Robust Feature Selection strategy detects a panel of microRNAs as putative diagnostic biomarkers in Breast Cancer

Abstract MicroRNAs represent a comprehensive class of short, single-stranded, non-coding RNA transcripts able to interfere with the translation of their targets or degrade them. Since these molecules are dysregulated in several malignancies, they represent reliable biomarkers in various contexts. In this study, the application of several Feature Selection methods uncovers a panel of 20 microRNAs, of which hsa-mir-337, hsa-mir-378c, and hsa-mir-483 are still poorly investigated in the context of Breast Cancer. This signature is capable of discriminating between healthy and tumoral samples, showing better classification performance when compared with differentially expressed microRNAs. Furthermore, a network-based centrality analysis on the gene targets of these transcripts highlighted CDC25, TPX2, KIF18B, CDCA3, TGFBR2, CAV1, TNS1 , and LHFPL6 as key dysregulated genes. This study provides in silico hypotheses as new insights for future in vivo or in vitro studies uncovering the role of these putative diagnostic biomarkers in Breast Cancer.