Jialin Liu, Maokun Liao, Jingchao Liu, Shuo Liang, Jin Xie, Dandan Liang, Mingzhao Du, Honghui Shi, Wei Song
{"title":"Metabolic remodeling and its hidden heterogeneity in uterine fibroids: comprehensive metabolomic profiling and mass spectrometry imaging.","authors":"Jialin Liu, Maokun Liao, Jingchao Liu, Shuo Liang, Jin Xie, Dandan Liang, Mingzhao Du, Honghui Shi, Wei Song","doi":"10.1007/s11306-025-02346-9","DOIUrl":null,"url":null,"abstract":"<p><strong>Introduction: </strong>As the most common benign gynecological tumor in women, uterine fibroids not only pose a serious threat to reproductive health but also directly impair fertility. The structural abnormalities of the uterus and metabolic disturbances they induce have become critical pathological contributors to infertility, recurrent miscarriage, and obstetric complications in reproductive-aged women. However, the underlying metabolic mechanisms of uterine fibroids remain poorly understood.</p><p><strong>Objective: </strong>This study aimed to explore metabolic remodeling of uterine fibroids from patients.</p><p><strong>Methods: </strong>We performed global metabolomics analysis on myometrium and uterine fibroid tissues by combining ultra-high performance liquid chromatography coupled with mass spectrometry (UHPLC-MS) analysis and gas chromatography coupled with mass spectrometry (GC-MS) analysis. Spatially resolved metabolomics was carried out to analyze intratumor metabolic heterogeneity via desorption electrospray ionization mass spectrometry imaging (DESI-MSI). Combined with machine learning, important metabolites related to uterine fibroids were identified.</p><p><strong>Results: </strong>This study enabled mapping a comprehensive metabolome atlas up to 825 metabolites in human myometrium and uterine fibroid tissues via combining UHPLC-MS with GC-MS. Metabolic shifts from myometrium to uterine fibroids were clearly observed, which was accompanied by large changes in metabolites and amino acid metabolic pathways to display metabolic remodeling of uterine fibroids. Combined with machine learning, a total of ten metabolites were identified to characterize metabolic properties of uterine fibroids. Furthermore, DESI-MSI was employed to effectively differentiate regions of hyaline degeneration from those devoid of such degeneration, thereby firstly highlighting the intrinsic metabolic heterogeneity present in uterine fibroids.</p><p><strong>Conclusion: </strong>The findings offer new insights into the metabolic pathophysiology of fibroids, which may aid in the development of targeted therapeutic strategies for this widespread gynecological disorder.</p>","PeriodicalId":18506,"journal":{"name":"Metabolomics","volume":"21 5","pages":"144"},"PeriodicalIF":3.3000,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Metabolomics","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1007/s11306-025-02346-9","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENDOCRINOLOGY & METABOLISM","Score":null,"Total":0}
引用次数: 0
Abstract
Introduction: As the most common benign gynecological tumor in women, uterine fibroids not only pose a serious threat to reproductive health but also directly impair fertility. The structural abnormalities of the uterus and metabolic disturbances they induce have become critical pathological contributors to infertility, recurrent miscarriage, and obstetric complications in reproductive-aged women. However, the underlying metabolic mechanisms of uterine fibroids remain poorly understood.
Objective: This study aimed to explore metabolic remodeling of uterine fibroids from patients.
Methods: We performed global metabolomics analysis on myometrium and uterine fibroid tissues by combining ultra-high performance liquid chromatography coupled with mass spectrometry (UHPLC-MS) analysis and gas chromatography coupled with mass spectrometry (GC-MS) analysis. Spatially resolved metabolomics was carried out to analyze intratumor metabolic heterogeneity via desorption electrospray ionization mass spectrometry imaging (DESI-MSI). Combined with machine learning, important metabolites related to uterine fibroids were identified.
Results: This study enabled mapping a comprehensive metabolome atlas up to 825 metabolites in human myometrium and uterine fibroid tissues via combining UHPLC-MS with GC-MS. Metabolic shifts from myometrium to uterine fibroids were clearly observed, which was accompanied by large changes in metabolites and amino acid metabolic pathways to display metabolic remodeling of uterine fibroids. Combined with machine learning, a total of ten metabolites were identified to characterize metabolic properties of uterine fibroids. Furthermore, DESI-MSI was employed to effectively differentiate regions of hyaline degeneration from those devoid of such degeneration, thereby firstly highlighting the intrinsic metabolic heterogeneity present in uterine fibroids.
Conclusion: The findings offer new insights into the metabolic pathophysiology of fibroids, which may aid in the development of targeted therapeutic strategies for this widespread gynecological disorder.
期刊介绍:
Metabolomics publishes current research regarding the development of technology platforms for metabolomics. This includes, but is not limited to:
metabolomic applications within man, including pre-clinical and clinical
pharmacometabolomics for precision medicine
metabolic profiling and fingerprinting
metabolite target analysis
metabolomic applications within animals, plants and microbes
transcriptomics and proteomics in systems biology
Metabolomics is an indispensable platform for researchers using new post-genomics approaches, to discover networks and interactions between metabolites, pharmaceuticals, SNPs, proteins and more. Its articles go beyond the genome and metabolome, by including original clinical study material together with big data from new emerging technologies.