A machine-learning classifier trained with microRNA ratios to distinguish melanomas from nevi

Abstract

The use of microRNAs as biomarkers has been proposed for many diseases including the diagnosis of melanoma. Although hundreds of microRNAs have been identified as differentially expressed in melanomas as compared to benign melanocytic lesions, limited consensus has been achieved across studies, constraining the effective use of these potentially useful markers. In this study we quantified microRNAs by next-generation sequencing from melanomas and their adjacent benign precursor nevi.We applied a machine learning-based pipeline to identify a microRNA signature that separated melanomas from nevi and was unaffected by confounding variables, such as patient age and tumor cell content. By employing the ratios of microRNAs that were either enriched or depleted in melanoma compared to nevi as a normalization strategy, the classifier performed similarly across multiple published microRNA datasets, obtained by microarray, small RNA sequencing, or RT-qPCR. Validation on separate cohorts of melanomas and nevi correctly classified lesions with 83% sensitivity and 71-83% specificity, independent of variation in tumor cell content of the sample or patient age.

MicroRNA expression classification for pediatric multiple sclerosis identification

“…(Afshar et al 2018;Hinchcliff et al 2019;Lancashire et al 2009;Shipp et al 2002) and some papers have been presented on NGS data (Leung et al 2016). Some recent work can be found on the application of (mostly) random forests and artificial neural networks to NGS miRNA data, applied to the search for biomarkers from saliva (Rosato et al 2019), urine (Ben-Dov et al 2016, and for melanoma (Torres et al 2018) or other tumors (Elias et al 2017;Liao et al 2018). Some works apply machine learning to microarray data for the study of MS (Acquaviva et al 2019;Fagone et al 2019), while an extensive literature search actually provides few results on machine learning applied to NGS data for adult MS (He et al 2019).…”