Circulating lncRNAs as biomarkers for severe dengue using a machine learning approach

Abstract

Objectives: Dengue virus (DENV) infection is a significant global health concern, causing severe morbidity and mortality. While many cases present as a mild febrile illness, some progress to life-threatening severe dengue (SD). Early intervention is essential to improve outcomes, but current predictive methods lack specificity, burdening healthcare systems in endemic regions. Circulating long non-coding RNAs (lncRNAs) have emerged as stable and promising biomarkers. This study explored the use of lncRNAs as predictive markers for SD.

Methods: Differential expression and qPCR arrays were employed to identify lncRNAs associated with SD. Candidate lncRNAs were validated, and their plasma levels were measured in a cohort of Vietnamese dengue patients (n =377) and healthy controls (n=128) at admission. Machine learning algorithms were applied to predict the probability of SD progression.

Results: The predictive model demonstrated high sensitivity and specificity, with an area under the curve (AUC) of 0.98 (95% CI: 0.96-1.0). At admission, it accurately identified 17 of 18 patients who later died as high-risk, compared to traditional warning signs, which flagged only 9 of these cases.

Conclusions: Our findings suggest that this panel of lncRNAs has the potential to predict SD, which could help reduce healthcare burden and improve patient management in endemic countries.

Keywords: Dengue virus; Dengue warning signs; Long non-coding RNAs; Machine learning; Severe dengue.