Abstract
BACKGROUND: Sperm quality defined by motility and morphology has critical implications for fertility and pregnancy outcomes. Small RNAs, including microRNAs (miRNAs) and Piwi-interacting RNAs (piRNAs), play regulatory roles and may reflect underlying sperm function. This study aimed to identify small RNA types in sperm based on motility and morphology, examine their correlation with sperm and clinical parameters, and develop diagnostic tools to predict pregnancy outcomes. METHODS: A total of 98 male partners of couples undergoing infertility treatment were included. Thirteen males provided 39 sperm samples categorized into three groups based on quality: A (good), B (intermediate), and C (poor), each with 1500 individually selected sperm. Additionally, 85 males contributed purified sperm samples with various spermatogenic impairments. Small RNA sequencing was performed followed by RT-qPCR validation. RESULTS: Small RNA sequencing revealed a diverse RNA landscape in sperm, with long non-coding RNA (lncRNA) being the most abundant. Regulatory RNAs such as miRNAs and piRNAs were present at varying levels. Differential expression analysis identified 16 miRNAs and 37 piRNAs significantly different between groups A and C. Strong correlations were observed between miRNA/piRNA expression and sperm motility and morphology in groups A and C, but not in group B. miRNA expression levels were associated with sperm quality and pregnancy outcomes, including embryo quality, β-hCG levels, and live birth. Notably, hsa-miR-15b-5p, hsa-miR-19a-5p, and hsa-miR-20a-5p were linked to sperm impairments and hormonal markers (β-hCG, FSH, and LH). Higher expression of these miRNAs was associated with negative β-hCG outcomes and poor IVF prognosis. Lower expression of hsa-miR-15b-5p and hsa-miR-20a-5p was found in G1 embryos compared to G2 embryos. These miRNAs were also significantly correlated with live birth outcomes: higher expression was linked to failed IVF, while lower expression was linked to successful live births. Diagnostic validation showed AUCs of 0.76, 0.71, and 0.74 for hsa-miR-15b-5p, hsa-miR-19a-5p, and hsa-miR-20a-5p, respectively. A combined model yielded an AUC of 0.75. CONCLUSION: These findings suggest that hsa-miR-15b-5p, hsa-miR-19a-5p, and hsa-miR-20a-5p could serve as potential biomarkers for assessing sperm quality and predicting pregnancy outcomes.