Reproductive Medicine Centre, Shenzhen Maternity & Child Healthcare Hospital, Fuqiang Road No.3012, Shenzhen, 51807, China.
Newborn Screening Centre, Shenzhen Maternity & Child Healthcare Hospital, Shenzhen, China.
Metabolomics. 2024 May 21;20(3):57. doi: 10.1007/s11306-024-02118-x.
Despite the clear clinical diagnostic criteria for necrozoospermia in andrology, the fundamental mechanisms underlying it remain elusive. This study aims to profile the lipid composition in seminal plasma systematically and to ascertain the potential of lipid biomarkers in the accurate diagnosis of necrozoospermia. It also evaluates the efficacy of a lipidomics-based random forest algorithm model in identifying necrozoospermia.
Seminal plasma samples were collected from patients diagnosed with necrozoospermia (n = 28) and normozoospermia (n = 28). Liquid chromatography-mass spectrometry (LC-MS) was used to perform lipidomic analysis and identify the underlying biomarkers. A lipid functional enrichment analysis was conducted using the LION lipid ontology database. The top 100 differentially significant lipids were subjected to lipid biomarker examination through random forest machine learning model.
Lipidomic analysis identified 46 lipid classes comprising 1267 lipid metabolites in seminal plasma. The top five enriched lipid functions as follows: fatty acid (FA) with ≤ 18 carbons, FA with 16-18 carbons, monounsaturated FA, FA with 18 carbons, and FA with 16 carbons. The top 100 differentially significant lipids were subjected to machine learning analysis and identified 20 feature lipids. The random forest model identified lipids with an area under the curve > 0.8, including LPE(20:4) and TG(4:0_14:1_16:0).
LPE(20:4) and TG(4:0_14:1_16:0), were identified as differential lipids for necrozoospermia. Seminal plasma lipidomic analysis could provide valuable biochemical information for the diagnosis of necrozoospermia, and its combination with conventional sperm analysis may improve the accuracy and reliability of the diagnosis.
尽管在男科领域存在明确的坏死精子症临床诊断标准，但该病症的确切发病机制仍难以捉摸。本研究旨在系统地描绘精液脂质组成图谱，并确定脂质生物标志物在准确诊断坏死精子症方面的潜力。此外，本研究还评估了基于脂质组学的随机森林算法模型在识别坏死精子症方面的效果。
从诊断为坏死精子症（n=28）和正常精子症（n=28）的患者中收集精液样本。采用液相色谱-质谱（LC-MS）技术进行脂质组学分析，并鉴定潜在的生物标志物。使用 LION 脂质本体数据库进行脂质功能富集分析。通过随机森林机器学习模型对前 100 个差异显著的脂质进行脂质生物标志物检验。
脂质组学分析鉴定出 46 种脂质类别，包括精液中 1267 种脂质代谢物。前 5 个富集的脂质功能如下：含≤18 个碳原子的脂肪酸（FA）、16-18 个碳原子的 FA、单不饱和 FA、18 个碳原子的 FA 和 16 个碳原子的 FA。对前 100 个差异显著的脂质进行机器学习分析，鉴定出 20 种特征脂质。随机森林模型确定了 AUC 值>0.8 的脂质，包括 LPE(20:4)和 TG(4:0_14:1_16:0)。
LPE(20:4)和 TG(4:0_14:1_16:0)被鉴定为坏死精子症的差异脂质。精液脂质组学分析可为坏死精子症的诊断提供有价值的生化信息，与传统精子分析相结合可能会提高诊断的准确性和可靠性。
