Background: Human papillomavirus (HPV)-based screening has substantially improved sensitivity for cervical cancer detection but remains limited by low specificity, leading to unnecessary colposcopy referrals. MicroRNAs (miRNAs) represent promising biomarkers for improving triage of HPV-positive women. This study evaluated the diagnostic and regulatory roles of selected miRNAs in cervical lesion progression using liquid-based cytology (LBC) specimens. Methods: Expression of six biologically relevant miRNAs (miR-15a-5p, miR-16-5p, miR-20b-5p, miR-155-5p, miR-34a-5p, and miR-140-3p) was analyzed across NILM, CIN II, CIN III, and cervical cancer (CC) samples. All miRNA analyses were performed using residual cellular material derived from the same liquid-based cytology (LBC) specimens collected during the HPV screening visit, without requiring any additional sampling prior to colposcopy. Diagnostic performance was assessed using ROC analysis. To capture regulatory dynamics beyond expression magnitude, correlation, and differential correlation (Δρ), network analyses were applied. Results: Stage-dependent changes in miRNA expression were observed across disease categories; however, expression magnitude alone did not fully explain diagnostic performance. Upregulated miRNAs, particularly miR-16-5p, miR-20b-5p, and miR-155-5p, demonstrated high diagnostic accuracy for distinguishing NILM from high-grade lesions and invasive cancer. In contrast, downregulated miRNAs showed limited diagnostic utility. Correlation analyses revealed progressive remodeling of miRNA co-expression networks, with the most pronounced changes occurring during the CIN II–to–CIN III transition. Notably, miRNAs with strong diagnostic performance did not uniformly function as network hubs, indicating distinct roles as biomarkers versus regulators of network dynamics. Conclusions: Cervical lesion progression is characterized not only by changes in miRNA expression levels but also by stage-specific reorganization of miRNA regulatory networks. Integrating diagnostic performance with network-level analysis enables improved identification of clinically robust triage markers and provides additional insight into regulatory instability associated with progression.
Pisarska et al. (Mon,) studied this question.