Next generation sequencing profiling identifies miR-574-3p and miR-660-5p as potential novel prognostic markers for breast cancer. (December 2015)

• Record Type: Journal Article
• Authors: Krishnan, Preethi; Ghosh, Sunita; Wang, Bo; Li, Dongping; Narasimhan, Ashok; Berendt, Richard; Graham, Kathryn; Mackey, John; Kovalchuk, Olga; Damaraju, Sambasivarao
• Is Part Of: BMC genomics. Volume 16:Number 1(2015)
• Page Start: 1
• Page End: 17
• Publication Date: 2015-12
• Subjects: microRNA -- Next generation sequencing -- Breast cancer -- Prognostic marker -- miR-574-3p -- miR-660-5p -- Reduction mammoplasty -- Overall survival -- Recurrence free survival -- TCGA; Genomes -- Periodicals; Gene mapping -- Periodicals; Genomics -- Periodicals; Base Sequence -- Periodicals; Chromosome Mapping -- Periodicals; Genetic Techniques -- Periodicals; Sequence Analysis, DNA -- Periodicals; 572.8605
• DOI: 10.1186/s12864-015-1899-0
• Languages: English
• ISSNs: 1471-2164

Abstract

Abstract Background Prognostication of Breast Cancer (BC) relies largely on traditional clinical factors and biomarkers such as hormone or growth factor receptors. Due to their suboptimal specificities, it is challenging to accurately identify the subset of patients who are likely to undergo recurrence and there remains a major need for markers of higher utility to guide therapeutic decisions. MicroRNAs (miRNAs) are small non-coding RNAs that function as post-transcriptional regulators of gene expression and have shown promise as potential prognostic markers in several cancer types including BC. Results In our study, we sequenced miRNAs from 104 BC samples and 11 apparently healthy normal (reduction mammoplasty) breast tissues. We used Case–control (CC) and Case-only (CO) statistical paradigm to identify prognostic markers. Cox-proportional hazards regression model was employed and risk score analysis was performed to identify miRNA signature independent of potential confounders. Representative miRNAs were validated using qRT-PCR. Gene targets for prognostic miRNAs were identified usingin silico predictions and in-house BC transcriptome dataset. Gene ontology terms were identified using DAVID bioinformatics v6.7. A total of 1, 423 miRNAs were captured. In the CC approach, 126 miRNAs were retained with predetermined criteria for good read counts, from which 80 miRNAs were differentially expressed. Of these, four and two miRNAs were significant for Overall Survival (OS) and Recurrence Free Survival (RFS), respectively. In the CO approach, from 147 miRNAs retained after filtering, 11 and 4 miRNAs were significant for OS and RFS, respectively. In both the approaches, the risk scores were significant after adjusting for potential confounders. The miRNAs associated with OS identified in our cohort were validated using an external dataset from The Cancer Genome Atlas (TCGA) project. Targets for the identified miRNAs were enriched for cell proliferation, invasion and migration. Conclusions The study identified twelve non-redundant miRNAs associated with OS and/or RFS. These signatures include those that were reported by others in BC or other cancers. Importantly we report for the first time two new candidate miRNAs (miR-574-3p and miR-660-5p) as promising prognostic markers. Independent validation of signatures (for OS) using an external dataset from TCGA further strengthened the study findings.