miSAM: Robust MicroRNA Expression Estimation Using Bi-Objective Evolutionary Learning Algorithm in Cancer Transcriptomics

MicroRNAs (miRNAs) play crucial regulatory roles in cancer biology, but accurately quantifying their expression remains a significant unmet challenge in single-cell and spatial transcriptomics. Current sequencing technologies predominantly capture polyadenylated messenger RNAs (mRNAs), rendering them incapable of directly profiling miRNAs, which lack poly(A) tails. To address this gap, we thus propose miSAM, a novel computational framework for estimation of miRNA expressions based on a bi-objective combinatorial genetic algorithm in conjunction with support vector regression. The miSAM jointly optimizes the selection of a minimal subset of mRNAs, called signatures, while maximizing the Spearman correlation coefficient (SCC) between inferred and actual miRNA expression levels. Evaluation on The Cancer Genome Atlas (TCGA)-BRCA dataset, comprising 1,095 breast cancer samples with expression profiles of 1,881 miRNAs and 19,937 mRNAs, demonstrates the effectiveness of miSAM. Using the top five prognostic miRNA biomarkers for breast cancer, miSAM achieved a mean SCC of 0.633 on the test set while utilizing only 32.6 mRNAs in average, significantly outperforming baseline approaches including: (1) differential expression filtering-based SVR using 887 mRNAs (SCC = 0.562), and (2) LASSO-based SVR using 147.4 mRNAs (SCC = 0.470). miSAM also outperformed XGBoost, which yielded a SCC ofapproximately 0.45–0.50 across various cancer types. Furthermore, the identified mRNAs signatures offer explainable insights into the regulatory associations between miRNAs and their corresponding mRNA targets. These results underscore miSAM’s potential as a robust, interpretable, and scalable tool for miRNA inference in spatial transcriptomics, single-cell sequencing analyses, and precision oncology.