Computational Identification of Dengue-Associated Comorbidities and Key Regulatory Genes from Peripheral Blood Mononuclear Cell Transcriptomes

Abstract

Dengue fever remains a major health concern, and variation in host immune responses contributes to differences in clinical severity. This study computationally investigated peripheral blood mononuclear cell (PBMC) transcriptomes to identify highly expressed sample-specific genes, potential dengue-associated comorbidities, and key regulatory genes across dengue fever, dengue haemorrhagic fever, dengue with plasma leakage, and dengue without plasma leakage. Publicly available RNA-seq datasets were collected, processed, and integrated. Highly expressed sample-specific genes were identified after excluding housekeeping genes, followed by Gene Ontology, KEGG pathway, and Disease Ontology enrichment analyses. Protein–protein interaction networks were then used to identify hub genes associated with enriched disease terms. The analyses identified BCL2, MYC, TNF, and TYMS as key genes linking the examined dengue conditions with distinct disease categories. Leukaemia-related disease terms were prominent in dengue fever and dengue haemorrhagic fever, with BCL2 identified in both conditions and MYC additionally identified in dengue haemorrhagic fever. In dengue with plasma leakage, TNF was associated with rheumatic and autoimmune disease terms. In dengue without plasma leakage, TNF was linked with rheumatic and tuberculosis-related terms, while TYMS was associated with multiple myeloma-related molecular patterns. These findings describe condition-specific gene–disease overlaps in PBMC transcriptomes and provide computational candidates for further investigation. Experimental and clinical validation is required before these predicted associations can be considered for diagnostic or therapeutic application.