":"2022-08-02T09:06:23Z","timestamp":1659431183000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Identification of stage-related and severity-related biomarkers and exploration of immune landscape for Dengue by comprehensive analyses"],"prefix":"10.1186","volume":"19","author":[{"given":"Nan","family":"Xiong","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiangming","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,8,2]]},"reference":[{"key":"1853_CR1","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1016\/j.ijid.2021.04.010","volume":"107","author":"J Zhang","year":"2021","unstructured":"Zhang J, Shu Y, Shan X, Li D, Ma D, Li T, Long S, Wang X, Pan Y, Chen J, et al. 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