Detection of high-grade neoplasia in air-dried cervical PAP smears by a microRNA-based classifier.

Recent studies have shown that changes in the expression levels of certain microRNAs correlate with the degree of severity of cervical lesions. The aim of the present study was to develop a microRNA-based classifier for the detection of high-grade cervical intraepithelial neoplasia (CIN ≥2) in cytological samples from patients with different high-risk human papillomavirus (HR-HPV) viral loads. For this purpose, raw RT-qPCR data for 25 candidate microRNAs, U6 snRNA and human DNA in air-dried PAP smears from 174 women with different cervical cytological diagnoses, 144 of which were HR-HPV-positive [40 negative for intraepithelial lesion or malignancy (NILM), 34 low-grade squamous intraepithelial lesions (L-SIL), 57 high-grade squamous intraepithelial lesions (H-SIL), 43 invasive cancers], were statistically processed.