| 朱媛媛,高 璐,王 珊,李时杰,高 敏,王嘉希.脂质组学技术在痤疮分级和性别差异中的研究[J].,2024,(21):4057-4063 |
| 脂质组学技术在痤疮分级和性别差异中的研究 |
| The Research of Lipidomics Analysis in Classification and Gender Differences of Acne Vulgaris |
| 投稿时间：2024-05-07 修订日期：2024-05-31 |
| DOI：10.13241/j.cnki.pmb.2024.21.010 |
| 中文关键词: 痤疮 脂质组学 机器学习 生物标志物 性别差异 |
| 英文关键词: Acne vulgaris Lipidomics Machine learning Biomarkers Serum Lipids |
| 基金项目: |
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| 中文摘要: |
| 摘要 目的：采用脂质组学技术，结合生物信息学分析，探究痤疮患者疾病进程及性别差异的血清脂质特征，找寻潜在的生物标志物，为进一步理解痤疮的发病机制和临床分级预测提供新的思路。方法：2021年11月至2022年8月于安徽医科大学第一附属医院皮肤科采集41例轻、中、重度痤疮患者血清，及26例健康对照组血清，采用液相色谱-串联质谱法（liquid chromatograph mass spectrometer, LC-MS）进行血清脂质组学分析。采用偏最小二乘法判别分析（partial least squares discrimination analysis, PLS-DA）对差异表达的脂质代谢物进行多变量统计分析；采用火山图及加权基因共表达网络分析（Weighted correlation network analysis, WGCNA）对脂质组数据进行深入解析，以筛选关键的候选脂类标志物，并通过三种机器学习模型，评估这些标志物在痤疮分级预测中的效果。结果：在痤疮的分级分析中，通过WGCNA分析将所有脂类分为13个模块，并筛选出与轻、中、重三个进程特异相关的3个脂质模块。其中，PC(24:1)、LPC(22:2)、PC(21:0)、PS(26:1e)在痤疮不同等级的区分中表现出良好的分类性能，在三种机器学习模型下均展现出0.9以上的AUC值，在痤疮分级预测中具有潜在应用价值。在性别分析中，通过WGCNA分析将所有脂类分为9个模块，进一步识别出CL(79:9)、PC(15:0_22:6)、PE(18:0_20:3)、PE(18:0_22:6)可能与性别差异机制存在关联。结论：本研究筛选出的关键脂质标志物不仅具有较高的分级预测准确性，而且为理解痤疮发病的性别差异机制提供了新的线索。 |
| 英文摘要: |
| ABSTRACT Objective: To investigate the progression of serum lipid profiles and gender differences in acne patients by integrating lipidomics and bioinformatics analysis. The goal is to uncover new insights and potential biomarkers to enhance our understanding of acne pathogenesis and aid in clinical classification prediction. Methods: Serum samples were collected from the Dermatology Department of the First Affiliated Hospital of Anhui Medical University, between November 2021 and August 2022, between November 2021 and August 2022. They were taken from 41 patients with mild, moderate and severe acne, along with 26 healthy individuals served as control. Liquid chromatography-tandem mass spectrometry (LC-MS) was utilized for serum lipid metabolomics analysis. Partial least squares discrimination analysis (PLS-DA) was employed for multivariate statistical analysis of differentially expressed lipid metabolites. Volcano plot and weighted gene co-expression network analysis (WGCNA) were applied to gain deeper insights into the lipidomics data and identify key candidate lipid biomarkers. The performance of these markers in acne classification prediction was assessed using three machine learning models. Results: In the acne classification analysis, all lipids were divided into 13 modules by WGCNA analysis, with three lipid modules specifically associated with various processes being singled out. Notably, PC(24:1), LPC(22:2), PC(21:0), and PS(26:1e) demonstrated strong classification performance in distinguishing different grades of acne, with AUC values exceeding 0.9 under all three machine learning models, suggesting their potential application value in acne classification prediction. In the gender analysis, nine lipid modules were identified through WGCNA analysis, with PC(24:1), LPC(22:2), PC(21:0), and PS(26:1e) potentially linked to gender difference mechanisms. Conclusion: The identified key lipid markers not only exhibit high classification prediction accuracy but also shed light on the gender difference mechanisms in acne pathogenesis, offering new insights for future research. |
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