Combined molecular and mathematical analysis of long noncoding RNAs expression in fine needle aspiration biopsies as novel tool for early diagnosis of thyroid cancer.

In presence of indeterminate lesions by fine needle aspiration (FNA), thyroid cancer cannot always be easily diagnosed by conventional cytology. As a consequence, unnecessary removal of thyroid gland is performed in patients without cancer based on the lack of optimized diagnostic criteria. Aim of this study is identifying a molecular profile based on long noncoding RNAs (lncRNAs) expression capable to discriminate between benign and malignant nodules. Patients were subjected to surgery (n = 19) for cytologic suspicious thyroid nodules or to FNA biopsy (n = 135) for thyroid nodules suspicious at ultrasound. Three thyroid-specific genes (TG, TPO, and NIS), six cancer-associated lncRNAs (MALAT1, NEAT1, HOTAIR, H19, PVT1, MEG3), and two housekeeping genes (GAPDH and P0) were analyzed using Droplet Digital PCR (ddPCR). Based on higher co-expression in malignant (n = 11) but not in benign (n = 8) nodules after surgery, MALAT1, PVT1 and HOTAIR were selected as putative cancer biomarkers to analyze 135 FNA samples. Cytological and histopathological data from a subset of FNA patients (n = 34) were used to define a predictive algorithm based on a Naïve Bayes classifier using co-expression of MALAT1, PVT1, HOTAIR, and cytological class. This classifier exhibited a significant separation capability between malignant and benign nodules (P < 0.0001) as well as both rule in and rule out test potential with an accuracy of 94.12% and a negative predictive value (NPV) of 100% and a positive predictive value (PPV) of 91.67%. ddPCR analysis of selected lncRNAs in FNA biopsies appears a suitable molecular tool with the potential of improving diagnostic accuracy.