Transcriptomic and bioinformatics analysis reveals the host response feature and potential treatment strategy of patients with dengue fever.

Background: The dengue virus (DENV) can cause various clinical syndromes and organ damage, known as dengue fever, with the probability of developing severe dengue. However, the underlying mechanisms of host response against DENV infection remain unclear, and there is still no specific medicine for dengue fever. In the present study, we revealed the transcriptomic features of the host factor in patients with DENV infection and explored potential therapeutic medication. Methods: The peripheral blood samples were taken from 42 people with dengue fever and 23 healthy volunteers. Transcriptome sequencing was carried out to evaluate the host response in patients with DENV infection. The differentially expressed genes (DEGs) were obtained and functional enrichment analysis was performed. Weighted gene co-expression network analysis (WCGNA) was used to screen for key modules associated with dengue. Machine learning algorithms were applied to identify the signature genes. The features of immune cell infiltration in dengue were subsequently evaluated using CIBERSORT. Finally, the potential therapeutic medication was predicted via SPIED3 and CoreMine database. Results: 4451 DEGs were screened, and significantly enriched in the RIG-I-like receptor signaling pathway, NOD-like receptor signaling pathway, Neutrophil extracellular trap formation, and IL-17 signaling pathway. WGCNA was implemented to obtain hub modules concerning dengue. The signature genes were selected via the intersection of the LASSO and random forest algorithms, containing Conclusions: Our study revealed the transcriptomic features of the host factor and immune cell infiltration in patients with DENV infection and predicted potential medication for clinical utilization.