An evolutionary learning-based method for identifying a circulating miRNA signature for breast cancer diagnosis prediction

Breast cancer (BC) is one of the most commonly diagnosed cancers worldwide. As key regulatory molecules in several biological processes, microRNA s (miRNA s) are potential biomark ers f or cancer. Understanding the miRNA mark ers that can detect BC ma y impro v e surviv al rates and de v elop ne w targeted therapeutic strategies. To identify a circulating miRNA signature f or diagnostic prediction in patients with BC, w e de v eloped an e v olutionary learning-based method called BSig . BSig est ablished a compact set of miRNAs as potential markers from 1280 patients with BC and 2686 healthy controls retrieved from the serum miRNA expression profiles for the diagnostic prediction. BSig demonstrated outstanding prediction performance, with an independent test accuracy and area under the receiver operating characteristic curv e w ere 99.90% and 0.99, respectively. We identified 12 miRNAs, including hsa-miR-3185, hsa-miR-3648, hsa-miR-4530, hsa-miR-4763-5p, hsa-miR-5100, hsa-miR-5698, hsa-miR-6124, hsa-miR-6768-5p, hsa-miR-6800-5p, hsa-miR-6807-5p, hsa-miR-642a-3p, and hsa-miR-6836-3p, which significantly contributed to w ards diagnostic prediction in BC. Moreo v er, through bioinf ormatics analy sis, this study identified 65 miRNA-target genes specific to BC cell lines. A comprehensive gene-set enrichment analysis was also performed to understand the underlying mechanisms of these target genes. BSig, a tool capable of BC detection and facilitating therapeutic selection, is publicly a v ailable at https:// github.com/ mingjutsai/ BSig.