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

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.