Profiling Precursor microRNAs of Breast Cancer From Total RNA Sequencing Data to Gain Insights Into Their Roles and Prognostic Values

ABSTRACT Breast cancer, a molecularly heterogeneous disease, is classified into hormone receptor‐positive luminal breast cancer (LBC), human epidermal growth factor receptor 2‐positive breast cancer, and triple‐negative breast cancer (TNBC). Precursor microRNAs (pre‐miRNAs), typically form hairpin structures with a length from 65 to 80 bases, are shown to play crucial roles in breast cancer carcinogenesis. We hypothesized that these pre‐miRNAs could have been sequenced in total RNA sequencing (RNA‐seq) and developed a novel algorithm to profile pre‐miRNAs from raw total RNA‐seq data. A total of 907 breast cancer samples curated by Malaysian Breast Cancer Genetic Study (MyBrCa) were profiled using this algorithm and a comparison was made between pre‐miRNA profiles and mature miRNA profiles obtained from The Cancer Genome Atlas (TCGA) dataset. We explored differentially expressed pre‐miRNAs in TNBC in comparison to LBC and conducted downstream functional analyses of the target genes. A prognostic signature was built by LASSO–Cox regression on selected pre‐miRNAs and validated internally and externally by MyBrCa and TCGA datasets, respectively. As a result, 10 common differentially expressed pre‐miRNAs were identified. Functional analyses of these pre‐miRNAs captured certain aggressive TNBC behaviors. Importantly, a pre‐miRNA signature composed of 4 out of these 10 pre‐miRNAs significantly prognosticated the breast cancer patients in the MyBrCa cohort and TCGA cohort, independent of conventional prognostic factors. In conclusion, this novel algorithm allows profiling pre‐miRNAs from raw total RNA‐seq data, which could be cross‐validated with mature miRNA profiles for cross‐platform comparison. The findings of this study underscore the importance of pre‐miRNAs in breast cancer carcinogenesis and as prognostic factors.