Metabolomic Analysis Points to Bioactive Lipid Species and Acireductone Dioxygenase 1 (ADI1) as Potential Therapeutic Targets in Poor Prognosis Endometrial Cancer

Metabolism pathway-based subtyping in endometrial cancer: An integrated study by multi-omics analysis and machine learning algorithms

Endometrial cancer (EC), the second most common malignancy in the female reproductive system, has garnered increasing attention for its genomic heterogeneity, but understanding of its metabolic characteristics is still poor. We explored metabolic dysfunctions in EC through a comprehensive multi-omics analysis (RNA-seq datasets from The Cancer Genome Atlas [TCGA], Cancer Cell Line Encyclopedia [CCLE], and GEO datasets; the Clinical Proteomic Tumor Analysis Consortium [CPTAC] proteomics; CCLE metabolomics) to develop useful molecular targets for precision therapy. Unsupervised consensus clustering was performed to categorize EC patients into three metabolism-pathway-based subgroups (MPSs). These MPS subgroups had distinct clinical prognoses, transcriptomic and genomic alterations, immune microenvironment landscape, and unique patterns of chemotherapy sensitivity. Moreover, the MPS2 subgroup had a better response to immunotherapy. Finally, three machine learning algorithms (LASSO, random forest, and stepwise multivariate Cox regression) were used for developing a prognostic metagene signature based on metabolic molecules. Thus, a 13-hub gene-based classifier was constructed to predict patients' MPS subtypes, offering a more accessible and practical approach. This metabolism-based classification system can enhance prognostic predictions and guide clinical strategies for immunotherapy and metabolism-targeted therapy in EC.

Metabolomics for Clinical Biomarker Discovery and Therapeutic Target Identification

As links between genotype and phenotype, small-molecule metabolites are attractive biomarkers for disease diagnosis, prognosis, classification, drug screening and treatment, insight into understanding disease pathology and identifying potential targets. Metabolomics technology is crucial for discovering targets of small-molecule metabolites involved in disease phenotype. Mass spectrometry-based metabolomics has implemented in applications in various fields including target discovery, explanation of disease mechanisms and compound screening. It is used to analyze the physiological or pathological states of the organism by investigating the changes in endogenous small-molecule metabolites and associated metabolism from complex metabolic pathways in biological samples. The present review provides a critical update of high-throughput functional metabolomics techniques and diverse applications, and recommends the use of mass spectrometry-based metabolomics for discovering small-molecule metabolite signatures that provide valuable insights into metabolic targets. We also recommend using mass spectrometry-based metabolomics as a powerful tool for identifying and understanding metabolic patterns, metabolic targets and for efficacy evaluation of herbal medicine.

Pregnane X receptor knockout mitigates weight gain and hepatic metabolic dysregulation in female C57BL/6 J mice on a long-term high-fat diet

Obesity is a significant risk factor for several chronic diseases. However, pre-menopausal females are protected against high-fat diet (HFD)-induced obesity and its adverse effects. The pregnane X receptor (PXR, NR1I2), a xenobiotic-sensing nuclear receptor, promotes short-term obesity-associated liver disease only in male mice but not in females. Therefore, the current study investigated the metabolic and pathophysiological effects of a long-term 52-week HFD in female wild-type (WT) and PXR-KO mice and characterized the PXR-dependent molecular pathways involved. After 52 weeks of HFD ingestion, the body and liver weights and several markers of hepatotoxicity were significantly higher in WT mice than in their PXR-KO counterparts. The HFD-induced liver injury in WT female mice was also associated with upregulation of the hepatic mRNA levels of peroxisome proliferator-activated receptor gamma (Pparg), its target genes, fat-specific protein 27 (Fsp27), and the liver-specific Fsp27b involved in lipid accumulation, apoptosis, and inflammation. Notably, PXR-KO mice displayed elevated hepatic Cyp2a5 (anti-obesity gene), aldo-keto reductase 1b7 (Akr1b7), glutathione-S-transferase M3 (Gstm3) (antioxidant gene), and AMP-activated protein kinase (AMPK) levels, contributing to protection against long-term HFD-induced obesity and inflammation. RNA sequencing analysis revealed a general blunting of the transcriptomic response to HFD in PXR-KO compared to WT mice. Pathway enrichment analysis demonstrated enrichment by HFD for several pathways, including oxidative stress and redox pathway, cholesterol biosynthesis, and glycolysis/gluconeogenesis in WT but not PXR-KO mice. In conclusion, this study provides new insights into the molecular mechanisms by which PXR deficiency protects against long-term HFD-induced severe obesity and its adverse effects in female mice.

Metabolomic-Based Approaches for Endometrial Cancer Diagnosis and Prognosis: A Review

Endometrial cancer, the most prevalent gynecological malignancy in developed countries, is experiencing a sustained rise in both its incidence and mortality rates, primarily attributed to extended life expectancy and lifestyle factors. Currently, the absence of precise diagnostic tools hampers the effective management of the expanding population of women at risk of developing this disease. Furthermore, patients diagnosed with endometrial cancer require precise risk stratification to align with optimal treatment planning. Metabolomics technology offers a unique insight into the molecular landscape of endometrial cancer, providing a promising approach to address these unmet needs. This comprehensive literature review initiates with an overview of metabolomic technologies and their intrinsic workflow components, aiming to establish a fundamental understanding for the readers. Subsequently, a detailed exploration of the existing body of research is undertaken with the objective of identifying metabolite biomarkers capable of enhancing current strategies for endometrial cancer diagnosis, prognosis, and recurrence monitoring. Metabolomics holds vast potential to revolutionize the management of endometrial cancer by providing accuracy and valuable insights into crucial aspects.

Lipidomics profiles of human spermatozoa: insights into capacitation and acrosome reaction using UPLC-MS-based approach

Introduction

Lipidomics elucidates the roles of lipids in both physiological and pathological processes, intersecting with many diseases and cellular functions. The maintenance of lipid homeostasis, essential for cell health, significantly influences the survival, maturation, and functionality of sperm during fertilization. While capacitation and the acrosome reaction, key processes before fertilization, involve substantial lipidomic alterations, a comprehensive understanding of the changes in human spermatozoa's lipidomic profiles during these processes remains unknown. This study aims to explicate global lipidomic changes during capacitation and the acrosome reaction in human sperm, employing an untargeted lipidomic strategy using ultra-performance liquid chromatography-mass spectrometry (UPLC-MS).

Methods

Twelve semen specimens, exceeding the WHO reference values for semen parameters, were collected. After discontinuous density gradient separation, sperm concentration was adjusted to 2 x 106 cells/ml and divided into three groups: uncapacitated, capacitated, and acrosome-reacted. UPLC-MS analysis was performed after lipid extraction from these groups. Spectral peak alignment and statistical analysis, using unsupervised principal component analysis (PCA), bidirectional orthogonal partial least squares discriminant analysis (O2PLS-DA) analysis, and supervised partial least-squares-latent structure discriminate analysis (PLS-DA), were employed to identify the most discriminative lipids.

Results

The 1176 lipid peaks overlapped across the twelve individuals in the uncapacitated, capacitated, and acrosome-reacted groups: 1180 peaks between the uncapacitated and capacitated groups, 1184 peaks between the uncapacitated and acrosome-reacted groups, and 1178 peaks between the capacitated and acrosome-reacted groups. The count of overlapping peaks varied among individuals, ranging from 739 to 963 across sperm samples. Moreover, 137 lipids had VIP values > 1.0 and twenty-two lipids had VIP > 1.5, based on the O2PLS-DA model. Furthermore, the identified twelve lipids encompassed increases in PI 44:10, LPS 20:4, LPA 20:5, and LPE 20:4, and decreases in 16-phenyl-tetranor-PGE2, PC 40:6, PS 35:4, PA 29:1, 20-carboxy-LTB4, and 2-oxo-4-methylthio-butanoic acid.

Discussion

This study has been the first time to investigate the lipidomics profiles associated with acrosome reaction and capacitation in human sperm, utilizing UPLC-MS in conjunction with multivariate data analysis. These findings corroborate earlier discoveries on lipids during the acrosome reaction and unveil new metabolites. Furthermore, this research highlights the effective utility of UPLC-MS-based lipidomics for exploring diverse physiological states in sperm. This study offers novel insights into lipidomic changes associated with capacitation and the acrosome reaction in human sperm, which are closely related to male reproduction.

Metabolomic Profiling in Children with Celiac Disease: Beyond the Gluten-Free Diet

Celiac disease (CD) is included in the group of complex or multifactorial diseases, i.e., those caused by the interaction of genetic and environmental factors. Despite a growing understanding of the pathophysiological mechanisms of the disease, diagnosis is still often delayed and there are no effective biomarkers for early diagnosis. The only current treatment, a gluten-free diet (GFD), can alleviate symptoms and restore intestinal villi, but its cellular effects remain poorly understood. To gain a comprehensive understanding of CD's progression, it is crucial to advance knowledge across various scientific disciplines and explore what transpires after disease onset. Metabolomics studies hold particular significance in unravelling the complexities of multifactorial and multisystemic disorders, where environmental factors play a significant role in disease manifestation and progression. By analyzing metabolites, we can gain insights into the reasons behind CD's occurrence, as well as better comprehend the impact of treatment initiation on patients. In this review, we present a collection of articles that showcase the latest breakthroughs in the field of metabolomics in pediatric CD, with the aim of trying to identify CD biomarkers for both early diagnosis and treatment monitoring. These advancements shed light on the potential of metabolomic analysis in enhancing our understanding of the disease and improving diagnostic and therapeutic strategies. More studies need to be designed to cover metabolic profiles in subjects at risk of developing the disease, as well as those analyzing biomarkers for follow-up treatment with a GFD.

Endometrial cancer diagnostic and prognostic algorithms based on proteomics, metabolomics, and clinical data: a systematic review

Endometrial cancer is the most common gynaecological malignancy in developed countries. Over 382,000 new cases were diagnosed worldwide in 2018, and its incidence and mortality are constantly rising due to longer life expectancy and life style factors including obesity. Two major improvements are needed in the management of patients with endometrial cancer, i.e., the development of non/minimally invasive tools for diagnostics and prognostics, which are currently missing. Diagnostic tools are needed to manage the increasing number of women at risk of developing the disease. Prognostic tools are necessary to stratify patients according to their risk of recurrence pre-preoperatively, to advise and plan the most appropriate treatment and avoid over/under-treatment. Biomarkers derived from proteomics and metabolomics, especially when derived from non/minimally-invasively collected body fluids, can serve to develop such prognostic and diagnostic tools, and the purpose of the present review is to explore the current research in this topic. We first provide a brief description of the technologies, the computational pipelines for data analyses and then we provide a systematic review of all published studies using proteomics and/or metabolomics for diagnostic and prognostic biomarker discovery in endometrial cancer. Finally, conclusions and recommendations for future studies are also given.