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

Srinivasulu Yerukala Sathipati; Ming-Ju Tsai; Nikhila Aimalla; Luke Moat; Sanjay K Shukla; Patrick Allaire; Scott Hebbring; Afshin Beheshti; Rohit Sharma; Shinn-Ying Ho

doi:10.1093/nargab/lqae022

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

NAR Genom Bioinform. 2024 Feb 24;6(1):lqae022. doi: 10.1093/nargab/lqae022. eCollection 2024 Mar.

Authors

Srinivasulu Yerukala Sathipati¹, Ming-Ju Tsai^{2

3}, Nikhila Aimalla⁴, Luke Moat¹, Sanjay K Shukla¹, Patrick Allaire¹, Scott Hebbring¹, Afshin Beheshti^{5

6}, Rohit Sharma⁷, Shinn-Ying Ho^{8

9

10}

Affiliations

¹ Center for Precision Medicine Research, Marshfield Clinic Research Institute, Marshfield, WI 54449, USA.
² Hinda and Arthur Marcus Institute for Aging Research at Hebrew Senior Life, Boston, MA 02131, USA.
³ Department of Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA 02131, USA.
⁴ Department of Internal Medicine-Pediatrics, Marshfield Clinic Health System, Marshfield, WI 54449, USA.
⁵ Blue Marble Space Institute of Science, Space Biosciences Division, NASA Ames Research Center, Moffett Field, CA94035, USA.
⁶ Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
⁷ Department of Surgical Oncology, Marshfield Clinic Health System, Marshfield, WI 54449, USA.
⁸ Institute of Bioinformatics and Systems biology, National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan.
⁹ College of Health Sciences, Kaohsiung Medical University, Kaohsiung 807378, Taiwan.
¹⁰ Biomedical Engineering, National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan.

Abstract

Breast cancer (BC) is one of the most commonly diagnosed cancers worldwide. As key regulatory molecules in several biological processes, microRNAs (miRNAs) are potential biomarkers for cancer. Understanding the miRNA markers that can detect BC may improve survival rates and develop new targeted therapeutic strategies. To identify a circulating miRNA signature for diagnostic prediction in patients with BC, we developed an evolutionary learning-based method called BSig. BSig established 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 curve were 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 towards diagnostic prediction in BC. Moreover, through bioinformatics analysis, 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 available at https://github.com/mingjutsai/BSig.