Identification of miRNA biomarkers for essential hypertension in small samples based on MPGAM.

Essential hypertension (EH) is one of the most widespread chronic diseases globally, with a multifactorial etiology. MicroRNAs (miRNAs) are key regulators in the development and progression of EH and hold great promise as biomarkers. However, reliably identifying EH-related miRNA biomarkers in small-sample datasets remains challenging. To address these limitations, we propose a novel computational framework, the Modular Probability-driven Global Analytical Method (MPGAM), tailored for biomarker discovery in small-sample settings. MPGAM integrates three key innovations: (1) the Dual-Index Nearest Neighbor Similarity Measure (DINNSM), which captures local similarity structures more accurately than conventional correlation-based methods; (2) a multi-dimensional module evaluation strategy that incorporates gene significance, module membership, and known hypertension-associated miRNAs; and (3) a Probability-based Global Sorting Method (PGSM), which ranks miRNAs across modules based on probabilistic enrichment. Using the GSE75670 dataset from the GEO database, MPGAM identified ten candidate miRNA biomarkers. In this study, identification refers to the data-driven selection of miRNAs that exhibit potential associations with EH. These may include both previously reported EH-related miRNAs and novel candidates that have not been documented in existing literature. Among these, eight have been previously reported to be associated with blood pressure, including four (hsa-miR-107, hsa-miR-210, hsa-miR-665, and hsa-miR-449a) cited in more than five independent studies. Target gene interaction analysis further suggests that these miRNAs may exert coordinated regulatory effects on EH-related pathways. Compared to existing methods, MPGAM demonstrated greater effectiveness in miRNA biomarker identification and offers an interpretable approach.